Authors’ response (11 February 2024)

GENERAL ASSESSMENT

Ionotropic glutamate receptors mediate the large majority of excitatory synaptic transmission in the brain. These receptors consist of four classes: AMPA, kainate, NMDA and delta receptors. NMDA receptors are obligate tetramers composed of two GluN1 and two GluN2 (or GluN3) subunits. Compared to other iGluRs, they have the particularity of requiring two different agonists for their channel to open: glycine binding on GluN1 and glutamate on GluN2.

Seljeset et al. investigate the molecular determinants controlling ligand potency and NMDAR activity at the level of the ligand-binding domains (LBDs), where the agonists bind. They identify a specific position, D732, whose mutation to either leucine or phenylalanine leads to a constitutively active GluN1 subunit, and thus to NMDARs activated solely by glutamate. This aspartate is well known in the field, since it is a highly conserved, signature residue in iGluRs that binds amino acid ligands, together with an arginine in the LBD upper lobe. Surprisingly, although glycine cannot further activate GluN1-D732L/GluN2Awt receptors, glycine site antagonists like 5,7-DCKA or CGP-78608 can still bind and inhibit NMDAR activity. This study is therefore very intriguing, as it raises new questions about something that was previously thought to be understood. By using a combination of unnatural amino acids and conventional mutagenesis, the authors propose that D732 contributes to glycine-mediated effects by changing local interactions with nearby residues. In addition, they show that this behavior is specific for the GluN1 subunit, since mutation of the equivalent aspartate in the GluN2 subunit does not yield constitutively activated GluN2 subunits. Finally, the authors identify a homomeric iGluR from the placozoan Trichoplax adhaerens, Trichoplax AKDF¹⁹³⁸³, in which this conserved aspartate is replaced by a tyrosine. When expressed in Xenopus oocytes, the channel shows constitutive activity. Mutation of the tyrosine into an aspartate, to convert Trichoplax AKDF¹⁹³⁸³ into a “classical” iGluR, decreases Trichoplax AKDF¹⁹³⁸³ constitutive current and allows this channel to be activated by glycine and D-serine. Interestingly, an adjacent residue that is a serine in most mammalian subunits is also a tyrosine in Trichoplax AKDF¹⁹³⁸³, and mutation of both tyrosines yields a glutamate-gated ion channel comparable to mammalian receptors. All of this suggests that the nature of the residue at position 732 influences not only ligand binding but also channel gating.

The study is technically sound, with appropriate controls, and uncovers intriguing properties of a position in GluN1 LBD at which specific side chain mutations can lock the subunit in an active state. Investigation of Trichoplast iGluR further reinforces these findings. This study should lead to a better understanding of how LBDs prime channel opening in iGluRs in the absence of agonists. In addition, co-agonist insensitive GluN1-D732L containing NMDARs could be used as tools to investigate the physiological consequences of NMDAR regulation by their co-agonist site. In contrast to previously engineered NMDARs activated solely by glutamate, which rely on the LBD being locked in its active state by cysteine bridges (Blanke and VanDongen, J Biol Chem 2008), GluN1-D37L/GluN2A NMDARs remain druggable (i.e. they can still be inhibited by glycine-site competitive antagonists). This is a great advantage when investigating the function of these receptors in a native context. The study identifies a few gaps that remain in our mechanistic understanding of D732’s role in channel gating. Particularly, it is unclear how subtle modification of residue side chains at position D732 lead to such drastic changes in function and why these effects are specific to GluN1 LBD. Also, why does mutation of D732 into isoleucine lead to a constitutively active GluN1 subunit, while mutation of a closely related leucine residue prevents activation of the receptor by glycine? The idea of a “hydrophobic plug” formed by D732L or D732F sidechains leading to constitutive activation would benefit from further validation since other hydrophobic substitutions (A, V, I, Y, and W) do not produce similar effects. Finally, it would be interesting to carry out further investigations of the role of the interaction between D732 and Q536 in open conformation stability. Thus, this paper puts forth interesting questions that could be addressed by future studies, for example molecular dynamics simulations and exploration of the LBD free energy landscapes (as in Yao et al., Structure 2013), to understand the impact of the GluN1-D732L mutation on GluN1 LBD conformational mobility.

RECOMMENDATIONS

Essential revisions:

1. Page 2, “These data show that essentially all substitutions at the GluN1-732 position decrease glycine potency, but leucine and phenylalanine substitutions also remove the requirement for glycine co-agonism in GluN1/GluN2A NMDA receptors”: One other hypothesis for the lack of glycine dependence of GluN1-D732I and D732Y + GluN2A receptors could be that the mutated receptors have a glycine potency so high that GluN1 LBD is already saturated by contaminating, ambient glycine. At this point in the paper, the authors cannot distinguish between one hypothesis or the other, therefore we suggest that this sentence be rephrased. Later in the text, control experiments with GluN1-R523K mutations that kill glycine binding and competition with 5,7-DCKA show that glycine-independent activation of GluN1-D732L/GluN2A mutants is not due to constitutive occupancy of GluN1 LBD by contaminating glycine.

ER1) We have now changed this to (page 4): “These data show that most substitutions at the GluN1-732 position decrease glycine potency, but leucine and phenylalanine substitutions alter GluN1 activity in such a way that leads to single-mutant NMDA receptors activated solely by glutamate.”

1. Does glycine insensitivity in GluN1-D732L/GluN2A NMDARs reflect a constitutively active GluN1 subunit or is this subunit locked in another conformational state that cannot be further modified by glycine? This could be answered by estimating the maximum open probability of GluN1-D732L/GluN2A NMDARs compared to their wt counterparts. To estimate Po, the authors could measure the kinetics of NMDA receptor current inhibition by MK801 (the slower MK801 inhibition, the lower the Po; see Chen et al., J. Neurosci 1999; Blanke and VanDongen, JBC 2008) in the presence of saturating agonist concentrations (100 μM Glu, 100 μM Gly for wt and only 100 μM Glu for mutant).

ER2) We have now assessed the rate of MK-801 block in glutamate-gated mutant and glycine + glutamate-gated WT receptors, and reshuffled text/figures, as this ties in well with ER4) below. MK-801 results now in Figure 3 on page 6, and main text on page 5: “In order to understand whether the glycine-insensitive GluN1-D732L subunit is in a constantly activated state or occupies a different conformation that may reflect an alternative to typical channel gating, we compared the kinetics of WT receptor and GluN1-D732L-containing receptor inhibition by the open-channel blocker MK-801, which can be used to evaluate maximum open probability of NMDARs ^26,30. We observed very similar kinetics of inhibition of WT and mutant receptors (Fig. 3A), indicating similar open probability in solely glutamate-gated GluN1-D732L-containing receptors and glutamate and glycine-gated WT receptors. This reflects unchanged maximum open probability in solely glutamate-gated NMDARs with disulfide-locked GluN1 LBDs assayed by single channel recordings ²⁷. This suggests that the GluN1-D732L subunit is in a constantly activated state.”

When viewed alongside high sensitivity of mutant subunits to DCKA - OS1) below - it’s difficult to conclude what sort of active state the mutant subunit adopts. We’ve assessed the best we can at the moment, and in this paper we’ll have to leave it at “here is the observation; here is some evidence ruling out various possibilities; and here is a receptor from another family that shows something remarkably consistent”. Future studies will have to establish exactly what state the mutant subunit adopts.

1. Page 4: The term “hydrophobic plug” is not fully justified since other hydrophobic residues do not lock GluN1 LBD in its active state.

ER3) We have replaced nearly all use of this term, in the title and in the main text, to e.g. “certain hydrophobic substitutions” or “L/F substitutions”.

1. Figure 2, redox sensitivity of GluN1-D732L/GluN2Awt: It would be helpful to explain the point of this experiment – maybe to investigate if the D732L mutation has an impact on the receptor gate rather than on the LBD? In any case, the authors should investigate the effect of DTT on the activity of wt GluN1/GluN2A receptors to determine whether there is an absence of an effect of the D732L mutant on redox sensitivity.

ER4) Indeed we were curious if D732L affected the gate via this allosteric route, rather than by just altering LBD conformation. And we have now shown the effect of DTT on WT receptors.

In addition to re-writing to better explain the point, as suggested, we have also re-written to follow on from new data/text on the whether the D732L mutation affects LBD, gating, etc: “We next questioned if D732L/F substitutions affect channel gating, rather than simply altering the LBD conformation. The gating machinery is complex, but it includes the peptide segment linking the C-terminal end of the LBD to membrane-spanning helix 4 (LBD-M4 linker, (11)). The LBD and LBD-M4 linker are confined by a C744—C798 disulfide, just four helical turns after D732, whose disruption by reduction enhances channel gating (28)). We considered that if the D732L/F substitution is coupled to channel gating via this route, then removal of the C744—C798 disulfide via the C744A mutation might alter glutamate-gated currents in GluN1-D732L-containing receptors. Alternatively, the typical enhancement by the reducing agent dithiothreitol (DTT) might differ in GluN1-D732L compared to WT receptors.”

And new Figure 3 now includes DTT effects on WT receptors.

1. Page 6: The authors find that mutation of Q536 decreases glycine potency and conclude there is an interaction between D732 and Q536. However, the effects of D732 and Q536 mutations could be independent, therefore the authors should consider mutating both residues together to look at the additive/non-additive effects of the mutations. Or perhaps, note in the Discussion that some sort of mutant cycle analysis or molecular dynamics simulation would be needed to rigorously test these ideas.

ER5) We have now made and tested a double mutant combining D732E and Q536N and performed mutant cycle analysis.

(We also tried to do this for Q536 side chain (regular mutations) and A734 main chain (non-canonical substitutions), but double mutants involving non-canonical amino acids at A734 were not successful – Figure S1.)

As is now shown in Figure 4D, the effects of the mutations are decidedly non-additive, yielding an Ω value of 0.05, corresponding to a reasonably high energetic coupling of ~7 kJ/mol. We have now added to the relevant section of the Results on page 8: “If an interaction between Q536 and D732 were energetically important for receptor activation, the effects of their mutations should be non-additive ³¹. We therefore tested glycine potency at double-mutant GluN1-Q536N/D732E-containing receptors and observed non-additive changes in EC₅₀, with a strong coupling value, Ω, of 0.05 (Fig. 4D). This deviation of Ω from unity, corresponding to an interaction energy of 7.4 kJ/mol is relatively high ³¹, confirming that Q536 and D732 are energetically coupled. We tried to analyse energetic coupling between Q536 and A734 via double mutants incorporating nonsense suppression at the A734 position, but unfortunately, attempts to incorporate Aah into such double mutants via nonsense suppression were unsuccessful (Fig. S1B).”

1. Page 6, “A hydrophobic plug does not cause constitutive activity in all NMDA receptor subtypes”: This title is misleading as it raises the expectation that the effect of GluN1-D732L has been investigated in the context of GluN1/GluN2A, GluN1/GluN2B, etc NMDARs. Instead, the equivalent mutation is made in the GluN2 subunit. We suggest using the word “subunit” rather than “subtype”.

ER6) We have changed this Results section title (page 8) to: “L/F substitutions do not cause constitutive activity in all NMDA receptor subunits”

1. Page 7, effect of GluN1-D732L in the context of GluN1/GluN3 NMDARs: We would not expect current to be observed with GluN1-D732L/GluN3 NMDARs, since locking GluN1 LBD in its active state desensitizes the receptors. The effect of the D732L mutation seems therefore conserved between GluN1/GluN2 and GluN1/GluN3 NMDARs. In addition, when using CGP, please cite Grand et al., Nat. Commun. 2018 since they were the first to use CGP as a tool to record GluN1/GluN3 currents.

ER7) We have now cited that paper specifically here (page 8) and inserted the following (page 8/9): “While this seems like inactivity of the mutant GluN1 subunit in GluN1(4a)/GluN3A, it could yet reflect the activity of constitutively active mutant GluN1 subunits in GluN1/GluN2A receptors, as GluN1 activity in GluN1/GluN3A receptors is known to cause more desensitization than activation (Grand et al 2018).”

1. Figure 5C: It is stated in the text that the aspartate position is “highly” conserved. However, no actual number or percentages are given for this statement. How does it compare to the residues in the highly conserved SYTANLAAF motif or other conserved positions? This sort of analysis does not need to be done for the entire receptor, but perhaps for glycine and glutamate binding residues and SYTANLAAF motif, to give a quantitative feel for statements about conservation. In addition, what other types of residues occupy this position in other species? And what was the number of species/subunits included in the analysis?

ER8) To clarify the level of conservation, we have added Table 1 (page 10) listing the % conservation of amino acids at selected positions.

In analyzing % conservation, we noticed that several iGluR sequences with gaps in the ligand-binding domain or channel-forming helices had escaped our filtering out incomplete sequences in our phylogenetic analysis. We therefore revisited our phylogenetic analysis, removed several incomplete sequences, and replaced Crassostrea gigas (a mollusc spiralian) iGluR sequences with Schmidtea mediterranea (a flatworm spiralian) sequences. This (1) means less sequences with gaps in the ligand-binding domain in our alignment/tree and (2) better covers the diversity of the lineage Spiralia now that we have sequences of Lingula anatina and Schmidtea mediterranea, which are more distantly related than Lingula anatina and Crassosttrea gigas (Laumer et al 2019, PMID:31690235; Marlétaz et al., 2019, PMID:30639106).

The result is a phylogenetic and amino acid sequence analysis of 204 iGluR genes (previous version had 212 genes) with the same overall topology as the previous version, including lambda, NDMA, epsilon, and AKDF iGluR families (Fig. 5B, page 9).

The number of subunits/genes used is stated in the Figure legend. The number of and reasoning behind the number of species used is described under Methods, Bioinformatic analyses: in exploring the conservation of the D732 residue, we have not tried to use as many iGluR sequences as possible; rather we have tried to assess this residue in a broad sample covering all (animal) iGluR families and from a careful selection of different animal lineages, while also avoiding fast-evolving species like Drosophila, which complicate tree topology. Hence our description of “two ctenophores, one poriferan, etc” under Methods, Bioinformatic analyses. In the main text (Results, page 9), we retain our original description: “We assembled diverse iGluR sequences, covering all animal lineages and animal iGluR families (Fig. 6A,B)…”

1. Figure 5, panel F: From what we understand, the authors created dose-response curves for wt Trichoplast AKDF¹⁹³⁸⁶³ based on steady-state currents and for Y742D/Y743S mutants based on peak currents. If this is the case, one cannot compare the two dose-response curves since peak current potentiation and steady-state inhibition likely reflect different conformational transitions.

ER9) We acknowledge this issue and that we can’t really say that ligand-activated D742 channels bind D-serine better than ligand-deactivated Y742 channels. But we think it’s fair to point out that mutant D742 channels react (by conducting current) to micromolar ligand concentrations whereas wildtype Y742 channels react (with decreased current) only to millimolar concentrations, and we have re-written to acknowledge the issue raised for this comparison (page 11): “Finally, we tried to assess whether position 742 determines ligand potency in addition to channel activity in AKDF¹⁹³⁸³ receptors. For these experiments we employed D-serine, as recovery from glycine-induced deactivation (Fig. 6C, far-left) and activation/desensitization (Fig. 6C, far-right) was very slow. Substantial deactivation of WT receptors was only induced by millimolar D-serine concentrations, whereas Y742D-containing mutants were activated by micromolar concentrations (Fig. 6D,E), with an EC₅₀ of 490 ± 120 µM at Y742D/Y743S (n = 4; Y742D EC₅₀ not assessed due to slow recovery from desensitization). Our measure of potency is confounded by the fact that deactivation (in WT channels) and activation (in mutant channels) are presumably coupled to D-serine binding via different conformational transitions. Nonetheless, we observe that a naturally occurring large hydrophobic side chain at the top of the β-strand preceding the αI helix leads to an AKDF homo-tetramer that shows constitutive activity and responds only to millimolar concentrations of D-serine. In contrast, “re-introducing” an aspartate to this position reinstates more typical ligand-dependent activation and sensitivity to micromolar concentrations of D-serine.”

Optional suggestions:

1. Figure 2, glycine/DCKA competition: It is difficult to understand how a GluN1 LBD-locked closed (active state) could still bind DCKA. If the open-to-close equilibrium of GluN1 LBD is displaced towards its closed state, then DCKA Ki should be shifted to the right compared to wt receptors. Additionally, DCKA inhibition kinetics should be slower if DCKA needs to “wait” for rare resting-like conformational changes to bind. Did the authors investigate DCKA potency and inhibition kinetics?

OS1) We have now investigated DCKA potency. DCKA capably inhibits GluN1-D732L/GluN2A-WT activity, and perhaps surprisingly, potency of DCKA at the mutant is greater than at wildtype. We suspect this is due to (1) the introduction of a hydrophobic leucine residue right next to an aryl group of DCKA, increasing DCKA affinity directly, (2) the absence of glycine binding to this site, so no need for competition, and (3) potentially other mechanisms such as cooperativity between subunits. Again, establishing the precise nature of our mutant LBD conformation here is for future structural and molecular dynamics studies. But we have described the results, along with our following interpretation, (page 4): “Whether increased DCKA potency in GluN1-D732L subunits derives from the now non-competitive nature of the inhibition in mutant receptors or from the introduction of a favourable hydrophobic interaction with the dichlorobenzene moiety of the inhibitor is unclear. But the high DCKA potency would suggest that the constitutively active GluN1-D732L subunit is, unexpectedly, not due to a permanently clamshell-closed LBD in the mutant. This may reflect the fact that extent of LBD closure is poorly correlated with agonist efficacy in GluN1 subunits, in contrast to AMPA receptor GluA2 subunits ²¹.”

1. The authors show in many panels that GluN1/GluN2A currents desensitize (e.g. Fig.1B, 3C, 4A). In Xenopus oocytes, NMDAR currents do not normally desensitize. We fear this desensitization might stem from contamination of the NMDA current by calcium-activated chloride channels, which can be activated by high quantities of barium when large NMDAR currents are measured. To avoid this problem, we advise that NMDA currents above 2 µA are avoided.

OS2) We have moved forward presuming that potential changes in current amplitude due to a small chloride flux doesn’t affect our measures of potency or ligand-selectivity. But in our new experiments, we’ve especially tried to avoid large currents.

1. Page 5, investigation of D732 state-dependent interactions: Mutation of residues near D732 to unnatural amino acids to replace the peptidic NH do not bring much information about the mechanisms of D732 action. The fact that the 734Aah and 735Vah cannot mimic the effect of the D732L mutation could be due to many factors, including the fact that changing the peptide bond probably changes the local structure of the LBD. Perhaps mention this in the discussion.

OS3) We have now acknowledged this possibility in the Results, right after we describe the decrease in glycine potency caused by the 734Aah mutation (page 7): “Although this may be due to local conformational changes due to altered main chain structure,…”

1. It is intriguing that the D732L mutation locks an active conformation of the GluN1 subunit but not the GluN2 subunit, suggesting two different mechanisms of LBD closure by glutamate and glycine. It would be interesting to look at the effect of the equivalent mutation on the GluN3 subunit to investigate if this locking effect is specific to glycine-binding LBDs or just to the GluN1 subunit.

OS4) We have now made and tested mutant GluN3A subunits D485L and D485F. Simply decreases glycine activity altogether (reflecting the effects of the mutations in GluN2A). Described on page 9: “Similarly, at oocytes injected with GluN1(4a)-WT and GluN3A-D845L or -D845F mRNAs, we saw no response to glycine alone or glycine in the presence of CGP 78608 (Fig 5D). Together, these results indicate that the induction of a constitutively active state by the D732L/F substitution is an exclusive feature of the GluN1 subunit, and the only conserved feature of the mutation in different subunits is a decrease in agonist potency.”

1. Page 9: Discussing the position of residue side chains from structures with 4 Å resolution does not seem relevant and would benefit from a caveat.

OS5) We want to retain our comparison of experiments with available structural data, so we have kept this but re-written to more openly acknowledge the caveat (page 12): “Indeed, in a cryo-electron microscopy (cryo-EM) study of GluN1/GluN2B receptors, D732 has only swung toward the ligand and away from A734 in a second of two putative pre-gating step structural models, although this is speculative considering the poor resolution of D732 side chains in those cryo-EM maps (12).”

1. Page 10: We don’t understand the point that the authors want to make with the activation of Aplysia californica. Please clarify.

OS6) He we were trying to say that “not much is required to change NMDARs from requisite co-agonism to single-ligand agonism”, either (a) in the lab via the D732L mutation or (b) naturally, as invertebrate NMDA receptors apparently show single-ligand agonism (results on invertebrate NDMARs in the literature). Further, we want to say that “by extension, we wonder if (c) in certain physiological situations, vertebrate NMDARs might indeed need only a single ligand.” We acknowledge this was unclear and – although it’s still speculative – we have now changed to (page 13): “Our work shows that only small changes in the GluN1 LBD are required for solely glutamate-gated currents in vertebrate GluN1/GluN2 receptors, and previous work suggests that invertebrate Drosophila melanogaster and Aplysia californica GluN1/GluN2 receptors can be activated by single ligands ^50,51. This suggests that NMDA receptors’ requirement of co-agonism is easily alleviated by certain mutations or conditions. As iGluR-modulatory proteins vary across cell types or even across neuronal compartments ^52,53 and NMDA receptor sequence varies across animals, it is foreseeable that in certain physiological settings, certain NMDA receptors might be activated by glutamate alone. But in most settings, certainly in vertebrates, it seems that glutamate-induced activation of NMDA receptors relies on a system of ambient glycine or D-serine ^54,55.”

1. In iGluRs, constitutive currents are often induced by mutations in the gate region, near the SYTANLAAF motif (e.g. lurcher mutations). Does the sequence around the gate of Trichoplast AKDF¹⁹³⁸⁶³ predict channel constitutive activity?

OS7) Our results with WT, single mutant Y742D, and double mutant Y742D/Y743S Trichoplax AKDF¹⁹³⁸³ receptors already show convincing evidence that the constitutive activity is via the Y742 and Y743 position: the tyrosine residues are unique to this leaky channel, and their mutation to more typical residues removes the leak current (Fig. 7B, page 11, revised manuscript).

But a look at upper M3 is warranted. As shown in Fig. 6C, AKDF¹⁹³⁸³ (YTANMAAFL) is quite similar to typical iGluRs (e.g. GluA2 YTANLAAFL). But one might ask about the single M/L difference in that motif, and we have therefore made and tested the M657L AKDF¹⁹³⁸³ mutant, comparing it with WT. Results show that this small M3 difference has little effect on channel activity. We have added this data in new Figure 7D and described it (page 11): “As channel activity of iGluRs also relies on the upper segment of the third membrane-spanning helix (M3, (34)), we also examined this segment in AKDF¹⁹³⁸³. AKDF¹⁹³⁸³ differs only subtly from most iGluRs with a methionine residue (M657) instead of leucine here (Fig. 6C), but we tested potential effects of this difference by mutating M657 to leucine. M657L activity was much like WT (Fig. 7D), however, confirming that divergence at Y742/Y743 and not the upper M3 segment determines the unique activity of AKDF¹⁹³⁸³.”

1. D-serine is another co-agonist that binds the GluN1 subunit. Compared to glycine, D-serine can make additional interactions with the lower lobe of GluN1 LBD. It would be interesting to look at D-serine dose-response curves in GluN1-D732L/GluN2A receptors: are these receptors also D-serine insensitive or can they be further activated by D-serine?

OS8) We have now measured the effects of D-serine on GluN1-D732L/GluN2A-WT receptors. As we now show in Figure 1B (green symbols), D-serine at increasing concentrations (100 nM through 100 μM) activates no additional current on top of the glutamate-gated current in mutant receptors. We have added to the end of the first Results paragraph (page 3): “Similarly, large currents were activated in mutant GluN1-D732L/GluN2A-WT receptors when 100 nM through 100 μM D-Serine was applied the presence of 100 µM glutamate (green in Fig. 1B).”

(This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

Author Response

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

eLife assessment

This study uses a multi-pronged empirical and theoretical approach to advance our understanding of how differences in learning relate to differences in the ways that male versus female animals cope with urban environments, and more generally how reversal learning may benefit animals in urban habitats. The work makes an important contribution and parts of the data and analyses are solid, although several of the main claims are only partially supported or overstated and require additional support.

Public Reviews:

We thank the Editor and both Reviewers for their time and for their constructive evaluation of our manuscript. We worked to address each comment and suggestion offered by the Reviewers in our revision—please see our point-by-point responses below.

Reviewer #1 (Public Review):

Summary:

In this highly ambitious paper, Breen and Deffner used a multi-pronged approach to generate novel insights on how differences between male and female birds in their learning strategies might relate to patterns of invasion and spread into new geographic and urban areas.

The empirical results, drawn from data available in online archives, showed that while males and females are similar in their initial efficiency of learning a standard color-food association (e.g., color X = food; color Y = no food) scenario when the associations are switched (now, color Y = food, X= no food), males are more efficient than females at adjusting to the new situation (i.e., faster at 'reversal learning'). Clearly, if animals live in an unstable world, where associations between cues (e.g., color) and what is good versus bad might change unpredictably, it is important to be good at reversal learning. In these grackles, males tend to disperse into new areas before females. It is thus fascinating that males appear to be better than females at reversal learning. Importantly, to gain a better understanding of underlying learning mechanisms, the authors use a Bayesian learning model to assess the relative role of two mechanisms (each governed by a single parameter) that might contribute to differences in learning. They find that what they term 'risk sensitive' learning is the key to explaining the differences in reversal learning. Males tend to exhibit higher risk sensitivity which explains their faster reversal learning. The authors then tested the validity of their empirical results by running agent-based simulations where 10,000 computersimulated 'birds' were asked to make feeding choices using the learning parameters estimated from real birds. Perhaps not surprisingly, the computer birds exhibited learning patterns that were strikingly similar to the real birds. Finally, the authors ran evolutionary algorithms that simulate evolution by natural selection where the key traits that can evolve are the two learning parameters. They find that under conditions that might be common in urban environments, high-risk sensitivity is indeed favored.

Strengths:

The paper addresses a critically important issue in the modern world. Clearly, some organisms (some species, some individuals) are adjusting well and thriving in the modern, human-altered world, while others are doing poorly. Understanding how organisms cope with human-induced environmental change, and why some are particularly good at adjusting to change is thus an important question.

The comparison of male versus female reversal learning across three populations that differ in years since they were first invaded by grackles is one of few, perhaps the first in any species, to address this important issue experimentally.

Using a combination of experimental results, statistical simulations, and evolutionary modeling is a powerful method for elucidating novel insights.

Thank you—we are delighted to receive this positive feedback, especially regarding the inferential power of our analytical approach.

Weaknesses:

The match between the broader conceptual background involving range expansion, urbanization, and sex-biased dispersal and learning, and the actual comparison of three urban populations along a range expansion gradient was somewhat confusing. The fact that three populations were compared along a range expansion gradient implies an expectation that they might differ because they are at very different points in a range expansion. Indeed, the predicted differences between males and females are largely couched in terms of population differences based on their 'location' along the rangeexpansion gradient. However, the fact that they are all urban areas suggests that one might not expect the populations to differ. In addition, the evolutionary model suggests that all animals, male or female, living in urban environments (that the authors suggest are stable but unpredictable) should exhibit high-risk sensitivity. Given that all grackles, male and female, in all populations, are both living in urban environments and likely come from an urban background, should males and females differ in their learning behavior? Clarification would be useful.

Thank you for highlighting a gap in clarity in our conceptual framework. To answer the Reviewer’s question—yes, even with this shared urban ‘history’, it seems plausible that males and females could differ in their learning. For example, irrespective of population membership, such sex differences could come about via differential reliance on learning strategies mediated by an interaction between grackles’ polygynous mating system and malebiased dispersal system, as we discuss in L254–265 (now L295–306). Population membership might, in turn, differentially moderate the magnitude of any such sex-effect since an edge population, even though urban, could still pose novel challenges—for example, by requiring grackles to learn novel daily temporal foraging patterns such as when and where garbage is collected (grackles appear to track this food resource: Rodrigo et al. 2021 [DOI: 10.1101/2021.06.14.448443]). We now introduce this important conceptual information— please see L89–96.

Reinforcement learning mechanisms:

Although the authors' title, abstract, and conclusions emphasize the importance of variation in 'risk sensitivity', most readers in this field will very possibly misunderstand what this means biologically. Both the authors' use of the term 'risk sensitivity' and their statistical methods for measuring this concept have potential problems.

Please see our below responses concerning our risk-sensitivity term.

First, most behavioral ecologists think of risk as predation risk which is not considered in this paper. Secondarily, some might think of risk as uncertainty. Here, as discussed in more detail below, the 'risk sensitivity' parameter basically influences how strongly an option's attractiveness affects the animal's choice of that option. They say that this is in line with foraging theory (Stephens and Krebs 2019) where sensitivity means seeking higher expected payoffs based on prior experience. To me, this sounds like 'reward sensitivity', but not what most think of as 'risk sensitivity'. This problem can be easily fixed by changing the name of the term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. We further apologise for not clearly explaining how our lambda parameter estimates such risk-sensitive foraging. To do so here, we need to consider our Bayesian reinforcement learning model in full. This model uses observed choice-behaviour during reinforcement learning to infer our phi (information-updating) and lambda (risksensitivity) learning parameters. Thus, payoffs incurred through choice simultaneously influence estimation of each learning parameter—that is, in a sense, they are both sensitive to rewards. But phi and lambda differentially direct any reward sensitivity back on choicebehaviour due to their distinct definitions. Glossing over the mathematics, for phi, stronger reward sensitivity (bigger phi values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For lambda, stronger reward sensitivity (bigger lambda values) means stronger internal determinism about seeking the non-risk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’. We hope this information, which we have incorporated into our revised manuscript (please see L153–161), clarifies the rationale and mechanics of our reinforcement learning model, and why lamba measures risk-sensitivity.

In addition, however, the parameter does not measure sensitivity to rewards per se - rewards are not in equation 2. As noted above, instead, equation 2 addresses the sensitivity of choice to the attraction score which can be sensitive to rewards, though in complex ways depending on the updating parameter. Second, equations 1 and 2 involve one specific assumption about how sensitivity to rewards vs. to attraction influences the probability of choosing an option. In essence, the authors split the translation from rewards to behavioral choices into 2 steps. Step 1 is how strongly rewards influence an option's attractiveness and step 2 is how strongly attractiveness influences the actual choice to use that option. The equation for step 1 is linear whereas the equation for step 2 has an exponential component. Whether a relationship is linear or exponential can clearly have a major effect on how parameter values influence outcomes. Is there a justification for the form of these equations? The analyses suggest that the exponential component provides a better explanation than the linear component for the difference between males and females in the sequence of choices made by birds, but translating that to the concepts of information updating versus reward sensitivity is unclear. As noted above, the authors' equation for reward sensitivity does not actually include rewards explicitly, but instead only responds to rewards if the rewards influence attraction scores. The more strongly recent rewards drive an update of attraction scores, the more strongly they also influence food choices. While this is intuitively reasonable, I am skeptical about the authors' biological/cognitive conclusions that are couched in terms of words (updating rate and risk sensitivity) that readers will likely interpret as concepts that, in my view, do not actually concur with what the models and analyses address.

To answer the Reviewer’s question—yes, these equations are very much standard and the canonical way of analysing individual reinforcement learning (see: Ch. 15.2 in Computational Modeling of Cognition and Behavior by Farrell & Lewandowsky 2018 [DOI: 10.1017/CBO9781316272503]; McElreath et al. 2008 [DOI: 10.1098/rstb/2008/0131]; Reinforcement Learning by Sutton & Barto 2018). To provide a “justification for the form of these equations'', equation 1 describes a convex combination of previous values and recent payoffs. Latent values are updated as a linear combination of both factors, there is no simple linear mapping between payoffs and behaviour as suggested by the reviewer. Equation 2 describes the standard softmax link function. It converts a vector of real numbers (here latent values) into a simplex vector (i.e., a vector summing to 1) which represents the probabilities of different outcomes. Similar to the logit link in logistic regression, the softmax simply maps the model space of latent values onto the outcome space of choice probabilities which enter the categorial likelihood distribution. We can appreciate how we did not make this clear in our manuscript by not highlighting the standard nature of our analytical approach—we now do so in our revised manuscript (please see L148–149). As far as what our reinforcement learning model measures, and how it relates cognition and behaviour, please see our previous response.

To emphasize, while the authors imply that their analyses separate the updating rate from 'risk sensitivity', both the 'updating parameter' and the 'risk sensitivity' parameter influence both the strength of updating and the sensitivity to reward payoffs in the sense of altering the tendency to prefer an option based on recent experience with payoffs. As noted in the previous paragraph, the main difference between the two parameters is whether they relate to behaviour linearly versus with an exponential component.

Please see our two earlier responses on the mechanics of our reinforcement learning model.

Overall, while the statistical analyses based on equations (1) and (2) seem to have identified something interesting about two steps underlying learning patterns, to maximize the valuable conceptual impact that these analyses have for the field, more thinking is required to better understand the biological meaning of how these two parameters relate to observed behaviours, and the 'risk sensitivity' parameter needs to be re-named.

Please see our earlier response to these suggestions.

Agent-based simulations:

The authors estimated two learning parameters based on the behaviour of real birds, and then ran simulations to see whether computer 'birds' that base their choices on those learning parameters return behaviours that, on average, mirror the behaviour of the real birds. This exercise is clearly circular. In old-style, statistical terms, I suppose this means that the R-square of the statistical model is good. A more insightful use of the simulations would be to identify situations where the simulation does not do as well in mirroring behaviour that it is designed to mirror.

Based on the Reviewer’s summary of agent-based forward simulation, we can see we did a poor job explaining the inferential value of this method—we apologise. Agent-based forward simulations are posterior predictions, and they provide insight into the implied model dynamics and overall usefulness of our reinforcement learning model. R-squared calculations are retrodictive, and they say nothing about the causal dynamics of a model. Specifically, agent-based forward simulation allows us to ask—what would a ‘new’ grackle ‘do’, given our reinforcement learning model parameter estimates? It is important to ask this question because, in parameterising our model, we may have overlooked a critical contributing mechanism to grackles’ reinforcement learning. Such an omission is invisible in the raw parameter estimates; it is only betrayed by the parameters in actu. Agent-based forward simulation is ‘designed’ to facilitate this call to action—not to mirror behavioural results. The simulation has no apriori ‘opinion’ about computer ‘birds’ behavioural outcomes; rather, it simply assigns these agents random phi and lambda draws (whilst maintaining their correlation structure), and tracks their reinforcement learning. The exercise only appears circular if no critical contributing mechanism(s) went overlooked—in this case computer ‘birds’ should behave similar to real birds. A disparate mapping between computer ‘birds’ and real birds, however, would mean more work is needed with respect to model parameterisation that captures the causal, mechanistic dynamics behind real birds’ reinforcement learning (for an example of this happening in the human reinforcement learning literature, see Deffner et al. 2020 [DOI: 10.1098/rsos.200734]). In sum, agent-based forward simulation does not access goodness-of-fit—we assessed the fit of our model apriori in our preregistration (https://osf.io/v3wxb)—but it does assess whether one did a comprehensive job of uncovering the mechanistic basis of target behaviour(s). We have worked to make the above points on the method and the insight afforded by agent-based forward simulation explicitly clear in our revision—please see L192–207 and L534–537.

Reviewer #2 (Public Review):

Summary:

The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find moderate support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as those observed in the Grackle data. In the third part, the authors present an evolutionary model, in which they show that learning strategies as observed in male Grackles do evolve in what they regard as conditions present in urban environments.

Strengths:

The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show moderately better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency, especially regarding data analysis.

Thank you—we are pleased to receive this positive evaluation, particularly concerning our efforts to improve scientific transparency via our study’s preregistration (https://osf.io/v3wxb).

Weaknesses:

One major weakness of this manuscript is the fact that the authors are working with quite low sample sizes when we look at the different populations of edge (11 males & 8 females), middle (4 males & 4 females), and core (17 males & 5 females) expansion range. Although I think that when all populations are pooled together, the sample size is sufficient to answer the questions regarding sex differences in learning performance and which learning processes might be used by grackles but insufficient when taking the different populations into account.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results (it might; so might muchneeded population replicates—see L310), but our Bayesian models still allow us to learn a lot from our current data. We now explain this in our revised manuscript—please see L452–457.

Another weakness of this manuscript is that it does not set up the background well in the introduction. Firstly, are grackles urban dwellers in their natural range and expand by colonising urban habitats because they are adapted to it? The introduction also fails to mention why urban habitats are special and why we expect them to be more challenging for animals to inhabit. If we consider that one of their main questions is related to how learning processes might help individuals deal with a challenging urban habitat, then this should be properly introduced.

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question. We have worked towards flushing out how urban-imposed challenges faced by grackles, such as the wildlife management efforts introduced in L64–65 (now L85–86), may apply to animals inhabiting urban environments more broadly; for example, we now include an entire paragraph in our Introduction detailing how urban environments may be characterised differently to nonurban environments, and thus why they are perhaps more challenging for animals to inhabit— please see L56–71.

Also, the authors provide a single example of how learning can differ between populations from more urban and more natural habitats. The authors also label the urban dwellers as the invaders, which might be the case for grackles but is not necessarily true for other species, such as the Indian rock agama in the example which are native to the area of study. Also, the authors need to be aware that only male lizards were tested in this study. I suggest being a bit more clear about what has been found across different studies looking at: (1) differences across individuals from invasive and native populations of invasive species and (2) differences across individuals from natural and urban populations.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49), and we are careful to call attention to the fact that these data cover both resident urban and non-urban species as well as urban invasive species (please see L49–50). We also revised our labelling of the lizard species (please see L44). We are aware only male lizards were tested but this information is not relevant to substantiating our use of this study; that is, to highlight that learning can differ between urbandwelling and non-urban counterparts. We hope the changes we did make to our manuscript satisfy the Reviewer’s general suggestion to add biological clarity.

Finally, the introduction is very much written with regard to the interaction between learning and dispersal, i.e. the 'invasion front' theme. The authors lay out four predictions, the most important of which is No. 4: "Such sex-mediated differences in learning to be more pronounced in grackles living at the edge, rather than the intermediate and/or core region of their range." The authors, however, never return to this prediction, at least not in a transparent way that clearly pronounces this pattern not being found. The model looking at the evolution of risk-sensitive learning in urban environments is based on the assumption that urban and natural environments "differ along two key ecological axes: environmental stability 𝑢 (How often does optimal behaviour change?) and environmental stochasticity 𝑠 (How often does optimal behaviour fail to pay off?). Urban environments are generally characterised as both stable (lower 𝑢) and stochastic (higher 𝑠)". Even though it is generally assumed that urban environments differ from natural environments the authors' assumption is just one way of looking at the differences which have generally not been confirmed and are highly debated. Additionally, it is not clear how this result relates to the rest of the paper: The three populations are distinguished according to their relation to the invasion front, not with respect to a gradient of urbanization, and further do not show a meaningful difference in learning behaviour possibly due to low sample sizes as mentioned above.

Thank you for highlighting a gap in our reporting clarity. We now take care to transparently report our null result regarding our fourth prediction; more specifically, that we did not detect credible population-level differences in grackles’ learning (please see L130). Regarding our evolutionary model, we agree with the Reviewer that this analysis is only one way of looking at the interaction between learning phenotype and apparent urban environmental characteristics. Indeed, in L282–288 (now L325–329) we state: “Admittedly, our evolutionary model is not a complete representation of urban ecology dynamics. Relevant factors—e.g., spatial dynamics and realistic life histories—are missed out. These omissions are tactical ones. Our evolutionary model solely focuses on the response of reinforcement learning parameters to two core urban-like (or not) environmental statistics, providing a baseline for future study to build on”. But we can see now that ‘core’ is too strong a word, and instead ‘supposed’, ‘purported’ or ‘theorised’ would be more accurate—we have revised our wording throughout our manuscript to say as much (please see, for example, L24; L56; L328). We also further highlight the preliminary nature of our evolutionary model, in terms of allowing a narrow but useful first-look at urban eco-evolutionary dynamics—please see L228–232. Finally, we now detail the theorised characteristics of urban environments in our Introduction (rather than in our Results; please see L56–71), and we hope that by doing so, how our evolutionary results relate to the rest of our paper is now better set up and clear.

In conclusion, the manuscript was well written and for the most part easy to follow. The format of eLife having the results before the methods makes it a bit harder to follow because the reader is not fully aware of the methods at the time the results are presented. It would, therefore, be important to more clearly delineate the different parts and purposes. Is this article about the interaction between urban invasion, dispersal, and learning? Or about the correct identification of learning mechanisms? Or about how learning mechanisms evolve in urban and natural environments? Maybe this article can harbor all three, but the borders need to be clear. The authors need to be transparent about what has and especially what has not been found, and be careful to not overstate their case.

Thank you, we are pleased to read that the Reviewer found our manuscript to be generally digestible. We have worked to add further clarity, and to tempter our tone (please see our above and below responses).

Reviewer #1 (Recommendations For The Authors):

Several of the results are based on CIs that overlap zero. Tone these down somewhat.

We apologise for overstating our results, which we have worked to tone down in our revision. For instance, in L185–186 we now differentiate between estimates that did or did not overlap zero (please also see our response to Reviewer 2 on this tonal change). We note we do not report confidence intervals (i.e., the range of values expected to contain the true estimate if one redoes the study/analysis many times). Rather, we report 89% highest posterior density intervals (i.e., the most likely values of our parameters over this range). We have added this definition in L459, to improve clarity.

The literature review suggesting that urban environments are more unpredictable is not convincing. Yes, they have more noise and light pollution and more cars and planes, but does this actually relate to the unpredictability of getting a food reward when you choose an option that usually yields rewards?

To answer the Reviewer’s question—yes. But we can see that by not including empirical examples from the literature, we did a poor job of arguing such links. In L43–49 we now give three empirical examples; more specifically, we state: “[...] experimental data show the more variable are traffic noise and pedestrian presence, the more negative are such human-driven effects on birds' sleep (Grunst et al., 2021), mating (Blickley et al., 2012), and foraging behaviour (Fernández-Juricic, 2000).” We note we now detail such apparently stable but stochastic urban environmental characteristics in our Introduction rather than our Results section, to hopefully improve the clarity of our manuscript (please see L56–71). We further note that we cite three literature reviews—not one—suggesting urban environments are stable in certain characteristics and more unpredictable in others (please see L59–60). Finally, we appreciate such characterisation is not certain, and so in our revision we have qualified all writing about this potential dynamic with words such as “apparent”, “supposed”, “theorised”, “hypothesised” etc.

It would be interesting to see if other individual traits besides sex affect their learning/reversal learning ability and/or their learning parameters. Do you have data on age, size, condition, or personality? Or, the habitat where they were captured?

We do not have these data. But we agree with the Reviewer that examining the potential influence of such covariates on grackles’ reinforcement learning would be interesting in future study, especially habitat characteristics (please see L306–309).

For most levels of environmental noise, there appears to be an intermediate maximum for the relationship between environmental stability and the risk sensitivity parameter. What does this mean?

There is indeed an intermediate maximum for certain values of environmental stochasticity (although the differences are rather small). The most plausible reason for this is that for very stable environments, simulated birds essentially always “know” the rewarded solution and never need to “relearn” behaviour. In this case, differences in latent values will tend to be large (because they consistently get rewarded for the same option), and different lambda values (in the upper range) will produce the same choice behaviour, which results in very weak selection. While in very unstable environments, optimal choice behaviour should be more exploratory, allowing learners to track frequently-changing environments. We now note this pattern in L240–248.

Reviewer #2 (Recommendations For The Authors):

L2: I'd encourage the authors to reconsider the term "risk-sensitive learning", at least in the title. It's not apparent to me how 'risk' relates to the investigated foraging behaviour. Elsewhere, risk-reward sensitivity is used which may be a better term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. In explaining our reinforcement model, we also now detail how risk relates to foraging behaviour. Specifically, in L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies why lamba measures risk-sensitivity, and why we continue to use this term.

L1-3: The title is a bit misleading with regard to the empirical data. From the data, all that can be said is that male grackles relearn faster than females. Any difference between populations actually runs the other way, with the core population exhibiting a larger difference between males and females than the mid and edge populations.

It is customary for a manuscript title to describe the full scope of the study. In our study, we have empirical data, cognitive modelling, and evolutionary simulations of the background theory all together. And together these analytical approaches show: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; and (3) risk-sensitive learning should be favoured in statistical environments characterised by purported urban dynamics. So, we do not feel our title “Leading an urban invasion: risk-sensitive learning is a winning strategy” is misleading with regard to our empirical data; it just doesn’t summarise only our empirical data. Finally, as we now state in L312–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences.

L13: "Assayed", is that correctly put, given that the authors did not collect the data?

Merrian-Webster defines assay as “to analyse” or “examination or determination as to characteristics”, and so to answer the Reviewer’s question—yes, we feel this is correctly put. We note we explicitly introduce in L102–103 that we did not collect the data, and we have an explicit “Data provenance” section in our methods (please see L342–347).

L42-46: The authors provide a single example of how learning can differ between populations from more urban and more natural habitats. I would like to point out that many of these studies do not directly confirm that the ability in question has indeed led to the success of the species tested (e.g. show fitness consequences). Then the authors could combine these insights to form a solid prediction for the grackles. As of now, this looks like cherry-picking supportive literature without considering negative results.

Here are some references that might be helpful in identifying relevant literature to cite:

Szabo, B., Damas-Moreira, I., & Whiting, M. J. (2020). Can cognitive ability give invasive species the means to succeed? A review of the evidence. Frontiers in Ecology and Evolution, 8, 187.

Griffin AS, Tebbich S, Bugnyar T, 2017. Animal cognition in a human-dominated world. Anim Cogn 20(1):1-6.

Kark, S., Iwaniuk, A., Schalimtzek, A., & Banker, E. (2007). Living in the city: Can anyone become an "urban exploiter"? Journal of Biogeography, 34(4), 638-651.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49). We are aware that direct evidence of fitness consequences is entirely lacking in the scientific literature on cognition and successful urban invasion; hence why such data is not present in our paper. But we now explicitly point out a role for likely fitness-affecting anthropogenic disturbances on sleep, mate, and foraging behaviour on animals inhabiting urban environments (please see L63–68). We hope these new data bolster our predictions for our grackles. Finally, the Reviewer paints a (in our view) inaccurate picture of our use of available literature. Nevertheless, to address their comment, we now highlight a recent meta-analysis advocating for further research to confirm apparent ‘positive’ trends between animal ‘smarts’ and successful ‘city living’ (please see L43).

L64: Is their niche historically urban, or have they recently moved into urban areas?

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question.

L66-67: This is an important point that is however altogether missing from the discussion.

We thank the Reviewer for highlighting a gap in our discussion regarding populationlevel differences in grackles’ reinforcement learning. In L310–312 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L68-71: The paper focuses on cognitive ability. The whole paragraph sets up the prediction of why male grackles should be better learners due to their dispersal behaviour. This example, however, focuses on aggression, not cognition. Here is a study showing differences in learning in male and female mynas that might be better suited:

Federspiel IG, Garland A, Guez D, Bugnyar T, Healy SD, Güntürkün O, Griffin AS, 2017. Adjusting foraging strategies: a comparison of rural and urban common mynas (Acridotheres tristis). Anim Cogn 20(1):65-74.

We thank the Reviewer for suggesting this paper. We feel it is better suited to substantiating our point in the Discussion about reversal learning not being indicative of cognitive ability—please see L276–277.

L73: Generally, I suggest not writing "for the first time" as this is not a valid argument for why a study should be conducted. Furthermore, except for replication studies, most studies investigate questions that are novel and have not been investigated before.

The Reviewer makes a fair point—we have removed this statement.

L80-81: Here again, this is left undiscussed later on.

By ‘this’ we assume the Reviewer is referring to our hypothesis, which is that sex differences in dispersal are related to sex differences in learning in an urban invader— grackles. At the beginning of our Discussion, we state how we found support for this hypothesis (please see L250–261); and in our ‘Ideas and speculation’ section, we discuss how these hypothesis-supporting data fit into the literature more broadly (please see L294–331). We feel this is therefore sufficiently discussed.

L77-81: This sentence is very long and therefore hard to read. I suggest trying to split it into at least 2 separate sentences which would improve readability.

Per the Reviewer’s useful suggestion, we have split this sentence into two separate sentences—please see L97–115.

L83: Please explain choice-option switches. I am not aware of what that is and it should be explained at first mention.

We apologise for this operational oversight. We now include a working definition of speed and choice-option switches at first mention. Specifically, in L107–108 we state: “[...] we expect male and female grackles to differ across at least two reinforcement learning behaviours: speed (trials to criterion) and choice-option switches (times alternating between available stimuli)”.

L83-87: Again, a very long sentence. Please split.

We thank the Reviewer for their suggestion. In this case we feel it is important to not change our sentence structure because we want our prediction statements to match between our manuscript and our preregistration.

L96-97: Important to not overstate this. It merely demonstrates the potential of the proposed (not detected) mechanism to generate the observed data.

As in any empirical analysis, our drawn conclusions depend on causal assumptions about the mechanisms generating behaviour (Pearl, J. (2009). Causality). Therefore, we “detected” specific learning mechanisms assuming a certain generative model, namely reinforcement learning. As there is overwhelming evidence for the widespread importance of value-based decision making and Rescorla-Wagner updating rules across numerous different animals (Sutton & Barto (2018) Reinforcement Learning), we would argue that this assumed model is highly plausible in our case. Still, we changed the text to “inferred” instead of “detected” learning mechanisms to account for this concern—please see L123–124.

L99: "urban-like settings" again a bit confusing. The authors talk about invasion fronts, but now also about an urbanisation gradient. Is the main difference between the size and the date of establishment, or is there additionally a gradient in urbanisation to be considered?

We now include a paragraph in our Introduction detailing apparent urban environmental characteristics (please see 56–71), and we now refer to this dynamic specifically when we define urban-like settings (please see L126–127). To answer the Reviewer’s question—we consider both differences. Specifically, we consider the time since population establishment in our paper (with respect to our behavioural and mechanistic modelling), as well as how statistical environments that vary in how similar they are to apparently characteristically urban-like environments, might favour particular learning phenotypes (with respect to our evolutionary modelling). We hope the edits to our Introduction as a whole now make both of the aims clear.

L11-112: Above the authors talk about a comparable number of switches (10.5/15=0.7), and here of fewer number of switches (25/35=0.71), even though the magnitude of the difference is almost identical and actually runs the other way. The authors are probably misled by their conservative priors, which makes the difference appear greater in the second case than in the first. Using flat priors would avoid this particular issue.

Mathematically, the number of trials-to-finish and the number of choice-optionswitches are both a Poisson distributed outcome with rate λ (we note lambda here is not our risk-sensitivity parameter; just standard notation). As such, our Poisson models infer the rate of these outcomes by sex and phase—not the ratio of these outcomes by sex and phase. So comparing the magnitude of divided medians of choice-option-switches between the sexes by phase is not a meaningful metric with respect to the distribution of our data, as the Reviewer does above. For perspective, 1 vs. 2 switches provides much less information about the difference in rates of a Poisson distribution than 50 vs 100 (for the former, no difference would be inferred; for the latter, it would), but both exhibit a 1:2 ratio. To hopefully prevent any such further confusion, and to focus on the fact that our Poisson models estimate the expected value i.e., the mean, we now report and graph (please see Fig. 2) mean and not median trialsto-finish and total-switch-counts. Finally, we can see that our use of the word “conservative” to describe our weakly informative priors is confusing, because conservative could mean either strong priors with respect to expected effect size (not our parameterisation) or weak priors with respect to such assumptions (our parameterisation). To address this lack of clarity, we now state that we use “weakly informative priors” in L457–458.

L126: It is not clear what risk sensitivity means in the context of these experiments.

Thank you for pointing out our lack of clarity. In L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies what risk sensitivity means and measures, with respect to our behavioural experiments.

L128-129: I find this statement too strong. A plethora of other mechanisms could produce similar patterns, and you cannot exclude these by way of your method. All you can show is whether the mechanism is capable of producing broadly similar outcomes as observed

In describing the inferential value of our reinforcement learning model, we now qualify that the insight provided is of course conditional on the model, which is tonally accurate. Please see L161.

L144: As I have already mentioned above, here is the first time we hear about unpredictability related to urban environments. I suggest clearly explaining in the introduction how urban and natural environments are assumed to be different which leads to animals needing different cognitive abilities to survive in them which should explain why some species thrive and some species die out in urbanised habitats.

Thank you for this suggestion. We now include a paragraph in our Introduction detailing as much—please see L56–71.

L162: "almost entirely above zero" again, this is worded too strongly.

In reporting our lambda across-population 89% HPDI contrasts in L185–186, we now state: “[...] across-population contrasts that lie mostly above zero in initial learning, and entirely above zero in reversal learning”. Our previous wording stated: ““[...] across-population contrasts that lie almost entirely above zero”. The Reviewer was correct to point out that this previous wording was too strong if we considered the contrasts together, as, indeed, we find the range of the contrast in initial learning does minimally overlap zero (L: -0.77; U: 5.61), while the range of the contrast in reversal learning does not (L: 0.14; U: 4.26). This rephrasing is thus tonally accurate.

L178-179: I think it should be said instead that the model accounts well for the observed data.

We have rephrased in line with the Reviewer’s suggestion, now stating in L217–218 that “Such quantitative replication confirms our reinforcement learning model results sufficiently explain our behavioural sex-difference data.”

L188-190: I am not convinced this is a general pattern. It is quite a bold claim that I don't find to be supported by the citations. Why should biotic and abiotic factors differ in how they affect behavioural outcomes? Also, events in urban environments such as weekend/weekday could lead to highly regular optimal behaviour changes.

Please see our response to Reviewer 1 on this point. We note we now touch on such regular events in L94–96.

L209-211: The first sentence is misleading. The authors have found that males and females differ in 'risk sensitivity', that their learning model can fit the data rather well, and that under certain, not necessarily realistic assumptions, the male learning type is favoured by natural selection in urban environments. A difference between core, middle, and edge habitats however is barely found, and in fact seems to run the other way than expected.

In our study, we found: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; (3) we are sufficiently certain risk-sensitive learning generates our sex-difference data, as our agentbased forward simulations replicate our behavioural results (not because our model ‘fits’ the data, but because we inferred meaningful mechanistic differences—see our response to Reviewer 1 on this point); and (4) under theorised dynamics of urban environments, natural selection should favour risk-sensitive learning. We therefore do not feel it is misleading to say that we mapped a full pathway from behaviour to mechanisms through to selection and adaptation. Again, as we now state in L311–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences. And we note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale—please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324.

L216: "indeed explain" again worded too strongly.

We have tempered our wording. Specifically, we now state in L218: “sufficiently explain”. This wording is tonally accurate with respect to the inferential value of agent-based forward simulations—please see L192–207 on this point.

L234: "reward-payoff sensitivity" might be a better term than risk-sensitivity?

Please see our earlier response to this suggestion. We note we have changed this text to state “risk-sensitive learning” rather than “reward-payoff sensitivity”, to hopefully prevent the reader from concluding only our lambda term is sensitive to rewards—a point we now include in L153–154.

L234-237: I think these points may be valuable, but come too much out of the blue. Many readers will not have a detailed knowledge of the experimental assays. It therefore also does not become clear how they measure the wrong thing, what this study does to demonstrate this, or whether a better alternative is presented herein. It almost seems like this should be a separate paper by itself.

We apologise for this lack of context. We now explicitly state in L275 that we are discussing reversal learning assays, to give all readers this knowledge. In doing so, we hope the logic of our argument is now clear: reversal learning assays do not measure behavioural flexibility, whatever that even is. The Reviewer’s suggestion of a separate paper focused on what reversal learning assays actually measure, in terms of mechanism(s), is an interesting one, and we would welcome this discussion. But any such paper should build on the points we make here.

L270-288: Somewhere here the authors have to explain how they have not found differences between populations, or that in so far as they found them, they run against the originally stated hypothesis.

We thank the Reviewer for these suggestions. In L310—313 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L284: should be "missing" not "missed out"

We have made this change.

L290-291: It is unclear what "robust interactive links" were found. A pattern of sexbiased learning was found, which can potentially be attributed to evolutionary pressures in urban environments. An interaction e.g. between learning, dispersal, and sex can only be tentatively suggested (no differences between populations). Also "fully replicable" is a bit misleading. The analysis may be replicable, but the more relevant question of whether the findings are replicable we cannot presently answer.

We apologise for our lack of clarity. By “robust” we mean “across population”, which we now state in L333. We again note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale— please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324. Finally, the Reviewer makes a good point about our analyses but not our findings being replicable. In L334 we now make this distinction by stating “analytically replicable”.

L306-315: I think you have a bit of a sample size issue not so much when populations are pooled but when separated. This might also factor in the fact that you do not really find differences across the populations in your analysis. When we look at the results presented in Figure 2 (and table d), we can see a trend towards males having better risk sensitivity in core (HPDI above 0) and middle populations (HPDI barely crossing 0) but the difference is very small. Especially the results on females are based on the performance of only 8 and 4 females respectively. I suggest making this clear in the manuscript.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results; it might; so might muchneeded population replicates—see L310. But our Bayesian models still allow us to learn a lot from our current data, and, at present, we infer no meaningful population-level behavioural or mechanistic differences in grackles’ behaviour. To make clear the inferential sufficiency of our analytical approach, we now include some of the above points in our Statistical analyses section in L452–457. Finally, we caution against speculating on any between-population variation, as we now highlight in L311—313 of our Discussion.

Figure 2: I think the authors should rethink their usage of colour in this graph. It is not colour-blind friendly or well-readable when printed in black and white.

We used the yellow (hex code: #fde725) and green (hex code: #5ec962) colours from the viridis package. As outlined in the viridis package vignette (https://cran.rproject.org/web/packages/viridis/index.html), this colour package is “designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing”.

Figure 3B: Could the authors turn around the x-axis and the colour code? It would be easier to read this way.

We appreciate that aesthetic preferences may vary. In this case, we prefer to have the numbers on the x-axis run the standard way i.e., from small to large. We note we did remove the word ‘Key’ from this Figure, in line with the Reviewer’s point about these characteristics not being totally certain.

I also had a look at the preregistration. I do think that there are parts in the preregistration that would be worth adding to the manuscript:

L36-40: This is much easier to read here than in the manuscript.

We changed this text generally in the Introduction in our revision, so we hope the Reviewer will again find this easier to read.

L49-56: This is important information that I would also like to see in the manuscript.

We no longer have confidence in these findings, as our cleaning of only one part of these data revealed considerable experimenter oversight (see ‘Learning criterion’).

L176: Why did you remove the random effect study site from the model? It is not part of the model in the manuscript anymore.

The population variable is part of the RL_Comp_Full.stan model that we used in our manuscript to assess population differences in grackles’ reinforcement learning, the estimates from which we report in Table C and D (please note we never coded this variable as “study cite”). But rather than being specified as a random effect, in our RL_Comp_Full.stan model we index phi and lambda by population as a predictor variable, to explicitly model population-level effects. Please see our code:

https://github.com/alexisbreen/Sex-differences-in-grackles- learning/blob/main/Models/Reinforcement%20learning/RL_Comp_Full.stan

L190-228: I am wondering if the model validation should also be part of the manuscript as well, rather than just being in the preregistration?

We are not sure how the files were presented to the Reviewer for review, but our study preregistration, which includes our model validation, should be part of our manuscript as a supplementary file.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors reported the biological role of RBM7 deficiency in promoting metastasis of breast cancer. They further used a combination of genomic and molecular biology approaches to discover a novel role of RBM7 in controlling alternative splicing of many genes in cell migration and invasion, which is responsible for the RBM7 activity in suppressing metastasis. They conducted an in-depth mechanistic study on one of the main targets of RBM7, MFGE8, and established a regulatory pathway between RBM7, MFGE8-L/MFGE8-S splicing switch, and NF-κB signaling cascade. This link between RBM7 and cancer pathology was further supported by analysis of clinical data.

Strengths:<br /> Overall, this is a very comprehensive study with lots of data, and the evidence is consistent and convincing. Their main conclusion was supported by many lines of evidence, and the results in animal models are pretty impressive.

Weaknesses:<br /> However, there are some controls missing, and the data presentation needs to be improved. The writing of the manuscript needs some grammatical improvements because some of the wording might be confusing.

Specific comments:<br /> (1) Figure 2. The figure legend is missing for Figure 2C, which caused many mislabels in the rest of the panels. The labels in the main text are correct, but the authors should check the figure legend more carefully. Also in Figure 2C, it is not clear why the authors choose to examine the expression of this subset of genes. The authors only refer to them as "a series of metastasis-related genes", but it is not clear what criteria they used to select these genes for expression analysis.

(2) Line 218-220. The comparison of PSI changes in different types of AS events is misleading. Because these AS events are regulated in different mechanisms, they cannot draw the conclusion that "the presence of RBM7 may promote the usage of alternative splice sites". For example, the regulators of SE and IR may even be opposite, and thus they should discuss this in different contexts. If they want to conclude this point, they should specifically discuss the SE and A5SS rather than draw an overall conclusion.

(3) In the section starting at line 243, they first referred to the gene and isoforms as "EFG-E8" or "EFG-E8-L", but later used "EFGE8" and "EFGE8-L". Please be consistent here. In addition, it will be more informative if the authors add a diagram of the difference between two EFGE8 isoforms in terms of protein structure or domain configuration.

(4) Figure 7B and 7C. The figures need quantification of the inclusion of MFGE exon7 (PSI value) in addition to the RT-PCR gel. The difference seems to be small for some patients.

Minor comments:<br /> The writing in many places is a little odd or somewhat confusing, I am listing some examples, but the authors need to polish the whole manuscript more to improve the writing.

(1) Line 169-170, "...followed by profiling high-throughput transcriptome by RNA sequencing", should be "followed by high-throughput transcriptome profiling with RNA sequencing".

(2) Line 170, "displayed a wide of RBM7-regulated genes were enriched...", they should add a "that" after the "displayed" as the sentence is very long.

(3) Line 213, "PSI (percent splicing inclusion)" is not correct, PSI stands for "percent spliced in".

(4) Line 216-217, the sentence is long and fragmented, they should break it into two sentences.

(5) Line 224, the "tethering" should be changed to "recognizing". There is a subtle difference in the mechanistic implication between these two words.

(6) Line 250, should be changed to "..in the ratio of two MFGE8 isoforms".

Author Response

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

We thank the reviewers for their valuable feedback which has improved this work greatly from its original form, and are elated to have such glowing reviews of the revised work published alongside the revised preprint. Reviewer 3 raises some final salient points, which deserve a brief address here.

Teeth: We thank the reviewer for clarifying their points. We do make the assumption that the ecological parameter space of toothed and beaked organisms will be comparable. Both are governed by the same set of physical principles and have the jaw bone as the most likely point of failure (teeth are harder than bone, and keratinous rhamphothecae are malleable and can be regrown with relative ease when deformed). Differences in stress/strain distribution between toothed and beaked organisms will occur but are already accounted for in our methods as we model both the teeth and rhamphotheca and will observe these different effects. We have added an explicit statement of this hypothesis to the Methods section of the manuscript.

Cranial kinesis: In our opinion, it is a safe assumption that the lower jaws of extant birds and enantiornithines are comparable. We do not see why the acquisition of kinesis in the upper jaw would generally affect the functional role of or constraints on the lower jaw. One possibility we discussed is that a quickly-moving kinetic premaxilla could let the lower jaw move a shorter distance during effective prey capture and lower the selection for speed (i.e. allow jaw-closing MA to remain higher). While we have added this possibility to our call for the investigation of cranial kinesis, we consider it too speculative to begin altering interpretations of fossil taxa. All raw measurement data remains available so that, if evidence is found for cranial kinesis having predictable effects on our measured parameters, future researchers can re-analyse our data and update any ecological predictions accordingly.

Organization: To our knowledge eLife format incorporates what one would think of as a Conclusions section into the Discussion. Our Discussion section currently contains 18 subheadings which should guide a reader to any specific topic of interest. The Discussion also progresses from a more narrow to broad focus which we and several colleagues find intuitive.

We thank all three reviewers once again for their feedback that has improved this work and their kind words throughout the process.

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

We thank all three reviewers for their detailed reviews, and generally agree with their feedback. To accompany the reviewed preprint of this manuscript, we wished to respond to comments from the reviewers so that they (and the public) will know what we are planning to incorporate in the revised manuscript we are currently preparing. If there are any comments on our plans in the meantime, please let us know.

• Reviewer 1, on concerns regarding identification of ontogenetic stage and comparison of taxa from different ontogenetic stages: It is fair to say that enantiornithine ontogeny is still poorly understood, though we believe all current evidence points to each specimen used in this study to being adequately mature for comparison to the extant birds used in the study. Stages of skeletal fusion are the standard method of assessing enantiornithine ontogeny (Hu and O'Connor 2017), and our comparison of histological work (Atterholt, Poust et al. 2021) to skeletal stages in Table S4 suggests a transition from juvenile to subadult in stage 0 or 1 and from subadult to adult within stage 3. Thus, the specimens we quantitatively examine in this study, all at stages 2 or 3 (Figure S10), are advanced subadults or adults. It is well-known that many living animals considered “adults” would be considered subadults or even juveniles to a palaeontologist (Hone, Farke et al. 2016). So, even if some individuals in this study are not fully skeletally mature, they should have obtained the morphology which they would possess for most of their lives and thus the morphology which undergoes selective pressure. We will add this context to the “Bohaiornithid Ontogeny” section and thank the reviewer for seeking more detail for this point.

• Reviewer 2, on need of a context figure: We have an artistic life reconstruction of a bohaiornithid in preparation, and can include that in the revised manuscript as a figure.

• Reviewer 2, on raptor claw categories: We explain these categories in-depth in a previous work (Miller, Pittman et al. 2023). However, we will now add a short summary of that explanation to this work so that this manuscript will become self-contained in this regard. In short, the “large raptor” category includes extant birds with records of regularly taking prey which cannot be encircled with the pes, while birds in the “small raptor” have no such records. As Reviewer 2 points out this does often follow phylogenetic lines, but not always. E.g. most owls specialise in taking small prey, but the great horned owl Bubo virginianus regularly takes mammals and birds larger than its pes (Artuso, Houston et al. 2020); and conversely we can only find reports of the common black hawk Buteogallus anthracinus taking prey samll enough for the pes to encircle (Schnell 2020) despite other accipiters frequently taking large prey. In both cases these taxa plot in PCA nearer to other large or small raptors (respectively) than to their phylogenetic relatives.

• Reviewer 3, on teeth vs beaks: We are not aware of any foods which are exclusive to toothed or beaked animals. There are some aspects of extant bird biology that may affect the way a certain diet may need to be adapted to which we do comment on, e.g. discussion of alternatives to the crop and ventriculus for processing plant matter in the Bohaiornithid Ecology and Evolution section. For functional studies, e.g. FEA, we have included the rhamphotheca in toothless models which serves the same role as teeth, to be a feeding surface. It should not matter, in theory, if the feeding surface is hard or soft as mechanical failure occurs in high stress/strain states regardless of the medium. If having teeth necessarily increases or decreses overall stress/strain relative to a beak (and from our work this does not appear to be the case), this would in turn necessarily limit dietary options. So, all models in our work should be directly comparable.

As an additional note on this topic, we address tooth shape in bohaiornithids at the end of the Bohaiornithid Ecology and Evolution section. We specifically note that their tooth shape is likley controlled by phylogeny in the current version, though we will add a note in the upcoming version that the morphospace of bohaiorntihid teeth overlaps that of many other clades with purportedly diverse diets, which is consistent with a hypothesis of diverse diets within the clade.

• Reviewer 3, on cranial kinesis: Our FE models should be unaffected by cranial kinesis, as these are two-dimensional and model the akinetic lower jaw only. Some mediolateral kinesis may be relevant in the mandible in the form of “wishboning” in different taxa, but its prevalence in extant birds is currently unknown. The preservation of enantiornithines (two-dimensionally and typically in lateral view) limits the ability to capture any mediolateral function regardless.

Our models of mechanical advantage do not account for any cranial kinesis. This is a necessary simplifcation. The nature of cranial kinesis in extant birds, and the role that it plays in feeding, is poorly understood. Cranial kinesis will increase gape, but we don’t yet know how/if it affects jaw closing force and speed (moreover, given the variation in quadrate and hinge morphology present in extant birds, this is also something that is likely to be highly diverse). We have therefore modelled the extant birds’ jaw closing systems as having one, akinetic out lever (the jaw joint to the bite point), to match the situation in our fossil taxa. This is a common simplification that has been used previously with success (Corbin, Lowenberger et al. 2015, Olsen 2017). However, we acknowledge that this simplification may introduce some error. Unfortunately, until the mechanics of cranial kinesis – and the variation in the anatomy and performance of kinetic structures in extant birds – are better understood, we cannot determine exactly what that error looks like. We therefore have greater confidence in the inter-species comparability this conservative, akinetic approach (in other words, we may not be making assumptions that are 100% accurate, but we are at least making the same assumption across all taxa, so it should be comparable in its error). We will add a section in the Mechanical Advantage and Functional Indices discussion calling for further research into the mechanics of cranial kinesis so future mechanical advantage work in birds can take this matter into account.

• Reviewer 3, on skull reconstruction: This issue is partly addressed in the Bohaiornithid Skull Reconstruction section, though we agree that adding more mentions of it in the MA and FEA Discussion sections and the Bohaiornithid Ecology and Evolution sections will benefit the manuscript. Most notably Shenqiornis and Sulcavis have similar ecological interpretations, but much of the Shenqiornis skull reconstruction uses Sulcavis bones. Longusunguis is the only other taxon which takes more than two bones from a different taxon, and in this case all but the quadrate are not used in any quanitative measurements. We have ensured that the skull reconstructions presented in Figure 2 show what portions of the skull come from what specimen so that as new material is discovered and phylogenetic relationships are updated it will be clear to future readers which parts of reconstructions will need to be updated.

• Reviewer 3, on data availability: All data including FEA models and raw measurement data are included in the same repository as the scripts, which we will make clear in the manuscript. Good catch on the data link being dead, we will publish it now.

As a final note, it was brought to our attention by another colleague that the original manuscript’s ancestral state reconstrction lacked an outgroup. An updated reconstruction using Sapeornis as an outgroup will be included in the revised manuscript. The addition of the outgroup does not change any conclusions of the manuscript.

We once again thank our reviewers for their valuable feedback and will submit a revised version of this manuscript for publication shortly. Please let us know if you have any additional comments after reading our response that we can take onboard in our revision.

References

Artuso, C., C. S. Houston, D. G. Smith and C. Rohner (2020). Great Horned Owl (Bubo virginianus), version 1.0. Birds of the World. A. F. Poole. Ithaca, NY, USA, Cornell Lab of Ornithology.

Atterholt, J., A. W. Poust, G. M. Erickson and J. K. O'Connor (2021). "Intraskeletal osteohistovariability reveals complex growth strategies in a Late Cretaceous enantiornithine." Frontiers in Earth Science 9: 640220.

Corbin, C. E., L. K. Lowenberger and B. L. Gray (2015). "Linkage and trade‐off in trophic morphology and behavioural performance of birds." Functional ecology 29(6): 808-815.

Hone, D. W. E., A. A. Farke and M. J. Wedel (2016). "Ontogeny and the fossil record: what, if anything, is an adult dinosaur?" Biology letters 12(2): 20150947.

Hu, H. and J. K. O'Connor (2017). "First species of Enantiornithes from Sihedang elucidates skeletal development in Early Cretaceous enantiornithines." Journal of Systematic Palaeontology 15(11): 909-926.

Miller, C. V., M. Pittman, X. Wang, X. Zheng and J. A. Bright (2023). "Quantitative investigation of Mesozoic toothed birds (Pengornithidae) diet reveals earliest evidence of macrocarnivory in birds." iScience 26(3): 106211.

Olsen, A. M. (2017). "Feeding ecology is the primary driver of beak shape diversification in waterfowl." Functional Ecology 31(10): 1985-1995.

Schnell, J. H. (2020). Common Black Hawk (Buteogallus anthracinus), version 1.0. Birds of the World. A. F. Poole and F. B. Gill. Ithaca, NY, USA, Cornell Lab of Ornithology.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary

This fascinating paper by M. Alfatah et al. describes work to uncover novel genes affecting lifespan in the budding yeast S. cerevisiae, eventually identifying and further characterizing a gene, YBR238C, now named AAG1 by the authors. The authors began by considering published gene sets pulled from the Saccharomyces genome database that described increases or decreases in either chronological lifespan or replicative lifespan in yeast. They also began with gene sets known to be downregulated upon treatment with the lifespan-extending TOR inhibitor rapamycin.

YBR283C was unique in being largely uncharacterized, downregulated upon rapamycin treatment, and linked to both increased replicative lifespan and increased chronological lifespan upon deletion.

The authors show that YBR283C may act to negatively regulate mitochondrial function, in ways that are both dependent on and independent of the stressresponsive transcription factor Hap4, largely by looking at relative expression levels of relevant mitochondrial genes.

In a hard-to-fully interpret but well-documented series of experiments the authors note that the two paralogues YBR283C and RMD9 (which have ~66% similarity) (a) have opposite effects when acting alone, and (b) appear to interact in that some phenotypes of ybr283c are dependent on RMD9.

A particularly interesting finding in light of the current literature and of the authors' strategy in identifying YBR283C is that changes in electron transport chain genes upon rapamycin treatment appear to be affected via YBR283C.

Based on a series of experiments the authors move to conclude the existence of "a feedback loop between TORC1 and mitochondria (the TORC1-Mitochondria-TORC1 (TOMITO) signaling process) that regulates cellular aging processes."

Strengths

Overall, this study describes a great deal of new data from a large number of experiments, that shed light on the potential specific roles of YBR238C and its paralog RMD9 in aging in yeast, and also underscore the potential of an approach looking for "dark matter" such as uncharacterized genes when seining the increasing deluge of published datasets for new hypotheses to test. This work when revised will become a valuable addition to the field.

Weaknesses

A paralog of YBR283C, RMD9, also exists in the yeast genome. While the authors indicate that part of their interest in YBR283C lies in its uncharacterized nature, its paralogue, RMD9, is not uncharacterized but is named due to its phenotype of Required for Meiotic nuclear Division, which is not mentioned or discussed anywhere in the manuscript currently.

In the context of the current work, in addition to the cited Hillen, H.S et al. and Nouet C. et al, the authors might be very interested in the 2007 Genetics paper "Translation initiation in Saccharomyces cerevisiae mitochondria: functional interactions among mitochondrial ribosomal protein Rsm28p, initiation factor 2, methionyl-tRNAformyltransferase and novel protein Rmd9p" (PMID: 17194786), which does not appear to be cited or discussed in the current version of the manuscript.

Thank you for your thorough and insightful review of our manuscript. We value your positive feedback and recognition of the strengths in our study. Your constructive comments have been carefully considered, leading to the inclusion of RMD9, identified as 'Required for Meiotic Nuclear Division,' and the addition of the relevant reference (PMID: 12586695) in the revised manuscript. This information has been incorporated into the second paragraph of the "The YBR238C paralogue RMD9 deletion decreases the lifespan of cells" results section.

Furthermore, we appreciate the reviewer's suggestion to include the 2007 Genetics paper on translation initiation in Saccharomyces cerevisiae mitochondria (PMID: 17194786). This citation has been integrated into our revised manuscript.

We believe that these revisions significantly strengthen the manuscript and address the concerns raised by Reviewer #1. We thank the reviewer for their time and valuable input.

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. Curiously, despite these selection criteria, the authors only use CLS as a proxy for cellular aging throughout their study and do not explore the effects of ybr238c deletion on RLS. This does not diminish their conclusions, but given the importance of this phenotype in their selection criteria, it is surprising that the authors did not choose to test both types of aging throughout their study.

Nonetheless, the authors demonstrate that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild-type cells, consistent with "accelerated aging" as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. However, the authors then link the increased CLS in Δybr238c yeast to mitochondrial function using only a handful of assays that do not directly test mitochondrial function. These include total cellular ATP levels, levels of reactive oxygen species, and the transcript levels of select nuclear-encoded mitochondrial genes. Yeast is well established to generate ATP through non-mitochondrial pathways such as glycolysis in fermentive conditions. While it is possible that the ATP levels assayed in the manuscript were tested in stationary phase, which would more likely reflect "mitochondrial function," the methods nor the figure legends contain these details, which are critical for the interpretation of these data. Similarly, ROS can be generated through non-mitochondrial pathways, and the transcription of nuclear-encoded mitochondrial genes is an indirect measure of mitochondrial function at best. Thus, the authors' proposed connection of ybr238c to mitochondrial function is correlative and should be substantiated with assays that more closely align with organellar function, such as respirometry or assaying the activity of oxidiative phosphorylation complexes. Finally, the authors attempt to tie the phenotypes of mitochondrial dysfunction caused by the deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the presentation of the data, such as reporting ATP levels as relative percentages or failing to perform appropriate statistical comparisons between conditions in which the authors derive conclusions, renders the data difficult to interpret. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but its influence on mitochondrial activity and ties to TORC1 signaling remain speculative.

We would like to express our gratitude to Reviewer #2 for the thoughtful feedback on our manuscript. We have carefully considered your comments and have made comprehensive revisions to address the concerns raised.

We appreciate the suggestion to investigate the role of YBR238C in replicative lifespan (RLS). However, we want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. Given the existing body of literature on this aspect, we chose not to duplicate these efforts in our study.

Instead, we focused our efforts on validating the role of YBR238C in chronological lifespan (CLS) phenotype, a finding reported in only one genome-wide study (reference 38). To enhance the comprehensiveness of our study, we performed analyses on different phenotypes, including mitochondria activity and oxidative stress, under both logarithmic-phase (condition for RLS) and stationary phase (condition for CLS). We now clearly indicate the logarithmic-phase/stationary phase conditions in the figure legends of the manuscript, specifying whether the conditions are relevant to RLS or CLS. Additional results of the new experiments have been included in the revised manuscript as supplementary figures (S3E-S3I).

To address concerns about the indirect nature of our mitochondrial function assays, we have performed relative mitochondria content (S3F), quantification of ROS levels from fermentative to stationary phase conditions (S3G), and assessment in respiratory glycerol medium (S3H), which provides a more direct insight into mitochondrial biology. Additionally, we have investigated the resistance of ybr238c∆ cells to H2O2 toxicity and found them to be more resistant compared to wild-type cells.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #2, and we hope these modifications address the concerns raised effectively.

Reviewer #3 (Public Review):

Summary: The study by Alfatah et al. presented a role for YBR238C in mediating lifespan through improved mitochondrial function in a TOR1-dependent metabolic pathway. The authors used a dataset comparison approach to identify genes positively modulating yeast chronological (CLS) and Replicative (RLS) lifespan when deleted, and their expression is reduced under Rapamycin treatment condition. This approach revealed an unknown, mitochondria-localized yeast gene YBR238C, and through mechanistic studies, they identified its paralogous gene RMD9 regulating lifespan in an antagonistic effect.

Strengths:

Findings have valuable implications for understanding the YBR238C-mediated, mitochondrial-dependent yeast lifespan regulation, and the interplay between two paralogous genes in the regulation of mitochondrial function represents an inserting case for gene evolution.

Weaknesses:

Overall, the implication/findings of this study are restricted only to the yeast model since these two genes do not have any homology in higher eukaryotes. The primary methods must be carefully designed by considering two different metabolic states: respiration-associated with CLS and fermentation-associated with RLS in a single comparative approach. Yeast CLS and RLS are two completely different processes. It is already known that most gene-regulating CLS is not associated with RLS or vice versa. The method section is poorly written and missing important information. The experimental approaches are poorly designed, and variability across the datasets (e.g., media condition "YPD," "SC" etc.) and their experimental conditions are not well described/considered; thus, presented data are not conclusive, which decreases the overall rigor of the study.

We sincerely appreciate your thorough review of our manuscript and your insightful comments. We acknowledge the limitation of our study being yeast-specific due to the absence of homologous genes in higher eukaryotes. However, we would like to highlight the significance of our findings in revealing a feedback loop between mitochondrial function and TORC1 signaling (TORC1-Mitochondria-TORC1 or TOMITO signaling process) in cellular lifespan regulation.

Our interpretation of the experimental results is grounded in recent literature. Two studies (references 62 and 63) support our findings by demonstrating TORC1 activation after mitochondrial electron transport chain dysfunction and the delay in brain pathology progression upon TORC1 inhibition, respectively. These studies, discussed in our manuscript, reinforce the relevance of our work in a broader biological context.

We recognize the importance of carefully designing our primary methods to account for the different metabolic states associated with cellular processes, such as respiration in cellular lifespan (CLS) and fermentation in replicative lifespan (RLS). We want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. To avoid duplicating these efforts, we have chosen not to reiterate these findings in our study. However, we have clarified the logarithmic-phase/stationary phase conditions in the figure legends, specifying their metabolic states relevance to RLS or CLS. Additionally, we have included new supplementary figures (S3E-S3I) to provide further details on the new experiments conducted.

We appreciate your feedback regarding the clarity and completeness of our method section. In the revised manuscript, we have invested additional effort to enhance the clarity of the method section, providing a more detailed account of the experimental procedures, including the missing information you identified.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #3, and we hope these modifications address the concerns raised effectively.

Reviewer #1 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) "TORC1 positively regulates aging, and its inhibition increases lifespan in various eukaryotic organisms including yeast and mammalian 13,26,27,29,30." Here I would suggest replacing "mammalian" with "mammals".

We have amended the sentence as recommended.

(2) "Next, we experimentally tested whether the transcriptome longevity signatures are associated with enhanced mitochondrial metabolism, whether the cellular energy level has gone up and cellular stress responses are induced with a switch to oxidative metabolism 47,48." Here I would replace "transcriptome longevity signatures is" with "transcriptome longevity signatures are".

We have amended the sentence as recommended.

(3) "Thus, HAP4-independent mechanism does exist through which YBR238C also affects cellular aging (Figure 3I)." I would replace "Thus, HAP4-independent" with "Thus, a HAP4-independent".

We have amended the sentence as recommended.

(4) "We examined other mitochondrial dysfunctional conditions to confirm that suppressive effect of rapamycin is not only specific to YBR238C-OE." I would change "that suppressive effect" to "that the suppressive effect".

We have amended the sentence as recommended.

(5) "Understanding the mechanism of aging will also require to understand the role of many genes of yet unknown function as YBR238C at the beginning of this work." I would switch "require to understand" to "require understanding".

We have amended the sentence as recommended.

(6) "The gene lists that modulate cellular lifespan in aging model organism yeast Saccharomyces cerevisiae were extracted from database SGD 22 and GenAge 23 (as of 8th November 2022)" "yeast" should not be italicized.

Corrected.

(7) Figure 1, panels C and D, ybr238c should be italicized.

Corrected.

(8) Figure 2B, top left-most (oxidative phosphorylation) network. I might consider repositioning some labels to make them more readable if possible.

Thank you for your feedback. The figure labels in Figure 2B are default from Metascape analysis, so repositioning isn't feasible. However, we have indicated in the figure legends that the full set of genes for functional enrichment analysis and the MCODE complex is available in Additional File 3.

(9) Figure 4E, rmd9, pet100, and cox6 should be italicized.

Corrected.

(10) Figure 5C, rmd9 and rmd9 ybr238c should be italicized. Corrected.

Reviewer #2 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) The presentation of data as heatmaps (Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, 6K) obfuscates the quantitative nature of the data. These data would be much stronger if presented as bar graphs with appropriate statistical analysis. If the authors prefer the visual of the heat map, there should be some statistical analysis performed to accompany these figures. This is particularly important for Figure 3D, in which the authors state "We found that HAP4 deletion significantly decrease the ETC complex I-V genes' expression" (bottom of page 8). As no statistical analyses were performed, the authors should refrain from using such language as it is unsupported by the data as analyzed.

Thank you for your insightful comments and suggestions regarding the presentation of our data. We appreciate the attention you have given to Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, and 6K.

In response to your feedback, we have carefully re-evaluated our approach. Considering the large volume of data associated with our lifespan analysis at different time points, we initially chose to visualize it using heatmaps to comprehensively capture the complexity of the results. However, we have now incorporated quantification information into the heatmaps.

For Figure 3D, which addresses the impact of HAP4 deletion on the expression of ETC complex I-V genes, we have replaced the heatmap with a bar graph. This modification allows for a clearer representation of the quantitative nature of the data. Moreover, we have conducted thorough statistical analyses comparing data between ybr238c∆ and ybr238c∆ hap4∆ to support the statements made in the text. The results of these analyses are now included in the revised figure. Moreover, we also replaced the Figure 6K heatmap with a bar graph.

We believe that these changes enhance the interpretability and robustness of our findings. We are grateful for your guidance, and we are confident that these adjustments will strengthen the overall quality of our manuscript.

(2) The presentation of ATP data, given its importance in supporting the core conclusions of this manuscript, is poor. The conditions under which yeast was collected are not reported, making these data impossible to interpret; total cellular ATP levels would be significantly altered and influenced by separate pathways in fermentive versus stationary phases. Minimally, the authors should describe the conditions of yeast growth (e.g., age, culture media) in which these measurements were made. The presentation of relative ATP percentages is problematic, particularly with measurements that deviate so far from wild-type ATP levels in conditions such as those in Figure 6A, in which the authors report that rapamycin induces a 1200% increase in cellular ATP. Previous papers have established that ATP levels in yeast hover around 4 mM and are stable through the cell cycle and across nutrient conditions (PMID: 30858198, 35438635). Given this, the reported ATP levels would be expected to be near 48 mM, which is strongly outside of the typically accepted values of 1-10 mM for this metabolite. Without understanding the contexts in which these measurements are made, as well as the absolute values for these measurements (which would be easily achievable through the use of a standard curve of ATP), these data are uninterpretable. Furthermore, it seems unlikely that yeast would be able to accommodate shifts of ATP levels that span an order of magnitude without dire cellular consequences, particularly during rapamycin treatment.

We appreciate the valuable feedback from the reviewer regarding the importance of providing detailed information on yeast growth conditions for interpreting ATP data. In response to this suggestion, we have enhanced the figure legends associated with the relevant figures to include a comprehensive description of the yeast growth conditions. This now specifies the age of the culture, culture media composition, and other pertinent parameters.

In addressing the concern raised about the rapamycin-induced ATP increase, we have carefully re-examined our experimental procedures. We performed additional experiments and confirmed the consistency of our findings in logarithmic-treated cultures. The results remain in alignment with our initial observations, reinforcing the reliability and reproducibility of our data.

(3) As stated above, the inference of mitochondrial function from cellular ATP levels, cellular ROS levels, and gene expression of a handful of nuclear-encoded genes is not sound. The authors should include further experimentation as evidence of mitochondrial functionality, such as respirometry or metabolic flux experiments.

Thank you for your constructive feedback on our manuscript. We appreciate your careful consideration of our work. In response to your concerns regarding the indirect nature of our mitochondrial function assays, we have implemented the following changes: We have incorporated additional assays to provide a more direct insight into mitochondrial biology. Specifically, we performed relative mitochondria content analysis (S3F) and quantified ROS levels under fermentative to stationary phase conditions (S3G). These assays offer a more direct and comprehensive assessment of mitochondrial function. Furthermore, we conducted experiments in respiratory glycerol medium (S3H) to complement our previous findings.

To further support our claims, we investigated the resistance of ybr238c∆ cells to H2O2 toxicity. Our results demonstrate that these cells exhibit increased resistance compared to wild-type cells. This additional evidence strengthens the link between mitochondrial function and cellular response to oxidative stress.

We believe these adjustments address your concerns and significantly enhance the robustness of our study. We hope you find these modifications satisfactory. We are grateful for your valuable input, which has undoubtedly improved the clarity and reliability of our findings.

(4) Multiple gene expression analyses are performed on n=2 measurements, and this should be bolstered by further replicates. Many bar graphs do not have accompanying statistics; these should be added. Some statistical tests are performed across inappropriate comparisons, such as Figure 3G, in which expression levels of mitochondrial genes in both deletion and overexpression strains should be compared to a wild-type control rather than to each other.

Thank you for your thorough review and constructive feedback on our manuscript. We appreciate your careful examination of our work. In response to your comments, we have made the following revisions to address your concerns: The multiple gene expression analysis in our study focused specifically on ETC genes. It is important to note that ETC genes themselves represent multiple replicates within the ybr238c deletion and overexpression cells, as illustrated in Figures 4D, 4G, and 6B.

We acknowledge and appreciate your observation regarding Figure 3G. To address this concern, we have revised the statistical comparisons. The expression levels of mitochondrial genes in the overexpression strain are now appropriately compared to a wild-type control. This correction has been applied in the figure that correctly corresponds to text in the manuscript.

(5) Figure 2B is uninterpretable as it stands, as most gene symbols are obscured.

We appreciate the reviewer's attention to Figure 2B and the feedback provided. Regarding the gene labels in Figure 2B, we would like to clarify that these labels are default outputs from the Metascape analysis, and unfortunately, repositioning them within the current figure layout isn't feasible without compromising the integrity of the information.

However, we have taken the reviewer's concern seriously and have made efforts to address the interpretability issue. To provide readers with access to the full set of genes for functional enrichment analysis and the MCODE complex, we have included this information in Additional File 3. The figure legends have been updated accordingly to guide readers to refer to Additional File 3 for a more detailed examination of the gene symbols and their annotations.

We hope that this solution addresses the concern raised by the reviewer.

(6) The conclusions to be drawn from Figure 3A are not clear, and this figure is cited only once in the text along with two other figures (page 8).

Thank you for your valuable feedback. We have carefully considered your comments and made revisions to improve the clarity of the conclusions drawn from Figure 3A.

(7) Figure 6K reports a range of 100-200% cell survival - how does a cell have 200% survival? Isn't survival binary (i.e., you survive or you are dead)? Perhaps this is meant to be relative to another condition; this should be more clearly stated in the figure, or the axis should be normalized to a maximum of 100% survival.

Thank you for your guidance and valuable feedback. Based on your recommendation, we have made significant changes to Figure 6K in the revised manuscript. Specifically, we replaced the heatmap with a bar graph to enhance clarity. Additionally, we would like to highlight that cell survival of combined treated cells is measured relative to the control treatment, which is considered 100% survival. This aims to provide a more accurate and comprehensible representation of the data. We believe these modifications contribute to a clearer presentation of our findings.

(8) The authors state that "TORC1 inhibition in yeast and human cells with mitochondrial dysfunction suppresses their accelerated aging." No studies of aging were done in human cells; survival in response to mitochondrial toxins does not reveal aging phenotypes. To state such is a substantial overstatement and should be amended to perhaps "cellular survival" rather than directly linked to aging.

We appreciate the careful review of our manuscript and the constructive feedback provided by the reviewer. In response to the concern raised regarding the statement about TORC1 inhibition and accelerated aging in human cells, we have revised the relevant passage as follows: "In turn, TORC1 inhibition in yeast and human cells with mitochondrial dysfunction enhances their cellular survival." We believe that this modification accurately reflects the outcomes of our experiments and addresses the concern raised by the reviewer. We would like to express our gratitude for the valuable feedback, which has contributed to the improvement of our manuscript. Thank you for your thoughtful consideration.

Reviewer #3 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

The authors should have attempted to fully characterize the RLS and CLS phenotype of strains lacking the YBR238C and RMD9 gene, the single most important gene identified in this study. Before further characterization, its association with aging must be tested to replicate findings from the literature. Although Figure 3 shows partially characterized CLS in SC medium, different media conditions could be tested, and the full spectrum of CLS lifespan curves should be represented. RLS phenotypes of these cells were not analyzed throughout the study.

We appreciate the suggestion to investigate the role of YBR238C in both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). However, it's essential to note that the involvement of YBR238C in the RLS phenotype has been previously documented in four studies (references 7, 39, 40, and 41). Considering the established literature on this matter, we chose not to duplicate these efforts in our study.

Our primary focus was on confirming the role of YBR238C in the chronological lifespan (CLS) phenotype, as indicated by a genome-wide study (reference 43). Accordingly, we also conducted an analysis of the role of RMD9 in CLS. The methods and figure legends explicitly state that CLS experiments for prototrophic CEN.PK113-7D strains were conducted in synthetic defined (SD) medium containing 6.7 g/L yeast nitrogen base with ammonium sulfate without amino acids and 2% glucose. For auxotrophic BY4743 strains, SD medium was supplemented with histidine (40 mg/L), leucine (160 mg/L), and uracil (40 mg/L).

It is important to clarify that SC medium was not used for CLS analysis. Instead, we employed SD medium, recommended for CLS analysis (reference 15; PMID: 22768836). The CLS experiments were conducted using three different methods, providing a comprehensive representation of the entire CLS lifespan (Figures 1C, 1D, 1E, and 1F).

While we did not present the Replicative Lifespan (RLS) phenotype explicitly, we performed experiments such as mitochondrial activity and ROS production under both CLS and RLS conditions. These additional analyses contribute valuable insights into the broader implications of YBR238C and RMD9 on cellular function.

We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We hope these explanations address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

In addition, authors include RNAseq data from Rapamycin-treated cells to identify differentially expressed genes. Notably, genes with decreased expression were used to compare KO strains' lifespan phenotype. Additional RNAseq analyses were performed on individual KO cells. The methodology section needs to be better written with information on which media and metabolic state that these cells are collected after treatment with rapamycin. If the cells are collected during logarithmic growth, the data can be compared with RLS aging gene sets only. A separate experiment has to be performed on stationary cells (respiratory) to collect RNAseq data after rapamycin treatment, then can be compared to the CLS aging gene set.

Thank you for your insightful comments and considerations regarding our methodology for obtaining Rapamycin response genes (RRGs). We appreciate the opportunity to address your concerns and provide further clarification on our experimental approach.

As mentioned in our manuscript, we obtained RRGs by treating logarithmic cells with 50 nM Rapamycin for 1 hour, and the details have been included in supplementary Figure S1C legends. Our primary objective was to compare these RRGs with agingassociated genes that modulate both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). We acknowledge the significance of this comparison and believe that our approach, treating logarithmic cells, is suitable for achieving this goal.

It is important to note that the use of a higher concentration of Rapamycin for treatment renders the cells less efficient in terms of growth, resulting in a very low optical density (OD) at 72 hours, as illustrated in Figure 6H. Unfortunately, due to this limitation in growth efficiency, obtaining Rapamycin response genes at the stationary phase was not feasible in our experimental setup.

As the experimental conditions vary among the reports and the gene expression signature significantly changes under different metabolic conditions, the media condition that samples are collected for RNAseq analyses should match the media condition that the lifespans of those KO strains are tested. However, more information needs to be detailed on these methodologies. For example, the transcriptomic signature of the YBR238C KO strain should be done under both fermentative and respiratory conditions to understand the true gene expression signature associated with CLS and RLS. Throughout the manuscript, these two metabolic conditions and associated lifespan types (CLS vs. RLS) are not differentiated and treated as the same, probably causing the biggest confounding effect that resulted in the identification of a single yeast-specific gene.

We obtained the transcriptomic signature of the YBR238C KO strain from logarithmic phase cultures. This consistency was maintained to align with the Rapamycin Response Genes (RRGs) obtained from logarithmic cells treated with rapamycin. Detailed methodology and metabolic status information is provided in the method section and relevant figure legends.

To broaden the scope of our study, we conducted analyses on various phenotypes, including mitochondrial activity and oxidative stress, under both logarithmic phase (relevant to Replicative Lifespan, RLS) and stationary phase (relevant to Chronological Lifespan, CLS). We have now explicitly indicated the logarithmic phase/stationary phase conditions in the figure legends of the manuscript, specifying their relevance to RLS or CLS.

Results from these additional experiments have been incorporated into the revised manuscript as supplementary figures (S3E-S3I). We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We trust that these explanations effectively address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

YBR238C gene KO effect on mitochondrial function missing comprehensive characterization. Whether the improved mito function caused by increased mtDNA copy number and/or increased mitochondrial number could be easily tested by analyzing normalizing RNAseq reads from mtDNA genes to reads from nucDNA genes. Data could be further combined with western blot specific to mito membrane proteins to analyze mito copy number.

Thank you for your insightful comments and suggestions. Following your recommendation, we conducted an assessment of relative mitochondrial content (see Figure S3F) and observed significantly higher mtDNA content in the ybr238c∆ compared to the wild type (see Figure S3F). Additionally, we have incorporated the methodology for mitochondrial DNA copy number analysis in the methods section.

The two paralogous gene interaction is an interesting observation. However, in yeast, it is known that deletion of one of the paralogous genes causes copy number amplification of the certain chromosome that the other paralogous gene is located, causing aneuploid chromosome. Many of the observed phenotypes can be associated with increased chromosome copy number and should be carefully tested. However, the authors did not consider this important point. Simply, using RNA seq data normalized read/per chromosome could be plotted to analyze the karyotype of YBR238C and RMD9 KO cells.

We appreciate your thoughtful consideration of our work and the suggestion to investigate chromosome copy number variations. While we did not directly test the chromosome copy, we want to highlight that our study extensively explores the impact of YBR238C on cellular lifespan through an RMD9-dependent mechanism (Figure 5). Deletion of YBR238C increases, whereas overexpression of YBR238C decreases the expression of its paralog, RMD9 (Figure 5F). Furthermore, this phenotype is associated with the lifespan of YBR238C-deleted and overexpressed cells. In our study, we have thoroughly investigated this aspect.

Author Response

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

We appreciate the care and the detail shown by the Reviewers. Their comments have made our article more focused and more accessible to a general audience.

We would like to begin with a comment about the last sentence of the “eLife assessment”. The evolution of metamorphosis in insects was a major triumph in animal evolution that subsequently impacted almost every aspect of plant and animal evolution in the terrestrial and freshwater aquatic biospheres. Unlike the metamorphoses of most other groups, whose evolutions are lost in time, insect evolution arose relatively recently (~400 mya) and insect orders have branched off at various points in this evolution and have persisted to modern times. Although these “relic” groups also have undergone millions of years of evolution and specialization, they still provide us with windows into how this progression may have come about. The study of these groups provides a unique opportunity to explore the mechanisms that underlie major life history shifts and should be of interest to anyone interested in evolution – not just entomologists.

Reviewer #1 (Public Review):

Summary:

This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:

• JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.

• JH redirects ecdysone-induced molting to direct formation of a more mature cuticle

• Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects

• The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

Strengths:

Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

Weaknesses:

This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

We appreciate the comments about the strengths and weaknesses of the paper. To deal with the weaknesses, we have condensed some of the Results to make them less cumbersome and the Discussion has been completely revised, keeping a sharp focus on the actions of JH in Thermobia embryos and how these actions relate to the status quo functions of JH in insects with metamorphosis. As part of the revision of the Discussion, we have replaced Figures 10 and 11.

Reviewer #1 (Recommendations For The Authors):

In keeping with my public review, this paper is very strong and I have very few suggestions for improvement. They are:

(1) Thermobia are extant insects and are not ancestral insects. It is likely that they retain features found in an insect ancestor. However, these insects have been evolving for a very long time, and for any one feature, many changes may have occurred, both gain and loss of gene function and morphology. Further, even for morphological features present in an extant species that are the same as an ancestor, genetic pathways regulating this feature may have changed over time (see for examples papers from the Haag and Pick labs). Although I realize this is a small, possibly almost semantic point, I feel it is important to be precise here. For example, in the title, "before" is speculative as there could have been a different role in the ancestor with the role in embryogenesis arising in lineages leading to Thermobia; similarly in the abstract, "this ancestral role of JH' is an overstatement since we cannot actually measure the ancestral role.

Since the title has already been cited in a Perspectives review, we decided to keep the title as is.

(2) I don't understand the results in Met and myo in Fig. 3B. Perhaps include them in the explanation of Fig.3 and not after the description of Fig. 4 and explain them in more detail (or perhaps not include them at all?). I don't really understand the statistical analysis of these panels either.

We have revised the figure legends to explain the statistics.

(3) Another point regarding language - talking about the embryo being "able" to go through a developmental stage implies decision-making. I would suggest dropping that wording (e.g, in the description of Fig. 5C). Similarly, in explaining Fig. 6B, it would be more correct to say "JH treatment no longer inhibited" than as written "could no longer inhibit" (implying 'no matter how hard it tried, it still couldn't do it')

We have removed the “can’t” wording. Figure 6 has been revised

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

The unique and intriguing aspect of juvenile hormone is its status quo action in the control of metamorphosis. Our reason for dealing with an insect group that branched off from the line of insects that eventually evolved metamorphosis, was to gain insight into the ancestral functions of this hormone. Our data from Thermobia as well as that from grasshoppers and crickets indicate that the developmental actions of JH were originally confined to embryogenesis where it promoted the terminal differentiation of the embryo. Its actions in promoting differentiation also included suppressing morphogenesis. This latter function was not pronounced during embryogenesis because JH only appeared after morphogenesis was essentially completed. However, it was a preadaptation that proved useful in more derived insects that delayed aspects of morphogenesis into the postembryonic realm. JH was then used postembryonically to inhibit morphogenesis until late in juvenile growth when JH disappears, and this inhibition is released.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

We disagree with the reviewer. An analogous situation would be in exploring gene function in which both gain-of-function and loss-of-function experiments often provide complementary insights into how a gene functions. We see JH effects only when its receptor, Met, is present and JH can induce its main effector protein, Kr-h1. The latter gives us confidence that we are looking at bona fide JH effects. We have also kept in mind, though, that the nature of the responding tissues is changing through time. Nevertheless, we see a consistent pattern of responses in the embryo and these can be related to its postembryonic effects in metamorphic insects.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

We have tried to dial back the speculation.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Our Figure 12 should put this into perspective.

Reviewer #2 (Recommendations For The Authors):

Main points

(1) Please, reduce the level of overinterpretation of ectopic treatment experiments with JHm, since the resulting observations represent effects, but not necessarily functions of JH.

We have revised this section to indicate that the “effects” of ectopic treatments provide insights into the function of JH. Using a genetic analogy, both “loss-of-function” and “gain-of-function” experiments provide insights into a given gene. (see response to Public Comments)

(2) Especially in the sections "JH and the progressive nature of embryonic molts" and "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories", and the entire "Discussion", please keep the level of speculation within reasonable limits, avoiding especially the inference of conclusions on the basis of speculation, itself based on previous speculation.

We have toned down some of the speculation and provided reasons why it is worth suggesting.

(3) Please revisit the argued roles of myoglianin in the story, in light of its effects as an inhibitor of JH production, repressing the expression of JHAMT, as has been reliably demonstrated in hemimetabolan species (DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R).

Our appreciation to the reviewer. We are more explicit about the relationship between JH and myo.

Minor points

(4) Please keep the consistency of the scientific binomial nomenclature for the species mentioned. For example, read "Manduca sexta" (in italics) at the first mention, and then "M. sexta" (in italics) in successive mentions (instead of reading "Manduca" on page 17, and then "Manduca sexta" on page 18, for example). The same for "Drosophila" ("Drosophila melanogaster" first, and then "D. melanogaster"), "Thermobia" ("Thermobia domestica" first, and then "T. domestica"), etc. In the figure legends, I recommend using the complete name: Thermobia domestica, in the main heading.

Where there is no possibility of confusion, we intend to use Thermobia, rather than T. domestica, etc. We think that it is easier for a non-specialist to read and it is commonly done in endocrine papers.

(5) There is no purpose in evolution and biological processes. Thus, I suggest avoiding expressions that have a teleological aftertaste. For example (capitals are mine), on p. 3 "appears to have been extended into postembryonic life where it acts TO antagonize morphogenic and allow the maintenance of a juvenile state".

We have tried to avoid teleological wording.

(6) The title "The embryonic role of juvenile hormone in the firebrat, Thermobia domestica, reveals its function before its involvement in metamorphosis" contains a redundancy ("role" and "function"), and an apparent obviousness ("before its involvement in metamorphosis"). I suggest a more straightforward title. Something like "Juvenile hormone plays developmental functions in the embryo of the firebrat Thermobia domestica, which predate its status quo action in metamorphosis".

As noted above, we are retaining the title since it has already been cited.

(7) Page 2. "The transition from larva to adult then occurred through a transitional stage, the pupa, thereby providing the three-part life history diagnostic of the "complete metamorphosis" exhibited by holometabolous insects (reviews: Jindra, 2019; Truman & Riddiford, 2002, 2019)". I suggest adding the reference ISBN: 9780128130209 9 7 8 - 0 - 1 2 - 8 1 3 0 2 0 - 9, as the most comprehensive and recent review on complete metamorphosis.

Done

(8) Page 3. "These severe developmental effects suggest that the developmental role of JH in insects was initially CONFINED to the embryonic domain" (capitals are mine). This appears contradictory with the observations of Watson, 1967, on the relationships between the apparition of scales and JH, mentioned shortly before by the authors.

This is explained in the Discussion. Although JH can suppress scale appearance in the J4 stage, we have not been able to show that scales appearance is caused by changes in the juvenile JH titer.

(9) Page 4. "we measured JH III levels during Thermobia embryogenesis at daily intervals starting at 5 d AEL". Why not before, like in the case of ecdysteroids? The authors might perhaps argue that the levels of Kr-h1 expression are consistently low from the very beginning, according to Fernandez-Nicolas et al, 2022 (reference cited later in the manuscript).

(10) Page 4. "Ecdysteroid titers through embryogenesis and the early juvenile instars were measured using the enzyme immunoassay method (Porcheron et al., 1989) that is optimized for detecting 20-hydroxyecdysone (20E)". The antibody generated by Porcheron (and now sold by Cayman) recognizes ecdysone and 20-hydroxyecdysone alike. But that's not relevant here. I would refer to "ecdysteroids" when mentioning measurements. Also in figure 2B (and "juvenile hormone III" without the formula, in Panel A, for harmonization). And I would not expand on specifications, like those at the beginning of page 5, or towards the end of page

We thank the reviewer for this important correction.

(12) ("the fact that we detected only a slight rise in ecdysteroids at this time (Fig 2B) is likely due to the assay that we used being designed to detect 20E rather than ecdysone").

Omitted.

(11) Page 5. "Low levels of Kr-h1 transcripts were present at 12 hr after egg deposition, but then were not detected until about 6 d AEL when JH-III first appeared". There is a very precise Kr-h1 pattern in Fernandez-Nicolas et al. 2023 (reference mentioned later in the manuscript).

(12) Page 5. "notably myoglianin (myo), have become prominent as agents that promote the competence and execution of metamorphosis in holometabolous and hemimetabolous insects (He et al., 2020; Awasaki et al., 2011)". See my note 3 above.

The myoglianin issue has been revised.

(13) Page 5. "a drug that suppresses JH production". Rather, "a drug that destroys the JH producing tissues". Why the way, do the authors know when the CA are formed in T. domestica embryo development?

We prefer to keep our original wording. There have been some cases in which precocene has blocked JH production but did not kill the CA cells. We do not have observations that show that 7EP kills the CA cells in Thermobia embryos.

(14) Page 5. "subsequent treatment with a JHm". I would say here that the JHm is pyriproxyfen, not on page 6 or page 7. Thus, to be consistent, after the first mention of "pyriproxyfen (JHm)" on page 5, I'd consistently use the abbreviation "JHm".

(15) Page 9. "Limb loss in such embryos was often STOCHASTIC, i.e., in a given embryo some limbs were completely lost while others were maintained in a reduced state" (capitals are mine). The meaning of "stochastic" is random, involving a random variable; it is a concept usually associated to probability theory and related fields. I suggest using the less specialized word "variable", since to ascertain that the values are really stochastic would require specific mathematical approaches.

We are still using stochastic because the loss is random.

(16) Page 10. "9E). Indeed, the JH treatment redirects the molt to be more like that to the J2 stage, rather than to the E2 (= J1) stage". Probably too assertive given the evidence available (see my points 1 and 2 above).

We do not see a problem with our conclusion. In response to the JHm treatment, the embryo produced a smooth, rather than a “pebbly” cuticle, failed to make the J1-specific egg tooth, and attempted to make cuticular lenses (a J2 feature). This ability of premature JH exposure to cause embryos to “skip” a stage is also seen in locusts (Truman & Riddiford, 1999) and crickets (Erezyilmaz et al., 2004). The JHm treatment resulted in the production of smooth cuticle, lack of a hatching tooth, and an attempt to make cuticular lenses.

(17) Page 11. "early JHM treatment", read "early JHm treatment".

Corrected

(18) Page 11. "likely. A target of JH, and likely Kr-h1, in Thermobia is myoglianin...". Please see my notes 1, 2, and especially 3, above.

This has been revised

(19) Page 13. "the locust, Locusta americana (Aboulafia-Baginshy et al.,1984)". Please read "the locust, Locusta migratoria (Aboulafia-Baginshy et al.,1984)".

Corrected

(20) Page 13 "Acheta domesticus" three times. The correct name now is "Acheta domestica", after harmonizing the declension of the specific name with the generic one. See additionally my note 4 above.

Acheta domesticus has been used in hundreds (thousands?) of papers since it was originally named by Linnaeus. We will continue to use it.

(21) Page 15, "(also called the vermiform larva (Bernays, 1971) redirects embryonic development to form an embryo with proportions, cuticular pigmentation, cuticular sculpturing and bristles characteristic of a nymph, while pronymph modifications, such as the cuticular surface sculpturing (Bernays, 1971)". The reference "Bernays, 1971" is indeed "Bergot et al., 1971".

There was a mistake in the references. The Bernays reference was omitted from the revised Discussion

(22) Page 16. "Since JH also induces Kr-h1 in embryos of many insects, including Thermobia". I'm not sure that this has been studied in many insects. In any case, any reference would be useful.

(23) Page 17. "Tribolium casteneum". Please read "Tribolium castaneum".

Changed

(24) Page 17. "...results in a permanent larva that continues to molt well after it has surpassed its critical weight (He et al., 2019)". The paper of He et al., 2019 is preceded by two key papers that previously demonstrate (and in hemimetabolan insects) that myoglianin is a determining factor in the preparation for metamorphosis: DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R). See my note 3 above.

Corrected in revision

(25) Page 18. "These persisting embryonic primordia join the wing primordia in delaying their morphogenesis into postembryonic life". This reader does not understand this sentence.

Made clearer in the revision.

(26) Page 18. "is first possible in the commercial silkworm (Daimon et al., 2015)". Please mention the scientific Latin name of the species, Bombyx mori.

(27) Page 19. "The functioning of farnesol derivatives in growth versus differentiation control extends deep into the eukaryotes.../... this capacity was eventually exploited by the insects to provide the hormonal system that regulates their metamorphosis". This information appears quite out of place.

We have retained this point.

(28) Page 21. Heading "Hormones". I suggest using the heading "Bioactive compounds", as neither pyriproxyfen nor 7-ethoxyprecocene are hormones.

Done

(29) Page 29, legend of figure 1. "Photomicrographs" is somewhat redundant. The technical word is "micrographs". "Thermobia domestica" appears in the explanation of panel C, but this is not necessary, as the name appears in the main heading of the legend.

Done

(30) Page 30, legend of figure 2. Panel B, see my comment 10 above. Why embryonic age is expressed in % embryo development in panel C (and in days in panels A and B)?

All have been converted to days AEL

(31) Page 35, legend of figure 5. "Photomicrograph" see my note 28 above.

Done

(32) Page 40, figure 10. In panel A, the indication of the properties of JH is misleading. The arrow going to promoting differentiation and maturation is OK, but the repression sign that indicates suppression of morphogenetic growth and cell determination seems to suggest that JH has retroactive effects. In panel B, I suggest to label "Flies" instead of "Higher Diptera", which is an old-fashioned term. In any case, see my general comments 1 and 2, above, about speculation.

Figure has been completely revised

(33) Figure 11. See my general comments 1 and 2, above, about speculation.

Figure has been revised

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

Strengths:

The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.

The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.

The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

Weaknesses:

The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

We agree completely and should be a feature of future work.

The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

We have tried to be less all-encompassing in the Discussion. The strongest comparisons can be made between ametabolous and hemimetabolous insects and we have focused most of the Discussion on the role of JH in that transition. We still include some discussion of holometabolous insects because the ancestral embryonic functions of JH may be somehow related to the unusual reappearance of JH in the prepupal period. We have reduced this discussion to only a few sentences.

Reviewer #3 (Recommendations For The Authors):

(1) The overall manuscript is very long (especially the discussion), and the main messages of the manuscript get lost in some of the details. I would suggest that the authors move some of the results to the supplementary material (e.g. it might be possible to put a lot of the detail of Thermobia embryogenesis into the supplementary text if the authors feel it is appropriate). The discussion contains a lot of speculation and I suggest the authors make this more concise. One example: At the moment there is a large section on the modification in JH function during the evolution of holo and hemi-metabolous life history strategies. There are some interesting ideas in this section and the authors do a good job of integrating their findings with the literature - but I would encourage the authors to limit the bulk of their discussion to the specific things that their results demonstrate. E.g. The first half of p17 contains too much detail, and the focus should be on the relationship with Thermobia (as at the bottom of p17).

Section has been revised and is more focused

(2) I would also suggest a thorough proofread of the manuscript, I have highlighted some of the errors/points of confusion that I found in the list below - but this list is unlikely to be exhaustive . We appreciate catching the errors. Hopefully the final version is better proofed.

(3) It might be me, but I found the wording in the second half of the abstract a bit confusing. Particularly the statement about the redeployment of morphogen systems - could this be stated more clearly?

Abstract has been revised.

(4) Introduction

a. "powered flight" rather than 'power flight'

Done

b. 'brought about a hemimetabolous lifecycle' implies causality which hasn't been shown and directionality to evolution - suggest 'facilitated the evolution of a hemi...". Similar comment for 'subsequent step to complete metamorphosis'.

c. Bottom of p2 - unclear whether you are referring to hemi- holo- or both

d. Suggest removing sentence beginning "besides its effects..." as the relevance of the role of JH in caste isn't clear.

Kept sentence but removed initial clause

e. State that Thermoia is a Zygentoma.

Done

f. Throughout - full species names on first usage only, T. domestica on subsequent usages.

We will continue to use genus names for the reason given above.

Gene names e.g. kr-h1 in italics.

g. 'antagonise morphogens"? rather than 'antagonise morphoentic'.

Done

(5) Results

a. Unclear why drawings are provided rather than embryonic images in Fig. 1A

We think that the points can be made better with diagrams.

b. Top of p4, is 'slot' the correct word?

Corrected

c. Unclear why the measurements of JHIII weren't measured before 5 days AEL, especially given that many of the manipulative experiments are at earlier time points than this. I appreciate that, based on kr-h1, levels that JHIII is also likely to be low.

d. Reference for the late embryonic peak of 20E being responsible for the J2 cuticle?

Clarified that this is an assumption

e. Clarify "some endocrine related transcripts" why were these ones in particular picked? Kr-h1 is a good transcriptional proxy for JH and Met is the JH-receptor, why myoglianin and not some of the other transcriptional proxies of neuroendocrine signalling?

Hopefully, the choice is clearer.

f. Fig 2C rather than % embryo development for the gene expression data please represent this in days (to be consistent with your other figures).

It is now consistent with other parts of figure.

g. In Fig. 3 the authors do t-tests, because there are three groups there needs to be some correction for multiple testing (e.g. Bonferroni) can the authors add this to the relevant methods section?

We think that pair-wise comparisons are appropriate.

h. Fig. 3 legend: you note that you treat stage 2 juveniles with 7EP - I couldn't tell what AEL this corresponded to.

This is after hatching so AEL does not apply.

i. Top of p7 'deformities' rather than 'derangements'?

Done

j. Regarding the dosage effects of embryonic abnormalities - it would be good to include these in the supp material, as it convinces the reader that the effects you have seen aren't just due to toxicity.

It is not clear what the objection is.

k. Bottom of p7 'problematic' not 'problematical'

Done

l. P8 Why are the clusters of Its important? - provide a bit more interpretation for the reader here.

This is clear in the revised version.

m. P9 Why is the modulation of transcription of kr-h1, met, and myo important in this context

Explained

n. P9 'fig. 7F'? there is no Fig. 5F

Thanks for catching the typo.

o. Fig. 7B add to the legend which treatment the dark and light points correspond to.

We think it is obvious from the labeling on Fig 7B.

(6) Discussion:

a. What do we know about how terminal differentiation is controlled in non-insect arthropods? Most of the discussion is focused on insects (which makes sense as JH is an insect-specific molecule), but if the authors are arguing the ancestral role of JH it would be useful to know how their findings relate to non-insect arthropods.

We have not been able to find any information about systemic signals being involved in non-insect arthropods.

b. There is no Fig. 5E (are they referring to 7E?)

Yes, it should have been Fig. 7E.

c. Is myoglianin a direct target of JH in other species?

Other reports are in postembryonic stages and show that myoglianin suppresses JH production. Our paper is the first examination in embryos and we find that the opposite is true – i.e., that JH treatment suppresses myoglianin production. We suspect that these two signaling systems are mutually inhibitory. It would be interesting to see whether treatment of a post-critical weight larva with JH (which would induce a supernumerary larval molt) would also suppress myoglianin production (as we see in Thermobia embryos).

d. P12 What is the evidence that JH interacts with the first 20E peak to alter the embryonic cuticle?

We are not sure what the issue is. The experimental fact is that treatment with JH before the E1 ecdysteroid peak causes the production of an altered E1 cuticle. We are faced with the question of why is this molt sensitive to JH when the latter will not appear until 3 or 4 days later? A possible answer is that the ecdysone response pathway has a component that has inherent JH sensitivity. The mosquito data suggest that Taiman provides another link between JH and ecdysone action

e. Top of p13 - this paragraph can be cut down substantially. Although this is evidence that JH can alter ecdysteriods - it is in a species that is 400 my derived from the target species. Is it likely to be the exact same mechanism? I would encourage the authors to distil and retain the most important points.

This paragraph has been shortened and focused.

f. Bottom of p13 - what does this study add to this knowledge?

The response of Thermobia embryos to JH treatment is qualitatively the same as seen in other short germband embryos. This similarity supports the assumption that the same responses would have been seen in their last common ancestor.

g. P19 the last paragraph in the conclusions is really peripherally relevant to the paper and is a bit of a stretch, I would encourage the authors to leave this section out.

We agree that it is a stretch. JH and its precursor MF are the only sesquiterpene hormones. How did they come about to acquire this function? We think it is worth pointing out the farnesol metabolites have been associated with promoting differentiation in various eukaryotes. An ancient feature of these molecules in promoting (maintaining?) differentiation may have been exploited by the insects to develop a unique class of hormones. It is worth putting the idea out to be considered.

h. P19 "conclusions" rather than 'concluding speculations'.

Changed as suggested.

Methods:

It is standard practice to include at least two genes as reference genes for RT-qPCR analysis (https://doi.org/10.1186/gb-2002-3-7-research0034, https://doi.org/10.1373/clinchem.2008.112797) If there are large-scale differences in the tissues being compared (e.g. as there are here during development) then more than two reference genes may be required and a reference gene study (such as https://doi.org/10.3390%2Fgenes12010021) is appropriate. Have the authors confirmed that rp49 is stably expressed during the stages of Thermobia development that they assay here?

We have explained our choice in the Methods.

Author Response

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

eLife assessment

This study is valuable as it sheds light on the pivotal role played by alterations in glycan metabolism within chondrocytes in the onset of cartilage degeneration and early onset of osteoarthritis (OA) through the process of hypertrophic differentiation of chondrocytes, giving insights into the identification of nascent markers for early-stage OA. Although the methods, data, and analyses broadly support the claims, the data shown by the authors are incomplete because the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs has not been fully elucidated. The authors' deductions stand to gain further credence through undertaking additional experiments aimed at analyzing the mechanisms underlying the changes in glycometabolism in cartilage, such as the meticulous identification of the target glycan molecules bearing core fucose and analysis of endochondral ossification in cartilage-specific Fut8 KO mice.

We wish to express our strong appreciation to the Reviewer for his or her insightful comments on our paper. We feel the comments have helped us significantly improve the paper. In particular, we wish to acknowledge the Reviewer’s highly valuable comments on the effect of Fut8 on endochondral ossification.

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

This study is valuable in that it may lead to the discovery of future OA markers, etc., in that changes in glycan metabolism in chondrocytes are involved in the initiation of cartilage degeneration and early OA via hypertrophic differentiation of chondrocytes. However, more robust results would be obtained by analyzing the mechanisms and pathways by which changes in glycosylation lead to cartilage degeneration.

Strengths:

This study is important because it indicates that glycan metabolism may be associated with pre-OA and may lead to the elucidation of the cause and diagnosis of pre-OA.

We thank reviewer #1 for their interest in our work and their overall positive report.

Weaknesses:

More robust results would be obtained by analyzing the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs.

To understand the mechanisms of cartilage degeneration induced by changes in glycometabolism, we attempted additional experiments using rescue experiments with external administration of TGF-β. We had shown that the addition of mannosidase to an organ culture system of normal wild-type mouse cartilage increased TGF-β gene expression from 6 hours (Fig. 3E) and that TGF-β expression was even suppressed in chondrocytes from Fut8 cKO mice (Fig. 4D). In addition to these results, an early OA model in which mannosidase is added to the cartilage was used to test the effect of exogenous TGF-β. As a result, under TGF-β treated conditions, no degenerative changes occurred when high-mannose type N-glycans were trimmed, and proteoglycan leakage during the recovery period was significantly reduced. This was considered to be a very useful finding and it was decided to include the experimental results in Figure 4F, rather than making them supplement data.

Reviewer #2 (Public Review):

Summary:

This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:

This paper is interesting in the clinical field, but unfortunately, the data is mostly descriptive and does not have a significant impact on the scientific community in general.

We thank reviewer #2 for their interest in our work and their overall positive report. In response to your comment about our attempts to show that glycan changes occur at the precursor stage of cartilage substrate degeneration and that this glycosylation is also what triggers substrate degeneration, we would like to add that reversing cartilage substrate degeneration is a very ambitious challenge. We are currently in the preparatory stages of characterizing the appropriate glycan-substrate relationships to 'rescue' cartilage tissue from degeneration, and we hope to use this approach to provide information on the pre-developmental stages of OA.

Weaknesses:

If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.

As the increase in NO was observed in the organ culture system and cartilage is a tissue without vascular invasion, we thought that the involvement of immune cells could be excluded. On the other hand, our research group has reported that chondrocytes themselves have inflammatory circuits (Ota et al., Arthritis Rheum. 2019. DOI:10.1002/art.41182), but as we did not find increased expression of NF-κB, an indicator of inflammatory amplifier activation, we concluded that inflammation was not involved in this study.

It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alpha-mannosidase administration increases core fucose.

The report by Toegel et al. that the synthesis of complex-type N-glycans (Man2a1, Mgat2) is predicted in human OA chondrocytes along with the expression of Fut8 also led to the expectation that administration of α-mannosidase would increase core fucose. However, there was no conclusive evidence that administration of α-mannosidase increased core fucose; in 1987, Vignon et al performed an enzyme assay on experimental OA cartilage (rabbit ACLT model) and showed that mannosidase was very high in operated joints and that its activity increased and decreased with the severity of fibrosis in the cartilage. The results suggest that glycoprotein hexose degradation is an early transient event in the enzymatic process of cartilage destruction. These findings led to the conception of a novel 'pre-OA model' in which mannosidase is added to the joint. The present study is valuable in its demonstration that glycometabolism is a driver of degeneration.

(see manuscript REF. 25, 9)

Toegel et al., Arthritis Res. Ther. 2013. DOI:10.1186/ar4330

Vignon et al., Clin Rheumatol. 1987. DOI:10.1007/BF02201026

Reviewer #3 (Public Review):

Summary:

In the manuscript "Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis", the authors investigate the glycan structural changes in the context of pre-OA conditions. By mainly conducting animal experiments and glycomic analysis, this study clarified the molecular mechanism of N-glycan core fucosylation and Fut8 expression in the extracellular matrix resilience and unrecoverable cartilage degeneration. Lastly, a comprehensive glycan analysis of human OA cartilage verified the hypothesis.

Strengths:

Generally, this manuscript is well structured with rigorous logic and clear language. This study is valuable and important in the early diagnosis of OA patients in the clinic, which is a great challenge nowadays.

We thank reviewer #3 for their interest in our work and their mainly positive report. This is precisely the purpose of our study, as we are primarily interested in the detection of conditions prior to the onset of OA.

Weaknesses:

I recommend minor revisions:

(1) I would suggest the authors prepare an illustrative scheme for the whole study, to explain the complex mechanism and also to summarize the results.

We would like to thank the reviewer for this comment and have created a new Figure 7 for the overall study scheme.

We included the following statement in the opening discussion part:

"The objective of this work was to provide novel and translational insights into pathogenesis of OA associated with changes in glycan structure. A graphical abstract summarizing our findings is shown in Fig. 7." (line199-201, p9)

(2) Including but not limited to Figures 2A-C, Figures 3A and C, Figure 4B, and Figures 5A and D. The texts in the above images are too small to read, I would suggest the authors remake these images.

The font size of the figures has been reviewed and revised throughout.

(3) The paper is generally readable, but the language could be polished a bit. Several writing errors should be realized during the careful check.

Thanks to your suggestion, I have noticed several writing errors. In addition, we have had the manuscript rewritten by an experienced scientific editor, who has improved the grammar and stylistic expression of the paper.

(4) As several species and OA models were conducted in this study, it would be better if the authors could note the reason behind their choice for it.

The authors agree with the reviewer's argument that since several species and OA models were performed in this study, it would be better to note the reason for their choice.

We first attempted to inject mannosidase into rabbits, matching the animal species to a previous paper showing that N-glycans are altered prior to degeneration of the cartilage matrix. Next, we checked whether similar changes occur in mouse cartilage after mannosidase treatment, assuming that we would verify this in genetically engineered mice. We then used the integrated glycome in human cartilage to see if the corefucosylation phenomenon detected was conserved across species.

For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA.

We included the following statements in each Methods part:

"We injected mannosidase into rabbit knee joints in accordance with a previous paper showing that N-type glycans are altered prior to cartilage matrix degeneration." (line289-290, p12)

"Organ culture experiments in mice were established to study the effects of mannosidase on articular cartilage without immunoreaction and in anticipation of later candidate gene research using transgenic mice." (line326-328, p14)

"To determine whether the glycosylation detected is conserved across species, we analyzed the total glycome in human cartilage." (line407-408, p17)

We included the following statements in the Discussion part:

"For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA." (line254-257, p11)

Reviewer #1 (Recommendations For The Authors):

(1) The cited literature states that core fucosylation by FUT8 has a chondroprotective effect via the TGF-β pathway and that the loss of these chondroprotective effects in Fut8 led to cartilage degeneration, but these need to be proven by experiment.

We agree that corefucosylation and the TGF-β signaling pathway are important lines of investigation. We have now acknowledged this and added in the revised manuscript that additional experiments have shown that TGF-β restores the protective effects of Fut8 cKO cartilage by external administration.

We included the following statements in the Results part:

"To evaluate whether TGF-β1 decreases cartilage degeneration after mannosidase stimulation, TGF-β1 was exogenously added to Col2-Fut8−/− cartilage in the presence of α-mannosidase stimulation for 24 h. The samples treated with TGF-β1 leaked significantly less PG following mannosidase stimulation compared to samples not treated with TGF-β1 (Fig. 4F)." (line143-147, p6-7)

We included the following statements in the Discussion part:

"Here, the exogenous addition of TGF-β1 rescued them from cartilage degeneration." (line274-275, p12)

(2) There are skeletal differences in cartilage-specific Fut8 KO mice compared to WT, and the effect of Fut8 on endochondral ossification should also be analyzed.

We agree that Fut8 is associated with various endochondral ossification processes (for example by the TGF-β signaling pathway). Moreover, we would like to thank the reviewer for the proposed experiment.

The growth curve was normal at birth, with differences beginning around weaning (~3 w for mice). Therefore, we evaluated the epiphyseal line of 4-week-old mice stained with toluidine, type 10 collagen, and proliferating cell nuclear antigen. This is similar to the epiphyseal growth plate phenotype of Smad3ex8/ex8 mice by Yang et al. and is consistent with the finding that Smad3 deficiency does not affect chondrogenesis during developmental stages, but the hypertrophic zone is increased in 3-4 week-old Smad3 KO mice. Chondrocytes in Fut8 cKO mice were suppressed of Tgf-β expression (Fig. 4D), suggesting that inhibition of TGF-β signaling, which is suppressive for late hypertrophic chondrocyte differentiation, led to the increased height of the hypertrophic zone.

The results suggested that the growth plate of Fut8 cKO mice had an enlarged hypertrophic layer and decreased primary trabecular bone. Because these results have important implications for the content of the paper, we have included the staining results in Figure 5 and added a graph quantitatively assessing the extent of the hypertrophic zone as supplementary Figure S6.

We included the following statement in the Results part:

"To assess the role of FUT8 in endochondral ossification, we performed an epiphyseal plate analysis of 4-week-old Col2-Fut8−/− mice. This uncovered a significant enlargement of the zone of hypertrophic chondrocytes in the growth plates of the long bones of Col2-Fut8−/− mice compared to controls (Fig. 5C, S6 Figure)." (line154-158, p7)

We included the following statement in the Discussion part:

"The high-mannose/corefucosylation relationship estimated function to maintain formed cartilage. In endochondral ossification, the Fut8 cKO growth plate had an enlarged hypertrophic zone and reduced primary spongiosa because it is involved in the next process of cartilage replacement into bone rather than the process of cartilage formation." (line214-217, p9)

Literature mentioned above (not included in manuscript):

Yang X, et al. TGF-beta/Smad3 signals repress chondrocyte hypertrophic differentiation and are required for maintaining articular cartilage. J Cell Biol. 2001;153(1):35–46.

(3) The DMM model analysis is performed with n=5 for each group. Please consider if the sample size is sufficient.

In the literature, the sample sizes for DMM models have varied in previous studies (Doyran et al., n=5; Liao et al., n=6-7; Ouhaddi et al., n=8). Therefore, we performed a preliminary test of the DMM in WT and Flox mice with n=3 each and a power analysis with the outcome set to the OARSI score at 8 weeks. This resulted in n=4. The sample size for this study was increased to n=5 to account for attrition. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately.

Literature mentioned above (not included in manuscript):

(1) Doyran B, Tong W, Li Q, Jia H, Zhang X, Chen C, et al. Nanoindentation modulus of murine cartilage: a sensitive indicator of the initiation and progression of post-traumatic osteoarthritis. Osteoarthr Cartil. 2017;25(1):108–17.

(2) Liao L, Zhang S, Gu J, Takarada T, Yoneda Y, Huang J, et al. Deletion of Runx2 in Articular Chondrocytes Decelerates the Progression of DMM-Induced Osteoarthritis in Adult Mice. Sci Rep. 2017 24;7(1):2371.

(3) Ouhaddi Y, Nebbaki SS, Habouri L, Afif H, Lussier B, Kapoor M, et al. Exacerbation of Aging-Associated and Instability-Induced Murine Osteoarthritis With Deletion of D Prostanoid Receptor 1, a Prostaglandin D2 Receptor. Arthritis Rheum. 2017;69(9):1784–95.

Reviewer #2 (Recommendations For The Authors):

This paper is suitable for publication in clinical Journals related to osteoarthritis and cartilage.

Identification of core fucosylated glycans from chondrocytes is essential for this type of paper.

We mentioned that we had identified similar corefucosylated glycans in isolated mouse chondrocytes from the cartilage (line117-118, p5), but we have now also added the following to the subtitle of the Results section to avoid any potential confusion: "Corefucosylated N-glycan was formed in resilient cartilage and its isolated chondrocyte" (line109, p5)

Thank you again for your comments on our paper. We trust that the revised manuscript is suitable for publication.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The coupling between cell polarity and cell cycle progression is an important aspect of symmetric and asymmetric cell division. Although there are several examples of cell cycle kinases phosphorylating polarity proteins, it has been difficult to assess the importance of these on cell division due to the strong and pleiotropic effects of manipulating these kinases. Here, the authors generate an analogue-sensitive allele of cdk1 in flies to tackle this question in neuroblasts (NBs) and sensory organ precursors (SOPs), two well characterised examples of asymmetric cell divisions. They show that partial Cdk1 inhibition (which still allows cell cycle progression) does not block Bazooka (PARD3 in mammals) polarization in NBs, but prevents coalescence of the Baz crescent, which has previously been shown to be an actomyosin-based process. They further identify a Cdk1 consensus site on Baz (S180) for which they generate a phospho-specific antibody, allowing them to show that this site is specifically phosphorylated in dividing NBs and SOPs. Although mutations at this site do not recapitulate the effect of Cdk1 on Baz coalescence, they do delay Miranda polarization in NBs and affect lateral inhibition and asymmetric cell division of SOPs. Finally, the authors show that human PARD3 can also be phosphorylated by Cyclin B/Cdk1 in vitro.

Major comments:

• Figure 2A: it would be good to show that polarization of Baz::GFP in consecutive divisions is maintained in cdk1as2 animals in the absence of 1-NA-PP1. We now show in Fig S2B a panel with two consecutive divisions of a cdk1as2 neuroblast in the absence of 1-NAP-PP1, followed by a third division in the presence of 1-NAP-PP1. The neuroblast shows high levels of Baz polarization in the two first divisions.

• The interpretation of the observed SOP phenotypes is complicated by the uneven expression of the pnr-GAL4 driver and the fact that it is expressed in epithelial cells rather than just SOPs. The authors could express their control and mutant Baz constructs under the control of neurP72-GAL4. It is not likely they would be able to deplete endogenous Baz as they have done in NBs, as neurP72-GAL4 is expressed too late to deplete most proteins before SOP division, but they could at least look at localization of the mutants and any possible gain-of-function phenotypes.

Following this suggestion, we have recombined Neur-GAL4 with UAS-delta RNAi to attempt to deplete both endogenous Baz::mScarlet and Delta while expressing our Baz::GFP constructs specifically in SOPs. Baz::mScarlet depletion was surprisingly efficient considering, as the reviewer points out, the late timing of Neur-GAL4 expression. However, the adult flies did not present any sensory organs transformations, perhaps because Delta might not be as efficiently depleted. We can at least rule out dominant-negative effects.

We thank the reviewer for his constructive feedback and as suggested, we now extensively analysed the localisation of the Baz-S180 mutants in SOPs and found significant defects. We describe these observations in a new Figure 6. Briefly, we observed that the Baz phosphomutants have localisation defects during the pIIa cell division but not the pI cell division. We also observed a very surprising mosaicism of expression of our UASz-driven constructs within the SOP lineage that allowed us to make a few interesting observations which should be of interest to SOP specialists. Briefly, mosaic expression of Baz::GFP within the SOP lineage allowed to analyse the relative contributions of pIIa and pIIb/pIIIb to different Baz cortical pools and revealed an unexpected cell non-autonomous mechanism controlling pIIb division orientation. We describe these findings in a new associated supplemental figure.

The authors speculate that Baz phosphorylation during lateral inhibition may be the reason for the observed excess specification of SOPs in the S180 mutants. This could easily be tested by looking at their antibody staining at earlier stages in the notum. Following this suggestion (also coming from Reviewer #2), we have stained nota between around 8h APF. We observed that patches of cells of the early notum display a strong Baz-pS180 phospho-signal. These patches partially overlap with the Delta-positive stripes in which lateral inhibition occurs (as described for example in (Corson et al., 2017), consistent with the possibility that Baz-S180 phosphorylation does somehow regulate lateral inhibition.

These new experiments clearly show that Baz can be phosphorylated on S180 in cells that do not divide asymmetrically. This led us to change the title.

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

Cell polarization in dividing cells, including stem cells, is typically coupled such that polarity can inform the architecture, orientation, and/or asymmetry during cell division. In Drosophila neural stem cells (neuroblasts/SOP), Par polarity is coupled to the cell cycle, but the nature of this coupling remains unclear. In this work, Loyer and colleagues report on impacts of CDK1 inhibition on Bazooka/Par3 localization and basal fate determinant localization. They provide evidence for a novel phosphorylation site that appears unique to asymmetrically dividing cells and may be involved in regulation of asymmetric division. Finally, they show that CDK1 can, at least in principle, phosphorylate human Par3 in vitro.

Overall, the major claims of the abstract appear supported by the experimental work; however, we think the title overstates the overall conclusions that can be drawn from the work.

Major comments:

• The major claim of the paper is the role of specific phosphorylation of S180 in asymmetrically dividing cells in polarization and sensory organ formation, which relies heavily on interpretation of S180A/D phosphomutants. The experiments are carefully performed and quantified, and are consistent with the conclusions drawn. However, we wondered if it possible that the phenotypes are not linked to phosphorylation (the authors acknowledge this in the Discussion)? In other words could the A/D mutants simply be weak Baz mutants? This could this potentially explain the extra-SOP phenotype if Baz function is generally altered, especially given that it is difficult to rationalise a role for SOP-specific phosphorylation in the processes that specify SOP cells in the precursor epithelial cells. The authors speculate that these early precursors may exhibit also phosphorylation, but this isn't examined. Chasing this down seems key to support the core titular claim of the paper. Following this suggestion (also coming from Reviewer #1), we have stained nota around 8h APF. We observed that patches of cells of the early notum display a strong Baz-pS180 phospho-signal. These patches partially overlap with the Delta-positive stripes in which lateral inhibition occurs (as described for example in Corson et al., 2017). This result is presented in Fig. 5H. As would be the case for any phosphomutant, this does not strictly rule out that the S180A and S180D could simply be weak Baz mutants, but it strongly supports the possibility that the lateral inhibition defects observed in these mutants result from defective Baz-S180 phosphorylation.

• Implicit in the core message of the paper is the elucidation of CDK1 regulation of polarity and specifically Baz. However, the connection between CDK1 and S180 (and Baz regulation overall) is relatively tenuous in this work. First, the S180A mutant does not phenocopy CDK1 inhibition with respect to basal determinant phenotypes, though obviously CDK1 may be more pleiotropic. Second, whether the CDK1 inhibition phenotype is linked to any effect on Baz/PAR behaviour is not really explored. Third, they do not test whether S180 phosphorylation is CDK1-dependent. We fully agree with these comments. We cannot think of any way of addressing the first two points, which would require fully inhibiting CDK1 and somehow maintaining neuroblasts in mitosis to examine how it impacts Baz localisation. We tried to arrest neuroblasts in mitosis and block the proteasome as this at least in HeLa cells led to persistence of mitosis when CDK1 was inhibited (Skoufias et al., 2007). However, neuroblasts arrested in mitosis by proteasome inhibition slipped out of mitosis.

However, concerning the third point, we now provide evidence showing that, at least in vitro, Drosophila BazS180 is phosphorylated by CDK1 (see below).

The method for quantifying domain signal only references prior work and should be described in this work. From our search of the cited reference, it appears to be peak signal intensity at a user specified point on the cortex. While this does not undermine the core findings as presented, it may not capture additional features that may be informative (domain size, fluorescence distribution, total signal etc.). For example domain coalescence would imply smaller, brighter domains, but similar total protein amounts, which appears to be the case from images, but isn't quantified per se. We now describe our method for quantifying average signal intensity in the middle of the Baz crescents. We agree that quantifying additional features to check whether they are affected by partial CDK1 inhibition would be interesting. However, doing so requires determining exactly where Baz crescents start and end. As Baz crescent edges in neuroblasts often end in a gradient rather than a sharp edge (Hannaford et al., 2018), we are not sure to be able to confidently do so in every case with the image quality of our dataset: we prioritised limiting photobleaching to accurately quantify the levels of endogenously expressed Baz rather than obtaining very sharp and high contrast images. This is further complicated by the fact that, depending on the depth of neuroblasts within the tissue and the orientation of their division relative to the imaging plane, the signal intensity of Baz crescents is quite variable, preventing a simple thresholding approach to arbitrarily determine the limits of crescents based on signal intensity. In short, accurately determining the size of crescents is very challenging.

The phosphospecific antibody signal is relatively weak, leading to relatively low signal to noise, which could compromise the ability to detect phospho-S180 in non-asymmetrically dividing cells or generally in cells in which Baz is not polarised and thus signal would be diffused around the cell rather than concentrated. Similar caveats could also apply to the lack of signal in interphase cells, where Baz may be less enriched at the cortex and not polarized. We are inclined to believe the authors conclusions, particularly given their examination of multiple cell types and tissues. However, it is a potential caveat as it may be most visible in polarised cells where it is asymmetrically enriched. We thank the reviewer for pointing this out. Given the fact that Baz levels at the neuroepithelial cells adherens junctions are similar, we are confident that Baz-S180 is phosphorylated in dividing neuroblasts but not in non-mitotic epithelial cells, which is at least consistent with our new finding that CDK1 phosphorylates Baz-S180 in vitro. However, we agree that we cannot strictly rule out that Baz-S180 is phosphorylated but below a detection threshold in mitotic neuroepithelial cells as cortical Baz levels decrease in these cells.

We have also gathered new data showing that, in the early notum, Baz-S180 is detected in epithelial cells that are not dividing asymmetrically, definitely ruling out the notion that Baz-S180 is strictly ACD-specific. We have changed the title of the paper accordingly, toned down the mention of apparently ACD-specific Baz-S180 phosphorylation in the abstract and now describe and discuss the fact that the apparent ACD-specificity of Baz-S180 phosphorylation is context-specific.

Examination of in vitro phosphorylation of human Par3D (Figure 6) seems out of place and does not add much. It is human, not Bazooka. They reveal 30 sites, 18 of which in both replicates, but most are not obvious CDK sites and the S180 equivalent site is missing. None of these sites is validated in vivo, at least in this work.

We fully agree with these comments. We initially attempted to purify both full length Baz and human PARD3 but only managed to purify small amounts of PARD3, which is why our analysis was limited to human PARD3. To circumvent these difficulties, we instead purified a smaller N-terminal fragment of Baz and PARD3, which was successful for both proteins and gave us much higher quantities of sample for analysis. Using two different approaches (Western blot with our phospho-specific antibody on Baz and targeted mass spectrometry on Baz and PARD3), we now show in a new Figure 7 that CDK1 phosphorylates Baz-S180 and PARD3-S187 in vitro.

Minor comments: Figure 1: Uses metaphase arrested cells, presumably colcemid, but colcemid is only noted in Figure 2. We now mention Colcemid in the legend of Figure 1. - Figure 2A: Scale bar is truncated. We have corrected this. - Figure 2A: Example images of control neuroblasts could be useful to readers. We now show control neuroblasts in Figure 2A. - Figure 2G' vs H': Because G' has two panels and H' has only one, we often confused the PKC and Mira box plots when comparing to Numb. Perhaps Mira could be in a separate sub panel or be more closely juxtaposed with Numb? The quantification of the Mira signal is now right next to Numb. - Whereas both Numb/Mira were examined in CDK1(as), only Mira is reported for the S180A/D experiments. Is there a Numb phenotype as well?

We actually co-stained Numb and Miranda in the dataset that we analysed in the S180A/D experiments shown in Fig 4E, F. We did not analyse Numb localisation in the first version we submitted because of a penetration issue of the Numb antibody: the Numb signal fades extremely fast as we image deeper in the tissue, causing large difference of signal intensity even within a single cell. This prevents us from performing any meaningful quantitative measurement of the Numb signal like the one we did in Fig. 2H, K, for which we did not encounter this issue. All our further immunostaining experiments with this antibody have had the same problem since then, even after using Triton concentrations up to 4% for permeabilization.

Nonetheless, following the reviewer’s question, we have at least performed a simple qualitative analysis of Numb localisation in this experiment. We observed that Numb localised to the basal pole in most cases in controls and Baz phosphomutants, but localised uniformly at the cortex in half the cases where Miranda showed very low levels of polarisation in metaphase in BazS180D mutants. This Numb localisation defect suggests a loss of function of the PAR complex whereas, intriguingly, the Miranda localisation defect suggests a gain of function of the PAR complex. These new observations are described in Fig. 4G-H’.

• The discussion of the notch / Baz phenotypes (Figure 5) is rather complicated and a bit difficult to follow. We agree with this, we have rewritten this part. This is further simplified by our new observation that Baz-S180 is phosphorylated in the early notum during lateral inhibition.

• Figure 5A: captions should indicate that RFP RNAi is depleting Baz. We have modified the figure accordingly.

• Box plots are used, but not described. i.e. outliers seem to be marked, but criteria unclear. Mean vs median, etc. We now describe boxplots in the legend in the first instance they are used (Fig 2A’), and in the material and methods
• Some grammatical mistakes:
• Title: neuroblast (no 's'),
• Page 1: Cell fate difference(s?) in the resulting daughter cells
• Page 4: (As) CDK1 inhibition with 10 μM 1-NA-PP1 prevents neuroblasts from cycling and causes metaphase- arrested neuroblasts to slip out of mitosis. (Reword)
• Page 6: increased levels of basal fate(no 's') determinants

We have corrected these mistakes.

Reviewer #2 (Significance (Required)):

The links between cell cycle and cell polarity are clearly important and remain poorly understood. Hence, the work addresses key conceptual/mechanistic questions relevant to our fundamental understanding of stem cell biology and regulation of polarity and asymmetric cell division. In our opinion, there are clearly some interesting observations in the manuscript, the experiments are performed carefully, and the data are generally well described. That said, overall, the work seems somewhat premature.

The direct impact of CDK1 on Baz behaviour remains somewhat unclear. The authors do a good job of limiting the concentration of inhibitor to decouple effects of cell cycle progression from CDK1 levels per se, but this does potentially impact the strength of the phenotypes they can detect and hence the observed phenotypes are relatively minor. Note that driving cells out of mitosis with stronger CDK1 inhibition clearly impacts Baz localization, so the 'real' effect of CDK1 inhibition on Baz could be stronger than reported here. It is also unclear whether the phenotypes observed are directly linked to CDK1 regulation of PAR polarity or an indirect effect of cell cycle control of other processes. The authors' suggestion that it could be related to defects in cortical actin organization, which is known to be cell cycle controlled, seems most likely, but neither this or other models are explored further. We agree but are not aware of any experiment that would allow testing full inhibition of CDK1 on membrane-bound Baz in mitotic neuroblasts. As mentioned above in our response to reviewer #1 we tried to arrest neuroblasts in mitosis and block the proteasome as this at least in HeLa cells led to persistence of mitosis when CDK1 was inhibited (Skoufias et al., 2007). However, neuroblasts arrested in mitosis by proteasome or Colcemid or both slipped out of mitosis upon inhibition of CDK1.

We agree it would be interesting to study how CDK1 affects the actomyosin network in neuroblasts but feel that this is somewhat beyond the scope of the manuscript.

Using phosphospecific antibodies, they report on a novel putative CDK1 phosphorylation site, but aside from looking like a consensus CDK1 site, whether this site is CDK1 dependent is not examined. Notably, the corresponding phosphomutants have modest effects and don't obviously account for the CDK1 inhibition phenotype, leaving it somewhat unclear whether it is under cell cycle regulation. We now provide a new figure 7 to address this point. As mentioned already above, using two different approaches (Western blot with our phospho-specific antibody on Baz and targeted mass spectrometry on Baz and PARD3 using), we now show in a new Figure 7 that CDK1 phosphorylates Baz-S180 and PARD3-S187 in vitro. Again, we cannot identify any experiment that would allow us testing whether S180 Baz is a direct target of CDK1 in vivo. The fact that we now report significant defects on Baz localisation in pIIa divisions, strongly suggests functional relevance and CDK1 seems a plausible kinase based on the new in vitro results.

The observation that S180 phosphorylation appears unique to asymmetrically dividing cells is very curious, but this observation is not followed up extensively. Again phenotypes of phosphomutants are quite modest, and while one can propose models to rationalise the phenotypes observed, these models are not fully explored. As mentioned above, we now show that Baz-S180 phoshorylation is not strictly ACD-specific and changed the title accordingly. We also have new data showing that the S180 phosphomutants of Baz have localisation defects in mitotic pIIa divisions (new figure 6). Therefore, this phosphorylation event on Baz can be linked to Baz’s cortical localisation and interestingly shows context dependency.

The findings that human Par3D can be phosphorylated by CDK1 in vitro do not add much particularly as they obtain a very large number of putative sites raising questions of specificity, the sites are not validated, and an S180 equivalent site was not identified. We agree that this has been a weakness which we feel we have addressed. We paste here the answer already provided above when replying to reviewer #1.

We initially attempted to purify both full length Baz and human PARD3 but only managed to purify small amounts of PARD3, which is why our phospho-proteomics analysis was limited to human PARD3. To circumvent these difficulties, we instead purified a smaller N-terminal fragment of Baz and PARD3, which was successful for both proteins and gave us much higher quantities of sample for analysis. Using two different approaches (Western blot with our phospho-specific antibody on Baz and phosphor proteomics on Baz and PARD3 using mass spectrometry), we now show in a new Figure 7 that CDK1 phosphorylates Baz-S180 and PARD3-S187 in vitro.

References

CORSON, F., COUTURIER, L., ROUAULT, H., MAZOUNI, K. & SCHWEISGUTH, F. 2017. Self-organized Notch dynamics generate stereotyped sensory organ patterns in Drosophila. Science, 356.

HANNAFORD, M. R., RAMAT, A., LOYER, N. & JANUSCHKE, J. 2018. aPKC-mediated displacement and actomyosin-mediated retention polarize Miranda inDrosophilaneuroblasts. eLife, 7__,__ 166.

SKOUFIAS, D. A., INDORATO, R. L., LACROIX, F., PANOPOULOS, A. & MARGOLIS, R. L. 2007. Mitosis persists in the absence of Cdk1 activity when proteolysis or protein phosphatase activity is suppressed. J Cell Biol, 179__,__ 671-85.

Author Response

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

We highly thank the editor and reviewers for their time and insightful comments and suggestions. We have made revisions by performing additional experiments and analysis, and clarified the items based on the suggestions.

Reviewer #1 (Public Review):

Summary of Author's Objectives:

The authors aimed to explore JMJD6's role in MYC-driven neuroblastoma, particularly in the interplay between pre-mRNA splicing and cancer metabolism, and to investigate the potential for targeting this pathway.

Strengths:

(1) The study employs a diverse range of experimental techniques, including molecular biology assays, next-generation sequencing, interactome profiling, and metabolic analysis. Moreover, the authors specifically focused on gained chromosome 17q in neuroblastoma, in combination with analyzing cancer dependency genes screened with Crispr/Cas9 library, analyzing the association of gene expression with prognosis of neuroblastoma patients with large clinical cohort. This comprehensive approach strengthens the credibility of the findings. The identification of the link between JMJD6-mediated premRNA splicing and metabolic reprogramming in MYC-driven cancer cells is innovative.

(2) The authors effectively integrate data from multiple sources, such as gene expression analysis, RNA splicing analysis, JMJD6 interactome assay, and metabolic profiling. This holistic approach provides a more complete understanding of JMJD6's role.

(3) The identification of JMJD6 as a potential therapeutic target and its correlation with the response to indisulam have significant clinical implications, addressing an unmet need in cancer treatment.

Weaknesses:

(1) The manuscript contains complex technical details and terminology that may pose challenges for readers without a deep background in molecular biology and cancer research. Providing simplified explanations or additional context would enhance accessibility.

We have provided simplified explanations for some terminology.

(2) It would be beneficial to explore whether treatment with JMJD6 inhibitors, both in vitro and in vivo, can effectively target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells.

Unfortunately, there is no potent and selective JMJD6 inhibitors available.

Reviewer #3 (Public Review):

Summary:

Jablonowski and colleagues studied key characteristics of MYC-driven cancers: dysregulated pre-mRNA splicing and altered metabolism. This is an important field of study as it remains largely unclear as to how these processes are coordinated in response to malignant transformation and how they are exploitable for future treatments. In the present study, the authors attempt to show that Jumonji Domain Containing 6, Arginine Demethylase And Lysine Hydroxylase (JMJD6) plays a central role in connecting pre-mRNA splicing and metabolism in MYC-driven neuroblastoma. JMJD6 collaborates with the MYC protein in driving cellular transformation by physically interacting with RNA-binding proteins involved in pre-mRNA splicing and protein regulation. In cell line experiments, JMJD6 affected the alternative splicing of two forms of glutaminase (GLS), an essential enzyme in the glutaminolysis process within the central carbon metabolism of neuroblastoma cells. Additionally, the study provides in vitro (and in silico) evidence for JMJD6 being associated with the anti-proliferation effects of a compound called indisulam, which degrades the splicing factor RBM39, known to interact with JMJD6.

Overall, the findings presented by Jabolonowski et al. begin to illuminate a cancer-promoting metabolic, and potentially, a protein synthesis suppression program that may be linked to alternative pre-mRNA splicing through the action of JMJD6 - downstream of MYC. This discovery can provide further evidence for considering JMJD6 as a potential therapeutic target for the treatment of MYC-driven cancers.

Strengths:

Alternative Splicing Induced by JMJD6 Knockdown: the study presents evidence for the role of JMJD6 in alternative splicing in neuroblastoma cells. Specifically, the RNA immunoprecipitation experiments demonstrated a significant shid from the GAC to the KGA GLS isoform upon JMJD6 knockdown. Moreover, a significant correlation between JMJD6 levels and GAC/KGA isoform expression was identified in two distinct neuroblastoma cohorts. This suggests a causative link between JMJD6 activity and isoform prevalence.

Physical Interaction of JMJD6 in Neuroblastoma Cells: The paper provides preliminary insight into the physical interactome of JMJD6 in neuroblastoma cells. This offers a potential mechanistic avenue for the observed effects on metabolism and protein synthesis and could be exploited for a deeper investigation into the exact nature, and implications of neuroblastoma-specific JMJD6 protein-protein interactions.

Weaknesses:

There are several areas that would benefit from improvements with regard to the current data supporting the claims of the paper (i.e., the conclusion presented in Figure 8).

Neuroblastoma Modelling Strategy: The study heavily relies on cell lines without incorporating patient derived cells/biomaterials. Using databases to fill gaps in the experimental design can only fortify the observations to a certain extent. A critical oversight is the absence of non-cancerous control cells in many figures, and the rationale for selecting specific cell lines for assays/approaches remains somewhat unclear. A foundational control for such experiments should involve the non-transformed neural crest cell line, which the authors have readily available. Are the observed splicing and metabolic effects of JMJD6 specific to neuroblastoma? Is there a neuroblastoma-specific JMJD6 interactome? Is MYC function essential?

In Vivo Modelling: The inclusion of a genetic mouse model combined with an inducible JMJD6 knockdown, would enhance the study by allowing examination of JMJD6's role during both tumor initiation and growth in vivo. For instance, the TH-MYCN mice overexpressing MYCN in neural crest cells, could be a promising choice.

Dependence on Colony Formation Assay: The study leans on 2D and semi-quantitative colony formation assays to assess malignant growth. To validate the link between the mechanistic insights discussed (e.g., reduced protein synthesis) and JMJD6-mediated malignant growth as a potential therapeutic target, evidence from in vivo or representative 3D models would be crucial.

Data Presentation and Rigor: The presented data is predominantly qualitative and necessitates quantification. For instance, Western blots should be quantified. The RNAseq, metabolism, and pulldown data should be transparently and numerically presented. The figure legends seem elusive and their lack of transparency (oden with regards to biological repeats, error bars, cell line used etc.) is concerning. Adequate citation and identification of all data sources, including online resources, are imperative. The manuscript would also benefit from a more rigorous depiction and quantification of RNA interference of both stable and transient knockdowns with quantitative validation at mRNA and protein levels.

Novelty Concerns: The emphasis on JMJD6 as a novel neuroblastoma target is contingent on the new mechanistic revelations about the JMJD6-centered link between splicing, metabolism, and protein synthesis. Given that JMJD6 has been previously linked to neuroblastoma biology, the rationale (particularly in Figure 1) for concentrating on JMJD6 may stem more from bias rather than data-driven reasoning.

Depth of Mechanistic Investigation: Current evidence lacks depth in key areas such as JMJD6-RNA binding. A more thorough approach would involve pinpointing specific JMJD6 binding sites on endogenous RNAs using techniques such as cross-linking and immunoprecipitation, paired with complementary proximity-based methodologies. Regarding the presented metabolism data, diving deeper into metabolic flux via isotope labeling experiments could shed light on dynamic processes like TCA and glutaminolysis. As it stands, the 'pathway cartoon' in Figure 6d appears overly qualitative.

Response: We agree with this reviewer that more in-depth studies are needed to understand the biological functions of JMJD6 in neuroblastoma. We have included one paragraph “limitation of the study” to point out that additional work needs to be done to address the comments from this reviewer.

We have also added details in figure legend to increase rigor.

Reviewer #1 (Recommendations For The Authors):

In this study, Jablonowski and colleagues identify the link between JMJD6-mediated pre-mRNA splicing and metabolic reprogramming in cancer cells, with implications for therapeutic response to splicing inhibitors. I have reviewed your manuscript and found it quite promising. However, there are some specific points that require further clarification and additional experiments. Please consider the following comments:

Major concerns:

(1) Regarding Figure 1d and e: to enhance the robustness of your findings, it would be beneficial to include additional datasets, such as the Kocak-649 dataset. It is important to narrow down the analysis to high-risk patient groups when examining survival rates, specifically to investigate whether the elevated expression of the 114 gene signature correlates with poor survival within this subgroup. Additionally, please consider conducting a more detailed breakdown of the subsets depicted in Fig. 1b to explore the association between their expression levels and patient survival rates.

Response: We have included the Kocak-649 datasets as Supplemental Figure 1. We have further analyzed the 114 gene signature in low-risk and high-risk patients, respectively, as Supplemental Figure 2.

(2) Fig. 2b: Similar to the previous comment, it would strengthen your findings to include survival rate analysis in more datasets, particularly in high-risk patient groups.

Response: We have further analyzed the association of JMJD6 with survival in low-risk and high-risk patients, respectively, as Supplemental Figure 3. Regardless of the risk factors, high expression of JMJD6 was associated with a poor outcome.

(3) In reference to Fig. S1D, please clarify the time point under investigation. It looks like siRNAs were utilized in this study. Ensure consistency between the siRNA # mentioned in the methods section and what is presented in Fig. S1d.

Response: We have clarified the time point under investigation in Fig. S1D (now as Fig. S4D). We have corrected the siRNA# on the method section.

Additionally, it would be beneficial to include data on knockdown efficacy and consider incorporating western blot results, similar to those presented in Fig. 2c.

Response: These experiments were performed as shown in Figure 4C. We assumed the knockdown efficiency was comparable.

Furthermore, I recommend analyzing the RNA-seq data from JMJD6-depleted BE(2)C cells to identify any alterations in the expression of neuronal differentiation signature genes, with the aim of exploring potential associations with changes in cell morphology showed in Fig. S1D.

Response: We have analyzed the data and indeed like this reviewer expected, we do see the upregulation of neuronal differentiation pathways. We have included the data as Fig. S7B.

(4) Fig. 4g: Confirm whether the data is related to GAC, and if so, where is the data for KGA?

Response: We apologize for this. KGA data was missed when we assembled the figure. We have added back as Figure 4H.

(5) In relation to Fig. 4, I suggest conducting experiments to individually silence GAC and KGA, if feasible (for instance, by targeting their 3'-UTRs). This would allow for a more in-depth investigation into whether GAC and KGA play essential roles in NB cell proliferation.

Response: As this reviewer suggested, we have performed the experiments to knock down GAC and KGA in BE2C cells, and we found that both isoforms seemed to be important for cell survival. We have included the data as Figure 5G-I. Additionally, we have also performed RNA-seq to understand the differential functions of GAC and KGA in neuroblastoma cells when they were overexpressed separately. We have included the data as Figure 5E,F, and Supplemental Figure 9.

(6) Fig. 5c: Could this protein synthesis reduction be attributed to an artificial overexpression of JMJD6? It would be interesting to investigate whether the genetic silencing of JMJD6 has an impact on total protein synthesis.

Response: This is a great question but could be very challenging to have a definitive answer. Since cells are not happy with knockdown of JMJD6, we may have a secondary effect resulting from activation of cell death. While we have successfully generated single cell JMJD6 CRISPR KO clones, the cells are not happy either. In the future, we may generate dTAG knockin cell line which will allow us to induce an acute protein degradation, and then we can assess if JMJD6 loss will consequently impact total protein synthesis.

(7) Fig. S7: the authors have shown that knocking down of JMJD6 in NB cells reduced cell proliferation (Fig. 2c-e). Please clarify how you obtained sufficient cells ader CRISPR knockout of JMJD6 clones and whether the cells remained healthy. It would be helpful to provide cell images.

Response: We harvested cells at different time points in Fig 2C-E, and we have added the information in Figure legends. Cells were not happy ader JMJD6 KD or KO. We therefore harvest cells for Western blot at an early time point while stained cells for survival effect at a late time point.

(8) Fig. 7f: Address the paradox where JMJD-knockdown cells grow slower (Fig. 2c-e), but these JMJD-KO4E5 cells grow at a similar rate compared to SKNAS-WT in the DMSO treatment group. Clarify whether this aligns with the results observed with shRNA results shown in Fig. 2c-e.

Response: The JMJD6 KO cells grew much slower than the wild-type cells. In these experiments, we intentionally seeded a lot more cells for JMJD6 KO clone so that we can have a comparable comparison for the cells with DMSO treatment.

Minor concerns:

(1) Fig. 2c: Please specify the time point for Fig. 2c to provide a clearer context for readers.

We have added the information.

(2) In Line 204, it is stated that 'Supplementary Table 3,' which describes the 'Correlation of JMJD6 KO and its co-dependency genes,' can actually be found in 'Supplementary Table 4.' Please clarify this discrepancy.

We apologize for this. We probably accidentally uploaded the duplicates. We have uploaded the new table in our revision.

(3) Line 207: The order of figures should be clarified. Fig. 3c should be mentioned before Fig. 3b in the text.

Yes, we did.

(4) In Line 216, it is mentioned that 'Supplementary Table 4,' which describes 'Differentially expressed genes by JMJD6 KD,' can actually be found in 'Supplementary Table 3.' Please provide clarification for this discrepancy.

We have corrected this.

(5) Line 244-247: Please provide clarification of this section to ensure readers can fully understand your point.

We have rephrased the sentence.

(6) Line 1048: Confirm whether Fig. 2c represents siRNA or shRNA, as the label in the graph does not match the figure legends.

Sorry for this. We have corrected.

(7) Line 1161: Provide clarification regarding the use of Image J from k, and in Line 1162, specify the source of Image J from l.

We apologized for the confusion of our description. We meant “Image J” sodware. We have corrected in Figure legend.

Reviewer #2 (Recommendations For The Authors):

Suggestions to authors:

Line 39 - suggest introducing JMJD6.

Response: We have added the full name of JMJD6.

Line 47 - suggest slightly rephrasing 'metabolic program that is coupled with...'.

We have made a slight change by changing “coupled” to “associate”.

Line 85 - please delete/replace 'exceptional'; proofread for inadequate use of ambiguous wording.

We have changed it as “significant”.

Line 141 - please concisely define 'high risk'.

We have defined it with a citation (line 142-146).

Line 143 - please concisely define 'event free'.

We have defined the event free and overall survival precisely (line 149, 150).

Line 153 - provide an adequate citation for 'cBioportal'.

We have added the citation (line166).

Line 161 - please state the utilized cell lines.

We have referenced to Materials and Methods (line 175).

Line 166 - please note that 'morphological changes' of a cell do not suffice to determine 'stemness', please rephrase.

We agreed and changed it to “regulate cellular differentiation” (line 181).

Line 182 - provide a quantifiable measure for color change and or remove observation from the narrative.

We have removed “indicative of acidic pH change” (line 198).

Line 185 - the statement commencing with 'It is believed...' requires referencing.

We have added references (line 200).

Line 187 - please provide an adequate citation for the 'JoMa1' neural crest-derived cells (J. Maurer and colleagues?).

We have added the reference (line 201).

Line 203 - please provide an adequate citation for 'DepMap'.

There is no citation specifically for DepMap and that’s why we can only provide the DepMap link.

Line 234 - please provide an adequate citation for 'two algorithms'.

We have provided the reference (line 265).

Line 265 - please provide a rationale for the choice of the three tested cell lines.

We have added definition by saying C-MYC overexpressed SKNAS, BE2C and SIMA with MYCN amplification (line 302, 303).

Line 279 - suggest rephrasing 'gaining more ATPs'.

We have removed these words as we do not have direct evidence to show ATP production (line 320).

Line 342 - suggest rephrasing 'are in the only gene signature'.

We have rephrased by saying “lysine demethylase (HDM) genes, including JMJD6, are present in the most significantly enriched gene signature in indisulam-sensitive cells” (line 416-416).

Line 424 - please state the source or all cell lines (commercial provider?).

We have added the source of cell lines.

Lines 438 to 442 - are STR and mycoplasma profiling data adequately presented in the manuscript?

We routinely test STR and mycoplasma for all cell lines cultured in hood in our Department every month.

Lines 520 onwards - is the JMJD6 knockout generation data (e.g., cell viability upon knockout) adequately presented in the manuscript? Why does the study depend on transient transfection of siRNAs for obtaining mechanistic results?

We created stable JMJD6 KO clones by selecting single cell with complete knockout. Cells are not happy ader KO. siRNA knockdown is a method for relatively acute depletion of JMJD6, which is easy and fast, and may be more reliable to assess the direct effect of JMJD6.

Figures: please provide adequate axis-labeling for all graphs (e.g., FIg2 b, and e).

We have added the axis labeling.

Discussion line 370 - what is meant by 'too harsh' - please use unambiguous phrasing to highlight limitations.

We have changed to “stringent”.

Please provide a study limitation paragraph.

We have added one limitation paragraph.

Limitation of the study

Our study focused on the understanding of JMJD6 function in neuroblastoma cell lines. In the future, we will consolidate our study by expanding our models to patient-derived xenograds, organoids, and neuroblastoma genetic models, in comparison with non-cancerous cells. Although we have identified a conserved interactome of JMJD6 in neuroblastoma cells, it remains to be determined whether it is neuroblastoma-specific and essential to MYC-driven cancers. The genome-wide RNA binding by JMJD6 in cancer cells and normal cells coupled with isotope labeling to dissect the metabolic effect of JMJD6 will enhance our understanding of the biological functions of JMJD6, awaiting future studies. Inability to target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells by pharmacologic inhibition of JMJD6 is another limitation, due to lack of selective and potent JMJD6 inhibitors.

Additional editing and proof-reading of the manuscript's narrative, figures, legends, and methods is highly recommended.

We have gone through the whole MS to have proof-reading.

Author Response

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

eLife assessment

This study presents a valuable finding on the distinct subpopulation of adipocytes during brown-to-white conversion in perirenal adipose tissue (PRAT) at different ages. The evidence supporting the claims of the authors is convincing, although specific lineage tracing of this subpopulation of cells and mechanistic studies would expand the work. The work will be of interest to scientists working on adipose and kidney biology.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors performed single nucleus RNA-seq for perirenal adipose tissue (PRAT) at different ages. They concluded a distinct subpopulation of adipocytes arises through brown-to-white conversion and can convert to a thermogenic phenotype upon cold exposure.

Strengths:

PRAT adipose tissue has been reported as an adipose tissue that undergoes browning. This study confirms that brown-to-white and white-to-beige conversions also exist in PRAT, as previously reported in the subcutaneous adipose tissue.

Response: We thank the reviewer for summarizing the strengths of our manuscript. However, we would like to clarify two points here. First, PRAT has been reported as a visceral adipose depot that contains brown adipocytes and a process of continuous replacement of brown adipocytes by white adipocytes has been previously suggested based on histological assessment. There is no evidence that PRAT undergoes browning, unless cold exposure is involved. Second, unlike the brown-to-white conversion, white-to-beige conversion in PRAT was not observed under normal conditions. The adipocyte population that arises from brown-to-white conversion (mPRAT-ad2) can respond to cold and restore their UCP1 expression. However, the adipocytes that arise from the mPRAT-ad2 subpopulation after cold exposure have a distinct transcriptome to that of cold-induced beige adipocyte in iWAT (Figure S7K) and are more related to iBAT brown adipocytes (Figure 6E). Therefore, it is more of a white-to-brown conversion in PRAT upon cold exposure rather than white-to-beige conversion and the underlying mechanism is likely different from the white-to-beige conversion in the subcutaneous adipose tissue.

Weaknesses:

(1) There is overall a disconnection between single nucleus RNA-seq data and the lineage chasing data. No specific markers of this population have been validated by staining.

Response: We are not sure what “this population” refers to. We assume that it is the Ucp1-&Cidea+ mPRAT-ad2 adipocyte subpopulation. If so, we did not identify specific markers for these adipocytes as shown in Figure 1H and statements in the Discussion section. mPRAT-ad2 is negative for Ucp1 and Cyp2e1, which are markers for mPRAT-ad1 and mPRAT-ad3&4, respectively. To visualize the mPRAT-ad2 adipocytes on tissue sections, we collected pvPRAT and puPRAT of Ucp1CreERT2;Ai14 mice one day after tamoxifen injection and stained with CYP2E1 antibody and BODIPY. The Tomato-&CYP2E1-&BODIPY+ cells represent the mPRAT-ad2 adipocytes. Based on such strategy, we revealed a significantly higher percentage of mPRAT-ad2 cells in puPRAT than pvPRAT (presented as Figure S3E in the revised manuscript).

(2) It would be nice to provide more evidence to support the conclusion shown in lines 243 to 245 "These results indicated that new BAs induced by cold exposure were mainly derived from UCP1- adipocytes rather than de novo ASPC differentiation in puPRAT". Pdgfra-negative progenitor cells may also contribute to these new beige adipocytes.

Response: We stained pvPRAT and puPRAT of the PdgfraCre;Ai14 mice with the adipocyte marker Plin1 and observed a 100% overlap between the tdTomato signal and the Plin1 staining, after examining a total of 832 and 628 adipocytes in pvPRAT and puPRAT of two animals (Figure S4). Plin1 stains all adipocytes, while the endogenous tdTomato labels both the adipocytes and blood vessels. This result suggests that all adipocytes in mPRAT are derived from Pdgfra-expressing cells, which is in line with a previous study that integrated several single-cell RNA sequencing data sets and showed that Pdgfra is expressed by virtually all ASPCs (Ferrero et al., 2020).

Also, we would like to point out that the cold-induced adipocytes in mPRAT resemble more to the brown adipocytes of iBAT than the beige adipocytes of iWAT (Figure 6E and S7K).

Ferrero, R., Rainer, P., and Deplancke, B. (2020). Toward a Consensus View of Mammalian Adipocyte Stem and Progenitor Cell Heterogeneity. Trends Cell Biol 30, 937-950.

(3) The UCP1Cre-ERT2; Ai14 system should be validated by showing Tomato and UCP1 co-staining right after the Tamoxifen treatment.

Response: We collected pvPRAT and puPRAT of 1- and 6-month-old Ucp1CreERT2;Ai14 mice one day after the last tamoxifen injection and stained with UCP1 antibody to check the overlap between the Tomato and UCP1signal. All Tomato+ cells were UCP1+, indicating 100% specificity of the Ucp1CreERT2; and the labelling efficiency was over 93% at both time points for both regions (Figure S3C-D).

Reviewer #2 (Public Review):

Summary:

In the present manuscript, Zhang et al utilize single-nuclei RNA-Seq to investigate the heterogeneity of perirenal adipose tissue. The perirenal depot is interesting because it contains both brown and white adipocytes, a subset of which undergo functional "whitening" during early development. While adipocyte thermogenic transdifferentiation has been previously reported, there remain many unanswered questions regarding this phenomenon and the mechanisms by which it is regulated.

Strengths:

The combination of UCP1-lineage tracing with the single nuclei analysis allowed the authors to identify four populations of adipocytes with differing thermogenic potential, including a "whitened" adipocyte (mPRAT-ad2) that retains the capacity to rapidly revert to a brown phenotype upon cold exposure. They also identify two populations of white adipocytes that do not undergo browning with acute cold exposure.

Anatomically distinct adipose depots display interesting functional differences, and this work contributes to our understanding of one of the few brown depots present in humans.

Weaknesses:

The most interesting aspect of this work is the identification of a highly plastic mature adipocyte population with the capacity to switch between a white and brown phenotype. The authors attempt to identify the transcriptional signature of this ad2 subpopulation, however, the limited sequencing depth of single nuclei somewhat lessens the impact of these findings. Furthermore, the lack of any form of mechanistic investigation into the regulation of mPRAT whitening limits the utility of this manuscript. However, the combination of well-executed lineage tracing with comprehensive cross-depot single-nuclei presented in this manuscript could still serve as a useful reference for the field.

Response: The sequencing depth of our data is comparable, if not better than previously published snRNA-seq studies on adipose tissue (Burl et al., 2022; Sarvari et al., 2021; Sun et al., 2020). Therefore, the depth of our data has reached the limit of the 3’ sequencing methods. Unfortunately, due to size limitation of the adipocytes, it is challenging to sort them for Smart-seq. We suspect that lack of specific markers for mPRAT-ad2 is partly due to its intermediate and plastic phenotype. Regarding the mechanistic regulation of mPRAT whitening, we believe that it is more suitable to leave such investigations for a separate follow-up and more in-depth study.

Burl, R.B., Rondini, E.A., Wei, H., Pique-Regi, R., and Granneman, J.G. (2022). Deconstructing cold-induced brown adipocyte neogenesis in mice. Elife 11. 10.7554/eLife.80167.

Sarvari, A.K., Van Hauwaert, E.L., Markussen, L.K., Gammelmark, E., Marcher, A.B., Ebbesen, M.F., Nielsen, R., Brewer, J.R., Madsen, J.G.S., and Mandrup, S. (2021). Plasticity of Epididymal Adipose Tissue in Response to Diet-Induced Obesity at Single-Nucleus Resolution. Cell Metab 33, 437-453 e435. 10.1016/j.cmet.2020.12.004.

Sun, W., Dong, H., Balaz, M., Slyper, M., Drokhlyansky, E., Colleluori, G., Giordano, A., Kovanicova, Z., Stefanicka, P., Balazova, L., et al. (2020). snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature 587, 98-102. 10.1038/s41586-020-2856-x.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) There is overall a disconnection between single nucleus RNA-seq data and the lineage chasing data. No specific markers of this population have been validated by staining.

(2) It would be nice to provide more evidence to support the conclusion shown in lines 243 to 245: "These results indicated that new BAs induced by cold exposure were mainly derived from UCP1- adipocytes rather than de novo ASPC differentiation in puPRAT". Pdgfra-negative progenitor cells may also contribute to these new beige adipocytes.

(3) The UCP1Cre-ERT2; Ai14 system should be validated by showing Tomato and UCP1 co-staining right after the Tamoxifen treatment.

Please see above for the responses.

Reviewer #2 (Recommendations For The Authors):

• Without specific lineage tracing it is not possible to conclude that the mPRAT-ad2 population converted to beige with CE. The authors should change this wording from "likely" to "possible".

Response: We have changed the word “likely” to “possible” in the text. Also, we would like to point out that the cold-induced adipocytes in mPRAT resemble more to the brown adipocytes of iBAT than the beige adipocytes of iWAT (Figure 6E and S7K).

• The sentence "precursor cells may be less sensitive to environmental temperature and have a limited contribution to mature adipocyte phenotypes through de novo adipogenesis after cold exposure." and others like it should be changed to indicate the acute timeframe of this experiment. It has been shown that the precursors make a more significant contribution to de novo beige adipogenesis with chronic cold exposure.

Response: We have modified the sentence as follows: “precursor cells may be less sensitive to acute environmental temperature drop and have a limited contribution to mature adipocyte phenotypes through de novo adipogenesis after cold exposure”. As mentioned above, the cold-induced adipocytes in mPRAT resemble more to the brown adipocytes of iBAT and therefore may have a different mechanism to the de novo beige adipogenesis with chronic cold exposure.

Material

Author Response

Note to the editor and reviewers.

All the authors would like to thank the editorial team and the two anonymous reviewers for their efforts and thoughtfulness in assessing our manuscript. We very much appreciate it and we all believe that the manuscript has been much improved in addressing the comments and suggestions made.

General considerations on the revised manuscript

We have applied extensive modifications to the manuscript with our main goal being the improvement of clarity. The Introduction has been changed mainly to introduce precisely our terminology and we have stuck to it in the rest of the manuscript. The Results section has been divided up into more defined sections. The discussion has been extensively re-written to improve clarity, following the suggestion of the reviewers. Main figures 1 and 4 have been modified with clearer schematics. Supplementary figures and legends have been modified and several supplementary schematic figures have been added to clearly present our interpretations for various data. We have added a Supplementary Discussion where the most detailed technical parts of our discussion are presented to avoid unnecessarily weighing down the main discussion, where our main conclusions are outlined. We have presented our mass photometry mixing experiment in a new supplementary figure, with detailed explanation. We have also expanded our discussion of in vivo and general relevance of our study.

Response to manuscript evaluation

Our manuscript has been evaluated as a valuable study and presenting solid experimental evidence. We appreciate the recognition of our work.

Two weaknesses were identified by reviewers: 1) our experiments do not completely exclude the possibility of an alternative nucleophile. This relates to the evaluation of our experimental evidence. 2) Our study does not address the in vivo relevance of the interface swapping phenomenon, which relate to the value of the study for the community.

Response to the evaluation of experimental evidence (Weakness #1):

We argued in the original manuscript that we have excluded completely the presence of an alternative nucleophile. This conclusion is based on a series of experiments which were presented in the originally submitted manuscript. These experiments are not discussed by the reviewers in relation to this main conclusion and therefore we suggest that they have not been properly evaluated. We believe our conclusion to be appropriately supported by these data (see our response to reviewer #1). In addition, the criticism of our gel-filtration data by reviewer #2 was based on a misinterpretation of Supplementary figure 1 b. We accept of course that the way the data was presented could be misleading and we assume responsibility for this. We have attempted to correct this by changing the main text and the figures legends and annotation. In conclusion, we believe that the evaluation of experimental evidence as presented in the revised manuscript could be upgraded to “convincing”.

Response to our study general relevance evaluation (weakness #2):

We agree with both reviewers about the in vivo relevance of our observation being an important question, not addressed so far. Indeed, the value of our study would be greatly increased by in vivo data and be of interest to a wider audience. However, we would like to argue that our study would interest a wider audience than initially stated for the following reasons: 1) Our study is the first evidence of interface swapping in vitro and will constitute a base to investigate this phenomenon both in vivo and in vitro. It will therefore interest a wide audience due to the potential involvement of interface swapping in a wide range of processes, such as recombination, evolution, and drug targeting (see also below). 2) DNA cleavage is the central mode of action of antibiotics targeting bacterial type II topoisomerases (i.e. topoisomerases “poisons”). This already established target is one of the few having produced new scaffolds and too few new antibacterial are in production to fulfill medical needs. The role of interface stability is also emerging as a modulator of the efficiency of topoisomerase poisons. See for instance (Germe, Voros et al. 2018, Bandak, Blower et al. 2023). By shedding light on interface dynamics, our study will be of interest to scientist interested in the development of these drugs. In addition, the heterodimer system can potentially produce detailed mechanistic information (Gubaev, Weidlich et al. 2016, Hartmann, Gubaev et al. 2017, Stelljes, Weidlich et al. 2018) not only on gyrase but also on other, dimeric type II topoisomerases or even other dimeric enzyme in general. We have amended the manuscript to make these points clearer. Therefore, we believe that the evaluation of the revised manuscript’s relevance could be upgraded to “important”.

Point-by-point response to the reviewer

Reviewer #1 (Public Review):

Germe and colleagues have investigated the mode of action of bacterial DNA gyrase, a tetrameric GyrA2GyrB2 complex that catalyses ATP-dependent DNA supercoiling. The accepted mechanism is that the enzyme passes a DNA segment through a reversible double-stranded DNA break formed by two catalytic Tyr residues-one from each GyrA subunit. The present study sought to understand an intriguing earlier observation that gyrase with a single catalytic tyrosine that cleaves a single strand of DNA, nonetheless has DNA supercoiling activity, a finding that led to the suggestion that gyrase acts via a nicking closing mechanism. Germe et al used bacterial co-expression to make the wild-type and mutant heterodimeric BA(fused). A complexes with only one catalytic tyrosine. Whether the Tyr mutation was on the A side or BA fusion side, both complexes plus GyrB reconstituted fluoroquinolone-stabilized double-stranded DNA cleavage and DNA supercoiling. This indicates that the preparations of these complexes sustain double strand DNA passage. Of possible explanations, contamination of heterodimeric complexes or GyrB with GyrA dimers was ruled out by the meticulous prior analysis of the proteins on native Page gels, by analytical gel filtration and by mass photometry. Involvement of an alternative nucleophile on the Tyr-mutated protein was ruled unlikely by mutagenesis studies focused on the catalytic ArgTyrThr triad of residues. Instead, results of the present study favour a third explanation wherein double-strand DNA breakage arises as a consequence of subunit (or interface/domain) exchange. The authors showed that although subunits in the GyrA dimer were thought to be tightly associated, addition of GyrB to heterodimers with one catalytic tyrosine stimulates rapid DNA-dependent subunit or interface exchange to generate complexes with two catalytic tyrosines capable of double-stranded DNA breakage. Subunit exchange between complexes is facilitated by DNA bending and wrapping by gyrase, by the ability of both GyrA and GyrB to form higher order aggregates and by dense packing of gyrase complexes on DNA. By addressing a puzzling paradox, this study provides support for the accepted double strand break (strand passage) mechanism of gyrase and opens new insights on subunit exchange that may have biological significance in promoting DNA recombination and genome evolution.

The conclusions of the work are mostly well supported by the experimental data.

Strengths:

The study examines a fundamental biological question, namely the mechanism of DNA gyrase, an essential and ubiquitous enzyme in bacteria, and the target of fluoroquinolone antimicrobial agents.

The experiments have been carefully done and the analysis of their outcomes is comprehensive, thoughtful and considered.

The work uses an array of complementary techniques to characterize preparations of GyrA, GyrB and various gyrase complexes. In this regard, mass photometry seems particularly useful. Analysis reveals that purified GyrA and GyrB can each form multimeric complexes and highlights the complexities involved in investigating the gyrase system.

The various possible explanations for the double-strand DNA breakage by gyrase heterodimers with a single catalytic tyrosine are considered and addressed by appropriate experiments.

The study highlights the potential biological importance of interactions between gyrase complexes through domain-or subunit-exchange

We thank the reviewer for their support, effort, and comments. The above is a great summary.

Weaknesses:

The mutagenesis experiments described do not fully eliminate the perhaps unlikely participation of an alternative nucleophile.

We agree that the mutagenesis experiment on its own does not fully eliminate the possibility of an alternative nucleophile. The number of residues mutated is limited, and therefore it is possible we have missed a putative alternative nucleophile.

However, we have other data and experiments supporting the conclusion that no alternative nucleophile exists. Therefore, we want to stress that our conclusion that no such alternative exist is based on these extra data. These data and experiments are not discussed by either reviewer despite being present in the original manuscript. This puzzled us and we have modified the manuscript and the figures in the hope that they, and their significance, would not be missed.

Briefly:

1) We have performed cleavage-based labeling of the nucleophile responsible for cleavage. This experiment is depicted in Figure 4. The nucleophilic activity of the residue involved results in covalent link between the polypeptide (that includes the residue) and radiolabeled DNA. Therefore, a polypeptide that includes an active nucleophile will be radiolabeled and visible, whereas a polypeptide that is missing an active nucleophile will remain unlabeled and invisible. We can distinguish the BA and the A polypeptide from their size. In the case of the BA.A complex both the BA polypetide and the A polypetide are radiolabeled and therefore both have an active nucleophile. In the case of the BAF.A complex, the unmutated A polypeptide is labeled, meaning that a nucleophile is still active. In contrast, the BAF polypeptide shows no detectable labeling. This result means that removing the hydroxyl group from the catalytic tyrosine abolishes any protein-DNA covalent link, suggesting that no other nucleophile from the BA polypetidic chain can substitute for the catalytic tyrosine hydroxyl group. This experiment excludes the possibility of an alternative nucleophile coming from the polypeptidic chain of either GyrA or GyrB. This experiment, described in figure 4, is not discussed by the reviewer. This experiment is similar in principle to early experiments identifying catalytic tyrosine in topoisomerases. See for instance, (Shuman, Kane et al. 1989).

2) The experiment above does not exclude a nucleophile coming from the solvent. To exclude this possibility, we have used T5 exonuclease (which needs a free 5’ DNA end to digest) and ExoIII (which need a free 3’ DNA end to digest). We have shown the reconstituted cleavage is not sensitive to T5 and sensitive to ExoIII. This shows that the 5’ end of the cleaved sites are protected by a bulky polypeptide impairing T5 activity, which is active in our reaction as shown by the digestion of a control DNA fragment. This experiment shows that the reconstituted cleavage is very unlikely to come from a small nucleotide potentially provided by the solvent. This experiment is described in the main text and the results are shown in supplementary figure 5. It is not mentioned by either reviewer.

3) Finally, we would like to emphasize our experiment comparing the BAF.A59 to BALLL.A59. The BALLL.A59 complex displays increased cleavage compared to BAF.A59. If this increased cleavage was due to an alternative nucleophile on the BALLL side, we would expect an accompanying increase in supercoiling activity since the BALLL.A59 possesses one CTD, which is sufficient for supercoiling. The fact that no increased supercoiling activity is observed strongly suggests subunit exchange reconstituting an A59 dimer, inactive for supercoiling but active for cleavage. We believe this somewhat complex observation to be quite significant and we have attempted to clarify the manuscript and discuss its full significance in several places.

Reviewer #1 (Recommendations For The Authors):

An interesting paper on DNA gyrase that explains a puzzling paradox in terms of the double-strand break mechanism.

Major points

1) The authors consider several mechanisms that could potentially explain their data. On page 15, the authors present the evidence against the nicking closing mechanism proposed by Gubaev et al. Throughout the manuscript, they indicate where their experimental results agree with this earlier work but should also indicate and account for differences. For example, Gubaev et al describe cross linking experiments that they claim rule out subunit exchange. These aspects should be clearly explained.

Thank you for the suggestion. We have re-written the discussion to address this point. We are extensively discussing experiments from (Gubaev, Weidlich et al. 2016), and offer our interpretation of apparently conflicting results. We suggest that their experiments are basically consistent with our data when correctly interpreted. To keep the main manuscript clear, we have added a supplementary discussion where experiments from (Gubaev, Weidlich et al. 2016) are discussed further in relation to our data.

2) Page 9. The experiments done to rule out the perhaps unlikely alternative nucleophile hypothesis relate to the possible role of the Arg and Threonine of the RYT triad. These residues are close to the DNA and therefore are prime candidates and attractive targets for mutagenesis. However, strictly speaking, the mutant enzyme data presented do not rule all possibilities. For example, Serine is often the nucleophile used by resolvases to effect DNA recombination via subunit exchange. The ideal experiment to rule out/rule in other nucleophiles would be to identify the residue(s) that become attached to DNA in the cleavage reaction.

Please see above. We have effectively ruled an alternative nucleophile with our cleavage-based labeling experiment and others that were present and discussed in the original manuscript but were missed. We have modified the manuscript and figures in order to make this point clearer than before.

3) p17. The readout for subunit exchange used by the authors is double-stranded DNA cleavage. Attempts to directly detect the formation of the DNA cleaving complexes GyrA2B2 and (GyrBA)2 (arising from subunit exchange between heterodimers) by mass photometry were not successful. Perhaps FRET would have been another approach to try as it could also detect interface and domain interchanges.

Directly detecting interface exchange directly by proximity experiment would be extremely useful. FRET would have to be done in the BAF.A + GyrB configuration where the amount of interface exchange is important. Now, we do not have the tools to do that and developing them would be outside the scope of the study. We propose cross linking experiment to be done in the future. We argue that the manuscript is convincing without these for now. This will be addressed in the future. This point, and other possible future experiments are now discussed in the discussion section.

4) The underlying canvas of this paper is the strand passage mechanism of gyrase. It would seem appropriate to include the papers first proposing it - Brown P.O and Cozzarelli N.R. (1979) and Mizuuchi K et al (1980).

We very much agree. These papers have now been added in the introduction as appropriate, highlighting the relationship between double-strand cleavage and the strand-passage mechanism.

5) Figure 1. The quality of the insets is poor. It is difficult to pick out the key catalytic residues and their disposition vis-a-vis DNA.

We agree, Figure 1 has been re-done and the schematic theme has been harmonized throughout the whole manuscript. We very much hope that clarity has improved. Thank you for the suggestion.

6) The experimental work is a very detailed analysis of a specific feature of engineered gyrase heterodimers. Making the work accessible to the general reader will be important. Using shorter paragraphs each with a specific theme might help. In particular, the second paragraph of the Results on p7, the section on p9 and bottom of p11, p13 and the first paragraph of the Discussion on p14 are each a page or more long. A shorter manuscript that avoids overinterpretation of the smaller details would also help.

We agree. We have now split long paragraphs into individual sections, with titles, in the Results. This structure is recapitulated at the beginning of the discussion, and we have split the discussion into shorter paragraphs, each with a unique point being made.

7) The impact of the Gubaev et al (2016) paper for the field in general, and as the catalyst for the present work should be better documented. Mention of this earlier paper and its significance at the beginning of the Abstract and elsewhere e.g in the Introduction might also help with a more logical organization of the current findings and result in a shorter paper (which would be easier to read).

We have added a reference to (Gubaev, Weidlich et al. 2016) in the abstract and have expanded our introduction

Minor points

1) Legends for Figs 2 and 6; Supplementary Figs 1 and 8. The designation of subfigures as a, b, c, d , e etc appears to be incorrect. Check throughout and in the text.

The manuscript has been checked for such errors.

2) Figure 2, and first paragraph p8. Peaks in Fig 2c should be labelled to facilitate discussion on p8.

Agreed, this has been done.

3) Supplementary Fig 4 and elsewhere in the manuscript. A variety of notations are used to denote phenylalanine mutants e.g. AsubscriptF, AsuperscriptF and AF. Check and use one format throughout.

Done

4) Figures showing gels include the label '+EtBr, +cipro'. This is somewhat confusing because EtBr was contained in the gel (not the samples) whereas cipro was included in the reaction. Modify or describe in the legend..

We have re-written the figure legend.

5) Supplementary Fig 4b describes a small effect on the ratio of linear to nicked DNA for the triple LLL mutant. Is this significant? How many times was the measurement made?

This has been addressed in the original manuscript in the supplementary data. In term of quantification, the experiment has been done 3 times for each prep, with the same GyrB prep and concentration. The standard error is displayed on the figure. This result is very reproducible and have been reproduced more than 3 times. No LLL cleavage assay showed more single-strand than double-strand cleavage. For the phenylalanine mutant, no cleavage assay showed more double-strand than single-strand cleavage.

6) Supplementary Fig 5 legend. Should 'L' read 'size markers' (and give their sizes)?

Yes indeed, we have modified the figure to clarify.

7) p11 line 5. Is this statement correct?

Yes, it is correct. Although we hope we are on the same line. When the Tyrosine is mutated on one side only of the heterodimer, both single- and double-strand cleavage are protected from T5 exonuclease digestion.

8) 12 last line should read...and supercoiling activity (not shown)..were

Thank you, done.

There are a number of typos throughout the text, for example:

Page 3 line..Difficult to conclude...what?

Page 3 para 3...Lopez....and Blazquez

We have corrected these typos and checked the whole manuscript.

Reviewer #2 (Public Review):

DNA gyrase is an essential enzyme in bacteria that regulates DNA topology and has the unique property to introduce negative supercoils into DNA. This enzyme contains 2 subunits GyrA and GyrB, which forms an A2B2 heterotetramer that associates with DNA and hydrolyzes ATP. The molecular structure of the A2B2 assembly is composed of 3 dimeric interfaces, called gates, which allow the cleavage and transport of DNA double stranded molecules through the gates, in order to perform DNA topology simplification. The article by Germe et al. questions the existence and possible mechanism for subunit exchange in the bacterial DNA gyrase complex.

The complexes are purified as a dimer of GyrA and a fusion of GyrB and GyrA (GyrBA), encoded by different plasmids, to allow the introduction of targeted mutations on one side only of the complex. The conclusion drawn by the authors is that subunit exchange does happen, favored by DNA binding and wrapping. They propose that the accumulation of gyrase in higher-order oligomers can favor rapid subunit exchange between two active gyrase complexes brought into proximity.

The authors are also debating the conclusions of a previous article by Gubaev, Weidlich et al 2016 (https://doi.org/10.1093/nar/gkw740). Gubaev et al. originally used this strategy of complex reconstitution to propose a nicking-closing mechanism for the introduction of negative supercoils by DNA gyrase, an alternative mechanism that precludes DNA strand passage, previously established in the field. Germe et al. incriminate in this earlier study the potential subunit swapping of the recombinant protein with the endogenous enzyme, that would be responsible for the detected negative supercoiling activity.

Accordingly, the authors also conclude that they cannot completely exclude the presence of endogenous subunits in their samples as well.

Strengths

The mix of gyrase subunits is plausible, this mechanism has been suggested by Ideka et al, 2004 and also for the human Top2 isoforms with the formation of Top2a/Top2b hybrids being identified in HeLa cells (doi: 10.1073/pnas.93.16.8288).

Germe et al have used extensive and solid biochemical experiments, together with thorough experimental controls, involving :

• the purification of gyrase subunits including mutants with domain deletion, subunit fusion or point mutations.

• DNA relaxation, cleavage and supercoiling assays

• biophysical characterization in solution (size exclusion chromatography, mass photometry, mass spectrometry)

Together the combination of experimental approaches provides solid evidence for subunit swapping in gyrase in vitro, despite the technical limitations of standard biochemistry applied to such a complex macromolecule.

We thank the reviewer for their supportive and considered comments.

Weaknesses

The conclusions of this study could be strengthened by in vivo data to identify subunit swapping in the bacteria, as proposed by Ideka et al, 2004. Indeed, if shown in vivo, together with this biochemical evidence, this mechanism could have a substantial impact on our understanding of bacterial physiology and resistance to drugs.

Thank you for this comment. Indeed, whether this interface exchange can happen in vivo and lead to recombination is a very important question. However, we believe that this is outside the scope of this study simply because of the amount of work one can fit into one paper. Proving that interface exchange can happen in vitro has already necessitated a number of non-trivial experiments and likewise investigating interface exchange in vivo will require a careful, long-term study (see our reply to reviewer #2 comment, who also raised this point). We can’t address it with one additional experiment with the tools we have. However, we very much hope to do it in the future.

Reviewer #2 (Recommendations For The Authors):

Specific questions and comments for the authors:

1) Complex identification during purification

The statement line 236-237 that "Our heterodimer preparation showed a single-peak on a gel-filtration column, distinct from the GyrA dimer peak" is not entirely clear. In Fig supp 1 b, how can the authors conclude from the superose 6 that GyrBA is separated from the GyrA dimer? Since they seem close in size 160/180kDa, they are unlikely to be well separated in a superose 6 gel filtration column. The SDS-PAGE seems to show both species in the same fractions #15-17 therefore it would not be possible to distinguish GyrBA. A from A2.

There appears to be some confusion about what Supp Fig. 1b shows. First, in all our gel filtration conditions both GyrBA and GyrA can’t exist as monomers at a significant concentration. Therefore, we can never observe the GyrBA monomer on a gel filtration column. Supp Fig. 1b shows the gel filtration profile of the BA.A heterodimer only. This is the output of the last, polishing step in the reaction. We analyze these results using SDS-PAGE. Therefore, the BA.A heterodimer will be denatured and separated into 2 polypeptides: GyrBA and GyrA, which migrates according to their size in an SDS-PAGE and forms two bands. These two bands do not represent two separate species in solution. They represent the separation of one species only, the BA.A heterodimer into its two, denatured, subunits: GyrA and GyrBA. We do not conclude from Supp Fig. 1 as a whole that GyrBA and the GyrA dimer are well separated, and this is not stated in the manuscript. We conclude that the BA.A dimer is fairly well separated from the GyrA dimer. They have significant different size (~260 kDa and ~180 kDa respectively) and form different peaks on a gel filtration column. The BA.A heterodimer has a GyrA subunit and therefore will shows a GyrA band on an SDS-PAGE, like the GyrA dimers but the two are obviously distinct in their quaternary structure. We are hoping that our new schematics and re-write of some of the results and figure legends will clarify this.

Panel 6 shows a different elution volume for the 2 species BA.A and A2 on an analytical S200 column, which appears better at separating the complexes in this size range.

Did the authors consider using a S200 column instead of superose 6 for the sample preparation, to optimize the separation of GyrBA. A from A2?

This is not a necessarily true statement (see above). We have not run the GyrA dimer on a Superose 6 column. The analysis was done on an s200 because extensive data for the GyrA dimer was already available with this, already calibrated column. We do not expect the Superose 6 to be worse in this size range. In fact, it might even be better. The Superose 6 profile in Supp. Fig. 1b shows BA.A only and no GyrA dimer. We have clarified the annotations in the figure to make this clearer.

Regarding the analytical gel filtration experiment, there is however an overlap in the elution volume in the analytical column, therefore how can the authors ensure there is no excess free A2 complex in the GyrBA. A sample?

Indeed, there is an overlap, but we argue that it is overstated. The important part of the overlap is where the maximum height of the GyrA peak is positioned compared to the BA.A trace, not where the traces intersect. This overlap is minimal. If a contaminating GyrA peak was hidden in the BA.A peak, it would have to be at least 10 times less intense than the BA.A peak. Since BA.A and GyrA dimer have roughly the same extinction coefficient, this means that a contamination would detectable at 10 % or even less. Our mass photometry further excludes such contamination.

Alternatively, the addition of a larger (cleavable) tag at the C-terminal end of the BA construct (therefore not disturbing dimer association) could allow to better distinguish the 2 populations already at the size exclusion step.

This is true and could allow cleaner purification. There are also other ways to achieve cleaner purification, like adding a secondary tag. However, like we argue in the manuscript, our contaminations are already minimal. It is questionable what benefits could be gained in changing the protocol. We also argue that the tandem tag method does not completely exclude contamination (Supplementary Discussion) and therefore we are not sure if this would be worth the time and expenditure.

2) GyrA and GyrB Oligomers:

In the mass photometry experiment, the authors explain that the low concentration of the proteins promotes dissociation of GyrA dimers, hence the detection of GyrA monomers instead of GyrA dimers, which are also detected in the GyrBA.A sample.

However, it cannot be concluded that the GyrA dimer is not formed in the condition of the gel filtration chromatography, at higher concentration.

In our mass photometry experiment, The BA.A sample is not as diluted as the GyrA dimer and much closer to our experimental condition. Since we have calculated the dissociation constant, we can calculate the expected level of dissociation (or reassociation). The level of dissociation is minimal in these conditions. If some dissociation is expected from the BA.A heterodimers, a very low amount of GyrBA monomer should also be present and yet they are not observed. We presume that it is because mass photometry is much more sensitive to GyrA (see our mixing mass photometry experiment that we have added). If the GyrA would reassociate at higher concentration, it would do so either with itself (forming a GyrA dimer) or with the GyrBA monomer, reforming the heterodimer. Assuming both GyrA dimer and heterodimer have the same dissociation constant, roughly one third of the GyrA monomer would reassociate with themselves. Assuming even complete reassociation of the GyrA dimer, this would leave only GyrA dimer accounting for 2% of the prep.

Another interpretation would be to assume that GyrBA monomers are not present at all and that GyrA monomer are reassociating only with themselves. This is not valid because of the following thermodynamic reason:

Since the profile for the GyrA dimer are collected at equilibrium, we should expect a ratio between GyrA monomer and dimers that follow the dissociation constant. In other words, if the GyrA monomer were in equilibrium with GyrA dimer we should expect a much higher dimer concentration already as the GyrA monomers are not as dilute. We do not observe a GyrA dimer peak in the BA.A profile, even though we can detect a low amount of GyrA dimer mixed with BA.A. Therefore, we conclude that the observed GyrA monomer must be in equilibrium with another dimerization partner, which is most probably the GyrBA monomer (see above). Therefore, only a minimal amount of GyrA dimer is expected to be formed at higher concentration by direct reassociation. This could probably increase if we let this solution-based exchange carry on for a long time at dissociation equilibrium. We have actually shown that this solution-based exchange is very slow and take several days because of the low dissociation at equilibrium.

The mass spectrometry analysis in Fig 2 confirms the presence of (monomeric) GyrA in the sample, despite different experimental conditions.

The concentration of heterodimer in the mass spectrometry experiment is actually higher than in the mass photometry experiment. This shows that self-reassociation of the GyrA monomer as suggested above is undetectable with mass spectrometry at higher concentration.

We considered that the “GyrA monomer” peak could be a contaminating GyrB monomer, which is ~90 kDa, which would explain the lack of reassociation. However, the mass spectrometry peak shows precisely the expected molecular weight of GyrA so we interpret this peak as arising from very limited dissociation of the BA.A heterodimer. The reassociation is limited at high concentration due simply to the fact that the difference in concentration between the mass photometry and our other experimental conditions is not that high. The GyrA dimer had to be diluted 400 times to see significant dissociation and yet even at this very low concentration the dissociation is far from complete.

Our general conclusions on the couple of point above is that we cannot completely exclude the presence of GyrA dimers being present, although they are undetectable in our working conditions either by mass photometry (lower concentration), Mass spectrometry (higher concentration) and even gel filtration (even higher concentration, see above). For the mass photometry, we have established that our detection threshold for a contamination is very low (see our mixing experiment).

Figure 2A: the authors state in the introduction that GyrB is a monomer in solution and then explain that the upper bands in the native gel are multimer of GyrB. Could the authors comment and provide the size exclusion profile of the Gyr B purification?

We have expanded our discussion of this. However, we have not been successful in collecting a gel filtration profile for GyrB. This is likely due to excessive oligomerization at the concentration we are using for gel filtration. We suggest that our mass photometry and Blue-Native PAGE experiment shows clearly that GyrB can be detected as a monomer in solution at the appropriate dilution. However, GyrB tends to oligomerize in a regular fashion (Consider especially Supp Fig. 8a), which suggest that it could align heterodimers on DNA in a linear, regular orientation. We have added a discussion of this.

Together the relevance of the oligomeric state of purified GyrA or GyrB should be clarified, relative to their role in subunit swapping.

We have added explanation in our discussion, while also trying to not be too speculative. Basically, we believe that GyrB oligomerization is likely to be involved. It is difficult to conclude for GyrA since no experiment has allowed us to test it. Therefore, the role of GyrA oligomerization, if any, is unclear. The GyrA tetramer is very prominent though and forms very easily. GyrB on the contrary forms longer oligomers more readily than GyrA and we surmise that this would help interface exchange. However, the structure of these GyrA and GyrB oligomers is not clear, which make it difficult to go beyond speculation on this. It would be a very interesting experiment if we were able to suppress GyrB oligomerization whilst conserving its ability to promote strand-passage and cleavage. Same goes for GyrA. Unfortunately, we are unable to do that at this time.

4) Subunit exchange

Line 320: the concept of subunit exchange in this context should be clearly explained. If one understands correctly, the authors mean that the BAF polypeptide, part of the BAF.A complex, could be replaced by a combination of B+A therefore forming a fully functional WT A2B2 gyrase complex.

Thank you for the suggestion. We have harmonized and clearly defined our terminology for interface swapping and subunit exchange in the introduction and attempted to be much more rigorous when referring to it.

A great effort has been done in this study to explain all the pros and cons of the experimental design but the length of the explanations may prevent readers outside of the field to fully appreciate the conclusions. This article would benefit from the addition of a few schematics to summarize the working hypothesis.

Thanks for the suggestion. We have added a series of schematics to illustrate our interpretation for each construct. As mentioned above the terminology has been more rigorously defined and updated throughout the manuscript.

5) Presence of endogenous GyrA

Line 419-425: it is quite difficult to follow the explanations regarding the possible contamination of the sample by endogenous GyrA.

Maybe these points should rather be addressed in the discussion, when debating the conclusions of Gubaev et al.

We agree. We have re-organized the Discussion doing just that. We added a Supplementary Discussion in which we further discuss the contamination problem in relation to (Gubaev, Weidlich et al. 2016).

Production of the subunits in another (non bacterial) expression system or a cell free system may prevent the association of endogenous protein.

Absolutely. We are planning on addressing this in the future, using the yeast expression system.

6) Mechanism for subunit swapping

Lines 588-595: As described by the authors the BA fusion shows decreased activity when compared with the WT probably due to limited conformational flexibility in absence of an additional linker sequence between the fused subunits.

The affinity of BA for A may possibly be reduced compared to the free A2B2 complex, due to a relative stiffness of the fusion upon full association with a free B subunit, as rightfully pointed by the authors.

If subunit exchange do happen in vitro, at least in the conditions of this study, the authors could assess the affinity of BA for A, when compared to the association of free B and A subunits

Experiments using analytical ultracentrifugation or surface plasmon resonance (SPR) may allow to determine the relative affinity of the BA +(A+B) compared to the A2B2 complex. This could be done also for the BALLL mutant and association with A59.

It would be extremely useful to measure the affinity of BA for A. However, this is difficult because of the high affinity of the interface. To measure a dissociation constant, one has to be able to measure the concentration of the monomer and the dimer at equilibrium. Because of this, the complex must be diluted enough to see any dissociation, making detection difficult. In practice, this also means that we cannot purify monomeric versions of these subunits. We therefore can’t perform “on-rate” study on an SPR surface, which would require flowing monomers on its partner subunit tethered to the SPR surface. However, we could perform “off-rate” studies, but the dissociation time is likely to be very long, making the measurement difficult. We have not tried it though, and it could turn out to be informative. An analysis of antibodies off-rate done in the past could provide a guideline for us to perform this experiment. Analytical ultracentrifugation is an excellent technique and could in theory provide information. In practice however it would be still necessary to dilute the complex enough to obtain significant dissociation at equilibrium, making detection difficult. As far as we are aware, analytical ultracentrifugation rely on UV absorbance for protein detection and therefore we probably would not detect our material at the necessary dilution. We are however open-minded about technique with very sensitive detection methods that could be used.

9) In vivo relevance

The study does not conclude on the subunits exchange in vivo, which have been suggested by earlier studies by Ikeda et al. To elaborate further on the relevance of such mechanism in the bacteria, experiments involving the fluorescent labeling of endogenous / exogenous mutant subunits may be required to provide further information on this phenomenon.

We completely agree that the in vivo relevance of such phenomena is the central question. Addressing this directly is not trivial though. Expressing both BA and A in vivo will results in random partnering and lead to a mix of dimers: A2 (1/4), BA2(1/4) and BA.A (1/2), assuming equal interface affinity. Therefore, to see subunit exchange in the same way as in vitro, one would have to get rid of the BA2 and A2 dimer together, or the BA.A dimer only. Our initial strategy to do that would be to engineer a specific dimer as being uniquely targeted for degradation. This could allow us to “get rid” of for instance the BA.A dimer. Subsequently, we would turn off the degradation and translation together and observe the rate of subunit exchange. This is not trivial though and would be the subject of a further study.

10) Figure 3: I guess the "intact" label refers to the supercoiled DNA (SC) ? It also appears as "uncleaved" in supp Figure 6. The same label for this topoisomer should be used throughout.

Thank you for pointing that out. It has now been corrected.

Bandak, A. F., T. R. Blower, K. C. Nitiss, R. Gupta, A. Y. Lau, R. Guha, J. L. Nitiss and J. M. Berger (2023). "Naturally mutagenic sequence diversity in a human type II topoisomerase." Proceedings of the National Academy of Sciences 120(28).

Germe, T., J. Voros, F. Jeannot, T. Taillier, R. A. Stavenger, E. Bacque, A. Maxwell and B. D. Bax (2018). "A new class of antibacterials, the imidazopyrazinones, reveal structural transitions involved in DNA gyrase poisoning and mechanisms of resistance." Nucleic Acids Res.

Gubaev, A., D. Weidlich and D. Klostermeier (2016). "DNA gyrase with a single catalytic tyrosine can catalyze DNA supercoiling by a nicking-closing mechanism." Nucleic Acids Res 44(21): 10354-10366.

Hartmann, S., A. Gubaev and D. Klostermeier (2017). "Binding and Hydrolysis of a Single ATP Is Sufficient for N-Gate Closure and DNA Supercoiling by Gyrase." J Mol Biol 429(23): 3717-3729. Shuman, S., E. M. Kane and S. G. Morham (1989). "Mapping the active-site tyrosine of vaccinia virus DNA topoisomerase I." Proc Natl Acad Sci U S A 86(24): 9793-9797.

Stelljes, J. T., D. Weidlich, A. Gubaev and D. Klostermeier (2018). "Gyrase containing a single C-terminal domain catalyzes negative supercoiling of DNA by decreasing the linking number in steps of two." Nucleic Acids Res.

Author Response

Reviewer #1 (Public Review):

The goal of the current study was to evaluate the effect of neuronal activity on blood-brain barrier permeability in the healthy brain, and to determine whether changes in BBB dynamics play a role in cortical plasticity. The authors used a variety of well-validated approaches to first demonstrate that limb stimulation increases BBB permeability. Using in vivo-electrophysiology and pharmacological approaches, the authors demonstrate that albumin is sufficient to induce cortical potentiation and that BBB transporters are necessary for stimulus-induced potentiation. The authors include a transcriptional analysis and differential expression of genes associated with plasticity, TGF-beta signaling, and extracellular matrix were observed following stimulation. Overall, the results obtained in rodents are compelling and support the authors' conclusions that neuronal activity modulates the BBB in the healthy brain and that mechanisms downstream of BBB permeability changes play a role in stimulus-evoked plasticity. These findings were further supported with fMRI and BBB permeability measurements performed in healthy human subjects performing a simple sensorimotor task. While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain. Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies. The authors could have also better integrated the RNAseq findings into mechanistic experiments, including testing whether the upregulation of OAT3 plays a role in cortical plasticity observed following stimulation. Overall, this study provides novel insights into how neurovascular coupling, BBB permeability, and plasticity interact in the healthy brain.

While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain.

We agree with the reviewer regarding the importance of examining sex differences on stimulation-evoked BBB changes. To address this issue we have: (1) clarified in the methods section that the human study involved both males and females; (2) added a section to the discussion highlighting the male bias as a key limitation of our animal experiments; and (3) stated that future work should examine whether stimulation-evoked BBB changes differ between makes and females.

Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies.

We are grateful for this comment and agree with the reviewer that the potential effects of anesthesia should be discussed. We have added the following discussion paragraph:

“A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018).”

Reviewer #2 (Public Review):

Summary:

This study builds upon previous work that demonstrated that brain injury results in leakage of albumin across the bloodbrain barrier, resulting in activation of TGF-beta in astrocytes. Consequently, this leads to decreased glutamate uptake, reduced buffering of extracellular potassium, and hyperexcitability. This study asks whether such a process can play a physiological role in cortical plasticity. They first show that stimulation of a forelimb for 30 minutes in a rat results in leakage of the blood-brain barrier and extravasation of albumin on the contralateral but not ipsilateral cortex. The authors propose that the leakage is dependent upon neuronal excitability and is associated with an enhancement of excitatory transmission. Inhibiting the transport of albumin or the activation of TGF-beta prevents the enhancement of excitatory transmission. In addition, gene expression associated with TGF-beta activation, synaptic plasticity, and extracellular matrix are enhanced on the "stimulated" hemisphere. That this may translate to humans is demonstrated by a breakdown in the blood-brain barrier following activation of brain areas through a motor task.

Strengths:

This study is novel and the results are potentially important as they demonstrate an unexpected breakdown of the blood-brain barrier with physiological activity and this may serve a physiological purpose, affecting synaptic plasticity.

The strengths of the study are:

1) The use of an in vivo model with multiple methods to investigate the blood-brain barrier response to a forelimb stimulation.

2) The determination of a potential functional role for the observed leakage of the blood-brain barrier from both a genetic and electrophysiological viewpoint.

3) The demonstration that inhibiting different points in the putative pathway from activation of the cortex to transport of albumin and activation of the TGF-beta pathway, the effect on synaptic enhancement could be prevented.

4) Preliminary experiments demonstrating a similar observation of activity-dependent breakdown of the blood-brain barrier in humans.

Weaknesses:

There are both conceptual and experimental weaknesses.

1) The stimulation is in an animal anesthetized with ketamine, which can affect critical receptors (ie NMDA receptors) in synaptic plasticity.

We agree that the potential effects of anesthesia should be considered. The Discussion was revised to address this point: “A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018)”

2) The stimulation protocol is prolonged and it would be helpful to know if briefer stimulations have the same effect or if longer stimulations have a greater effect ie does the leakage give a "readout" of the stimulation intensity/length.

Thank you for this important comment. We are also very curious about the potential relationship between stimulation magnitude/duration and subsequent leakage and have added the following statement to the discussion:

“Future studies should also explore the effects of stimulation magnitude/duration on BBB modulation, as well as the stimulation threshold between physiological and pathological increase in BBB permeability.”

Our current findings indicate that a one-minute stimulation does not affect vascular permeability or SEP and we aim to test additional stimulation paradigms in future studies.

3) For some of the experiments (see below), the numbers of animals are low and the statistical tests used may not be the most appropriate, making the results less clear cut.

We appreciate this comment and have revised the statistical analysis of Figure 1J,K. We now use a nested t-test to test for differences between rats (as opposed to sections). The differences remain significant (EB, p=0.0296; Alexa, p=0.0229). The text was modified accordingly.

4) The experimental paradigms are not entirely clear, especially the length of time of drug application and the authors seem to try to detect enhancement of a blocked SEP.

Thank you for pointing this out. Figures 2&3 were revised for clarification and a ‘Drug Application’ subsection was added to the methods section.

5) It is not clear how long the enhancement lasts. There is a remark that it lasts longer than 5 hours but there is no presentation of data to support this.

Thank you for this comment. As the length of experiments differed between animals, the exact length could not be specifically stated. To clarify this point, we revised the text to indicate that LTP was recorded until the end of each experiment (between 1.5-5 hours, depending on the condition the animal was in). We also added a panel to figure 2 (Figure 2d) with exemplary data showing potentiation 60, 90, and 120 min post stimulation.

6) The spatial and temporal specificity of this effect is unclear (other than hemispheric in rats) and even less clear in humans.

Our animal experiments (using both in vivo imaging and histological analysis) showed no evidence of BBB modulation outside the cortical somatosensory area corresponding to the limbs. We looked at the entirety of the coronal section of the brain and found enhancement solely in the somatosensory area corresponding to limb. The right side of panels h and i in Figure 1 show an x20 magnification of the section, focusing on the enhanced area. The whole section was not shown, as no fluorescence was found outside the magnified area. Moreover, our quantification showed that the enhancement was specific to the contralateral and not ipsilateral somatosensory cortex (Figure 1 j-k).

We agree that temporal specificity needs to be further explored, and we have now stated that in the discussion: “Future studies are needed to explore the BBB modulating effects of additional stimulation protocols – with varying durations, frequencies, and magnitudes. Such studies may also elucidate the temporal and ultrastructural characteristics that may differentiate between physiological and pathological BBB modulation.”

We also agree that larger studies are needed to better understand the specificity of the observed effect in humans, and to account for potential inter-human variability in vascular integrity and brain function due to different schedules, diets, exercise habits, etc.

8) The experimenters rightly use separate controls for most of the experiments but this is not always the case, also raising the possibility that the application of drugs was not done randomly or interleaved, but possibly performed in blocks of animals, which can also affect results.

Thank you for pointing out this lack of clarity. We have now highlighted that drug application was done randomly.

9) Methyl-beta-cyclodextrin clears cholesterol so the effect on albumin transport is not specific, it could be mediating its effect through some other pathway.

We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10uM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is observed at 5mM mβCD and not at 2.5mM/5mM (Koudinov & Koudinova, 2001). This point was added to the discussion.

10) Since the breakdown of the blood-brain barrier can be inhibited by a TGF-beta inhibitor, then this implies that TGFbeta is necessary for the breakdown of the blood-brain barrier. This does not sit well with the hypothesis that TGF-beta activation depends upon blood-brain barrier leakage.

Thank you for pointing out this lack of clarity. We have added a discussion paragraph that clarifies our hypothesis: “As mentioned above, albumin is a known activator of TGF-β signaling, and TGF-β has a well-established role in neuroplasticity. Interestingly, emerging evidence suggests that TGF-β also increases cross-BBB transcytosis (Betterton et al., 2022; Kaplan et al., 2020; McMillin et al., 2015; Schumacher et al., 2023). Hence, we propose the following two-part hypothesis for the TGF-β/BBB-mediated synaptic potentiation observed in our experiments: (1) prolonged stimulation triggers TGF-β signaling and increased caveolae-mediated transcytosis of albumin; and (2) extravasated albumin induces further TGF-β signaling, leading to synaptogenesis and additional cross-BBB transport – in a self-reinforcing positive feedback loop. Future research is needed to examine the validity of this hypothesis.

Reviewer #3 (Public Review):

Summary:

This study used prolonged stimulation of a limb to examine possible plasticity in somatosensory evoked potentials induced by the stimulation. They also studied the extent that the blood-brain barrier (BBB) was opened by prolonged stimulation and whether that played a role in the plasticity. They found that there was potentiation of the amplitude and area under the curve of the evoked potential after prolonged stimulation and this was long-lasting (>5 hrs). They also implicated extravasation of serum albumin, caveolae-mediated transcytosis, and TGFb signalling, as well as neuronal activity and upregulation of PSD95. Transcriptomics was done and implicated plasticity-related genes in the changes after prolonged stimulation, but not proteins associated with the BBB or inflammation. Next, they address the application to humans using a squeeze ball task. They imaged the brain and suggested that the hand activity led to an increased permeability of the vessels, suggesting modulation of the BBB.

Strengths:

The strengths of the paper are the novelty of the idea that stimulation of the limb can induce cortical plasticity in a normal condition, and it involves the opening of the BBB with albumin entry. In addition, there are many datasets and both rat and human data.

Weaknesses:

The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

The conclusions are not compelling however because of a lack of explanation of methods and quantification.

Thank you for this comment. In the revised paper, we expanded the Methods section to better describe the procedures and approaches we used for data analysis.

It also is not clear whether the prolonged stimulation in the rat was normal conditions.

We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

1) In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

2) Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

3) The levels of SEP potentiation we observed are similar to those reported in:

a) Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

b) Humans following a 15 min task (McGregor et al., 2016).

This important point is now presented in the discussion.

Reviewer #1 (Recommendations For The Authors):

The discussion would benefit from additional discussion of the potential impacts of sex and anesthesia in their findings.

We agree with the reviewer and have added the following paragraph to the discussion:

“A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can potentially alter neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketaminexylazine anesthesia, which unlike other anesthetics, was shown to maintain robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018). Another limitation of our animal study is the potentially non-specific effect of mβCD – an agent that disrupts caveola transport but may also lead to cholesterol depletion (Keller & Simons, 1998). To mitigate this issue, we used a very low mβCD concentration (10uM), orders of magnitude below the concentration reported to deplete cholesterol (Koudinov et al). Lastly, our animal study is limited by the inclusion of solely male rats. While our findings in humans did not point to sex-related differences in stimulation-evoked BBB modulation, larger animals and human studies are needed to examine this question.”

The figure text is quite small.

Thank you for pointing this out, we revised all figures and increased font size for clarity.

Including pharmacological concentrations within the figure legends would improve the readability of the manuscript.

Thank you for this suggestion, the figure legends were modified accordingly.

In methods for immunoassays the 5 groups could be more clear by stating that there are 3 timepoints for stimulation experiments. There is a typo in this section where the 24-hour post is stated twice in the same sentence.

Thank you for pointing this out, the text was modified accordingly.

Reviewer #2 (Recommendations For The Authors):

1) In Figure 1, J and K seem to indicate that in these experiments the statisitics were done per slice and not per animal. This is not a reasonable approach, a repeat measure ANOVA or averaging for each animal are more appropriate statistical approaches.

We thank the reviewer for pointing this out. The statistical analysis for Figure 1j,k was modified. We now use a nested ttest to test for differences between rats and not sections. The differences are still significant (EB, p=0.0296; Alexa, p=0.0229). The manuscript was modified accordingly.

2) In Figure 2, the protocol does not seem to give much idea about time course. There was a stimulation test for 1 minute before and then 1 minute after the 30-minute stimulation train. How was potentiation assessed for the next 5 hours and where are the data?

Potentiation was assessed by repeating 1min test stim every 30 min for the duration of the experiment, we added a panel to show late potentiation, see response above.

3) In Figure 2, there is a notable lack of controls eg the effect of sham stimulation and application of saline. These are important as the drift of response magnitude can be a problem in long experiments.

We did test for the potential presence of response drift, by examining whether SEPs of non-stimulated animals change over time (at baseline, 30 or 60 minutes of recording; n=6). No statistical differences were found. Our analysis focused on using each animal as its own control (i.e., comparing baseline SEP to SEP post albumin perfusion), because SEP studies highlight the importance of comparing each animal to its own baseline, due to the large inter-animal variability (All et al., 2010; Mégevand et al., 2009; Zandieh et al., 2003).

4) Figure 3 a is not clear – were the drugs applied throughout?

Thank you for pointing this out. We have revised Figure 3 a to show that the drugs were applied for 50 min before the stimulation.

5) In Figure 3 panel d is repeated in panel j. This needs correcting

Thank you. This mistake was fixed.

6) In LTP-type experiments usually the antagonist is applied during the stimulation and then washed out. This avoids the problem in this figure in which CNQX effectively blocks transmission and so it is not possible to detect any enhancement if it were there. Eg in panel e, CNQX block transmission, and then the assessment is performed when the AMPA receptors are blocked after 30 minutes of stimulation. If receptors are blocked no enhancement will be detectable. Moreover, surely the question is the ratio of the effect of 30-minute stimulation on the SEP in the presence of CNQX and so the statistics should be done on the fold change in the SEP following 30-minute stimulation in the presence of CNQX.

Thank you. The protocol might have been misrepresented in the original figure. We modified Fig 3a to clarify that the antagonists were indeed washed out upon stimulation start to make sure the receptors are not blocked during the test stimulation following the 30 min stimulation. In addition, we tested for the difference in fold change between 30 min stim, and 30 min stimulation following antagonists wash-in (Fig 3f and Fig S2a).

7) Interesting in Figure f, stimulation, albumin, and AP5 all seem to have the same enhancement of the SEP. Is the lack of effect of 30-minute stimulation in the presence of AP5, a ceiling effect ie AP5 has enhanced the SEP, and no further enhancement from stimulation is possible.

This is a very interesting point that will require further research.

8) SJN seems to block neurotransmission. What is the mechanism? The same analysis as for CNQX should be performed ie what is the fold change not compared to baseline but in the presence of SJN.

Our quantification showed that SJN did not significantly reduce the SEP max amplitude, and we therefore did not include this graph in the figure.

9) Please acknowledge that the effect of mbetaCD is non-specific. There is a large literature on the effects of cholesterol depletion on LTP.

We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10µM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is only observed at a concentration of 5mM (Koudinov & Koudinova, 2001). This point is now discussed under the discussion paragraph describing the study’s limitations.

10) k&l seem to have used the same control in which case they should not be analysed separately (they are all part of the same experiment).

We agree with the reviewer and have revised the figure accordingly.

11) The difference in gene expression in Figure 4 would be more convincing if it could be prevented by for example a TGFbeta inhibitor.

We agree and acknowledge the impact such experiments could provide. We plan to incorporate these experiments into our future studies.

12) Figure 5 seems to indicate bilateral and widespread BBB modulation arguing that this may be a non-specific effect. Panel g should look at other neocortical regions eg occipital cortex.

We agree and thank the reviewer for this comment. We revised the figure to include other cortical areas, such as the frontal and occipital cortices (Figure 5g)

Minor comments

1) Paired data eg in Fig 2D are better represented by pairing the dots usually with a line.

2) Please correct the %fold baseline in axes in graphs which show % change for baseline.

3) Figure 4 is not correctly referred to in the text.

We agree with all the points raised by the reviewer and revised the figures and text accordingly.

Reviewer #3 (Recommendations For The Authors):

The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

Major concerns:

Methods need more explanation. Rationales need more justification. Examples are provided below.

Throughout many sections of the paper, sample sizes and stats are often missing. For stats, please provide p-values and other information (tcrit, U statistic, F, etc.)

Thank you, we added the relevant information where it was missing throughout the manuscript.

For transcriptomics, they might have found changes in BBB-related genes if they assayed vessels but they assayed the cortex.

We agree with the reviewer that this would be a very interesting future direction. The present study could not include this kind of analysis due to lack of access to vasculature isolation methods or single-cell RNA seq.

What were the inclusion/exclusion criteria for the subjects?

Thank you for pointing out this lack of clarity. The methods section (under ‘Magnetic Resonance Imaging’ – ‘Participants’) was expanded to include the following:

“Male and female healthy individuals, aged 18-35, with no known neurological or psychiatric disorders were recruited to undergo MRI scanning while performing a motor task (n=6; 3 males and 3 females). MRI scans of 10 sex- and age- matched individuals (with no known neurological or psychiatric disorders) who did not perform the task were used as control data (n=10; 5 males and 5 females.

Were they age and sex-matched?

They were, indeed, age and sex-matched. This was now clarified in the relevant Methods section.

Were there other factors that could have influenced the results?

Certainly. Human subjects are difficult to control for due to different schedules, diets, exercise habits, and other factors that may impact vascular integrity and brain function. Larger multimodal studies are needed to better understand the observed phenomenon.

Fig. 1. Images are very dim. Text here and in other figures is often too small to see. Some parts of the figures are not explained.

Our apologies. Figures and legends were revised accordingly.

Fig 2a, f. I don't see much difference here- do the authors think there was?

We agree that the difference may not be visually obvious. The quantification of trace parameters (amplitude and area under curve) does, however, reveal a significant SEP difference in response to both stimulation (panels X and y) and albumin (panels z and q).

Fig 3 d and j seem the same.

We thank the reviewer for noticing. This was a copy mistake that was now rectified.

Lesser concerns and examples of text that need explana9on:

Introduction

Insulin-like growth factor is transported. From where to where?

The text was edited to clarify that this was cross-BBB influx of insulin-like growth factor-I.

RMT that underlies the transport of plasma proteins was induced by physiological or non-physiological stimulation.

This was shown without stimulation, in normal physiology of young and aged healthy mice. The text was edited to clarify this point.

What was the circadian modulation that was shown to implicate BBB in brain function?

The text was edited for clarity.

Results

When the word stimulation is used please be specific if whiskers are moved by an experimenter, an electrode is used to apply current, etc.

We have now moved the ‘Stimulation protocol’ section closer to beginning of the Methods and emphasized that we administered electrical stimulation to the forepaw or hindlimb using subdermal needle electrodes.

Please explain how the authors are convinced they localized the vascular response.

The vascular response was localized via: (1) visual detection of arterioles that dilated in response to stimulation (due to functional hyperemia / neurovascular coupling) [figure 1 d]; and (2) quantitative mapping of increased hemoglobin concentration (Bouchard et al., 2009) [Figure 1 b]. This is now mentioned in the methods (under ‘In vivo imaging’) and results (under the ‘Stimulation increases BBB permeability’).

"30 min of limb stimulation" means what exactly? 6 Hz 2mA for 30 min?

Thank you. The text was revised for clarity (Methods under ‘Stimulation protocol’):

“The left forelimb or hind limb of the rat was stimulated using Isolated Scmulator device (AD Instruments) attached with two subdermal needle electrodes (0.1 ms square pulses, 2-3 mA) at 6 Hz frequency. Test stimulation consisted of 360 pulses (60 s) and delivered before (as baseline) and after long-duration stimulation (30 min, referred throughout the text as ‘stimulation’). In control and albumin rats, only short-duration stimulations were performed. Under sham stimulation, electrodes were placed without delivering current.”

Histology that was performed to confirm extravasation needs clarification because if tissue was removed from the brain, and fixed in order to do histology, what is outside the vessels would seem likely to wash away.

Thank you for pointing out the need to clarify this point. The Histology description in the Methods section was revised in the following manner:

“Albumin extravasacon was confirmed histologically in separate cohorts of rats that were anesthetized and stimulated without craniotomy surgery. Assessment of albumin extravasacon was performed using a well-established approach that involves peripheral injection of either labeled-albumin (bovine serum albumin conjugated to Alexa Flour 488, Alexa488-Alb) or albumin-labeling dye (Evans blue, EB – a dye that binds to endogenous albumin and forms a fluorescent complex), followed by histological analysis of brain tissue (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Obermeier et al., 2013; Veksler et al., 2020). Since extravasated albumin is taken up by astrocytes (Ivens et al., 2007; Obermeier et al., 2013), it can be visualized in the brain neuropil after brain removal and fixation (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Veksler et al., 2020). Five rats were injected with Alexa488-Alb (1.7 mg/ml) and five with EB (2%, 20 mg/ml, n=5). The injections were administered via the tail vein. Following injection, rats were transcardially perfused with…”

It is not clear why there was extravasacon contralateral but not ipsilateral if there are cortical-cortical connections.

Interpersonally, we also did not observe ipsilateral SEP in response to limb stimulation, with evidence of SEP and BBB permeability only in the contralateral sensorimotor region. This finding is consistent with electrophysiological and fMRI studies showing that peripheral stimulation results in predominantly contralateral potentials (Allison et al., 2000; Goff et al., 1962).

After injection of Evans blue or Alexa-Alb, how was it shown that there was extravasacon?

Extravasalon in cortical sections was visualized using a fluorescent microscope (Figure 1 h-i). Since extravasated albumin is taken up by astrocytes, fluorescent imaging can be used for visualizing and quantifying labeled albumin (Ahishali & Kaya, 2020; Ivens et al., 2007; Knowland et al., 2014). Here is the relevant methods excerpt:

“Coronal sections (40-μm thick) were obtained using a freezing microtome (Leica Biosystems) and imaged for dye extravasacon using a fluorescence microscope (Axioskop 2; Zeiss) equipped with a CCD digital camera (AxioCam MRc 5; Zeiss).”

How is a sham control not stimulated - what is the sham procedure?

In the sham stimulation protocol electrodes were placed, but current was not delivered. A section titled ‘Stimulation protocol’ was added to the methods to clarify this point.

What was the method for photothrombosis-induced ischemia?

The procedure for photothrombosis-induced ischemia is described under the Methods section ‘Immunoassays’ – ‘Enzyme-linked immunosorbent assay (ELISA) for albumin extravasalon’:

“Rats were anesthetilzed and underwent … photothrombosis stroke (PT) as previously described (Lippmann et al., 2017; Schoknecht et al., 2014). Briefly, Rose Bengal was administered intravenously (20 mg/kg) and a halogen light beam was directed for 15 min onto the intact exposed skull over the right somatosensory cortex.”

Fig 1d. All parts of d are not explained.

Thank you for pointing this out. In the revised manuscript, the panels of this figure were slightly reordered, and we made sure all panels are explained in the legend.

e. Is the LFP a seizure? How physiological is this- it does not seem very physiological.

Thank you for your comment. We believe that this activity is not a seizure because it lacks the typical slow activity that corresponds to the “depolarizalon shir” observed during seizures (Ivens et al., 2007; Milikovsky et al., 2019; Zelig et al., 2022).

f. Permeability index needs explanation. How was the area chosen for each rat? Randomly? Was it the same across rats?

We have now revised the Methods section to provide a clearer description of the permeability index calculation and the choice of the imaging area:

“Across all experiments, acquired images were the same size (512 × 512 pixel, ~1x1 mm), centered above the responding arteriole. Images were analyzed offline using MATLAB as described (Vazana et al., 2016). Briefly, image registration and segmentation were performed to produce a binary image, separating blood vessels from extravascular regions. For each extravascular pixel, a time curve of signal intensity over time was constructed. To determine whether an extravascular pixel had tracer accumulation over time (due to BBB permeability), the pixel’s intensity curve was divided by that of the responding artery (i.e., the arterial input function, AIF, representing tracer input). This ratio was termed the BBB permeability index (PI), and extravascular pixels with PI > 1 were identified as pixels with tracer accumulation due to BBB permeability.”

g. For Evans blue and Alexa-Alb was the sample size rats or sections?

Thank you for this question. We revised the statistical analysis for Figure 1j,k to appropriately asses the differences between rats. We used a nested t-test to test for differences between rats (and not sections). The differences remained significant (EB, p=0.0296; Alexa, p=0.0229) and the text was modified accordingly.

h, i, j need more contrast and/or brightness to appreciate the images. Arrows would help. The text is too small to read.

Thank you. This issue was addressed in the revised paper.

To induce potentiation, 6 Hz 2 mA stimuli were used for 30 min. Please justify this as physiological.

Thank you for the comment. We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

1. In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

2. Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

3. The levels of SEP potentiation we observed are similar to those reported in:

a. Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

b. Humans following a 15 min task (McGregor et al., 2016).

We have revised the Discussion of the paper to clarify this important point.

The test stimulus to evoke somatosensory evoked potentials was 1 min. Was this 6 Hz 2 mA for 1 min? Please justify.

Yes. We chose these parameters as these ranges were shown to induce the largest changes in blood flow (with laserdoppler flowmetry) and summated SEP (Ngai et al., 1999), corresponding with our findings. We also show that these stimulation parameters do not induce changes in BBB permeability nor synaptic potentiation, therefore served as test control.

How long after the 30 min was the test stimulus triggered- immediately? 30 sec afterwards?

The test stimulus was applied 5 min afterwards to allow for BBB imaging protocol (now explained in the Methods section).

How were amplitude and AUC measured? Baseline to peak? For AUC is it the sum of the upward and downward deflections comprising the LFP?

Yes, and yes. This is now clarified in the ‘Analysis of electrophysiological recordings’ section in the Methods.

How was the same site in the somatosensory cortex recorded for each animal?<br /> Potentiation was said to last >5 hrs. How often was it measured? Was potentiation the same for the amplitude and the AUC?

The location of the cranial window over the somatosensory cortex was the same in all rats. The location of the specific responding arteriole may change between animals, but the recording electrode was places around the responding arteriole in the same approaching angle and depth for all animals.

As the length of experiments differed between animals, the exact length could not be specifically stated. We therefore revised the text to clarify that LTP was recorded until the end of each experiment (depending on the animal condition, between 1.5-5 hours) and added a panel to figure 2 (Figure 2f) with exemplary data showing potentiation 120 min (2hr) post stimulation.

Why was 25% of the serum level of albumin selected- does the brain ever get exposed to that much? Was albumin dissolved in aCSF or was aCSF chosen as a control for another reason?

Yes, albumin was dissolved in aCSF and the solution was allowed to diffuse through the brain. The relatively high concentration of albumin was chosen to account for factors that lower its effective tissue concentration:

1. The low diffusion rate of albumin (Tao & Nicholson, 1996).

2. The likelihood of albumin to encounter a degradation site or a cross-BBB efflux transporter (Tao & Nicholson, 1996; Zhang & Pardridge, 2001).

Figure 2.

a. Please show baseline, the stimulus, and aftier the stimulus.

Please point out when there was stimulacon.

What is the inset at the top?

The inset on top is the example trace of the stimulus waveform, the legend of the figure was modified for clarity.

b. Please show when the stimulus artifact occurred. The end of the 1-minute test stimulus period is fine. Why are the SEPs different morphologies? It suggests the different locations in the cortex were recorded.

What is shown is the averaged SEP response over 1min test stimulus, each SEP is time locked to each stimulus. Regarding SEP waveform, it does indeed show different morphology between animals, as sometimes different arterioles respond to the stimulation, and we localize the recording to the responding vessel in each rat. However, in each rat the recording is only from one location. Once the electrode was positioned near the responding arteriole it was not moved.

d, e. What are the stats?

h, i. Add stats. Are all comparisons Wilcoxon? Please provide p values.

The comparisons were performed with the Wilcoxon test. We now state that and provide the exact p values.

j. What was selected from the baseline and what was selected during Albumin and how long of a record was selected?

What program was used to create the spectrogram?

What is meant by changes at frequencies above 200 Hz, the frequencies of HFOs?

The Method section (under ‘Electrophysiology – Data acquisition and analyses’) has been revised for clarification. Spectrogram was created with MATLAB and graphed with Prism. For analysis, we selected a 10 min recorded segment before starting albumin perfusion, and 10 min after terminating albumin perfusion.

When the cortial window was exposed to drugs, what were concentrations used that were selective for their receptor? How long was the exposure?

Was the vehicle tested?

We have revised the Methods section (under ‘Animal preparation and surgical procedures - Drug application’) to clarify the duration and concentration used and justification. All blockers were exposed for 50 min. The vehicle was an artificial cerebrospinal fluid solution (aCSF).

For PSD-95, what was the area of the cortex that was tested?

Were animals acutely euthanized and the brain dissected, frozen, etc?

We have revised the Methods section (under ‘Immunoassays’) for clarity.

What is mbetaCD?

The full term was added to the results section. It is also mentioned in the Methods.

Is SJN specific at the concentration that was chosen? Did it inhibit the SEP?

In the concentration used in our experiments, SJN is a selective TGF-β type I receptor ALK5 inhibitor (see (Gellibert et al., 2004)).

Fig. 3b. It looks like CNQX increased the width of the vessels quite a bit. Please explain.

For AP5, very large vessels were imaged, making it hard to compare to the other data.

The vascular dilation in response to the stimulation under CNQX was similar to that seen under “normal” conditions (i.e. aCSF). As for AP5, in some experiments the responding arteriole was in close proximity to a large venule that cannot be avoidable while imaging. For quantification we always measured arterioles within the same diameter range.

e. Sometimes CNQX did not block the response after 30 min stimulation. Why?

CNQX is washed out before the 30 min stimulation starts, so it is not expected to block the response to stimulation. However, in some cases the response to stimulation was lower in amplitude, likely due to residual CNQX that did not wash out completely.

Regarding DEGs, on the top of p 10 what are the percentages of?

In this analysis we tested in each hemisphere how many genes expressed differentially between 1 and 24 hours post stimulation (either up- or down- regulated). The results were presented as the percentages of differentially expressed genes in each hemisphere (13.2% contralateral, and 7.3% ipsilateral). The text was rephrased for clarity.

Please add a ref for the use of the JSD metric methods and support for its use as the appropriate method. Other methods need explanation/references.

References were added to the text to clarify. The Jensen-Shannon Divergence metric is commonly used to calculate the statistical pairwise distance among two distributions (Sudmant et al., 2015). From comparing a few different distance metric calculations including JSD, our results were similar irrespective of the distance metric applied. Therefore, we demonstrate the variability between paired samples of stimulated and non-stimulated cortex of each animal at two time points following stimulation (24 h vs. 1 h) using JSD.

What synaptic plasticity genes were selected for assay and what were not?

What does "largely unaffected" mean? Some of the genes may change a small amount but have big functional effects.

The selected genes of interest were taken from a large list compiled from previous publications (see (Cacheaux et al., 2009; Kim et al., 2017)) and are well documented in gene ontology databases and tools (e.g., Metascape, (Zhou et al., 2019)).

We agree that the term ‘largely unaffected’ is suboptimal, and we rephrased this section of the results to indicate that “No significant differences were found in BBB or inflammation related genes between the hemispheres”. We also agree that a small number of genes can have big functional effects. Future studies are needed to better understand the genes underlying the observed BBB modulation.

Please note that Slc and ABCs are not only involved in the BBB.

Thank you. We modified the text to no longer specify that these are BBB-specific transporters.

Please explain the choice of the stress ball squeeze task, and DCE.

DCE is a well-established method for BBB imaging in living humans, and it is cited throughout the manuscript. The ball squeeze task was chosen as it is presumed to involve primarily sensory motor areas, without high-level processing (Halder et al., 2005). This is now stated in the discussion.

What is Gd-DOTA?

Gd-DOTA is a gadolinium-based contrast agent (gadoterate meglumine, AKA Dotarem). Text was revised for clarity. Please see the Methods section under ‘Magnetic Resonance Imaging’ - ‘Data Acquisition’.

What does a higher percentage of activated regions mean- how was activacon defined and how were regions counted?

Higher percentage of activated regions refers to regions in which voxels showed significant BOLD changes due to the motor task preformed. The statistical approaches and analyses are detailed in the Methods section under ‘Magnetic Resonance Imaging - Preprocessing of functional data, and fMRI Localizer Motor Task’.

Figure. 4

Was stimulation 1 min or 30 min.?

30 min, Text has been revised for clarity.

What is the Wald test and how were p values adjusted-please add to the Stats section.

The Methods section under ‘Statistical analysis’ was revised to clarify this point.

Is there a reason why p values are sometimes circles and otherwise triangles?

The legend was revised to explain that ”Circles represent genes with no significant differences between 1 and 24 h poststimulation. Upward and downward triangles indicate significantly up- and down- regulated genes, respectively.”

How can a p-value be zero? Please explain abbreviations.

The p-value is very low (~10-10) and therefore appears to be zero due to the scale of the y-axis.

Fig. 5b.

There are unexplained abbreviations.

The x on the ball and hand is not clear relative to the black ball and hand.

Thank you for noticing. We revised the figure for clarity.

c. What was the method used to make an activator map and what is meant by localizer task?

The explanation of the “fMRI Localizer Motor Task” section in the methods was revised for added clarity.

f. What is the measurement "% area" that indicates " BBB modulation"?

Is it in f, the BBB permeable vessels (%)? f. Please explain: "Heatmap of BBB modulated voxels percentage in motor/sensory-related areas of task vs. controls."

The %area measurement indicates the percentage of voxels within a specific brain region that have a leaky BBB. See Methods.

Is Task - the control?

Yes.

Supplemental Fig. 2.

Why is AUC measured, not amplitude?

The amplitude, and now also the AUC are shown in Figure 3.

b. There is no comparison to baseline. The arrowhead points to the start of stimulation but there is no arrowhead marking the end.

In the revised paper we added a grey shade over the stimulation period to better visualize the difference to baseline. In this panel we wanted to show that NMDA receptor antagonist did not block the SEP, while AMPA receptor antagonist did.

c. In the blot there are two bands for PSD95- which is the one that is PSD95? There is no increase in PSD95 uncl 24 hrs but in the graph in d there is. In the blot, there is a strong expression of PSD95 ipsilateral compared to contralateral in the sham-why?

What is the percent change fold?

The PSD-95 is the top and larger band. The lower band was disregarded in the analysis. The example we show may not fully reflect the group statistics presented in panel d. Upon quantification of 8 animals, PSD-95 is significantly higher 30 min and 24 hours post stimulation in the contralateral hemisphere. No significant changes were found in sham animals. The % change fold refers to the AUC change compared to baseline. This panel was now incorporated in Figure 3 (panel h), and the title was corrected to “|AUC|, % change from baseline”.

Supplemental Fig. 4.

a. If ipsilateral and contralateral showed many changes why do the authors think the effects were only contralateral?

Our gene analysis was designed to complement our in vivo and histological findings, by assessing the magnitude of change in differentially expressed genes (DEGs). This analysis showed that: (1) the hemisphere contralateral to the stimulus has significantly more DEGs than the ipsilateral hemisphere; and (2) the DEGs were related to synaptic plasticity and TGF-b signaling. These findings strengthen the hypothesis raised by our in vivo and histological experiments.

Supplemental Fig. 5 includes many processes not in the results. Examples include dorsal cuneate and VPL, dynamin, Kir, mGluR, etc. The top right has numbers that are not mentioned. If the drawings are from other papers they should be cited.

The drawings of Figure 5 are original and were not published before. This hypothesis figure points to mechanisms that may drive the phenomena described in the paper. The legend of the figure was revised to include references to mechanisms that were not tested in this study.

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Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Rohde et al. discuss how single cells isolated from the presomitic mesoderm of the zebrafish embryo follow a cell-autonomous differentiation "programme", which is dependent on the initial anteroposterior position in the embryo.

Strengths:<br /> This work and in particular the comparison to cellular behaviour in vivo presents a detailed description of the oscillatory system that brings the developmental biology forward in their understanding of somitogenesis.<br /> The main novelty lies in the direct comparison of these isolated single cells to single cells tracked within the developing embryo. This allows them to show that isolated cells follow a similar path of differentiation without direct contact to neighbours or the presence of external morphogen gradients. Based on this, the authors propose an internal timer that starts ticking as cells traverse the presomitic mesoderm, while external signals modify this behaviour.

Weaknesses:<br /> There are a few things that would clarify the current statement or might be added in a reasonable amount of time to further increase the relevance of this study:<br /> - My main point of concern is the precision of dissection. The authors distinguish cells isolated from the tailbud and different areas in the PSM. They suggest that the cell-autonomous timer is initiated, as cells exit the tailbud.<br /> This is also relevant for the comparison of single cells isolated from the embryo and cells within the embryo. The dissection will always be less precise and cells within the PSM4 region could contain tailbud cells (as also indicated in Figure 1A), while in the analysis of live imaging data cells can be selected more precisely based on their location. This could therefore contribute to the difference in noise between isolated single cells and cells in the embryo. This could also explain why there are "on average more peaks" in isolated cells (p. 6, l. 7).<br /> This aspect should be considered in the interpretation of the data and mentioned at least in the discussion.<br /> (It does not contradict their finding that more anterior cells oscillate less often and differentiate earlier than more posterior ones.)

- Here, the authors focus on the question of how cells differentiate. The reverse question is not addressed at all. How do cells maintain their oscillatory state in the tailbud? One possibility is that cells need external signals to maintain that as indicated in Hubaud et al. 2014. In this regard, the definition of tailbud is also very vague. What is the role of neuromesodermal progenitors? The proposal that the timer is started when cells exit the tailbud is at this point a correlation and there is no functional proof, as long as we do not understand how cells maintain the tailbud state. These are points that should be considered in the discussion.

- The authors observe that the number of oscillations in single cells ex vivo is more variable than in the embryo. This is presumably due to synchronization between neighbouring cells via Notch signalling in the embryo. Would it be possible to add low doses of Notch inhibitor to interfere with efficient synchronization, while at the same time keeping single cell oscillations high enough to be able to quantify them?

In the same direction, it would be interesting to test if variation is decreased, when the number of isolated cells is increased, i.e. if cells are cultured in groups of 2,3 or 4 cells, for instance.

- It seems that the initiation of Mesp2 expression is rather reproducible and less noisy (+/- 2 oscillation cycles), while the number of oscillations varies considerably (and the number of cells continuing to oscillate after Mesp2 expression is too low to account for that). How can the authors explain this apparent discrepancy?

- The observation that some cells continue oscillating despite the upregulation of Mesp2 should be discussed further and potential mechanism described, such as incomplete differentiation.

- Fig. 3 supplement 3 B missing

Reviewer #2 (Public Review):

Pheochromocytoma (PCC), a rare neuroendocrine tumor, is currently considered malignant, but non-surgical treatment options are very limited and there is an urgent need for more basic research to support the development of new therapeutic approaches. In the present work, the authors described the intra- and inter-tumor heterogeneity by performing scRNA-seq on tumor samples from five patients with PCC, and evaluated the corresponding PASS scores.

Strengths: The tumor microenvironment of PCC was characterized and potential molecular classification criteria based on single-cell transcriptomics were proposed, offering new theoretical possibilities for the treatment of PCC. The article is logically written and the results are clearly presented.

Weaknesses: I still have concerns about some of the article's content. My main concerns are: In this study, the authors seem to have demonstrated the inaccuracy of a subjective score (PASS) by another objective means (scRNA-seq). In fact, the multiparametric scoring systems such as PASS are no longer endorsed in the 2022 WHO guidelines. The PASS scoring system does not have a high positive predictive value for risk stratification of PCC metastasis, but "rule-out" of metastasis risk with a PASS score of <4 seems to be fairly reliable. Could the authors please explain why the PASS scores were chosen rather than the GAPP, m-GAPP, or COPPS scoring systems? If possible, please try to emphasize the importance and necessity of using the PASS scoring system, either by replacing it with a more acceptable scoring system or by deleting the relevant part, which does not seem to be very relevant to the subject of the article.

Moreover, I noted the following statement in the text "There are no studies reporting the composition of immune cells in PCCs. The few published studies investigating the immune microenvironment of PCCs have been limited to the expression of PDL1 at the histological level and to assessment of the tumor mutation burden (TMB) at the genomic level, and these results only seem to suggest that PCCs are immune-cold (Bratslavsky et al, 2019; Guo et al, 2019; Pinato et al, 2017)." This statement is very wrong. The reason for this error may be that the authors did not adequately search and read the relevant literature. I noticed that almost all references in this paper are dated 2021 and earlier, which is surprising. Please update the references cited in this paper in a comprehensive and detailed manner; referring to literature published too early may lead to inadequate discussion or even one-sided or incorrect conclusions and conjectures.

For example, the text statement "Combined with previously reported negative regulatory effects of kinases (such as RET, ALK, and MEK) on HLA-I expression on tumor cells (Brea et al., 2016; Oh et al., 2019), we speculate that the possible reason for inability in recruiting CD8+ T cells of kinase-type PCCs is the downregulation of HLA-I in tumor cells regulated by RET, while the mechanism of immune escape in metabolism-type PCCs (with antigen presentation ability) needs to be further explored. Our results also indicate that the application of immunotherapy to metabolism-type PCCs is likely unsuitable, while kinase-type PCCs may have the potential of combined therapy with kinase inhibitors and immunotherapy." is rather one-sided; in fact, the presence of immune escape in PCC, as the malignancy with the lowest tumor mutation compliance, has been well characterized, and the low number of infiltrating T cells in tumor tissue may be influenced by a variety of factors, such as the release of catecholamines, the expression of inhibitory receptors on the surface of T cells, and so on, although genetic mutation still plays the most crucial role. The Discussion section also has a lot of information that needs to be updated or corrected and expanded, so please rewrite the above section with sufficiently updated references.

Below I have listed some references for the authors to read:

Tufton N, Hearnden RJ, Berney DM, et al. The immune cell infiltrate in the tumour microenvironment of phaeochromocytomas and paragangliomas. Endocr Relat Cancer. 2022;29(11):589-598. Published 2022 Sep 19. doi:10.1530/ERC-22-0020<br /> Jin B, Han W, Guo J, et al. Initial characterization of immune microenvironment in pheochromocytoma and paraganglioma. Front Genet. 2022;13:1022131. Published 2022 Dec 7. doi:10.3389/fgene.2022.1022131<br /> Celada L, Cubiella T, San-Juan-Guardado J, et al. Pseudohypoxia in paraganglioma and pheochromocytoma is associated with an immunosuppressive phenotype. J Pathol. 2023;259(1):103-114. doi:10.1002/path.6026<br /> Calsina B, Piñeiro-Yáñez E, Martínez-Montes ÁM, et al. Genomic and immune landscape Of metastatic pheochromocytoma and paraganglioma. Nat Commun. 2023;14(1):1122. Published 2023 Feb 28. doi:10.1038/s41467-023-36769-6

Here is where they begin the process with the two plants to conduct their experiment.

Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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1. General Statements

We thank the reviewers for their excellent work that greatly improved our work. We are very content that reviewer #1 considered our work to be “novel, interesting and important for understanding the mitochondrial biology of PD”. This reviewer also valued our work as “a significant advancement” and suggested further study of the relationship of CISD1 (dimerization) to general mitophagy/autophagy. We addressed this in the already transferred revision (version 1, v1).

Also reviewer #2 considered our work to be “an exciting and well-executed piece of research focusing on the defects in iron homeostasis observed in Parkinson's disease which a wide audience will appreciate”. This reviewer had a very specific suggestion on how to improve our manuscript which makes a lot of sense and is feasible. As the suggested experiments include fly breeding and behavioral analysis, these experiments will be included in the second revision to be uploaded as soon as possible (version 2, v2).

Finally, reviewer #3 gathered that parts of our results “are confirmatory to recently published work” but also appreciated that our results established that iron-depleted apo-Cisd is an important determinant of toxicity which has not been shown before. I would like to comment here, that in contrast to the paper mentioned by this reviewer, our contribution includes data from dopaminergic neurons obtained from human patients suffering from familial Parkinson’s disease that demonstrate the same increase in apo-Cisd levels as the flies. This reviewer mainly suggested that the manuscript would be improved by a more balanced discussion of the strengths and weaknesses of the study and more circumspection in interpretation of data which we did in the revised version of our manuscript. We also added data on the expression levels of Cisd and apo-Cisd in transgenic flies as also suggested.

2. Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity):

Summary: The manuscript focuses on mitochondrial CISD1 and its relationship to two Parkinson's disease (PD) proteins PINK1 and Parkin. Interestingly, CISD1 is a mitochondrial iron sulfur binding protein and an target of Parkin-mediated ubiquitinylation. Disruption of iron metabolism and accumulation of iron in the brain has long since been reported in PD but the involvement of iron sulfur binding is little studied both in vivo and in human stem cell models of PD. This work addresses the relationship between CISD1 and two mitochondrial models of PD (PINK1 and Parkin) making use of in vivo models (Drosophila), PINK1 patient models (iPSC derived neurons) and Mouse fibroblasts. The authors report a complex relationship between CISD1, PINK1 and Parkin, where iron-depleted CISD1 may illicit a toxic gain of function downstream of PINK1 and Parkin.

Major comments:

The conclusions are overall modest and supported by the data. One question remains unaddressed. Is mitochondrial CISD1 a downstream target that specifically mediates PINK1 and Parkin loss of function phenotypes or are the phenotypes being mediated because CISD1 is downstream of mitophagy in general?

It would be interesting to know what happens to CISD1 (dimerization?) upon initiation of mitophagy in wild type cells? Would dissipation of mitochondrial membrane potential be sufficient to induce changes to CISD1 in wild type cells or PINK1 deficient cells? Since iron chelation is a potent inducer of mitophagy (Loss of iron triggers PINK1/Parkin-independent mitophagy. George F G Allen, Rachel Toth, John James, Ian G Ganley. EMBO Reports (2013)14:1127-1135) it would be useful to show one experiment addressing the role of CISD1 dimerization under mitochondrial depolarizing and non-depolarizing conditions in cells.

Based on the overall assumption of the reviewer that our work is “novel, interesting and important for understanding the mitochondrial biology of PD” and “a significant advancement” we understand the word “modest” here as meaning “not exaggerated”. To address this question, we studied CISD1 dimerization in response to more classical activators of mitophagy namely FCCP and antimycin/oligomycin which had no significant effect on dimerization suggesting that this phenotype is more pronounced under iron depletion. These data are shown in the new Fig. 2c.

Alternatively, the authors should discuss the topic of mitophagy (including PINK1-parkin independent mitophagy), the limitation of the present study not being able to rule out a general mitophagy effect and previous work on the role of iron depletion on mitophagy induction in the manuscript.

The data and the methods are presented in such a way that they can be reproduced.

The experiments are adequately replicated and statistical analysis is adequate.

Minor comments:

Show p values even when not significant (ns) since even some of the significant findings are borderline < p0.05.

Here, I decided to leave it as it is, because the figures became very cluttered and less easy to understand. Borderline findings are however indicated and mentioned in the text.

Because the situation for CISD1 is complicated (overexpression, different models etc.) it would be helpful if in the abstract the authors could summarize the role. E.g. as in the discussion that iron-depleted CISD1 could represent a toxic function.

The abstract has been completely rewritten and now mentions the potential toxic function of iron-depleted CISD1.

If there is sufficient iron (accumulation in PD) why would CISD1 be deactivated? Perhaps that could be postulated or discussed in a simplified way?

We actually think that apo-CISD1 without its iron/sulfur cluster is incapable of transferring its Fe/S cluster to IRP1 and IRP2. This then results in increased levels of apo-IRP1/2 and subsequent changes that lead to iron overload. Such a sequence of events would place CISD1 upstream of the changes in iron homeostasis observed in PD and models of PD. This is now discussed in more detail.

In the methods section both reducing and non-reducing gel/Western blotting is mentioned but the manuscript only describes data from blots under reducing conditions. Are there blots under non-reducing conditions that could be shown to see how CISD1 and dimerized CISD1 resolve?

We now show these blots as supplemental data in new supplemental Figure 2.

In the results section, PINK1 mutant flies, it is said that the alterations to CISD1 (dimerization) are analogous to the PINK1 mutation patient neurons. The effect is seen in old but not young flies. Since iPSC-derived neurons are relatively young in the dish, would one not expect that young flies and iPSC-derived neurons have similar CISD1 phenotypes? Could the authors modify the text to reflect that? or discuss the finding in further context.

We only studied one time point in PINK1 mutation patient neurons and controls. It would indeed be interesting whether neuronal aging (as far as this can be studied in the dish) would result in increased CISD1 dimerization. This is now discussed.

Reviewer #1 (Significance):

The strengths of this work are in the novelty of the topic and the use of several well established in vivo and cell models including patient-derived neurons. The findings discussed in the text are honest and avoid over-interpretation. The findings are novel, interesting and important for understanding the mitochondrial biology of PD.

We thank the reviewer for their kind words.

Limitations include the lack of strong phenotypes in the CISD1 models and the lack of robust, sustained and consistent increase in CISD1 dimers in the patient and fly models (just significant because of variability). The relationship of CISD1 (dimerization) to general mitophagy/autophagy is not shown here.

We do not completely agree with the assumption that all CISD1 models lack a strong phenotype. At least the CISD1-deficient fibroblasts exhibit a strong phenotype consisting of fragmented mitochondria and increased oxidative stress. The lack of a strong phenotype in Cisd-deficient flies could actually hint to a potential compensatory mechanism that could also protect the Pink1 mutant x Cisd-deficient double-knockout flies. It is correct that the increase in CISD1/Cisd dimers in the PD models are not overwhelming but – as also mentioned by the reviewer – this could be increased in “older” cultures. This is now discussed in more detail. As suggested by the reviewer, we have now added experiments that study the relationship between CISD1 dimerization and conventional mitophagy as described above.

There is a significant advancement. So far researchers were able to describe the importance of iron metabolism in PD (For example refer to work from the group of Georg Auburger such as PMID 33023155 and discussion of therapeutic intervention such as reviewed by Ma et al. PMID: 33799121) but few papers describe involvement of iron sulfur cluster proteins specifically (such as Aconitase) in relation to PINK1 and parkin (these are cited). The fact that CISD1 is a protein of the mitochondrial outer membrane makes it particularly interesting and further studies looking more closely at the interaction of CISD1 with mitochondrial proteins associated with PD will be of interest.

We thank the reviewer for pointing out these excellent publications. Key et al present an enormous wealth of data on protein dysregulation of wildtype and Pink1-/- fibroblast cell lines upon perturbation of the iron homeostasis (Key et al, 2020). Both cell lines exhibit a downregulation of CISD1 levels upon iron deprivation with the agent 2,2′ -Bipyridine possibly as a compensatory mechanism to limit the toxic gain of function of iron-depleted CISD1. The other paper, Ma et al. is a recent review on changes in iron homeostasis in PD and PD models (Ma et al, 2021). Both papers are now cited in the manuscript.

This paper describes CISD1 as a new and relevant player in PINK1 and Parkin biology. Further work could lead to exploration of whether CISD1 could be a therapeutic target, considering its role in maintaining mitochondrial redox and mitochondrial health. This is of particular interest to mitochondrial biologists and pre-clinical research in PD.

This preprint was reviewed by three scientists whose research focus in the mitochondrial biology underlying Parkinson's disease. The group has a special interest in the functions of the mitochondrial outer membrane. We work with several cell models of Parkinson's disease and work with patient donated samples. We do not have expertise in Drosophila models of PD nor the quantification of iron described in the manuscript.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary: In the paper entitled 'Mitochondrial CISD1 is a downstream target that mediates PINK1 and Parkin loss-of-function phenotypes', Bitar and co-workers investigate the interaction between CISD1 and the PINK1/Parkin pathway. Mutations in PINK1 and PARKIN cause early onset Parkinson's disease and CISD1 is a homodimeric mitochondrial iron-sulphur binding protein. They observed an increase in CISD1 dimer formation in dopaminergic neurons derived from Parkinson's disease patients carrying a PINK1 mutation. Immuno-blots of cells expressing CISD1 mutants that affects the iron sulphur cluster binding and as well as cells treated with iron chelators, showed that the tendency of CISD1 to form dimers is dependent on its binding to iron-sulphur clusters. Moreover, the Iron-depleted apo-CISD1 does not rescue mitochondrial phenotypes observed in CISD1 KO mouse cells. Finally, In vivo studies showed that overexpression of Cisd and mutant apo-Cisd in Drosophila shortened fly life span and, using a different overexpression model, apo-Cisd caused a delay in eclosion. Similar as patient derived neurons, they observed an increase in Cisd dimer levels in Pink1 mutant flies. Additionally, the authors showed that double mutants of Cisd and Pink1 alleviated all Pink1 mutant phenotypes, while double mutants of Prkn and Cisd rescued most Prkn mutant phenotypes.

Major comments:

1) The authors observed an increase in the levels of Cisd dimers in Pink1 mutant flies and removing Cisd in Pink1 mutant background rescues all the mutant phenotypes observed in Pink1 mutant flies, suggesting that the Cisd dimers are part and partial of the Pink1 mutant phenotype. The authors also generated a UAS_C111S_Cisd fly which can overexpress apo-Cisd. Overexpression of the C111S_Cisd construct with Tub-Gal4 showed a developmental delay. Since apo-Cisd forms more dimeric Cisd, my question is: does the strong overexpression (e.g. with Tub-Gal4) of the C111S_Cisd in wild type flies shows any of the Pink1 mutant phenotypes? If not, the authors should mention this and elaborate on it.

We thank the reviewer for their comments. In fact, we only observed very few flies ecclosing after overexpression of wildtype Cisd or C111S Cisd using the strong tubP-Gal4 driver during development. We considered these very few flies to be escapees (also indicated by the rather low induction of Cisd mRNA suggesting compensatory downregulation) and only used them to conduct the analysis shown in Figure 4c-e. This is now mentioned in more detail in the manuscript.

2) Figure 6g: Shows the TEM pictures of the indirect flight muscles of Pink1 mutant flies and Pink1, Cisd double mutants. To me, the Picture of Pink1 mutant mitochondria is not very convincing. We expect swollen (enlarged) mitochondria with disrupted mitochondrial matrix. However, this is not clear in the picture. Moreover, in my opinion, Figure6 g, is missing an EM Picture of the Cisd mutant indirect flight muscles.

We now show exemplary pictures from Pink1 mutant and DKO in a higher magnification which better demonstrate the rounded Pink1 mutant mitochondria and the disrupted cristae structure. EM pictures of all four genotypes in different magnifications are now shown in new supplemental Figure 6.

3) OPTIONAL: The authors suggest that most probably apo-Cisd, assumes a toxic function in Pink1 mutant flies and serves as a critical mediator of Pink1-linked phenotypes. If this statement is correct, we can hypothesize that increasing apo-Cisd in Pink1 mutant background should worsen the pink1 mutant defects.

Therefore, I suggest overexpressing Cisd1 wild type (and/or C111S Cisd) in pink1 mutant flies, as pink1 is on the X chromosome, and mild overexpression of Cisd1 with da is not lethal, these experiments could be done in 3-4 fly crosses and hence within 1.5 - 2 months.

We have set up this experiment and will report in the second revision (v2) of our manuscript.

Since Pink1 mutant flies contain higher levels of endogenous Cisd dimers, we can expect that overexpression of wild type Cisd will result in an even stronger increase of dimers. If these dimers indeed contribute to Pink1 mutant phenotypes we can expect that overexpression of Cisd will result in a worsening of the Pink1 mutant phenotypes.

We have set up this experiment and will report in the second revision of our manuscript.

Minor Comments:

-) In the Introduction (Background) there are some parts without references:

E.g., there is not a single reference in the following part between

'However, in unfit mitochondria with a reduced mitochondrial membrane potential ...&... compromised mitochondria safeguards overall mitochondrial health and function.'

We thank the reviewer for pointing out this flaw. We have now added a suitable reference to the introduction.

-) In the introduction there is some confusion about the nomenclature used in the article: e.g. following comments are made in the text: Cisd2 (in this publication referred to as Dosmit) or fly Cisd2 (in this publication named MitoNEET).

However, the names Dosmit and MitoNEET do not appear in the manuscript (except in references)

The literature and nomenclature for CISD1 are indeed confusing. We have now revised the introduction.

-) Figure 1: I am not sure why some gels are shown in this figure. The two last lanes of figure 1c are redundant and Figure 1c' which is also not mentioned in the text, is also a repetition of figure 1c.

The blots in 1c and 1c’ represent all data points (different patients and different individual differentiations) shown in the quantification in 1d. This is now explained better in the revised manuscript.

-) The authors mention in material and methods that T2A sites are used at the C-terminus of CISD1 to avoid tagging of CISD1. However, this is not entirely true as T2A will leave some amino acids (around 20) after the self-cleaving and therefore CISD1 will be tagged.

This is indeed true and we have now changed the wording in the revised manuscript.

-) In figure 5 P1 is used to abbreviate Pink1 mutants, however P1, to me, refers to pink1 wild type. It would be clearer to abbreviate Pink1 mutants as P1B9 in the graphs as B9 is the name of the mutant pink1 allele.

We thank the reviewer for pointing out this flaw. We have now altered Fig. 5 to be clearer.

-) In figure 7: Parkin is abbreviated both as Prkn and as Park

We thank the reviewer for pointing out this flaw, we indeed mixed up both names because it is complicated. The gene symbol is Prkn, the fly line is called Park25. We have now clarified this in the text and Fig. 7.

-) I suggest changing the title. Recently an article (Ham et al, 2023 PMID: 37626046) was published showing similar genetic interactions between Pink1/Prkn and Cisd. However, the article of Ham et al, 2023 was focused on Pink1/Prkn regulation of ER calcium release, while this article is more related to iron homeostasis. I suggest that the title shows this distinction.

This is indeed a very good suggestion. We have now altered the title to “Iron/sulfur cluster loss of mitochondrial CISD1 mediates PINK1 loss-of-function phenotypes”.

Reviewer #2 (Significance):

In general, this is an exciting and well-executed piece of research focusing on the defects in iron homeostasis observed in Parkinson's disease which a wide audience will appreciate. Very recently, a similar genetic interaction between Cisd and Pink1/Prkn in flies was published (Ham et al, 2023 PMID: 37626046) however, from a different angle. While, Ham et al focused on the role of Pink1/Prkn and Cisd in IP3R related ER calcium release, this manuscript approaches the Pink1/Prkn - Cisd interaction from an iron homeostasis point of view. Since, iron dysregulation contributes to the pathogenesis of Parkinson's disease, the observations in this manuscript are relevant for the disease. Hence, the work is sufficiently novel and deserves publication. However, additional experiments are suggested to strengthen the authors' conclusions.

We thank the reviewer for their kind words. As mentioned above, these additional experiments are on their way and will be included in version 2 of our revised manuscript (v2).

I work on Drosophila models of Parkinson's disease

Referees cross-commenting

I agree with the reviewer number 1 that it would be interesting to investigate CISD1 dimerisation status during mitophagy.

As mentioned above, we now studied CISD1 dimerization in response to more classical activators of mitophagy namely FCCP and antimycin/oligomycin which had no significant effect on dimerization suggesting that this phenotype is more pronounced under iron depletion. These data are shown in the new Fig. 2c.

Reviewer #3 (Evidence, reproducibility and clarity):

Here the authors provide evidence that Cisd is downstream of Parkin/Pink1 and suggest that the levels of apo-Cisd correlate with neurotoxicity. The data presented generally supports the conclusions of the authors and will be useful to those in the field. The manuscript would be improved by a more balanced discussion of the strengths and weaknesses of the study and more circumspection in interpretation of data.

We thank the reviewer for their comments aimed to improve our manuscript. We have now discussed the strengths and weaknesses of our study in more detail.

Introduction. While iron has been implicated in Parkinson's disease, it is an overstatement to say that disruption in iron metabolism contributes significantly to the pathogenesis of the disease.

There is certainly a plethora of data implicating perturbed iron homeostasis in PD as also pointed out by reviewer #1. We have tried to tone down our wording in the text and added a recent review on the topic (Ma et al, 2021) as also suggested by reviewer #1.

Introduction. The discussion of the various names for Cisd2 is important, but confusing as written. Specifically, the use of "this" makes the wording unclear.

We thank the reviewer for pointing out this flaw. We have altered the wording in the introduction.

Methods. It would be preferable to use heterozygous driver lines or a more similar genetic control rather than w-1118.

The exact controls were indeed not well explained in the Methods section, this has been corrected in the revised version. In brief, homozygous driver and UAS lines were indeed used in Fig. 4, this will be addressed in the second revision of our manuscript together with the experiments reviewer #2 suggested. The data shown in Fig. 5, 6, and 7 all used w1118 as control because all other fly strains are on the same genetic background.

Page 10. It appears that the PINK1 lines have been described previously. The authors should clarify this point and ensure that the new data presented in the current manuscript (presumably the mRNA levels, Fig. 1a) is indicated, as well as data that is confirmatory of prior findings (Fig. 1b).

Yes, these PINK1 lines have been described previously as pointed out in the manuscript. The original paper did not quantify the PINK1 mRNA levels shown in Fig. 1a. The blots shown in Fig. 1b are from new differentiations and have also not been shown before but confirm findings published in Jarazo et al. (Jarazo et al, 2022). This has been clarified in the revised version of our manuscript.

Fig. 3 legend. There is a typographical error, "ne-way ANOVA."

We thank the reviewer for pointing out this flaw. This has been corrected in the revised version.

Page 15. The nature of the Pink1-B9 mutant should be specified.

We now added a supplemental Figure 1 that depicts the specific mutation in these flies.

Fig. 4. Levels of mutant and wild type Cisd should be compared in transgenic flies.

We now added a quantification of mutant and wildtype Cisd levels to the new Figure 4d.

Fig. 5b,d. The striking change seems to be the decrease in dimers in young Pink1 mutant animals, not the small increase in dimers in the older Pink1 mutants.

It is always difficult to find a “typical” picture that reflects all changes observed in quantitative data. This Figure actually shows a decrease of total Cisd levels in young flies in Fig. 5c but no difference of the dimer/monomer ratio in Fig. 5d.

Fig. 5f. Caution should be used in interpreting the results. Deferiprone has toxicity to wildtype flies (trend) and may simply be making sick Pink1 mutants sicker.

There is certainly a tendency for wildtype flies to thrive less in food containing deferiprone. To make this more obvious, we have now added the exact p value (0.0764, which we don’t consider borderline but a tendency) to this figure and mention this fact in the text.

Fig. 5e. The data are hard to interpret. The number of animals is very small for a viability study and the strains are apparently in different genetic backgrounds, though this is not clearly specified. The experiment in Supplementary Fig. 1 appears better controlled and supports the Pink1 data; however, a similar concern pertains to Fig. 7. The authors may thus wish to be more circumspect in their interpretation, especially of the Parkin data.

In Fig 5e we quantified total iron levels and the Fe3+/Fe2+ ratio using capillary electrophoresis-inductively coupled plasma mass spectrometry (CE-ICP-MS). Although indeed not so many flies were used in this quantification, the results are highly significant. If the reviewer was referring to Fig. 5f, we agree that this experiment was not well (to be honest, even wrongly explained) which we corrected in the revised version of this manuscript. We thank the reviewer for pointing out this flaw.

Reviewer #3 (Significance):

The major significance of the study is in putting downstream of Parkin/Pink1 (largely confirmatory to recently published work) and suggesting that the levels of apo-Cisd are an important determinant of toxicity. The work will be of interest to those in the field.

3. Description of the revisions that have already been incorporated in the transferred manuscript

The changes already carried out and included in the transferred manuscript (v1) are indicated above in bold orange. All changes pending on ongoing experiments to be included in the second revision of the manuscript are indicated above in bold magenta.

4. Description of analyses that authors prefer not to carry out

All changes suggested by the reviewers were addressed (v1) or will be addressed (v2).

References

Jarazo J, Barmpa K, Modamio J, Saraiva C, Sabaté-Soler S, Rosety I, Griesbeck A, Skwirblies F, Zaffaroni G, Smits LM, et al (2022) Parkinson’s Disease Phenotypes in Patient Neuronal Cultures and Brain Organoids Improved by 2-Hydroxypropyl-β-Cyclodextrin Treatment. Mov Disord 37: 80–94

Key J, Sen NE, Arsović A, Krämer S, Hülse R, Khan NN, Meierhofer D, Gispert S, Koepf G & Auburger G (2020) Systematic Surveys of Iron Homeostasis Mechanisms Reveal Ferritin Superfamily and Nucleotide Surveillance Regulation to be Modified by PINK1 Absence. Cells 9

Ma L, Gholam Azad M, Dharmasivam M, Richardson V, Quinn RJ, Feng Y, Pountney DL, Tonissen KF, Mellick GD, Yanatori I, et al (2021) Parkinson’s disease: Alterations in iron and redox biology as a key to unlock therapeutic strategies. Redox Biol 41: 101896

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Reply to the reviewers

1. General Statements [optional]

__We thank all the reviewers for their time and their constructive criticism, based on which we will revise our manuscript. All our responses are indicated in red. __

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

The manuscript by Nguyen and Cheng is investigating the timing and mechanism of cessation of neuroblasts in the pupal optic lobe. Previous studies by several groups have determined the spatial and temporal factors required for the neuroepithelial to neuroblast transition and neuroblast to neural/glycogenesis in third instar larvae such that neuroblasts are eliminated. The mechanism of elimination of neuroblasts in the VNC or mushroom bodies have been investigated, but the mechanism(s) and the timing of elimination of medulla neuroblasts has not been investigated. The authors suggest that medulla neuroblasts are eliminated via a combination of mechanisms including apoptosis, prospero induced size symmetric terminal differentiation and a switch to gliogenesis by gcm expression. Expression of Tailless also was found to affect the timing of medulla neuroblast termination. They also ruled out several mechanisms such as ecdysone pulses.

Major comments

Clearly written and logical flow to experiments and results not over interpreted.

Clearly show that the neuroblast number and size decrease (12 to 18 hrs) and are eliminated by 30 hours

Figure 2a Marking of the Neuroepithelium. Would be more convincing if shown by PatJ expression and is clonal analysis. While the following panels use PatJ in clones suggesting are NE and NBs present it is more difficult to put into the context in the higher magnification images (Figure 2 D- M) and the Miranda expression in F' seems to be the entire lobe and it is not clear if would be any NE which does not agree with what is shown in panel A.

We will perform clonal analysis using MARCM to show that the elimination of medulla NBs (marked by Dpn) is accompanied by the depletion of NE (marked by PatJ). For Figure 2 D, E, I, L, we will change the images to the whole lobes to clearly show the shift in the NE-NB transition upon Notch OE/KD.

Is difficult to see the neuroblasts in Figure 2 D D" and E. The figure does not match what is stated in the results in that the neuroblasts are difficult to observe. If the point is that there is fewer NE cells and more neuroblasts then this is hard to see. It has been previously shown that with Notch RNAi clones prematurely extrude form the NE (Egger 20210; Keegan 2023) and could be expressing more Neuroblast markers but this is not visible in the panels as shown. Are the images single focal plane or maximum projections? Imaging more deeply in the brain or viewing in cross section would account for these possibilities. The possibility that are more neuroblasts but not all at the surface of the OL should be addressed as this could also alter the overall results.

Figure 2 is key to first point of the paper so needs to be addressed.

The images are single focal plane of the superficial layer of the medulla. We will specify this information in the figure legends. We will include cross-section of the notch RNAi clones to show the delamination of precocious NBs.

Minor comments

Why express volume of DPN in clone volume. Would make the point more clear and more strong be to express as number of NB in the 3-D volume of the clone. This measurement occurs in several figures.

We will redo the quantification as suggested.

Use of Miranda to mark NBs is unclear in Figure 2. Perhaps more clear in B&W.

We will redo the staining with Dpn instead of Mira to mark medulla NBs. Figures will be presented in B&W as suggested.

Make clear in figures (or figure legend) if single focal plane or projections.

We will do so.

It is unclear what percentage of NB the Gal4 line eyR16F10 are expressed in. Veen 2023 state that the GAL4 is also expressed in neurons and at different levels whether deeper within the brain or superficially on the surface of the brain. At 16 APF it is expressed but it is not clear whether it is in all cells at a low level or only within a few cells

We will further characterize the expression of eyR16F10-GAL4 in the pupal medulla as suggested.

Some RNAi lines referenced as previously validated and other are not. For example: EcR, Oxphos, Med27, Notch need references or confirmation of specificity to the intended target (qRT)

We will perform RT-qPCR to validate the use of UAS-med27 RNAi. For RNAi stocks such as UAS-EcR RNAi, UAS-Atg1 RNAi, UAS-notch RNAi that have been previously used in other publications, we will provide appropriate references.

At least 2 animals per genotype were used. While I appreciate the technical difficulty of working in pupae this seems a bit low in terms of number of samples and data would be more robust with more numbers.

Any experiments in which less than 3 animals were used, we will redo the experiments.

Reviewer #1 (Significance (Required)):

This provides mechanism and timing for the elimination of neuroblasts (NE to NB) that arise from the medulla. As these are most similar to mammalian brain development (Radial glial to NSC) this information provides more context to interpret the formation of glial and neurons in the adult optic lobe given the effect on timing and mechanisms of elimination.

This paper would be of interest to developmental biologist who work with Drosophila or mice who are looking at neural development. An understanding of how neural diversity is achieved and the mechanisms behind this that can be dysfunctional in terms of etiology of neural diseases. Is a well done study for the most part that would be improved by clarifying some data and provided more replicates for robustness of the data.

I am a developmental biologist working with Drosophila in larval and adult neural development.

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

Lineages of neural stem cells are of great interest to understand how many neural types are generated. They produce very diverse neurons, often in a highly stereotyped series. However, they must terminate their life when the animal becomes functional or if neurons need time to become mature before birth.

In the Drosophila optic lobes, neural stem cells are produced over a period of several days by a wave of neurogenesis that transforms a neuroepithelium into neural stem cells that undergo a series of temporal patterning steps. It has been reported that they finish their life when a symmetric division generates glial cells. The authors however analyze the end of a particular lineage, that of the latest born neural stem cells of the medulla.

The paper shows that neural stem cells stop being produced when the neuroepithelium is consumed. But how do the latest born neural stem cells stop their lineage?

The results show that they do so by several means, which is quite unexpected: they may die from apoptosis, or autophagy, by becoming glioblasts or by a terminal symmetric division.

There are no major issues affecting the conclusions

• The paper shows that the end of production of neural stem cells occurs the neuroepithelium is completely transformed. The experiments performed by the authors are fine and show that, if the transition is delayed, neural stem cells terminate their life later, and vice versa. However, the lifespan of the neural stem cells is not affected by the timing of the transition. Therefore, these experiments do not tell us how neural stem cells terminate their life, which is the central question of the study. The discussion should be written accordingly and the title and the model in Fig 6 modified to reflect the importance of the end of life of the stem cells, the main theme of the paper.

We agree that our said experiments did not elucidate how NBs terminate at the end of neurogenesis. Nevertheless, our aim is to show that the timing of NB termination in the medulla is dependent on the timing of the NE-NB transition.

In Supplementary Figure 1, we showed that factors previously shown to be involved in NB termination in other lineages did not play similar roles in the medulla NBs. Thus, we think that NB termination in the medulla is likely regulated at the levels of the NE, but not the NBs themselves. Although we have briefly mentioned this in our manuscript, we hope by conducting the experiments suggested by the reviewer (see below), we can subsequently modify our model in Figure 6 and our discussion.

• The authors talk about Pros-dependent symmetric division and gliogenic switch as two separate processes, but these may be two sides of the same phenomenon. Tll+ gcm+ neural stem cells undergo Pros-dependent cell cycle exit, generating glial progeny. If the authors agree with this, could they update their model (and discussion) to reflect the fact that gliogenic switch occurs via a Pros-dependent symmetric division, and these are not two separate processes independently contributing to the depletion of the neural stem cell pool? Ideally, a triple staining between Dpn, Pros, and gcm would show that the symmetrically dividing cells seen by the authors are committed to the glial fate.

We will further test how gliogenesis is affected in pros RNAi clones. The results may shed light on whether Pros-mediated symmetric division is required for Gcm-mediated gliogenesis in the medulla. Regarding the model, we have summarized our findings and suggestions in Figure 5K, however, we will integrate this information into our final model.

In Figure 5C, we showed that at 12h APF, there are Dpn+ NBs in the medulla that expressed both Pros and Gcm, suggesting that it is very likely that Pros is upstream of Gcm to induce the glial cell fate switch of the medulla NBs.

• Why were Notch RNAi experiments assessed for the presence of neural stem cells at P12 and gcm RNAi experiments at P24? Given that most optic lobe neural stem cells disappear between P12-18, a subtle effect of gcm RNAi may have been missed. Do the authors have data for gcm RNAi at P12?

We hypothesized that the timing of NE-NB transition affects the timing of NB termination in the medulla. Because Notch KD was previously shown to induce precocious NE-NB transition in the OL, meaning that medulla NBs are born prematurely, we expected that this manipulation will lead to a corresponding premature elimination of the NBs. In contrast, gcm RNAi which inhibits the switch into the glial cell fate of the NBs, is expected to prolong the neurogenic phase of the NBs, and thereby, their persistence by 24h APF when WT NBs are eliminated.

• The authors should acknowledge that the inhibition of either apoptosis or autophagy alone may not be fully sufficient to prevent the death of NBs. In mushroom body neural stem cells, both processes must be inhibited simultaneously to produce a strong effect on their survival (Pahl et al. 2019, PMID 30773368).

We will add this information in our discussions.

• There is an important missing point that should be addressed: is there a specific point in time when all neural stem cells must stop their lineage wherever they are in the temporal series and either die or divide symmetrically? One possibility that is not discussed is that most neural stem cells end their life through a gliogenic symmetric division while those that were generated late must stop en route and die by apoptosis and/or autophagy. This would solve the strange diversity of end-of-life, which could be easily addressed by identifying the temporal stage of the neural stem cells that undergo apoptosis

We agree that it would be of interest to understand how there are diverse mechanisms by which medulla NBs terminate during pupal development. To address if temporal progression is involved in apoptosis of the medulla NBs, we will first characterize the expression of some temporal TFs (e.g., Ey, Slp, Tll) at 12h APF when we found a subset of medulla NBs undergo apoptosis in the wildtype animals.

Minor suggestions:

We agree with these minor modifications.

• Line 46: Specify that there are 8 type II neural stem cells in each hemisphere*.

• The statement in lines 181-182 that "cell death, and not autophagy, makes a minor contribution to..." should be replaced with "apoptosis, and not autophagy," as autophagy is also a type of cell death.

• The authors should adjust the logic of the section "Medulla neuroblasts terminate during early pupal development": Describe the wild-type pattern first (the decrease in the number of neural stem cells and their size with age) and then describe the perturbations aimed at disrupting the number and the size of neural stem cells

• Line 151 should refer to Fig. 2I-K, not Fig. 2J-K.

**Referees cross-commenting**

How can NBs die by different mechanisms?? This might only happen is they are in a different states, an issue that is not addressed.

it has been shown that optic lobe NBs end their life by a symmetric, gliogenic last division at the end of the last temporal window, and not by PCD.

It is likely, and the authors do hint at it, that NBs only die by PCD when they prematurely interrupt the temporal series in early pupation when neurons synchronously start undergoing maturation.

I believe that the authors should explain this, if this is indeed their model, and show that NBs die while still in early temporal windows.

Reviewer #2 (Significance (Required)):

Lineages of neural stem cells are of great interest to understand how many neural types are generated. They produce very diverse neurons, often in a highly stereotyped series. However, they must terminate their life when the animal becomes functional or if neurons need time to become mature before birth.

In the Drosophila optic lobes, neural stem cells are produced over a period of several days by a wave of neurogenesis that transforms a neuroepithelium into neural stem cells that undergo a series of temporal patterning steps. It has been reported that they finish their life when a symmetric division generates glial cells. The authors however analyze the end of a particular lineage, that of the latest born neural stem cells of the medulla.

The paper shows that neural stem cells stop being produced when the neuroepithelium is consumed. But how do the latest born neural stem cells stop their lineage?

The results show that they do so by several means, which is quite unexpected: they may die from apoptosis, or autophagy, by becoming glioblasts or by a terminal symmetric division.

There are no major issues affecting the conclusions

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

Summary

In this manuscript, the authors address the timing and mechanisms responsible for the termination of medulla neuroblasts in Drosophila visual processing centres, also known as optic lobes. Through time course experiments the authors demonstrate the medulla NBs are completely eliminated by 30h APF during early pupal development. By manipulating the Notch signalling pathway as well as proneural genes such as lethal of scute, the authors show that altering the NE-NB transition is sufficient to change the timing of NB termination. In contrast, ecdysone signalling and components of the mediator complex, known to terminate proliferation of central brain NBs, are not required for the termination of medulla NBs. Medulla NBs sequentially express a variety of temporal transcription factors to promote cellular diversity, however, the authors demonstrate that altering temporal factors such as Ey, Sco or Hth, does not affect the timing of the medulla NBs termination. Interestingly however overexpression of the transcription factor tailless can cease medulla NB termination via the conversion of type I to type II NB fate. They further go on to show the importance of the differentiation factor, Prospero, in promoting the differentiation of medulla NBs as well as terminating medulla neurogenesis during pupal development. Finally, in addition to differentiation, the authors show another mechanism responsible for the cessation of neurogenesis which is the commencement of gliogenesis. Through manipulation of the neurogenic to gliogenic switch by knockdown or overexpressing the glial regulatory gene, gcm, the authors show that even though the downregulation of gcm is is not sufficient to induce NB persistence, gcm overexpression can cause premature termination of NBs.

Major comments:

• Are the key conclusions convincing?

Yes, the key conclusions are convincing with proper controls, quantifications and statistical analyses.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The conclusion that temporal transcription factors (TTF) do not affect the timing of medulla NB termination is somewhat preliminary. The authors investigated a simplified temporal series including Homothorax, Eyeless, Sloppy-paired, Dichaete and Tailless. However, there are additional temporal factors that have not been examined for their potential involvement in medullar NB termination. Previous reports have identified several other temporal factors that play a role in medulla TTF cascade, such as, SoxNeuro (SoxN) and doublesex-Mab related 99B (Dmrt99B) that start their expression in the NE similar to Hth, however, Dmrt99B is likely to be repressed much later than Hth (Li, Erclik et al. 2013, Zhu, Zhao et al. 2022). At this point, it remains challenging to completely rule out the possibility that other temporal factors play a role in medullar NB termination or have redundant functions in regulating the timing of medulla NB cessation. It is suggested to tone down this claim and provide a brief discussion on alternative possibilities, citing relevant papers on the functions of other temporal factors in medullar NBs.

We agree.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Loss of pros by RNAi caused the formation of ectopic NBs and the NBs persist even at 24h APF. Do these NBs persist at 30h or 48h APF? Does overexpression of Pros result in early termination of medulla NBs?

We will do these experiments in clones as suggested.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

Yes, I believe the suggested experiments are realistic in terms of time and resources, with an estimation of 3 months to complete the experiments.

• Are the data and the methods presented in such a way that they can be reproduced?

Yes.

• Are the experiments adequately replicated and statistical analysis adequate?

The experiments are straight forward and were performed with proper controls, supported by quantifications and proper statistical analyses. However, there is no mention about how many replicates were used.

We will add this information in our Material and Methods section.

Minor comments:

1. The authors use the eyR6F10-Gal4 driver in certain experiments. The eyR6F10-Gal4 driver is however expressed only in a subset of medulla NBs. Can the authors comment on what percentage of medulla NBs is the driver expressed in? We will characterize this.

Does the EGFR signalling pathway or JAK/STAT pathway affect the timing of termination of medulla NBs? Experiments are not necessary. The author can speculate on their roles.

We will modify our discussion accordingly.

Figure 1C has a p value of only 0.03 (*) but shows a strong reduction in the number of Dpn+ cells from 12h to 18h, etc. Is this correct? Also, is the p value the same for the comparison between 12h and 24h as well as 12h and 30h APF?

Yes. P-values showed no significant differences between 28-24h and 24-30h APF.

The controls in figure 2B and to some extent figure 2H show one major outlier (much higher than the other brain lobes in the control). Will the removal of this outlier affect the significance/ p-value of the experiment?

No, removing the outliers do not change the statical results.

In figure 2B what is the p-value between 12h and 18h APF? Is it *** as well?

No, it’s not significant.

Line 84 of the introduction introduces Tll, Gcm and Pros for the first time in the manuscript and should be written out in full.

We will change this.

• Are prior studies referenced appropriately?

Yes.

• Are the text and figures clear and accurate?

Yes.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Quite a few of data mentioned in the manuscript have been described as data not shown. I think it would be nice to show quantifications or representative images in the supplementary figures.

We will add the data which was previously not shown.

Reviewer #3 (Significance (Required)):

Since the mechanisms by which medulla NBs are terminated are currently unknow, this is an important and interesting study to understand how medulla neuroblasts in the optic lobe are terminated. The balance between stem cell maintenance and differentiation is critical for proper brain development and the results presented in this paper are impactful. Furthermore, Drosophila melanogaster is an excellent model to study stem cell niches and neuroblast temporal patterning. The authors provide key mechanisms namely cell death, Pros-mediated differentiation and the gliogenic switch that contribute to a better understanding of how the NB progenitor pool can be terminated in the Drosophila OL, which is largely supported by the data.

• Place the work in the context of the existing literature (provide references, where appropriate).

So far, most work in this field has focused on the regulation of the temporal factors to promote the progression of the TTF transcriptional cascade and thereby diversity of the neural progenitors (Li, Erclik et al. 2013, Naidu, Zhang et al. 2020, Ray and Li 2022, Zhu, Zhao et al. 2022). Furthermore, work on pathways such as EGFR and Notch signalling that allows the proneural wave to progress and subsequently induce neuroblast formation in a precise and orderly manner have also been studied (Yasugi, Umetsu et al. 2008, Yasugi, Sugie et al. 2010). Here, considering previous literature, the authors move one step forward to determine how and when these neuroblast progenitors cease proliferation during development thus providing mechanisms for the regulation of the neuroepithelial stem cell pool, its timely conversion into NSCs and the switch from neurogenesis to gliogenesis thus providing important implications for brain size determination and function.

• State what audience might be interested in and influenced by the reported findings.

Stem cell research, neurobiologists and developmental biologists.

• Define your field of expertise

Stem cells, developmental biology

Author Response:

We thank the reviewers and editor for their careful analysis of our manuscript and their appreciation of its strengths. Our plans to address the reviewers’ concerns regarding the weaknesses of the study are outlined below.

Reviewing Editor (Public Review):

“Weaknesses mainly concern the experiments and arguments leading to the authors' notion that Cav3 channels may partially compensate for the loss of Cav1.4 calcium currents in cone synapses. It is possible that the non-conducting Cav1.4 variant supports synapse development and the Cav3 channel then provides the calcium influx. However, in its current state, the study does not unequivocally assess Cav3 expression in wild-type cones, it lacks direct evidence of Cav3 expression and upregulation, e.g. via single cell transcriptomics, immunolabeling, or an elaboration on electrophysiology, and it does not test the authors' earlier idea that Cav1.4 might couple to intracellular calcium stores at photoreceptor synapses.”

Current transcriptomic studies indicate that Cav3 transcripts are present at extremely low levels compared to that for Cav1.4 in cones of young mice (PMID 26000488, summarized in PMID 35650675), adult mice (PMID: 36807640), macaque (PMID 30712875), and human (PMID 31075224). Thus, it was somewhat surprising that Davison et al reported the presence of low voltage activated (LVA) Cav3-like currents with amplitudes that were ~50% of that for the Cav1 current in mouse cones at -40 mV (PMID 35803735). Using similar pharmacological criteria as Davison et al, we did not find functional evidence for a LVA current in cones of wild-type (WT) mouse retina: the Ca2+ current in our recordings was suppressed by the Cav1 antagonist isradipine (Fig 3a) but minimally affected in the expected voltage range by the Cav3 antagonist ML218 (Fig 3b). In WT mouse, voltage clamp steps from -90 mV to more depolarized voltages failed to show a transient inward current at onset (Fig 2e), which is a hallmark of LVA calcium currents. In addition, by standard physiological and pharmacological critera, we could not identify LVA currents in cones of ground squirrel (Fig.3c,d) and macaque retina (Supp. Fig.S3). Our results argue against a significant role for LVA currents in mammalian cones.

A problem that we discovered (as did Davison et al, their Fig.2C) was that Cav3 blockers (e.g., ML218 and Z944) have non-specific actions on the high voltage activated (HVA) Ca2+ current (presumably mediated by Cav1.4) in WT mouse cones. This is clearly shown in our Supp. figure S1a-b where ML218 causes a dose-dependent negative shift in the I-V relationship but also inhibition of current density in HEK293T cells transfected with Cav1.4. We are planning a second study to thoroughly characterize these actions of ML218 and Z944 on Cav1 channels as the results are important for understanding the actions of these drugs in cell-types with mixed populations of Cav1 and Cav3 channels.

A second problem is that dihydropyridines (DHP) used in both our study and that of Davison et al (e.g., isradipine, nifedipine) incompletely and slowly block Cav1 channels at negative membrane potentials (PMID: 12853422). Due to the slow kinetics of DHP block, Cav1 currents in the presence of such blockers can appear to inactivate rapidly (see Fig.6A in PMID 11487617). Thus, the Cav current recorded in the presence of DHP blockers in WT mouse cones may represent unblocked Cav1.4-mediated currents that appear rapidly inactivating, and therefore misconstrued as being mediated by Cav3 channels.

Given the caveats of the pharmacological approach, we agree that stronger evidence is needed to rule out a small contribution of Cav3 channels in WT mouse cones. As mentioned in our text, we have found that currently available Cav3 antibodies produce similar patterns of immunofluorescence in WT and corresponding Cav3 KO retina so analysis at the level of Cav proteins is not possible. Thus, we are planning to compare the relative expression of Cav channel genes in cones using drop-seq experiments of G369i KI and WT mouse retina. We also plan to elaborate on our electrophysiological dissection of the HVA and LVA currents.

Among the 3 Cav3 subtypes, Cav3.2 was the only one detected in mouse cones by Davison et al using nested RT-PCR (PMID 35803735). Thus, we obtained the Cav3.2 mouse strain from JAX (B6;129-Cacna1htm1Kcam/J) and generated a Cav3.2 KO/G369i KI double mutant mouse strain. If the Cav3 current that appears in the G369i KI cones is mediated by Cav3.2, then it should be undetectable in cones of the double mutant mice. Moreover, if these Cav3.2 channels contribute to the residual cone synaptic responses in G369i KI mice, then the double mutant mice should be deficient in this regard. We will test these predictions in patch clamp recordings and ERGs.

Finally, we will conduct Ca2+ imaging experiments in cone terminals of the WT vs G369i KI mice to test whether increased coupling of Cav channels to intracellular Ca2+ release may be involved in cone synaptic responses of the G369i KI mice.

Reviewer #1 (Public Review):

Weaknesses:

“The major criticism that I have of the study is that it infers Ca channel molecular composition based solely on pharmacological analysis, which, as the authors note, is confounded by the cross-reactivity of many of the "specific" channel-type antagonists. The authors note that Cav3 mRNAs have been found in cones, but here, they do not perform any analysis to examine Cav3 transcript expression after G369i-KI nor do they examine Ca channel transcript expression in monkey or squirrel cones, which serve as controls of sorts for the G369i-KI (i.e. like WT mouse cones, cones of these other species do not seem to exhibit LVA Ca currents).”

Actually, we also used non-pharmacological (i.e., electrophysiological) criteria to back up our interpretation that Cav3 channels contribute to the Cav current in cones primarily in the absence of functional Cav1.4 channels. For example, in Fig.2, we show that the Ca2+ current in G369i KI and Cav1.4 KO mice exhibit the hallmarks of the Cav3 channel (negative activation and inactivation voltages and window current, rapid inactivation), which are quite distinct from the Ca2+ currents in WT cones. In recordings of ground squirrel and macaque cones (Supp.Figs.S2-3), negative holding voltages do not unmask a LVA current according to various criteria. In addition to the transcriptomic approaches described above, we plan to elaborate on the electrophysiological evidence for the absence of a LVA current in WT mouse cones as part of the revision.

“Secondarily, in Maddox et al. 2020, the authors raise the possibility that G369i-KI, by virtue of having a functional voltage-sensing domain-might couple to intracellular Ca2+ stores, and it seems appropriate that this possibility be considered experimentally here.”

We will conduct Ca2+ imaging experiments in cone terminals of the WT vs G369i KI mice to test whether increased coupling of Cav channels to intracellular Ca2+ release may be involved in cone synaptic responses of the G369i KI mice.

“As a minor point: the authors might wish to note - in comparison to another retinal ribbon synapse-that Zhang et al. 2022 (in J. Neuroscience) performed a study of mouse rod bipolar cells found a number of LVA and HVA Ca conductances in addition to the typical L-type conductance mediated by Cav1-containing channels.”

We are aware of the extensive evidence for the expression of Cav3 channels in retinal bipolar cells (PMID 11604141, 22909426, 19275782, 35896423) and our recordings of cone bipolar cells in ground squirrel confirm this (Supp. Fig.S2D). We could add reference to this work in our revision.

Reviewer #2 (Public Review):

Weaknesses:

“The major critiques are related to the description of the Cav1.4 knock-in mouse as "sparing" function, which can be remedied in part by a simple rewrite, and in certain places, the data may need to be examined more critically. In particular, the authors should address features in the data presented in Figures 6 and 7 that seem to indicate that the retina of the Cav1.4 knock-in is not intact, but the interpretation given by the authors as "intact" is not appropriate and made without rigorous statistical testing.”

We intended to use “sparing” and “intact” to indicate that cone synapses are present and to some extent functional, in contrast to their complete absence in the Cav1.4 KO mouse. However, we recognize this may be misinterpreted as “normal”. As suggested by the reviewer, we will revise our statistical analyses and text to clarify that cone synaptic responses do indeed differ significantly in G369i KI as compared to WT mice. We feel that this will be a strong addition to the study and will emphasize the key point that Cav3 cannot fully compensate for loss of Cav1.4 with respect to cone synapse structure and function.

Reviewer #3 (Public Review):

Weaknesses:

“The study has been expertly performed but remains descriptive without deciphering the underlying molecular mechanisms of the observed phenomena, including the proposed homeostatic switch of synaptic calcium channels. Furthermore, a relevant part of the data in the present paper (presence of T-type calcium channels in cone photoreceptors) has already been identified/presented by previous studies of different groups (Macosko et al., 2015; pmid 26000488; Davison et al., 2021; pmid 35803735; Williams et al., 2022; pmid 35650675). The degree of novelty of the present paper thus appears limited.”

We respectfully disagree that our paper lacks novelty. As indicated by Reviewer 2, a major advance of our study is in providing a mechanism that can explain the longstanding conundrum that congenital stationary night blindness type 2 mutations that would be expected to severely compromise Cav1.4 function do not produce complete blindness. We also disagree that the presence of T-type channels in cone photoreceptors has been unequivocally demonstrated, as the non-biased transcriptomic approaches show very little Cav3 transcript expression in mouse cones (PMIDs 26000488, 35650675, 36807640), macaque cones (PMID 30712875), and human cones (PMID 31075224). Transcription may not equate to translation, particularly at low expression levels. We also note that the one study to date that suggests a functional contribution of Cav3 channels in mouse cones (Davison et al., 2021; pmid 35803735) used a DHP to isolate the “LVA” current, which is problematic as described above. Our demonstration of minimal or undetectable Cav3-type currents in mammalian cones using physiological and pharmacological approaches, while a negative result, adds important context to the recent literature. As described in our response to the editor’s review, our planned revisions include testing whether Cav3 transcripts are upregulated in G369i KI cones and whether the Cav3.2 subtype suggested to be present in cones (PMID 35803735) contributes to Cav currents in these cells using Cav3.2 KO and Cav3.2 KO/G369i KI double mutant mice.

Author Response

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

Response to reviews

We would like to extend our thanks to the reviewers who took the time to carefully read our paper and provide thoughtful insights and suggestions on how to strengthen our conclusions. All reviewers agreed that our study presented strong data supporting a role for triglyceride lipase brummer (bmm) in regulating testis lipid droplets and spermatogenesis in Drosophila, and that our findings advance our understanding of lipid biology during sperm development. Reviewers made several helpful suggestions on how to strengthen our manuscript even further. Below, we outline how we revised our manuscript in response to reviewer comments to ensure we clearly communicate our data and conclusions with readers, and properly contextualize our findings.

REVIEWER 1

In this study, the authors investigate the role of triglycerides in spermatogenesis. This work is based on their previous study (PMID: 31961851) on triglyceride sex differences in which they showed that somatic testicular cells play a role in whole body triglyceride homeostasis. In the current study, they show that lipid droplets (LDs) are significantly higher in the stem and progenitor cell (pre-meiotic) zone of the adult testis than in the meiotic spermatocyte stages. The distribution of LDs anti-correlates with the expression of the triglyceride lipase Brummer (Bmm), which has higher expression in spermatocytes than early germline stages. Analysis of a bmm mutant (bmm[1]) - a P-element insertion that is likely a hypomorphic - and its revertant (bmm[rev]) as a control shows that bmm acts autonomously in the germline to regulate LDs. In particular, the number of LDs is significantly higher in spermatocytes from bmm[1] mutants than from bmm[rev] controls. Testes from males with global loss of bmm (bmm[1]) are shorter than controls and have fewer differentiated spermatids. The zone of bam expression, typically close to the niche/hub in WT, is now many cell diameters away from the hub in bmm[1] mutants. There is an increase in the number of GSCs in bmm[1] homozygotes, but this phenotype is probably due to the enlarged hub. However, clonal analyses of GSCs lacking bmm indicate that a greater percentage of the GSC pool is composed of bmm[1]-mutant clones than of bmm[rev]-clones. This suggests that loss of bmm could impart a competitive advantage to GSCs, but this is not explored in greater detail. Despite the increase in number of GSCs that are bmm[1]-mutant clones, there is a significant reduction in the number of bmm[1]-mutant spermatocyte and post-meiotic clones. This suggests that fewer bmm[1]-mutant germ cells differentiate than controls. To gain insights into triglyceride homeostasis in the absence of bmm, they perform mass spec-based lipidomic profiling. Analyses of these data support their model that triglycerides are the class of lipid most affected by loss of bmm, supporting their model that excess triglycerides are the cause of spermatogenetic defects in bmm[1]. Consistent with their model, a double mutant of bmm[1] and a diacylglycerol Oacyltransferase 1 called midway (mdy) reverts the bmm-mutant germline phenotypes.

There are numerous strengths of this paper. First, the authors report rigorous measurements and statistical analyses throughout the study. Second, the authors ulize robust genetic analyses with loss-of-function mutants and lineage-specific knockdown. Third, they demonstrate the appropriate use of controls and markers. Fourth, they show rigorous lipidomic profiling. Lastly, their conclusions are appropriate for the results. In other words, they don't overstate the results.

We thank the Reviewer for their positive assessment of our paper.

There are a few weaknesses. Although the results support the germline autonomous role of bmm in spermatogenesis, one potential caveat that the mdy rescue was global, i.e., in both somatic and germline lineages. The authors did not recover somatic bmm clones, suggesting that bmm may be required for somatic stem self-renewal and/or niche residency. While this is beyond the scope of this paper, it is possible that somatic bmm does impact germline differentiation in a global bmm mutant.

In the revised manuscript, we made several changes to address these points.

1) We now clearly state when we used global versus germline-only loss of mdy to rescue bmm mutant phenotypes in the testis.

“Notably, at least some of the effects of global loss of mdy on bmm1 males can be attributed to the germline:

RNAi-mediated knockdown of mdy in the germline of bmm1 males partially rescued the defects in testis size (Figure 4I; Kruskal-Wallis rank sum test with Dunn’s multiple comparison test) and GSC variance (Figure S5J; p=4.5 x 10-5 and 8.2 x 10-3 by F-test from the GAL4- and UAS-only crosses, respectively).”

“Importantly, testes isolated from males with global loss of both bmm and mdy (mdyQX25/k03902;bmm1) had fewer LD than testes dissected from bmm1 males (Figures 5D, S5I; one-way ANOVA with Tukey multiple comparison test).”

2) We also discuss the possibility that somatic bmm may play a role in germline differentiation in a global bmm mutant, and present phenotypic data on somatic bmm1 clones.

“We also reveal a potential non-cell-autonomous role for somatic bmm. While there was no difference in the ratio of Zd-1-positive cells between homozygous clones and heterozygous clones in animals carrying the bmm1 or bmmrev alleles at 14 days post clone induction (Figure S4O; Kruskal-Wallis rank sum test), the distance from the hub to the Zd-1 positive clones reside was significantly decreased in bmm1 homozygous clones (Figure S4P; Kruskal-Wallis rank sum test). Together, these data indicate bmm may play a cell-autonomous role in germline cells, and potentially a non-cell-autonomous role in somatic cells, to regulate spermatogenesis.”

3) Finally, we clarify that we were unable to assess somatic LD. Specifically, this was a technical issue as the dye we use to visualize testis LD is incompatible with staining protocols to identify somatic cells. As a result, we were unable to count LD in somatic clones with confidence.

“While we were unable to assess LD in bmm1 somatic clones, our data when taken together reveals a previously unrecognized cell-autonomous role for bmm as a regulator of testis LD in germline cells.”

Regarding data presentation, I have a minor point about Fig. 3L: why aren't all data shown as box plots (only Day 14 bmm[rev] does).

In our revised manuscript Figure 4L does present a boxplot across all genotypes and times; the appearance of ‘no boxes’ is simply due to the large number of datapoints with a value of zero, which compress the box near the X-axis.

Finally, the authors provide a detailed pseudotime analysis of snRNA-seq of the testis in Fig. S2A-D, but this analysis is not sufficiently discussed in the text.

In the revised manuscript we added text to describe our pseudotime analysis of single-cell RNA seq data in more detail.

“Using pseudotime analysis, we arranged the germline (Figure S2A) and the somatic cells (Figure S2B) based on their annotated developmental trajectory. The expression pattern of bmm in the germline matched our observation with bmm-GFP reporter (Figure S2C). While levels of the bmm-GFP reporter were lower in somatic cells, single-cell RNA sequencing data identified bmm expression in the somatic lineage that was higher in cells at later stages of development (Figure S2D). Additional neutral lipid- and lipid droplet-associated genes such as lipid storage droplet-2, Seipin, Lipin, and midway also showed differential regulation during differentiation (Figure S2C, S2D). Combined with our data on the location of testis LD, these data suggest that bmm upregulation in both somatic and germline cells during differentiation corresponds to the downregulation of testis LD. Supporting this, germline GFP levels were negatively correlated with testis LD in bmm-GFP flies (Figure 2A, 2C), suggesting regions with higher bmm expression had fewer LD.”

Overall, the many strengths of this paper outweigh the relatively minor weaknesses. The rigorously quantified results support the major aim that appropriate regulation of triglycerides are needed in a germline cell-autonomous manner for spermatogenesis.

This paper should have a positive impact on the field. First and foremost, there is limited knowledge about the role of lipid metabolism in spermatogenesis. The lipidomic data will be useful to researchers in the field who study various lipid species. Going forward, it will be very interesting to determine what triglycerides regulate in germline biology. In other words, what functions/pathways/processes in germ cells are negatively impacted by elevated triglycerides. And as the authors point out in the discussion, it will be important to determine what regulates bmm expression such that bmm is higher in later stages of germline differentiation.

We agree with the reviewer about the many interesting future directions for this project. We added a model figure in the revised manuscript to visualize our findings and highlight remaining questions about how bmm and triglycerides support normal spermatogenesis in Drosophila (Fig. 6).

REVIEWER 2

Summary:

Here, the authors show that neutral lipids play a role in spermatogenesis. Neutral lipids are components of lipid droplets, which are known to maintain lipid homeostasis, and to be involved in non-gonadal differentiation, survival, and energy. Lipid droplets are present in the testis in mice and Drosophila, but not much is known about the role of lipid droplets during spermatogenesis. The authors show that lipid droplets are present in early differentiating germ cells, and absent in spermatocytes. They further show a cell autonomous role for the lipase brummer in regulating lipid droplets and, in turn, spermatogenesis in the Drosophila testis. The data presented show that a relationship between lipid metabolism and spermatogenesis is congruous in mammals and flies, supporting Drosophila spermatogenesis as an effective model to uncover the role lipid droplets play in the testis.

We thank the Reviewer for their positive assessment of our paper.

Strengths and weaknesses:

The authors do a commendably thorough characterization of where lipid droplets are detected in normal testes: located in young somatic cells, and early differentiating germ cells. They use multiple control backgrounds in their analysis, including w[1118], Canton S, and Oregon R, which adds rigor to their interpretations. The authors employ markers that identify which lipid droplets are in somatic cells, and which are in germ cells. The authors use these markers to present measured distances of somatic and germ cell-derived lipid droplets from the hub. Because they can also measure the distance of somatic and germ cells with age-specific markers from the hub, these results allow the authors to correlate position of lipid droplets with the age of cells in which they are present. This analysis is clearly shown and well quantified.

The quantification of lipid droplet distance from the hub is applied well in comparing brummer mutant testes to wild type controls. The authors measure the number of lipid droplets of specific diafteters, and the spatial distribution of lipid droplets as a function of distance from the hub. These measurements quantitatively support their findings that lipid droplets are present in an expanded population of cells further from the hub in brummer mutants. The authors further quantify lipid droplets in germline clones of specified ages; the quantitative analysis here is displayed clearly, and supports a cell autonomous role for brummer in regulating lipid droplets in spermatocytes.

Data examining testis size and number of spermatids in brummer mutants clearly indicates the importance of regulating lipid droplets to spermatogenesis. The authors show beautiful images supported by rigorous quantification supporting their findings that brummer mutants have both smaller testes with fewer spermatids at both 29 and 25C. There is also significant data supporting defects in testis size for 14-day-old brummer mutant animals compared to controls. The comparison of number of spermatids at this age is not significant, which does not detract from the story but does not support sperm development defects specifically caused by brummer loss at 14 days. Their analysis clearly shows an expanded region beyond the testis apex that includes younger germ cells, supporting a role for lipid droplets influencing germ cell differentiation during spermatogenesis.

We thank the reviewer for pointing out this inaccuracy in our manuscript. In the revised manuscript we chose more precise language to describe defects in 14-day-old bmm mutants:

“Defects in testis size were also observed at 14-day post eclosion; suggesting testis size defects persist later into the life course (Figure S4C; Welch two-sample t-test). In contrast, the number of spermatid bundles per testis was not significantly different between bmm1 and bmmrev males at this age (Figure S4D; Welch two-sample ttest), potentially due to a large decrease in the number of spermatid bundles in 14-day-old bmmrev males (Figure 4C, S4D).”

The authors present a series of data exploring a cell autonomous role for brummer in the germline, including clonal analysis and tissue specific manipulations. The clonal data indicating increased lipid droplets in spermatocyte clones, and a higher proportion of brummer mutant GSCs at the hub are convincing and supported by quantitation. The authors also show a tissue specific rescue of the brummer testis size phenotype by knocking down mdy specifically in germ cells, which is also supported by statistically significant quantitation. The authors present data examining the number of spermatocyte and post-meiotic clones 14 days aeer clonal induction. While data they present is significant with a 95% confidence interval and a p value of 0.0496, its significance is not as robust as other values reported in the study, and it is unclear how much information can be gained from that specific result.

We thank the reviewer for raising this point. In the revised manuscript we displayed the p-value clearly in the text and on the figure to ensure our statistical output is clear for readers to evaluate our conclusions regarding bmm mutant clones 14 days after clone induction. We also state that the finding should be reproduced by others given that the statistical significance of this result was not as strong as our other data.

“Because we observed significantly fewer bmm1 spermatocyte and spermatid clones at 14 days after clone induction (Figure 4K,4L; p = 0.0496, Kruskal-Wallis rank sum test), these effects on germline development may represent a cell-autonomous role in regulating spermatogenesis for bmm in this cell type. Given that the statistical significance of this finding was not as strong as for our other data, future studies should repeat this experiment with more samples.”

The authors do a beautiful job of validating where they detect brummer-GFP by presenting their own pseudotime analysis of publicly available single cell RNA sequencing data. Their data is presented very clearly, and supports expression of brummer in older somatic and germline cells of the age when lipid droplets are normally not detected. The authors also present a thorough lipidomic analysis of animals lacking brummer to identify triglycerides as an important lipid droplet component regulating spermatogenesis.

Impact:

The authors present data supporting the broad significance of their findings across phyla. This data represents a key strength of this manuscript. The authors show that loss of a conserved triglyceride lipase impacts testis development and spermatogenesis, and that these impacts can be rescued by supplementing diet with medium chain triglycerides. The authors point out that these findings represent a biological similarity between Drosophila and mice, supporting the relevance of the Drosophila testis as a model for understanding the role of lipid droplets in spermatogenesis. The connection buttresses the relevance of these findings and this model to a broad scientific community.

We thank the Reviewer very much for their positive assessment of our paper!

REVIEWER 3

In this manuscript, Chao et al seek to understand the role of brummer, a triglyceride lipase, in the Drosophila testis. They show that Brummer regulates lipid droplet degradation during differentiation of germ and somatic cells, and that this process is essential for normal development to progress. These findings are interesting and novel, and contribute to a growing realisation that lipid biology is important for differentiation.

We thank the Reviewer for their positive comments about our manuscript.

Major comments:

1) The data in Figs 1 and 2, while helpful in setting the scene, do not add much to what was previously shown by the same group, namely that lipid droplets are present in both early germ cells and early somatic cells in the testis, and that Bmm regulates their degradation (PMID: 31961851). Measuring the distance of lipid droplets from the hub, while helpful in quantifying what is apparent, that only stem and early differentiated stages have lipid droplets, is not as informative as the way data are presented later (Fig. 2I), where droplets in specific stages are measured. Much of this could be condensed without much overall loss to the manuscript.

We thank the reviewer for this comment. In our revised manuscript we edited the first part of the paper while still preserving the detailed characterization that builds upon our previous paper.

2) It would be important to show images of the clones from which the data in Fig. 2I are generated. The main argument is that Bmm regulates lipid droplets in a cell autonomous manner; these data are the strongest argument in support of this and should be emphasised at the expense of full animal mutants (which could be moved to supplementary data).

We thank the reviewer for this comment. In the revised manuscript we added a figure showing lipid droplets in control and bmm mutant spermatocyte clones in Fig. 3A, 3B with a quantification of this data in Figure 3C.

Similarly, the title of Fig. S2 ("brummer regulates lipid droplets in a cell autonomous manner") should be changed as the figure has no experiments with cell (or cell-type)-specific knockdowns/mutants. This figure does show changes in lipid droplets in both lineages in bmm mutants, so an appropriate title could be "brummer regulates lipid droplets in both germ and soma".

We thank the reviewer for this comment, we adjusted the Figure 2 legend title in the revised manuscript to “brummer regulates lipid droplets in both germline and somatic cells of the testis”.

3) Interestingly, the clonal data show that bmm is dispensable in germ cells until spermatocyte stages, as no increase in lipid droplet number is seen until then. This should be more clearly stated, as it indicates that the important function of Bmm is to degrade lipid droplets at the transition from spermatogonial to spermatocyte stages. This is consistent with the phenotypes observed in which late stage germ cells are reduced or missing. However, the effect on niche retention of the mutant GSCs at the expense of neighbouring wildtype GSCs is hard to explain. Are lipid droplets in mutant GSCs larger than in control? Is there any discernible effect of bmm mutation on lipids in GSCs? Additionally, bam expression is delayed, suggesting that bmm may have roles on cell fate in earlier stages than its roles that can be detected on lipid droplets.

We thank the reviewer for this comment. We included more text in the revised manuscript to clarify the key role bmm plays in regulating lipid droplets at the spermatogonia-spermatocyte transition.

“Because we observed no significant effect of cell-autonomous bmm loss on LD at any other stage of germline development (Figure 3C), this suggests bmm function is not required to regulate LD at early stages of germ cell development. Instead, our data suggests bmm plays a role in regulating LD at the spermatogonia-spermatocyte transition.”

We also added more detail to our description of how bmm affects lipid droplets in cells at the earliest stages of germline development.

“Given that we detected no effect of cell-autonomous bmm loss on the number of GSC LD (Fig. 3C), more work will be needed to understand how bmm regulates GSC at a stage prior to its effects on LD number.”

4) The bmm loss-of-function phenotype could be better described. Some of the data is glossed over with little description in the text (see for example the reference to Fig. 3A-C). For instance, in the discussion, the text states "loss of bmm delays germline differentiation leading to an accumulation of early-stage germ cells" (p13, l.25960). However, this accumulation has not been clearly shown, or at least described in the manuscript. Most of the data show a reduction (or almost complete absence) of differentiated cell types. This could indeed be due to delayed differentiation, or alternatively to a block in differentiation or to death of the differentiated cells. The clonal data presented show a decrease in the number of cells recovered, but do not allow inferences as to the timing of differentiation, making it hard to distinguish between the various possibilities for the lack of differentiated spermatids. Apart from data showing that GSCs are more likely to remain at the niche, no further data are shown to support the fact that mutant germ cells accumulate in early stages. While additional experiments could help resolve some of these issues, much of this could also be resolved by tempering the conclusions drawn in the text.

We thank the reviewer for these comments. In the revised manuscript we temper our conclusions regarding bmm’s precise role in spermatogenesis by discussing different mechanisms (e.g. differentiation or death) that could lead to the phenotypes we observe.

“This regulation is important for sperm development, as our data indicates that loss of bmm causes a decrease in the number of differentiated cell types. This reduction in differentiated cell types may be attributed to a delay in differentiation, a block in differentiation, or to a loss of differentiated cells through cell death. Future studies will therefore be essential to resolve why bmm loss causes a reduction in differentiated cell types.”

5) In the discussion (p.14, l-273 onwards), the authors suggest that products of triglyceride breakdown are important for spermatogenesis. However, an alternative interpretation of the results presented here (especially those using the midway mutant) could be that triglycerides impede normal differentiation directly. Indeed, preventing the cells' ability to produce triglycerides in the first place can rescue many of the defects observed. A better discussion of these results with a model for the function of triglycerides and their by-products would be a great improvement to this manuscript.

We thank the reviewer for this comment. To ensure our data is clearly communicated with readers, we added a model to the paper suggesting how triglyceride and its by-products influence spermatogenesis (Fig. 6) and text to clarify that triglyceride could potentially impeded differentiation.

“It will also be important to determine whether it is the loss of metabolites produced by bmm’s enzymatic action, or an increase in triglycerides, that leads to the reduction in differentiated cell types during spermatogenesis. Together, these experiments will provide critical insight into how triglyceride stored within testis LD contributes to overall cellular lipid metabolism during spermatogenesis.”

Together, these changes will strengthen our overall finding that bmm-mediated regulation of testis triglyceride is important for normal sperm development. Because our findings in flies align with and extend data from rodent models, the developmental mechanisms we uncovered about how triglyceride lipase bmm regulates testis lipid droplets and sperm development will likely operate in other species.  

Reviewer #1 (Recommendations For The Authors):

I have a minor concern about methodology: how were spermatocytes identified? I ask because data in Figure 3 indicate that there is a significant delay in germline differentiation in the bmm[1] mutant, with relatively smaller germ cells throughout the apical half of the testis. Typical large spermatocyte-like cells are not clearly obvious to me in Fig. 3.

We thank the Reviewer for suggesting we add more clarity to how we identified spermatocytes. We state in the revised manuscript how we identify spermatocytes:

“Cells in the testis region occupied by primary spermatocytes were identified by their large cell size and decondensed chromosome staining occupying three nuclear domains [120].”

Also, we note that while it is difficult to see where the bmm1 testis have spermatocytes in Fig. 4E, this is due to the large number of early-stage cells in this close-up image. The spermatocytes can be more easily seen in Fig. 4I and 4I’ when the whole testis is included in the image.    

Reviewer #2 (Recommendations For The Authors):

• Lines 197-198 mention "Boule-positive area," "individualization complexes," and "waste bags." It would be helpful to the reader to explain what these measurements are to help contextualize the data shown related to these statements.

We thank the Reviewer for this comment. We added the following text to the revised manuscript:

“Because Boule-positive area, individualization complexes, and waste bags are all markers for later stages in sperm development, these data indicate the loss of bmm causes a reduction in differentiated cell types.”

• Line 162 states a defect in sperm development observed in 14-day-old bmm[1] males, but the data presented in Figure S3D does not show a significant difference. The words "sperm development" should be removed from this sentence.

We thank the Reviewer for pointing out this inaccurate statement. We fixed the statement as follows in the revised manuscript:

“Defects in testis size were also observed at 14-day post eclosion; suggesting testis size defects persist later into the life course (Figure S4C; Welch two-sample t-test). In contrast, the number of spermatid bundles per testis was not significantly different between bmm1 and bmmrev males at this age (Figure S4D; Welch two-sample ttest), potentially due to a large decrease in the number of spermatid bundles in 14-day-old bmmrev males (Figure 4C, S4D).”

• Line 294 has a typo: "regulating" should likely be "regulated"

We thank the Reviewer for pointing out this mistake, which we corrected.

• Line 456 should include the length of time for heat shock

We thank the Reviewer for pointing out this omission. We now include these details:

“Adult males were collected at 3-5 days post-eclosion and heat-shocked three times at 37°C for 30 min followed by a 10 min rest period at room temperature between heat shocks.”

• Methods section beginning on Line 442 might include an explanation of how hub area was quantified.

We thank the Reviewer for this suggestion. We now include the following information:

“Hub size was measured by quantifying FasIII-positive area of the testis.”

• Figure 1 legend could benefit from adding a statement on how spermatocytes (arrowheads) were identified

We thank the Reviewer for this suggestion, we now refer the reader to the more detailed description in the methods section.

• Figure 2A should present the merged panel in A' first. The legend states that Panel A shows Lipid Droplets, but LipidTox is not shown until A'.

We thank the Reviewer for this suggestion, we now clarify that the text refers to panels A-A''''.

• Figure 2I would benefit from a key, to emphasize that these are individual cell clones, highlighting the idea of cell autonomous effects of bmm in the spermatocytes. Showing example images of spermatocyte clones with increased lipid droplets could also emphasize this result. The legend for this panel should note the statistical test done to confirm significance in the SC result.

We agree with the Reviewer and have added images of the LD in bmm1 spermatocyte clones in Figure 3B, and the quantification in Figure 3C. We explicitly state the significance of this result and the statistical test in Figure 3 legend.

• In Figure 3, the cell autonomous data clearly indicates that there are higher proportions of bmm mutant GSCs occupying the hub compared to control GSCs. It could be worth stating whether this observation indicates an increased ability of bmm mutant GSCs to compete for occupying space at the hub.

We thank the Reviewer for pointing out this potential implication of our data, which we acknowledge in the revised version of our manuscript:

“Future studies will also need to confirm whether bmm1 mutant GSCs show an increased ability to occupy space at the hub.”

• In Figure 4, I suggest changing the title of Panel B to "Proportion of significant species in each lipid class" for clarity.

We made this change in the Figure 5 legend (Figure 5 is the corresponding figure in the revised manuscript).

• It could be valuable to quantify the number of spermatids in the germline specific mdy knockdown, which would lend additional support to a cell autonomous requirement for bmm in spermatogenesis

We added a sentence to the revised manuscript recognizing that this is an interesting experiment for studies on the role of germline triglyceride in promoting spermatogenesis.

“While future studies will need to test whether germline-specific loss of mdy also rescues spermatid number defects in bmm1 males, our data suggest bmm-mediated regulation of testis triglyceride plays a previously unrecognized role in regulating sperm development.”

Reviewer #3 (Recommendations For The Authors):

1) bmm-GFP does not show expression in somatic cells yet previous work by the same group has shown a requirement for bmm in the testis soma using C587-Gal4.

We thank the Reviewer for raising this issue. While the reporter shows low GFP expression in the somatic cells, the single-cell RNA sequencing data we analyze suggests bmm is expressed in these cells. We address this issue in the revised manuscript as follows:

“While levels of the bmm-GFP reporter were lower in somatic cells, single-cell RNA sequencing data identified bmm expression in the somatic lineage that was higher in cells at later stages of development (Figure S2D).”

2) p.11 l.200-202 "Because we recovered fewer bmm1 spermatocyte and spermatid clones 14 days after clone induction (Figure 3K,3L; Kruskal-Wallis rank sum test), this effect on germline development represents a cell-autonomous role for bmm." This sentence should be rephrased as the phenotype could be a combination of autonomous roles within the germline and non-autonomous roles in supporting cyst cells.

“We also reveal a potential non cell-autonomous role for somatic bmm. While there was no difference in the ratio of Zd-1-positive cells between homozygous clones and heterozygous clones in animals carrying the bmm1 or bmmrev alleles at 14 days post clone induction (Figure S4O; Kruskal-Wallis rank sum test), the distance from the hub to the Zd-1 positive clones reside was significantly decreased in bmm1 homozygous clones (Figure S4P; Kruskal-Wallis rank sum test). Together, these data indicate bmm may play a cell-autonomous role in germline cells, and potentially a non-cell-autonomous role in somatic cells, to regulate spermatogenesis.”

3) The labelling in Fig. 3 is confusing - presumably the graph in 3C refers to spermatid bundles [this comment applies to other figures showing spermatid bundle numbers], not individual spermatids, while the graph in 3G refers to the proportion of the total GSC pool that is contained within the clone. The data in Fig. 3C are not described in the main text.

We adjusted the confusing labelling to ‘spermatid bundles’ from ‘number of spermatids’, as suggested. We also changed the title of panel Fig. 3G (now 4G) as suggested and men5oned Fig. 3C (now Fig. 4C) in the text.

4) On p.9, comments are speculative or seek to draw comparisons with the broader literature and would seem to belong more to the discussion (eg "our data suggests flies are a good model to study how bmm/ATGL influences sperm development" - also there is a typo, it should be "suggest").

We thank the Reviewer for raising concern about our speculative statement; we changed the text as follows in the revised manuscript:

“This identifies similarities between flies and mice in fertility-related phenotypes associated with whole-body loss of bmm/ATGL.”

5) The length of the heat shocks used for clone induction should be specified in the methods (rather than just the period in between heat shocks).

We now include more information on clone induction:

“Adult males were collected at 3-5 days post-eclosion and heat-shocked three times at 37°C for 30 min followed by a 10 min rest period at room temperature between heat shocks. Amer heat-shock, the flies were incubated at room temperature until dissection.”

6) p.8 l.132 "bmm-GFP accurately reproduces changes to bmm mRNA levels". This sentence should be rephrased.

We thank the Reviewer for this comment and rephrased the sentence:

“We first examined bmm expression in the testis by isolating this organ from flies carrying a bmm promoter driven GFP transgene (bmm-GFP) that recapitulates many aspects of bmm mRNA regulation [77].”

7) p.9 l.172 "we used germline-specific marker" should read "we used an antibody against the germline-specific marker".

We corrected this inaccurate statement in our revised manuscript.

8) p.10 several lines, "GSC" should be "GSCs".

We corrected this inaccurate use of GSC in our revised manuscript.

9) p.13 l.247 should read "variance in GSC numbers".

Thank you, this error was fixed.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Rebuttal for Review Commons to:

“A specific innate immune response silences the virulence of Pseudomonas aeruginosa in a latent infection model in the Drosophila melanogaster host”

We thank the reviewers for their careful scrutiny of our manuscript. We believe that we have addressed satisfactorily the points raised by the reviewers and that our revised manuscript is definitely improved. Our replies below are in blue and use a distinctive font.

Reviewer #1

__Evidence, reproducibility and clarity __

This works describes a latent Drosophila intestinal infection, which spreads systemically, with a direct systemic Drosophila infection using a common laboratory strain of Pseudomonas aeruginosa. The major observation of this study is that P. aeruginosa can cause a latent infection via its passage through the gut (as opposed to being injected). In doing so it exhibits cell rounding (instead of elongation), reduced cell motility, loss of O5-antigen, antibiotic resistance, ability to cross the intestinal barrier and circulate in the hemolymph and infiltrate the host tissue underneath the cuticle. In addition, latent infection bacteria induce all brunches of the systemic response: the Imd pathway, phagocytosis, and the melanization cascade. Moreover, the melanization pathway protects the host from a secondary systemic infection with various types of bacterial and fungal microbes.

An issue that needs to be clarified is the sensitivity of P. aeruginosa virulence to its biochemical environment. The authors note that. For example, liquid bacterial culture in BHI induces the latent form of bacteria. So the growth conditions and the infection media play a major role in the infection process. They authors need to clarify further the effect of media and infection vehicles, sucrose (high/low), LB, and BHI (as well as temperature) on the latent phenotype.

Temperature is definitely an important parameter and bacteria appear to be somewhat more virulent at 25°C. This point is now addressed in the Material and Methods section (lines 675-679) and in Fig. S6I.

As regards the influence of the composition of the infection solution, it does not seem to be a critical parameter that we have described in the context of continuous feeding on the bacterial solution (Limmer et al., PNAS, 2011). In preliminary experiments, we had tried LB or BHB medium to grow the bacteria and this did not make any difference (see Panel A below for LB [BHB used in our experiments]). As regards the sucrose concentration to the infection solution, we have tried two concentrations and did not observe any difference as regards the establishment of the latent infection. (see panel B below for 50 mM sucrose [100mM used in our experiments]). Of note, P. aeruginosa does not grow on sucrose solution alone. However, a latent infection was still established upon feeding the flies with PAO1 in sucrose alone, albeit likely with a mildly increased virulence, in the absence of any BHB medium (see panels C-D) below.

A) Comparison of LB vs. BHB B) Establishment of latent infection with 50mM sucrose

C) Establishment of a latent infection with a sucrose-only bacterial solution D) Colonization of host tissues by PAO1 ingested in a sucrose-only bacterial solution Minor issues: -Lines 579-581> How were the PAO1GFP/RFP constructed (details are needed)

Done; please, see lines 641-643.

-Figure 1D and other figures > CFUs given as Log2 are unconventional. One cannot easily deduce the burden unless e.g. translate 2e10 to ~1000 and 2e30 to ~10e9 CFUs.

True, but bacterial titer increases by a factor of two at each division cycle. Even though we have previously used a Log10 representation, we now prefer using a Log2 representation. This representation has also been used by other authors in the field, e.g., Duneau et al., eLife, 2019.

-Figire S1DB (now S1C)> "but from the outside of the gut". The given experiment does not prove that statement.

This issue has been already dealt with in the Nehme et al. PLoS Pathogens 2007 article, as cited in the manuscript. We further provide in Fig. S1B pictures documenting the presence of bacteria associated with visceral muscles. Finally, we also show that the gut lumen is essentially cleared of bacteria after a period of feeding on a sucrose solution or gentamicin. Hence, most bacterial colonies originate from the outer layer of the gut. We clarify the issue in the text (lines 154-158).

-Lines 146-7 > data are missing in support to the statement.

We have now added Fig. S1B to document that the gentamicin treatment does work, as actually does feeding on sucrose solution alone, as previously documented in Limmer et al., 2011 (Fig. S2B). Of note, we cannot exclude that a few bacteria remain, especially in the crop, but those would be at very low titer. Please, see also reply to Reviewer 2.

-Figure S1C > The effect of injury seems to be huge, and may account for much/most of the differences observed (including those between latent and active infection). This is further supported by Figure4A, injury may account for gut collapse and/or systemic stress.

It is well known that injury alone induces the systemic IMD pathway response 6 hours after injury but largely subsides by 24 hours. The point of Fig. S1C is that the level of induction reached during latent infection is very low as compared to that observed during a systemic infection, here obtained for reference with an Escherichia coli injection and to a lesser extent with a PBS injection. In our latent infection model, we do not perform any injury, except as noted by the reviewer in Fig. 4: the effects of an experimental injury are observed only while the bacteria are crossing the intestinal barrier and hardly any effect is observed when the injury is performed on day 10 (Fig. S4B).

-Figure S1D > How was "fated to die" assessed?

The fluorescent flies were sorted out and their subsequent survival was monitored. As compared to nonfluorescent flies from the same batch, they died within two days of sorting them.

-Figure 3B/10th day > Average line is misplaced.

We thank the reviewer for pointing out this problem. The line is not the average but the median. We have now added a precise description of the bars to all the figure legends.

-Lines 382-5 > what is the evidence of gut damage (or the absence of it)? How do the bacteria escape the gut?

The absence of major gut damages has been documented in Limmer et al, PNAS, 2011. How the bacteria escape the gut remains an open question (intracellular and/or paracellular route).

-Lines437-442 > The distinction between dormant P. aeruginosa in the fly tissues and persister cells (upon antibiotic treatment) cannot be justifies on the basis of relative bacterial numbers in the two systems. The extent of resistance to antibiotics though my serve that purpose.

In our latent infection model, most of the bacteria that have crossed the gut barrier become dormant and are associated with tissues, except at the beginning of the infection. In contrast, when a bacterial culture is treated with antibiotics, most of the bacteria are killed by the treatments and only a few ones persist, likely because of an inactive metabolism. Thus, the induction of dormancy in our latent infection model does not rely on the selection of a few metabolically-inactive bacteria able to withstand an immune response or an antibiotic treatment.

Significance

The study is a significant advance to our knowledge. Notwithstanding further explanations, it provides a solid basis of understanding active versus dormant bacteria. It further establishes a mode of intestinal to systemic infection as a tool for further explorations.

Reviewer #2

Evidence, reproducibility and clarity

Summary: In this study, Chen and colleagues investigated a new latent infection model for Pseudomonas aeruginosa using Drosophila melanogaster as a host. First, the authors established a new model for latent Pseudomonas infection. The key feature of this model is the translocation of P. aeruginosa from the gut to the hemolymph and the colonization of fly tissues by the dormant bacteria. Bacteria that translocated from the gut appeared strikingly different in morphology and resistance to antibiotics compared to bacteria that were directly injected. Next, the authors suggest that melanization but not the Imd pathway or hemocytes are necessary to promote dormancy and colonization of fly tissues. Finally, flies with latent P. aeruginosa infection exhibit improved survival after secondary infections in a melanisation-dependent manner. The study reports an interesting model for latent infection, provides insights into the host factors promoting latency and describes some of the consequences of such latent infection for the host. However, some of the conclusions are not fully supported by the data and need further experimental evidence.

Major comments: 1. The latent infection model requires some clarifications. First, temperature. Could the authors explain why they used 18 {degree sign}C and could low temperature contribute to the establishment of dormancy?

As shown in Fig.S6I, the latent infection model is less compelling at 25°C in terms of survival curves, which may reflect an increased rate of spontaneous reactivation of the virulence, a phenomenon we have not studied at 25°C. In another manuscript in preparation (Lin et al.), we actually show that a small heat shock does contribute to reactivation of the bacteria, an issue that is outside of the scope of the present study. Please, see also reply to reviewer 1.

Second, the use of gentamycin. How does gentamicin affect PAO1 outside the gut? From Fig.1C It looks like the cfus in the hemolymph diminished rapidly after gentamicin treatment (around day 3), suggesting the potential effect of the antibiotic. Once the bacteria have crossed the gut and entered the hemolymph, they could still be affected by feeding flies the antibiotic. Is there a possibility that gentamicin treatment is a stress factor that could trigger or facilitate the transition to dormancy? The authors could test this experimentally either by omitting the antibiotic and assessing dormancy or by feeding injected flies with gentamycin and scoring dormancy.

We had actually tested the issue about the potential role of gentamicin outside of the gut compartment. We have thus fed flies on different concentrations of gentamicin and monitored the survival of those flies to the injection of PAO1 bacteria (please, see Figure below). When flies were feeding on the highest concentration of gentamicin tested, 32 mg/mL, they were succumbing fast to the PAO1 challenge, but not as fast as nontreated positive control PAO1 injected flies. The use of lower concentrations (16, 8, and 4 mg/mL) led to a progressively stronger protection from PAO1 injection that inversely correlated with the gentamicin dose. We interpret the data with high gentamicin concentrations as an indication that gentamicin at such concentrations is likely directly toxic to the flies, an issue that could be experimentally tested but is not relevant to this study. Interestingly, lower doses led to a much-decreased protection from PAO1 (2mg/mL) to no protection at the dose we use to establish latent infection (100 µg/mL). Thus, these data show that gentamicin can pass the gut barrier when provided at high concentrations, down to 2 mg/mL. However, there is no proof of such a passage at the dose we use. In keeping with this latter possibility, we made a control experiment in which the gentamicin treatment step was replaced by simply feeding on the sucrose solution: the bacterial titer decreased in the hemolymph at the same rate as for gentamicin-treated flies (new Fig. S1F), demonstrating that ingested gentamicin does not contribute to the decreased titer. Rather, the likely depletion of the “source”, that is PAO1 in the gut lumen, best accounts for this phenomenon.

We have now cited references which document a lack of permeability of the gut barrier to ingested gentamicin in vertebrate animals (lines 130-133).

As regards the possibility that gentamicin acts as a stress factor on bacteria, our data do not support this possibility, as a latent infection is established in the absence of gentamicin by just feeding the flies on a sucrose solution. We had previously reported that flies fed with P. aeruginosa for up to three days do not succumb within the next two weeks when they are fed on a sterile sucrose solution after having ingested the bacterial solution (Limmer et al., PNAS, 2011; Fig. 1C). Under the conditions of two days of PAO1 ingestion, we document in novel Fig. S1G that the carcass is equally well colonized under these conditions.

Figure: impact of gentamicin ingestion at diverse concentrations on the survival of injected PAO1 bacteria. The ingested antibiotics can act on bacteria present in the hemocoel at concentrations over 2 mg/mL and not at that used in our experiments (100 µg/mL).

Does melanization really induce the dormant state of the bacteria? I am not sure the provided data fully support this claim. Addressing these questions might provide a stronger evidence: Fig. 2 A-F: What causes the morphological changes of the bacteria? Melanization or the passage through the gut? Do authors see the same changes in bacteria retrieved from PO-deficient mutant flies? Fig. 2G: Do the authors see the same resistance of PAO1 that colonized PO mutant flies to antibiotics?

In a novel Fig. S4, we now document comprehensively the physiological state of PAO1 bacteria fed to PO-deficient flies. We find that these bacteria are susceptible to antibiotics treatment as they can be rescued from death by the injection of antibiotics on day 3 (Fig. S4A-B). Second, they show a mixed phenotype in terms of colony morphology (Fig. S4C) and bacterial morphology and cell wall properties: even though most bacteria appeared to have kept a rounded morphology, they predominantly (about 75%) expressed the O5-LPS antigen. We interpret these data in terms of a slower transition to virulence than in a septic injury model. Melanization thus strongly contributes to the establishment of latency, even though it is likely that other factors contribute to the establishment of dormancy, but at best provide a minor contribution.

How do PO mutants behave after PAO1 injection? Are they similarly more susceptible?

PPO1-PPO2 mutants are not more susceptible to PAO1 injection than wt controls (new Fig. S3C).

Fig. 3F: PPO1 is believed to be the fast-acting PPO, whereas PPO2 is deployed later in infection.

This statement is based on experimental data gained on larvae, not adults. It is not really clear whether the about 10% adult hemocytes that express PPO2 actually contain crystals, in as much as the adult may be better oxygenated than larvae that grow in a hypoxic environment (description by the laboratory of Prof. Jiwon Shim of a role for PO in respiration at the latest EDRC meeting).

How does the Western blot look for PPO1? Will it show an early induction of melanization that could drive the change into the dormant state?

We provide below a characterization of the PPO antibody we use by Western blot analysis. This antibody had originally been raised by the late Dr. Hans-Michael Müller against a PPO from mosquito cell lines, hence explaining its cross-reaction to both * Drosophila PPO1 and PPO2 (Muller, H.M., Dimopoulos, G., Blass, C., and Kafatos, F.C. (1999)). A hemocyte-like cell line established from the malaria vector Anopheles gambiae expresses six prophenoloxidase genes. J Biol Chem 274*, 11727-11735.). It follows that at least one PO is partially cleaved at day 2 and that both are fully cleaved by day6 of the establishment of the latent infection (Fig. 3F, Fig. S3F).

Figure: characterization of the antibody raised against A. gambiae PPO

Alternatively, the induction of melanization could also be measured with an L-DOPA test.

This experiment is not needed given the explanation provided above.

Fig. 3E: Melanization prevents the growth of PAO1 adhering to tissues, as shown in Fig. 3E. One can see higher levels of cfus in the carcass in PO deficient flies compared to wt flies. However, after, 5 days, there is no difference in the cfus of wt and mutant flies anymore. If the growth inhibition was melanization mediated, would we not expect a consistent growth of bacteria in PO mutants? How to explain the drop in cfus in PO deficient mutants?

This observation is difficult to account for and the explanations we can put forward at this stage are somewhat speculative. It appears that bacteria found in the tissues in PO-deficient flies have a morphology found in in vitro culture and within the gut, which does not correlate with virulence but also not with the avirulence state since they are LPS O5 positive. Given the shallow survival curves, we envision that there is a progressive release of bacteria from the tissue and then quick proliferation in the hemolymph in a few flies that would then die, but at a frequency too low to reliably ascertain in our hemolymph titer data, with a few flies displaying a high titer (Fig. 3D). By day5, the decreased titer in the carcass may reflect the progressive depletion of tissue-associated bacteria as they progressively become planktonic.

Fig. 5D: How do PAO1 bacteria react to Levofloxacin treatment? Do they still go into the dormant state? Do they still attach to tissues? The authors should show that Levofloxacin treatment leads to the same dormant state as gentamycin before interpreting the results of this experiment.

Taken together, our data yield a mixed result. When levofloxacin was fed for two days to latently-infected flies, we found that colonization was not altered (Fig. S2D’), in contrast to a septic injury model in which injected bacteria were susceptible to the ingested antibiotics (Fig. S2D”). Following the reviewer’s query, we have further monitored survival and bacterial colonization in the levofloxacin ingestion model. Fig. S2D had already demonstrated that ingested levofloxacin protects the flies from injected PAO1. Fig. S6F shows that the double mutant PO bacteria are protected from ingested PAO1 by the ingestion of this antibiotics. When we monitored the bacterial burden, we found for both wild-type and double PO mutant flies that the bacteria had been cleared in some 50% of the flies. The exact interpretation of the wild-type data is not straightforward. On the one hand, the colonizing bacteria may have become susceptible to the antibiotics even though they remained dormant. On the other hand, they might have been reactivated in their virulence and thus become secondarily susceptible to the antibiotics. For the double PO mutants, the 40% bacteria remaining may witness the mixed bacterial state of PAO1 in these mutants, as documented in Fig. S4. Nevertheless, the important point is that bacteria are unlikely to contribute to the demise of secondarily infected flies since they have been cleared in at least 50% of the flies, yet the secondarily challenged flies become susceptible only when the relevant melanization genes are affected. The nonPAO1-infected controls succumb faster to the infection than infected ones: the protection against secondary infections is provided by the activation of the melanization cascade by colonizing PAO1 bacteria, even if the colonization is transient in the levofloxacin treatment.

We have altered the main text to reflect these novel data: lines 387-403.

Minor comments:

Lines 68-72. Mechanisms that are listed are not specific against Gram-negative bacteria but rather general. Please correct.

We are of course aware of this. If it is general, it also applies to Gram-negative bacteria that are the focus of this article. Actually, an earlier version of the manuscript just mentioned the IMD pathway, hence the reference to Gram-negative bacteria. However, the Toll pathway is also required in the host defense against some Gram-negative pathogens such as P. aeruginosa. We have now deleted “Gram-negative” in this corrected version.

Line 95. In - as?

We are not sure we understand this comment. We have now added a reference documenting that P. aeruginosa can be found in rotting fruits (line 97).

Lines 182-187. Some background information is needed. What is O5 LPS antigen? What motivated the authors to look at it specifically?

The O-antigen is a long-chain polysaccharide motif that constitutes the outermost part of the cell wall. It varies according to the strain. We have added a couple of references that refer to O-antigen (line 198). We had actually already found out this result (unpublished) with the Serratia marcescens Db11 O-antigen (O18) that was not found in bacteria that had crossed the gut. The loss of the O5 antigen changes the surface of the bacterium and likely its interactions with tissues and/or the immune system. In the case of Serratia, we suspect that the loss of its O-antigen allows the bacterium to be phagocytosed in an eater-dependent manner.

Fig. 3C: Why PPO1 and Hayan and PPO1,2 and Sp7 are compared but not mutant vs wild type?

The reason is that it was obviously significant. We have now added the comparisons to wild-type in the revised figure.

How precise is estimation of bacteria in the carcass?

Even though it is not possible to measure how precise these measures are, they are nevertheless reproducible making us confident that they provide an estimate of the rough number of these bacteria found associated to tissues.

How do the authors prevent dissemination of the bacteria during dissection? I wonder if some bacteria might by lost during the dissection (when removing the gut and ovaries) or if you carry over some bacteria from the hemolymph into the carcass measurement? How to make sure, that the bacteria you recover were really adherent and were not leftover from the hemolymph?

It is not possible to prevent dissemination as we cannot fix the tissues and bacteria if we make cfu counts. However, the finding that bacteria are found in the hemolymph only for the first three days, with a distinct morphology from tissue-associated bacteria, and not at later time points make us confident that this is not an issue, which suggests that the bacteria are rather tightly attached to the tissues. As regards contamination of tissues by hemolymph, it is also not an issue since the hemolymph titers are so low. However, when the bacteria are actively proliferating to high levels, this is a legitimate concern.

I am also curious how the differences in the cfu levels between whole fly and carcass can be explained (Fig. 1D). After day 5 there are almost no bacteria left in the hemolymph, however, if you compare cfus in the whole fly vs. the carcass, one can see that the whole fly cfus are rising from day 4 onwards. Where do these bacteria come from if not from the hemolymph?

To assess the statement of the reviewer, we now have included the numerical values of the medians of the bacterial burdens displayed in Fig. 1D. There is no increased bacterial burden in whole flies between days 5 to 12; however, the titer is increased at days 15 and 22. Whether this slight increase is biologically relevant is questionable given the spread of the data (see also reply to previous point on the precision of measures). We cannot rigorously exclude that there might be a low-level proliferation of colonizing bacteria late in the latent infection, which has been observed in specific conditions of reactivation of dormant bacteria (Lin et al., in preparation).

Fig. S4D: If the protection to secondary PAO1 infection is not mediated via Imd or phagocytosis, is it mediated via melanization? How do melanization mutants (increased or decreased) respond to PAO1 secondary infection?

We have performed the experiment (Fig. S6A-B) and found that the protection was abrogated. As noted in the main text, the interpretation is however difficult since the bacteria are no longer in a dormancy state in the PPO mutants.

Significance

This study suggests that host factors, particularly specific immune responses, could drive the latent infections. Hence, besides bacterial mechanisms that received significant attention, we should not underestimate the host's contribution to promoting the latent state in bacteria.

Reviewer #1 (Public Review):

Summary: This work is an extension of their earlier work published in Sci Adv in 2021, wherein they showed that DTD2 deacylates N-ethyl-D-aminoacyl-tRNAs arising from acetaldehyde toxicity. The authors (Kumar et al.) in this study, investigate the role of archaeal/plant DTD2 in the deacylation/detoxification of D-Tyr-tRNATyr modified by multiple other aldehydes and methylglyoxal (produced by plants). Importantly, the authors take their biochemical observations to plants, to show that deletion of DTD2 gene from a model plant (Arabidopsis thaliana) makes them sensitive to the aldehyde supplementation in the media especially in the presence of D-Tyr. These conclusions are further supported by the observation that the model plant shows increased tolerance to the aldehyde stress when DTD2 is overproduced from the CaMV 35S promoter. The authors propose a model for the role of DTD2 in the evolution of land plants. Finally, the authors suggest that the transgenic crops carrying DTD2 may offer a strategy for stress-tolerant crop development. Overall, the authors present a convincing story, and the data are supportive of the central theme of the story.

Strengths: Data are novel and they provide a new perspective on the role of DTD2, and propose possible use of the DTD2 lines in crop improvement.

Weaknesses: (a) Data obtained from a single aminoacyl-tRNA (D-Tyr-tRNATyr) have been generalized to imply that what is relevant to this model substrate is true for all other D-aa-tRNAs (term modified aa-tRNAs has been used synonymously with the modified Tyr-tRNATyr). This is not a risk-free extrapolation. For example, the authors see that DTD2 removes modified D-Tyr from tRNATyr in a chain-length dependent manner of the modifier. Why do the authors believe that the length of the amino acid side chain will not matter in the activity of DTD2? (b) While the use of EFTu supports that the ternary complex formation by the elongation factor can resist modifications of L-Tyr-tRNATyr by the aldehydes or other agents, in the context of the present work on the role of DTD2 in plants, one would want to see the data using eEF1alpha. This is particularly relevant because there are likely to be differences in the way EFTu and eEF1alpha may protect aminoacyl-tRNAs (for example see description in the latter half of the article by Wolfson and Knight 2005, FEBS Letters 579, 3467-3472).

Note added after revision: The authors have addressed all my concerns by doing additional experiments and by providing convincing arguments. I am happy to conclude that all my concerns on the weaknesses of the work have been nicely addressed. The already convincing story is now stronger.

Author Response

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

The reviewers make some suggestions aimed towards increasing the clarity of the manuscript, and I suggest that the authors examine those carefully. In particular, the figure is difficult to read and could contain additional information to help the reader's interpretation. For example, Reviewer 1 suggests including sample age estimates alongside depth, while Reviewer 3 also notes that there is missing information in the figure. Apart from the figure, Reviewer 1 suggests two additional analysis to help explain the amount of mammoth DNA recovered, which they observe is much higher than previous similar investigations. This would seem to be an important issue to address, given the surprising nature of the findings. In addition to this larger issue, the Reviewer makes a few important suggestions for supplementary material that may be needed to support the authors' statements.

Some additional recommended edits -- in particular to the text and included references to related studies -- are suggested by Reviewers 2 and 3, and both commented on the lack of a publicly-available data repository. The authors may also wish to comment on or revisit their differential treatment of wooly mammoth vs. wooly rhinoceros samples, though I suspect this has more to do with low read numbers for the rhinos.

Thank you very much for the positive assessment of our manuscript and clear suggestions for revision. We address these points below.

Reviewer #1 (Recommendations For The Authors):

I have a few suggestions that might further improve the manuscript:

It is difficult for the reader to follow which core slices exactly have been sampled and sequenced. The authors mention 23 samples were taken from core LK-001 and 16 samples from core LK-007. From the text it remains unclear to me what the exact age of each of these samples is. Figure 1 shows the depth at which the LK-001 core was sampled, maybe sample age estimates could be included here.

Thanks for pointing this out. We have added approximate ages to Figure 1, added the depth range to the text (“from 1.5 to 80 cm”; l. 73-74, caption Figure 1), and reworked the table of the sampling depths in the supplement.

Line 84-87. The authors mention the retrieval of DNA from several expected Arctic taxa, however no further data regarding these findings is given in the manuscript. It would be useful to report the same numbers for these species as the ones given for the Mammuthus and woolly rhinoceros, which would allow for a comparison of the relative abundance of the DNA between these species. Are the expected Arctic species for instance at much higher (DNA) abundance in the samples? It would also be interesting to know if the authors discovered DNA from extant species that are unlikely to have occurred in the geographic region. A (supplementary)table listing the number of mapped reads to each of the respective mitogenomes for each sequence library would be useful for the reader.

We added a supplementary table (S8) indicating the numbers of reads assigned to mammals.

Line 90: I am somewhat amazed by the amount of mammoth DNA the authors recovered from these cores. A total depth of over 400X of the mitogenome is quite extraordinary and I am not aware of any ancient sediment study to date that has retrieved a similar amount of data. For instance, the Wang et al. 2021 paper, which the authors cite, sequenced over 400 samples and did not find any mammoth DNA in 70% of those. For the 30% of samples showing signs of mammoth DNA they retrieved on average 530 sequence reads. In this study the authors find on average ~20.000 reads, in 22 out of the 23 sequence libraries. This makes me wonder if the way the mapping was performed has been too lenient, resulting in possible spurious mappings? To really confirm the authenticity of the mammoth (and woolly rhino data) I would suggest two additional analysis:

1) Mapping all the sequence libraries to a reference consisting of the complete Asian-elephant genome (for instance https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_024166365.1/), the complete human genome (+mitogenome) and the Asian elephant mitogenome. This could possibly reduce spurious mappings as conserved regions between the genomes are filtered out and could also reduce the possible mapping of NUMTS. If the authors could show that after such a mapping approach a significant number of reads are still assigned to the Asian elephant part (including the mitogenome) of the reference, the reported findings would be strengthened.

2) I also suggest to construct a mitochondrial haplotype network from the obtained DNA, while also including previously published Asian and African elephants as well as previously published mammoth mitogenomes. If the obtained haplotypes indeed show that they cluster within the known haplotype diversity of mammoth, that would be strong support for the authenticity of the data

The same analysis could be considered for the woolly rhino data, although the lower read numbers might make this analysis challenging.

We agree that the amount of mammoth DNA is surprising, which is why we opted for further laboratory experiments for confirmation of the hybridization capture results of the first core, i.e., 1) DNA extraction from a second core of a different lake, 2) a quantitative PCR approach (ddPCR), and 3) metabarcoding. Our results of the highly specific ddPCR and metabarcoding assays confirmed considerable amounts of mammoth DNA in two sediment cores of different lakes, thus we have no doubts regarding the authenticity of the data. Considering the large amount of mammoth DNA, the high number of reads, and particularly the high mitogenome coverage, we argue that the effect of some spurious mapping is negligible and does not affect the main outcome and conclusions of our study. Although we agree that a haplotype network would be interesting, such analyses would stretch beyond the focus of this publication.

Line 91: The authors mention negative controls (extraction and library blanks) did not produce any reads assigned to mammals. This is quite remarkable, as in my experience low levels of (human)contamination are almost always present in the blanks. Could the authors comment on why they think the blanks did not show any signal of mammalian DNA?

The hybridization capture enrichment and the filtration and mapping procedures likely eliminated human contamination. Also, the data were mapped against Arctic mammal mitogenomes, which did not include human reference sequences. However, six of the sediment samples contained human sequences (now shown in supplementary table S8), albeit at low read counts (mean = 65)

Line 97: "mapping suggested that the sequences throughout the core originated from multiple individuals" The authors do not provide any supporting data showing this. I think that an analysis (for instance based on allele frequencies) has to be included in manuscript to support this claim.

We agree that his claim was not sufficiently supported. We performed further analyses including genomic data of previously retrieved mammoth remains and assigned our data to these haplogroups; the results were added to the main text and are shown as a figure (Fig. 2).

Line 98: "Signatures of post-mortem DNA decay were comparably minor."

Do the authors know if the used hybridisation enrichment method can distort the measurement of post-mortem damage? Are for instance reads with C-T substitutions less likely to be captured by the baits?

To our knowledge, there is no study suggesting that damaged sites are less likely to be captured. In general, the hybridization capture procedure is not overly specific, and studies report that DNA is readily and preferentially captured as long as the difference between baits and DNA is not above 10%.

Line 100: "The proportions of bases did not suggest a substantial deviation from those in the reference genomes or in the closest extant relative of Mammuthus, the Asian elephant (Elephas maximus)."

It is not clear to me what the authors mean by this. Could the authors explain how this was measured and what their interpretation of this result is?

We realize that the sentence was unclear. We meant that the nucleotide composition was similar to that of the reference genomes or the closest extant relative. However, as we do not consider this important for the argument, we have removed this sentence from the manuscript.

Given the high number of recovered mammoth reads in the samples, it would be interesting to know how much mammoth reads are present in the sample before enrichment capture with the baits. Shotgun sequencing the raw extract of one of the samples with the highest number of mammoth reads might allow for a rough estimate of mammoth DNA abundance compared to the other extant species (e.g. reindeer, Arctic lemming and hare) found in the sample(s). This could give further clarification about the extent of stratigraphy disturbance and its overall effect on the DNA based community reconstruction. However, this is just a suggested additional analysis and not something I believe crucial for supporting the overall findings in this manuscript.

We fully agree that this would be a highly interesting and informative additional analysis to perform. It was, however, not possible to perform this additional analyses in the course of the current experiments.

Finally, I could not find a public link to the (sequence)data produced in this study. I strongly encourage the authors to make their data publicly available.

Thank you for pointing this out. We have added a Data Availability paragraph, including the respective reference.

Reviewer #2 (Recommendations For The Authors):

In the Discussion it is mentioned that the reasons for Mammoth extinction are not entirely clear but are largely attributed to sudden climate warming (and add some relevant citations). However, there is also abundant literature that suggest humans also played a role in their extinction (for instance, a recent one, Damien et al. (2022) at Ecology Letters 25: 127-137).

We agree with the reviewer and have added some the recent citation highlighting the possible influence of humans.

One possibility to add further interest to this paper would be to conduct a phylogenetic tree with the Mammoth mitogenome(s) retrieved and a reference dataset; it could be interesting to know where do they fall in the phylogeny -already abundant with tens of individuals- and maybe it could be even possible to roughly estimate their date. There are some papers that report many Mammoth mitogenomes, including of course some from Siberia; for instance Chang et al. (2017) at Sci Reports and also Fellow Yates et al. (2017) also at Sci Reports (the latter mainly from Central Europe).

We are well aware of the amount of mt genomes available for mammoth, and such an analyses would be an interesting addition, potentially also offering the possibility to date the DNA. However, the analyses was hampered and would be less secure for this dataset, as our sequences display quite some variation among each other, suggesting that we have a mix of multiple mt genomes, which we cannot readily distinguish. We thus refrain from this, also because we instead provide multiple lines of evidence for the existence of the mammoth DNA in the surface sediment core (metabarcoding, ddPCR).

Minor points:

-Correct wooly to woolly

Revised.

-In the sampling description it is not totally clear if the samples were taken at 1 cm each (it is mentioned that core LK-001 is sliced in the field at 1-cm steps for radiometric dating and later it is explained that 23 samples were analyzed from this core, but it is unclear if they represent 23 cm of core)

-Maybe the authors could briefly define some terms such as "talik"

Revised.

Reviewer #3 (Recommendations For The Authors):

Maybe I missed this but I could not find a data availability statement or the location of the repository

We have added a Data Availability paragraph, including the respective reference.

It would be good to see some additional analysis on the distribution of the woolly rhinoceros DNA through the sediment core - like the figure for the mammoth i.e read numbers vs depth.

We have added to the supplements a table showing the numbers of assigned mammal reads over the core depths (Table S8). However, as rhinoceros reads are considerable rarer in our results, we did not produce a figure.

Would it be possible to be more explicit about the multiple mammoth individuals, could you calculate a minimum number or haplotypes for example.

We agree that his claim was not sufficiently supported and added results from additional analyses (incl. Fig. 2). Please see our response above.

Based on the aim stated in the introduction, the analysis of the Arctic biodiversity of this area is missing, it would be nice to see these result added or maybe the focus needs to be changed for clarity.

We now explicitly state that this objective pertains to a different study, which is currently still in preparation for publication.

The single main figure needs a bit more consideration. For example in panel A - there was no information on the transformation performed or what the general trend line refers to. Do the results in panel B refer to all 22 libraries? What is the x-axis in Panel C and what do the coloured lines refer to? Additionally, I think the figure needs to be in higher resolution with increased text size on all axes.

We revised the figure and the caption for clarity and readability.

Finally this might be an accidental typo - but when referring to the sample aged at around 8,677 years in text it states this the 36.5 cm sample (line 130 and 192), but the supplementary says this is the 51cm sample (Table S6). This would maybe impact potential conclusions. Would you be able to clarify this.

Thank you for noting this error, we revised it.

Based on Du Bois’s chapter, provide three examples of post-Civil War lawlessness and anti-Black violence, disenfranchisement, and economic exploitation between 1865 and 1877.

What are some of the major problems with the Dunning school interpretation of Reconstruction?

What does it overlook and misrepresent?

These are the scale sets on my K+E 4181-3 slide Rule

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The present study provides a phylogenetic analysis of the size prefrontal areas in primates, aiming to investigate whether relative size of the rostral prefrontal cortex (frontal pole) and dorsolateral prefrontal cortex volume vary according to known ecological or social variables.

I am very much in favor of the general approach taken in this study. Neuroimaging now allows us to obtain more detailed anatomical data in a much larger range of species than ever before and this study shows the questions that can be asked using these types of data. In general, the study is conducted with care, focusing on anatomical precision in definition of the cortical areas and using appropriate statistical techniques, such as PGLS. That said, there are some points where I feel the authors could have taken their care a bit further and, as a result, inform the community even more about what is in their data.

We thank the reviewer for this globally positive evaluation of our work, and we appreciate the advices to improve our manuscript.

The introduction sets up the contrast of 'ecological' (mostly foraging) and social variables of a primate's life that can be reflected in the relative size of brain regions. This debate is for a large part a relic of the literature and the authors themselves state in a number of places that perhaps the contrast is a bit artificial. I feel that they could go further in this. Social behavior could easily be a solution to foraging problems, making them variables that are not in competition, but simply different levels of explanation. This point has been made in some of the recent work by Robin Dunbar and Susanne Shultz.

Thank you for this constructive comment, and we acknowledge that the contrast between social vs ecological brain is relatively marginal here. Based also on the first remark by reviewer 3, we have reformulated the introduction to emphasize what we think is actually more critical: the link between cognitive functions as defined in laboratory conditions and socio-ecological variables measured in natural conditions. And the fact that here, we use brain measures as a potential tool to relate these laboratory vs natural variables through a common scenario. Also, we were already mentioning the potential interaction between social and foraging processes in the discussion, but we are happy to add a reference to recent studies by S. Shultz and R. Dunbar (2022), which is indeed directly relevant. We thank the reviewer for pointing out this literature.

In a similar vein, the hypotheses of relating frontal pole to 'meta-cognition' and dorsolateral PFC to 'working memory' is a dramatic oversimplification of the complexity of cognitive function and does a disservice to the careful approach of the rest of the manuscript.

We agree that the formulation of which functions we were attributing to the distinct brain regions might not have been clear enough, but the functional relation between frontal pole and metacognition in the one hand, and DLPFC and working memory on the other hand, have been firmly established in the literature, both through laboratory studies and through clinical data. Clearly, no single brain region is necessary and sufficient for any cognitive operation, but decades of neuropsychology have demonstrated the differential implication of distinct brain regions in distinct functions, which is all we mean here. We have made a specific point on that topic in the discussion (cf p. 16). We have also reformulated the introduction to clarify that, even if the relation between these regions and their functions (FP/ metacognition; DLPFC/ working memory) was clear in laboratory conditions, it was not clear whether this mapping could be used for real life conditions. And therefore whether that simplification was somehow justified beyond the lab (and the clinics), and whether these neuro-cognitive concepts could be applied to natural conditions, are indeed critical questions that we wanted to address. The central goal of the present study was precisely to evaluate the extent to which this brain/cognition relation could be used to understand more natural behaviors and functions, and we hope that it appears more clearly now.

One can also question the predicted relationship between frontal pole meta-cognition and social abilities versus foraging, as Passingham and Wise show in their 2012 book that it is frontal pole size that correlates with learning ability-an argument that they used to relate this part of the brain to foraging abilities. I would strongly suggest the authors refrain from using such descriptive terms. Why not simply use the names of the variables actually showing significant correlations with relative size of the areas?

We basically agree with the reviewer, and we acknowledge the lack of clarity in the introduction of the previous manuscript. There were indeed lots of ambiguity in what we were referring to as ‘function’, associated with a given brain region. « Function » referred to way to many things! We have reformulated the introduction not only to clarify the different types of functions that were attributed to distinct brain regions in the literature but also to clarify how this study was addressing the question: by trying to articulate concepts from neuroscience laboratory studies with concepts from behavioral ecology and evolution using intuitive scenarios. We hope that the present version of the introduction makes that point clearer.

The major methodological judgements in this paper are of course in the delineation of the frontal pole and dorsolateral prefrontal cortex. As I said above, I appreciate how carefully the authors describe their anatomical procedure, allowing researchers to replicate and extend their work. They are also careful not to relate their regions of interest to precise cytoarchitectonic areas, as such a claim would be impossible to make without more evidence. That said, there is a judgement call made in using the principal sulcus as a boundary defining landmark for FP in monkeys and the superior frontal sulcus in apes. I do not believe that these sulci are homologous. Indeed, the authors themselves go on to argue that dorsolateral prefrontal cortex, where studied using cytoarchitecture, stretches to the fundus of principal sulcus in monkeys, but all the way to the inferior frontal sulcus in apes. That means that using the fundus of PS is not a good landmark.

We thank the reviewer for his kind remarks on our careful descriptions. But then, it is not clear whether our choice of using the principal sulcus as a boundary for FP in monkeys vs the superior frontal sulcus in apes is actually a judgement call. First, and foremost, there is no clear and unambiguous definition of what should be the boundaries of the FP. By contrast with cytoarchitectonic maps, but clearly this is out of reach here. In humans and great apes we used Bludau et al 2014 (i.e. sup frontal sulcus), and in monkeys, we chose a conservative landmark that eliminated area 9, which is traditionally associated with the DLPFC (Petrides, 2005; Petrides et al, 2012; Semendeferi et al, 2001).

Of course, any definition will attract criticism, so the best solution might be to run the analysis multiple times, using different definitions for the areas, and see how this affects results.

Indeed, functional maps indicate that dorsal part of anterior PFC in monkeys is functionally part of FP. But again, cytoarchitectonic maps also indicate that this part of the brain includes BA 9, which is traditionally associated with DLPFC (Petrides, 2005; Petrides et al, 2012). As already pointed out in the discussion, there is a functional continuum between FP and DLPFC and our goal when using PS as dorsal border was to be very conservative and to exclude the ambiguous area. But we agree with the reviewer that given that this decision is arbitrary, it was worth exploring other definitions of the FP volume. So, we did complete a new analysis with a less conservative definition of the FP, to include this ambiguous dorsal area, and it is now included in the supplementary material. Maybe as expected, including the ambiguous area in the FP volume shifted the relation with socio-ecological variables towards the pattern displayed by the DLPFC (ie the influence of population density decreased). The most parsimonious interpretation of this results is that when extending the border of the FP region to cover a part of the brain which might belong to the DLPFC, or which might be somehow functionally intermediate between the 2, the specific relation of the FP with socio-ecological variables decreases. Thus, even if we agree that it was important to conduct this analysis, we believe that it only confirms the difficulty to identify a clear boundary between FP and DLPFC. Again, we have clearly explained throughout the manuscript that we admit the lack of precision in our definitions of the functional brain regions. In that frame, the conservative option seems more appropriate and for the sake of clarity, the results of the additional analysis of a FP volume that includes the ambiguous area is only included in the supplementary material.

If I understand correctly, the PGLS was run separately for the three brain measure (whole brain, FP, DLPFC). However, given that the measures are so highly correlated, is there an argument for an analysis that allows testing on residuals. In other words, to test effects of relative size of FP and DLPFC over and above brain size?

Generally, using residuals as “data” (or pseudo-data) is not recommended in statistical analyses. Two widely cited references from the ecological literature are:

Garcia-Berthou E. (2001) On the Misuse of Residuals in Ecology: Testing Regression Residuals vs. the Analysis of Covariance. Journal of Animal Ecology, 70 (4): 708-711.

Freckleton RP. (2002). On the misuse of residuals in ecology: regression of residuals vs. multiple regression. Journal of Animal Ecology 71: 542–545. https://doi.org/10.1046/ j.1365-2656.2002.00618.x.

The main reason for this recommendation is that residuals are dependent on the fitted model, and thus on the particular sample under consideration and the eventual significant effects that can be inferred.

In the discussion and introduction, the authors discuss how size of the area is a proxy for number of neurons. However, as shown by Herculano-Houzel, this assumption does not hold across species. Across monkeys and apes, for instance, there is a different in how many neurons can be packed per volume of brain. There is even earlier work from Semendeferi showing how frontal pole especially shows distinct neuron-to-volume ratios.

We appreciate the reviewer’s comment, but the references to Herculano-Houzel that we have in mind do indicate that the assumption is legitimate within primates.

Herculano-Houzel et al (2007) show that the neuronal density of the cortex is well conserved across primate species (but only monkeys were studied). The conclusion of that study is that using volumes as a proxy for number of neurons, as a measure of computational capacity, should be avoided between rodents and primates (and as they showed later, even more so with birds, for which neuronal density is higher). BUT within primates, since neuronal densities are conserved, volume is a good predictor of number of neurons. Gabi et al (2016) provide evidence that the neuronal density of the PFC is well conserved between humans and non-human primates, which implies that including humans and great apes in the comparison is legitimate. In addition, the brain regions included in the analysis presumably include very similar architectonic regions (e.g. BA 10 for FP, BA 9/46 for DLPFC), which also suggests that the neuronal density should be relatively well conserved across species. Altogether, we believe that there is sufficient evidence to support the idea that the volume of a PFC region in primates is a good proxy for the number of neurons in that region, and therefore of its computational capacity.

Semendeferi and colleagues (2001) pointed out some differences in cytoarchitectonic properties across parts of the FP and discussed how these properties could 1) be used to identify area 10 across species 2) be associated with distinct computational properties, with the idea that thicker ‘cell body free’ layers would leave more space for establishing connections (across dendrites and axons). This pioneering work, together with more recent imaging studies on functional connectivity (e.g. Sallet et al, 2013) emphasize the critical contribution of connectivity pattern as a tool for comparative anatomy. But unfortunately, as pointed out in the discussion already, this is currently out of reach for us.

We acknowledge the limitations, and to be fair, the notion of computational capacity itself is hard to define operationally. Based on the work of Herculano-Houzel et al, average density is conserved enough across primates (including humans) to justify our approximation. We have tried to define our regions of interest using both anatomical and functional maps and, thanks to the reviewer’s suggestions, we even tried several ways to segment these regions. Functional maps in macaques and humans do not exactly match cytoarchitectonic maps, presumably because functions rely not only upon the cytoarchitectonics but also on connectivity patterns (e.g. Sallet et al, 2013).

In sum, we appreciate the reviewer’s point but feel that, given the current understanding of brain functions and the relative conservation of neuronal density across primate PFC regions, the volume of a PFC region seems to be reasonable proxy for its number of neurons, and therefore its computational capacity. We have added these points to the discussions, and we hope that the reader will be able to get a fair sense of how legitimate is that position, given the literature.

Overall, I think this is a very valuable approach and the study demonstrates what can now be achieved in evolutionary neuroscience. I do believe that they authors can be even more thorough and precise in their measurements and claims.

Reviewer #2 (Public Review):

In the manuscript entitled "Linking the evolution of two prefrontal brain regions to social and foraging challenges in primates" the authors measure the volume of the frontal pole (FP, related to metacognition) and the dorsolateral prefrontal cortex (DLPFC, related to working memory) in 16 primate species to evaluate the influence of socio-ecological factors on the size of these cortical regions. The authors select 11 socio-ecological variables and use a phylogenetic generalized least squares (PGLS) approach to evaluate the joint influence of these socio-ecological variables on the neuro-anatomical variability of FP and DLPFC across the 16 selected primate species; in this way, the authors take into account the phylogenetic relations across primate species in their attempt to discover the influence of socio-ecological variables on FP and DLPF evolution.

The authors run their studies on brains collected from 1920 to 1970 and preserved in formalin solution. Also, they obtained data from the Mussée National d´Histoire Naturelle in Paris and from the Allen Brain Institute in California. The main findings consist in showing that the volume of the FP, the DLPFC, and the Rest of the Brain (ROB) across the 16 selected primate species is related to three socio-ecological variables: body mass, daily traveled distance, and population density. The authors conclude that metacognition and working memory are critical for foraging in primates and that FP volume is more sensitive to social constraints than DLPFC volume.

The topic addressed in the present manuscript is relevant for understanding human brain evolution from the point of view of primate research, which, unfortunately, is a shrinking field in neuroscience.

We must not have been clear enough in our manuscript, because our goal is precisely not to separate humans from other primates. This is why, in contrast to other studies, we have included human and non-human primates in the same models. If our goal had been to study human evolution, we would have included fossil data (endocasts) from the human lineage.

But the experimental design has two major weak points: the absence of lissencephalic primates among the selected species and the delimitation of FP and DLPFC. Also, a general theoretical and experimental frame linking evolution (phylogeny) and development (ontogeny) is lacking.

We admit that lissencephalic species could not be included in this study because we use sulci as key landmarks. We believe that including lissencephalic primates would have introduced a bias and noise in our comparisons, as the delimitations and landmarks would have been different for gyrencephalic and lissencephalic primates. Concerning development, it is simply beyond the scope of our study.

Major comments.

1) Is the brain modular? Is there modularity in brain evolution?: The entire manuscript is organized around the idea that the brain is a mosaic of units that have separate evolutionary trajectories:

"In terms of evolution, the functional heterogeneity of distinct brain regions is captured by the notion of 'mosaic brain', where distinct brain regions could show a specific relation with various socio-ecological challenges, and therefore have relatively separate evolutionary trajectories".

This hypothesis is problematic for several reasons. One of them is that each evolutionary module of the brain mosaic should originate in embryological development from a defined progenitor (or progenitors) domain [see García-Calero and Puelles (2020)]. Also, each evolutionary module should comprise connections with other modules; in the present case, FP and DLPFC have not evolved alone but in concert with, at least, their corresponding thalamic nuclei and striatal sector. Did those nuclei and sectors also expand across the selected primate species? Can the authors relate FP and DLPFC expansion to a shared progenitor domain across the analyzed species? This would be key to proposing homology hypotheses for FP and DLPFC across the selected species. The authors use all the time the comparative approach but never explicitly their criteria for defining homology of the cerebral cortex sectors analyzed.

We do not understand what the referee is referring to with the word ‘module’, and why it relates to development. Same thing for the anatomical relation with subcortical structures. Yes, the identity of distinct functional cortical regions relies upon subcortical inputs during development, but clearly this is neither technically feasible, nor relevant here anyways.

We acknowledge, however, that our definition of functional regions was not precise enough, and we have updated the introduction to clarify that point. In short, we clearly do not want to make a strong case for the functional borders that we chose for the regions of interest here (FP and DLPFC), but rather use those regions as proxies for their corresponding functions as defined in laboratory conditions for a couple of species (rhesus macaques and humans, essentially).

Contemporary developmental biology has showed that the selection of morphological brain features happens within severe developmental constrains. Thus, the authors need a hypothesis linking the evolutionary expansion of FP and DLPFC during development. Otherwise, the claims form the mosaic brain and modularity lack fundamental support.

Once again, we do not think that our definition of modules matches what the reviewer has in mind, i.e. modules defined by populations of neurons that developed together (e.g. visual thalamic neurons innervating visual cortices, themselves innervating visual thalamic neurons). Rather, the notion of mosaic brain refers to the fact that different parts of the brain are susceptible to distinct (but not necessarily exclusive) sources of selective pressures. The extent to which these ‘developmental’ modules are related to ‘evolutionary’ modules is clearly beyond the scope of this paper.

Our goal here was to evaluate the extent to which modules that were defined based on cognitive operations identified in laboratory conditions could be related (across species) to socio-ecological factors as measured in wild animals. Again, we agree that the way these modules/ functional maps were defined in the paper were confusing, and we hope that the new version of the manuscript makes this point clearer.

Also, the authors refer most of the time to brain regions, which is confusing because they are analyzing cerebral cortex regions.

We do not understand why the term ‘brain’ is more confusing than ‘cerebral cortex’, especially for a wide audience.

2) Definition and delimitation of FP and DLPFC: The precedent questions are also related to the definition and parcellation of FP and DLPFC. How homologous cortical sectors are defined across primate species? And then, how are those sectors parcellated?

The authors delimited the FP:

"...according to different criteria: it should match the functional anatomy for known species (macaques and humans, essentially) and be reliable enough to be applied to other species using macroscopic neuroanatomical landmarks".

There is an implicit homology criterion here: two cortical regions in two primate species are homologs if these regions have similar functional anatomy based on cortico-cortical connections. Also, macroscopic neuroanatomical landmarks serve to limit the homologs across species.

This is highly problematic. First, because similar function means analogy and not necessarily homology [for further explanation see Puelles et al. (2019); García-Cabezas et al. (2022)].

We are not sure to follow the Reviewer’s point here. First, it is not clear what would be the evolutionary scenario implied by this comment (evolutionary divergence followed by reversion leading to convergence?). Second, based on the literature, both the DLPFC and the FP display strong similarities between macaques and humans, in terms of connectivity patterns (Sallet et al, 2013), in terms of lesion-induced deficit and in terms of task-related activity (Mansouri et al, 2017). These criteria are usually sufficient to call 2 regions functionally equivalent. We do not see how this explanation is "highly problematic" as it is clearly the most parsimonious based on our current knowledge.

Second, because there are several lissencephalic primate species; in these primates, like marmosets and squirrel monkeys, the whole approach of the authors could not have been implemented. Should we suppose that lissencephalic primates lack FP or DLPFC?

We understand neither the reviewer’s logic, nor the tone. We understand that the reviewer is concerned by the debate on whether some laboratory species are more relevant than others for studying the human prefrontal cortex, but this is clearly not the objective of our work. As explained in the manuscript, we identified FP and DLPFC based on functional maps in humans and laboratory monkeys (macaques), and we used specific gyri as landmarks that could be reliably used in other species. And, as rightfully pointed out by reviewer 1, this is in and off itself not so trivial. Of course, lissencephalic animals could not be studied because we could not find these landmarks, but why would it mean that they do not have a prefrontal cortex? The reviewer implies that species that we did not study do not have a prefrontal cortex, which makes little sense. Standards in the field of comparative anatomy of the PFC, especially when it implies rodents (lissencephalic also) include cytoarchitectonic and connectivity criteria, but obviously we are not in a position to address it here. We have, however, included references to the seminal work of Angela Roberts and collaborator in the discussion on marmosets prefrontal functions, to reinforce the idea that the functional organization is relatively well conserved across all primates (with or without gyri on their brain) (Dias et al, 1996; Roberts et al, 2007).

Do these primates have significantly more simplistic ways of life than gyrencephalic primates? Marmosets and squirrel monkeys have quite small brains; does it imply that they have not experience the influence of socio-ecological factors on the size of FP, DLPFC, and the rest of the brain?

Again, none of this is relevant here, because we could not draw conclusions on species that we cannot study for methodological reasons. The reviewer seems to believe that an absence of evidence is equivalent to an evidence of absence, but we do not.

The authors state that:

"the strong development of executive functions in species with larger prefrontal cortices is related to an absolute increase in number of neurons, rather than in an increase in the ration between the number of neurons in the PFC vs the rest of the brain".

How does it apply to marmosets and squirrel monkeys?

Again, we do not understand the reviewer’s point, since it is widely admitted that lissencephalic monkeys display both a prefrontal cortex and executive functions (again, see the work of Angela Roberts cited above). Our goal here was certainly not to get into the debate of what is the prefrontal cortex in a handful of laboratory species, but to evaluate the relevance of laboratory based neuro-cognitive concepts for understanding primates in general, and in their natural environment.

References:

García-Cabezas MA, Hacker JL, Zikopoulos B (2022) Homology of neocortical areas in rats and primates based on cortical type analysis: an update of the Hypothesis on the Dual Origin of the Neocortex. Brain structure & function Online ahead of print. doi:doi.org/ 10.1007/s00429-022-02548-0

García-Calero E, Puelles L (2020) Histogenetic radial models as aids to understanding complex brain structures: The amygdalar radial model as a recent example. Front Neuroanat 14:590011. doi:10.3389/fnana.2020.590011

Nieuwenhuys R, Puelles L (2016) Towards a New Neuromorphology. doi:10.1007/978-3-319-25693-1

Puelles L, Alonso A, Garcia-Calero E, Martinez-de-la-Torre M (2019) Concentric ring topology of mammalian cortical sectors and relevance for patterning studies. J Comp Neurol 527 (10):1731-1752. doi:10.1002/cne.24650

Reviewer #3 (Public Review):

This is an interesting manuscript that addresses a longstanding debate in evolutionary biology - whether social or ecological factors are primarily responsible for the evolution of the large human brain. To address this, the authors examine the relationship between the size of two prefrontal regions involved in metacognition and working memory (DLPFC and FP) and socioecological variables across 16 primate species. I recommend major revisions to this manuscript due to: 1) a lack of clarity surrounding model construction; and 2) an inappropriate treatment of the relative importance of different predictors (due to a lack of scaling/normalization of predictor variables prior to analysis). My comments are organized by section below:

We thank the reviewer for the globally positive evaluation and for the constructive remarks. Introduction:

• Well written and thorough, but the questions presented could use restructuring.

Again, we thank the reviewer, and we believe that this is coherent with some of the remarks of reviewer 1. We have extensively revised the introduction, toning down the social vs ecological brain issue to focus more on what is the objective of the work (evaluating the relevance of lab based neuro-cognitive concepts for understanding natural behavior in primates).

Methods:

• It is unclear which combinations of models were compared or why only population density and distance travelled tested appear to have been included.

The details of the model comparison analysis were presented as a table in the supplementary material (#3, details of the model comparison data), but we understand that this was not clear enough. We have provided more explanation both in the main manuscript and in the supplements. All variables were considered a priori; however, we proceeded beforehand to an exploratory analyses which led us to exclude some variables because of their lack of resolution (not enough categories for qualitative variables) or strong cross-correlations with other quantitative variables. There were much more than three variables included in the models but the combination of these 3 (body mass, daily traveled distance and population density) best predicted (had the smallest AIC) the size of the brain regions. We provide additional information about these exploratory analyses in the supplementary material, sections 2 and 3.

• Brain size (vs. body size) should be used as a predictor in the models.

We do not understand the theoretical reason for replacing body size by brain size in the models. Brain size is not a socio-ecological variable. And of course, that would be impossible for modeling brain size itself. Or is it that the reviewer suggests to use brain size as a covariate to evaluate the effects of other variables in the model over and above the effect on brain size? But what is the theoretical basis for this?

• It is not appropriate to compare the impact of different predictors using their coefficients if the variables were not scaled prior to analysis.

We thank the Reviewer for this comment; however, standardized coefficients are not unproblematic because their calculations are based on the estimated standard-deviations of the variables which are likely to be affected by sampling (in effect more than the means). We note that the methods of standardized coefficients have attracted several criticisms in the literature (see the References section in https://en.wikipedia.org/wiki/Standardized_coefficient). Nevertheless, we now provide a table with these coefficients which makes an easy comparison for the present study. We also updated tables 1, 2 and 3 to include standardized beta values.

Reviewer #1 (Recommendations For The Authors):

N/A

Reviewer #2 (Recommendations For The Authors):

Contemporary developmental biology has showed that the brain of all mammals, including primates, develops out of a bauplan (or blueprint) made of several fundamental morphological units that have invariant topological relations across species (Nieuwenhuys and Puelles 2016).

At some point in the discussion the authors acknowledge that:

"Our aim here was clearly not to provide a clear identification of anatomical boundaries across brain regions in individual species, as others have done using much finer neuroanatomical methods. Such a fine neuroanatomical characterization appears impossible to carry on for a sample size of species compatible with PGLS".

I do not think it would be impossible to carry such neuroanatomical characterization. It would take time and effort, but it is feasible. Such characterization, if performed within the framework of contemporary developmental biology, would allow for well-founded definition and delineation of cortical sectors across primate species, including lissencephalic ones, and would allow for meaningful homologies and interspecies comparisons.

We do not see how our work would benefit from developmental biology at that point, because it is concerned with evolution, and these are very distinct biological phenomena. We do not understand the reviewer’s focus on lissencephalic species, because they are not so prevalent across primates, and it is unlikely that adding a couple of lissencephalic species will change much to the conclusions.

Minor points:

• Please, format references according to the instructions of the journal.

Ok - done

• The authors could use the same color code across Figures 1, 2, and 3.

Ok – done

• The authors say that group hunting "only occurs in a few primate species", but it also occurs in wolves, whales, and other mammalian species.

We focus on primates here, these other species are irrelevant. Again, this is beside the point.

Reviewer #3 (Recommendations For The Authors):

My comments are organized by section below:

Introduction:

• Well written and thorough

• The two questions presented towards the end of the intro are not clear and do not guide the structure of the methods/results sections. I believe one it would be more appropriate to ask if: 1) the relative proportions of the FP and DLPFC (relative to ROB) are consistent across primates; and 2) if the relative size of these region is best predicted by social and/ or ecological variables. Then, the results sections could be organized according to these questions (current results section 1 = 1; current results sections 2, 3, 4 = 2.1, 2.2, 2.3)

As explained above, we agree with the reviewer that the introduction was somehow misleading and we have edited it extensively. We do not, however, agree with the reviewer regarding the relative (vs absolute) measure. We have discussed this in our response to reviewer 1 regarding the comparison of regional volumes as proxies for number of neurons. The best predictor of the computing capacity of a brain region is its number of neurons, but there is no reason to believe that this capacity should decrease if the rest of the brain increases, as implied by the relative measure that the reviewer proposes. That debate is probably critical in the field of comparative neuroanatomy, and confronting different perspectives would surely be both interesting and insightful, but we feel that it is beyond the scope of the present article.

Methods:

• While the methods are straightforward and generally well described, it is unclear which combinations of models were compared or why only population density and distance travelled tested appear to have been included (in e.g., Fig SI 3.1) even though many more variables were collected.

We agree that this was not clear enough, and we have tried to improve the description of our model comparison approach, both in the main text and in the supplementary material.

• Why was body mass rather than ROB used as a predictor in the models? The authors should instead/also include analyses using ROB (so the analysis is of FP and DLPFC size relative to brain size). Using body mass confounds the analyses since they will be impacted by differences in brain size relative body size.


Again, we have addressed this issue above. First, body size is a socio-ecological variable (if anything, it especially predicts energetic needs and energy expenditure), but ROB is clearly not. We do not see the theoretical relevance of ROB in a socio-ecological model. Second, from a neurobiological point of view, since within primates the volume of a given brain region is directly related to its number of neurons (again, see work of Herculano-Houzel), which is a good proxy for its computing capacity, we do not see the theoretical reason for considering ROB.

• It is not appropriate to compare the impact of different predictors using their coefficients if the variables were not scaled prior to analysis. The authors need to implement this in their approach to make such claims.

We thank the reviewer again for pointing that out. We have addressed this question above.

• Differences across primates in terms of frontal lobe networks throughout the brain should be acknowledged (e.g., Barrett et al. 2020, J Neurosci).

We have added that reference to the discussion, together with other references showing that the difference between human and non-human primates is significant, but essentially quantitative, rather than qualitative (the building blocks are relatively well conserved, but their relative weight differs a lot). Thank you for pointing it out.

I hope the authors find my comments helpful in revising their manuscript.

And we thank again the reviewer for the helpful and constructive comments.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This well done and interesting paper examining the connection between TXNIP and GDF15. The main thrust is that TXNIP upregulation chemotherapies, such as Oxa, results in an a down regulation of GDF15 early in tumorigenesis. Later in tumorigenesis, TXNIP upregulation is less pronounced, elevating GFP15 resulting in a blockage of tumor suppressive immune responses. Generally the work is convincing. For example, it's clear that TXNIP is up regulated by Oxa in an ROS and MondoA-dependent manner. Likewise its quite clear TXNIP loss reads to an upregulation of GDF15. However, it's also quite clear that Oxa suppresses GDF15 in a manner that appears to be completely independent of TXNIP. The writing in the paper implies strongly that there is a mechanistic connection between TXNIP and GDF15, but no experiments investigate this possibility.

We feel this is very fair and is reflective of a) perhaps an overemphasis of the TXNIP knockout observation and supportive tissue data, which suggests a relationship but not a mechanistic understanding b) an underemphasis of the data in Figure 3 that shows a decrease in GDF15 after oxaliplatin treatment in TXNIP knockout lines.

We have addressed these concerns in several ways:

1. We have carried out knockdown experiments using siRNA for ARRDC4, which we felt, given its regulation by MondoA and ROS, and homology to TXNIP, may also regulate GDF15. This was found to be the case and may explain the data in Figure 3. At the very least it shows that other factors involved in oxidative stress management may have similar impacts – a form of functional redundancy. Lines 553-559 “Finally, given our previous data (Figure S4) we looked to assess the role of ARRDC4 on GDF15 expression. In the absence of oxaliplatin, knocking down ARRDC4 in DLD1 and HCT15 cells drove an increase in GDF15. When challenged with oxaliplatin, both ARRDC4 and TXNIP expression increased and GDF15 decreased. When the ARRDC4 knockdown was challenged TXNIP increased further and GDF15 decreased further (Figure S6G-J). Given the common regulatory pathways and homology between TXNIP and ARRDC4, and their similar functional roles, we suggest these data are evidence of redundancy within this system. “

We have included some context in the discussion:

Lines 930-933: “Further support for both TXNIP and ARRDC4’s role in regulating GDF15 after the induction of ROS comes from a pan cancer meta-analysis assessing the impact of metformin (which has been reported to inhibit ROS) on gene expression. Here the top two downregulated genes were TXNIP and ARRDC4 and the top four upregulated genes were DDIT4, CHD2, ERN1 and GDF1572

We have tempered the text:

Lines 522-524 “It is important to note however that here we saw clear evidence that TXNIP was not solely responsible for the downregulation of GDF15 post oxaliplatin treatment, with decreased levels seen in knockout lines (Figure 3C-G, S5E).”

Lines 926-929 “It must be stressed that these data do not place TXNIP as the sole regulator of GDF15, for example ARRDC4 can also be seen to regulate GDF15. We envisage TXNIP as one of a number of ROS-dependent GDF15 regulators, with this redundancy potential evidence of the importance of this regulatory framework.”

We have carried out additional analysis detailed in the discussion and in Figure S12 which suggests TXNIP impacts MYC function, as reported elsewhere (detailed below). For ease, the key paper can be accessed through this link https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001778

Lines 934-956: “The main shortcoming of this paper is the lack of mechanistic understanding linking TXNIP to GDF15. There are 650 transcription factors that have been shown, or are predicted, to bind to GDF15 promoter and/or enhancer regions. By assessing our list of differentially expressed genes (Suppl. Table 1-2) for the presence of these factors we identified 6 GDF15 binding TFs that show significantly decreased expression after oxaliplatin treatment in both cell lines (ATF4, MYC, SREBF1, PHB2, HBP1, KLF9). There was only one, MYC, that was downregulated by oxaliplatin treatment (validated; Figure S12A), and with this downregulation partially being rescued in a matched TXNIP knockout line (Figure S12B). We then observed that c-myc has been shown or is predicted to bind to promoter/enhancer regions of the top five transcriptomic and proteomic differentials in TXNIP knockout lines, including TXNIP itself (apart from C16orf90). Even with c-myc’s promiscuity (binds to 10-20% of all promoters/enhancers) this may be suggestive of a specific relationship. Finally, when looking at the correlations between these 6 TFs and TXNIP and GDF15 in the TCGA COAD dataset, MYC has the greatest and most significant negative correlation to TXNIP (r=-0.4631 p=1.42e-28) and the greatest and most significant positive correlation to GDF15 (r=0.4653 p=7.32e-29). ATF4 and PHB2 are the other TFs in the list, that show the same significant trends (Figure S12C), and therefore may play a role in the TXNIP-independent oxaliplatin-dependent regulation of GDF15. Further exploration of these additional TFs is outside the scope of the current manuscript.

MYC’s role in bridging from TXNIP to GDF15 is further supported by a recent paper which shows that TXNIP is “a broad repressor of MYC genomic binding” and that “TXNIP loss mimics MYC overexpression”73. Furthermore, the inter-dependent regulatory relationship between MondoA, TXNIP, and MYC has been seen in a variety of models74, whilst the impact of NAC on MYC-dependent pathways has been seen in lymphoma75. These studies lend credence to the idea that MYC is the most likely TXNIP-regulated TF that regulates GDF15 in our systems.”

It seems equally likely that TXNIP and GDF15 represent independent parallel pathways. Even if TXNIP is a direct regulator of GDF15, it's also clear that other "factors" up or down-regulated by Oxa also contribute to the regulation of GDF15. These are not explored and even though TXNIP is highly regulated genes shown Figure 2 that are not identified or discussed that may also be contributing to GDF15 regulation.

As mentioned above, the new data suggests that at least one other factor, ARRDC4, can regulate GDF15 (changes upon oxaliplatin treatment) and that MYC is a potential mechanistic bridge between TXNIP and GDF15. Whilst assessing for the transcription factor that may link TXNIP and GDF15 we found an additional 5 TXNIP-independent factors (ATF4, PHB2, SREBF1, HBP1, KLF9) that bind to GDF15 promoter/enhancer regions and are downregulated post-oxaliplatin treatment. When looking at correlations between these factors and GDF15 in the TCGA COAD dataset, ATF4 and PHB2 correlate most closely with GDF15 (when removing MYC) and so we would cautiously suggest that these may be the most pertinent. This data is now included.

Further, the experiments treating PBMCs with conditioned media contain other cytokines/factors, in addition to GDF15, that likely also contribute the observed effects on the different immune cells understudy. The conditioned media from GDF15 knock out cells are a good experiment, but the media is not rigorously tested to see what other cytokines/factors might have also been depleted.

The TXNIP knockout media is the same as that analysed by mass spec and the protein array, however as the reviewer states there is no analysis (excluding assessing for the presence or absence of GDF15) on the double knockout supernatant or over-expression supernatant. The text has been corrected as follows:

Lines 675-679. “In light of other secreted factors being seen to be regulated by TXNIP (Figure 3A-B), we included double knockouts (TXNIP and GDF15 knockout; GTKO) as well as an overexpression system (GDF15a) to test for GDF15 specific effects. However, we do not know the impact of knocking out or overexpressing GDF15 on the broader secretome.”

Perhaps a GDF15 complementation experiment would help here.

We felt that the association between GDF15 and Treg induction is reasonably well established in the literature, and so once we saw that the supernatant from our GDF15 overexpression system (+/- CD48 blockade) complemented what has already been demonstrated, we were encouraged. However we needed more – hence the TCGA data and IHC staining.

Finally, even if completely independent, a TXNIP/GDF15 ratio does seem to have utility in determining chemo-therapeutic response.

We agree – we feel that conceptually this may be the most interesting part of the project and is an example of what can be done with these tools.

Other major points: 1. Please label the other highly regulated genes shown in Fig 2A and B. Might they also explain some of the underlying biology. This could be on the current figures or in a supplement, though the former is preferred.

Many thanks – we have done this.

Please address why the TXNIP induction is so much less in patient-derived organoids vs. cell line spheroids (Fig S2). By the western blots, TXNIP inductions in the organoids looks quite modest. Further, the text is quite cryptic and implies that the "upregulation" is similar in both organoids and spheroids.

You are absolutely correct. Many apologies, the wording has changed:

Lines 320-323 “In both models we observed the upregulation of TXNIP mRNA (Figure S2E-H) and TXNIP protein (Figure S2I-L) after oxaliplatin treatment, with spheroids showing greater responsiveness. This difference is most likely due to culturing conditions or differences in the number and location of cycling cells.”

We have two possible explanations. Firstly the media in which the organoids are cultured contains a lower glucose concentration than that used for the spheroids. As per some of our new data (Figure S3 – later in the rebuttal), the upregulation of TXNIP after oxaliplatin is glucose dependant, with lower concentrations resulting in less of a differential. Secondly, while restricted to the periphery, the Ki67 signal in DLD1 spheroids is quite pronounced indicating that, within the outer zone, many cells (probably the majority) are in the S/G1/G2 phase of the cell cycle at any given point in time (figure below this text).

This is not the case for the organoids, where the Ki67 (and pCDK1) signal is quite weak, and only sporadic in the outer layer. So we believe that there are many more rapidly cycling cells in the most drug-exposed layer of spheroids when compared to the comparable region in organoids. As the spheroid cells are likely cycling more rapidly, they would also be expected to be more adversely affected by the drug within the finite drug treatment window. Indeed, these spheroids grow large, and quite quickly. If the organoid cells are cycling more slowly and if, within the cell layer most exposed to drug, these cycling cells are less abundant, then the TXNIP response may well be subdued in organoids when compared with spheroids.

We have decided to not include the above (full) explanation and figure within the new draft, as we feel it may distract from the central message. However do let ourselves and the editor know if you disagree.

What was the rationale of performing the MS experiment on control and TXNIP KO DLD1 cells in the absence of oxaliplatin? The other experiments in Fig 3 clearly show that Oxa can repress GDF15 even in the absence of TXNIP, which implicates other pathways. ARRDC4? Or something else? This needs to be addressed.

We adopted this approach because of the order in which the assays occurred and technical issues surrounding the use of post-oxaliplatin treated supernatant. By the time we moved to the proteomics we had already identified, and validated, GDF15 as our number one candidate (initially from the protein array), in terms of response to oxaliplatin and dependence on TXNIP. This led us to the next stage of the project – to assess the environmental impacts of this factor in vitro before validation in situ. To do this, aware of the issue of contaminated recombinant GDF15, we decided early on to use cell line supernatant. We carried out some pilot studies on immune cells using supernatant from oxaliplatin treated cell lines and we had several technical issues (difficulty in determining the correct controls, immune cell death…). This changed the emphasis to using supernatant from knockout models rather than knockout and treated models. Before we began these assays in earnest we wanted to assess exactly what was enriched in TXNIP knockout supernatant and so we turned to proteomics. When this further validated GDF15, we then generated GDF15 and TXNIP/GDF15 knockouts to further elucidate GDF15’s role specifically.

With regards the other pathways, as you correctly predicted, ARRDC4 also appears to regulate GDF15 – many thanks for helping with this line of enquiry. Please see earlier in the rebuttal for more details and the data.

The data in 3J with the MondoA knockdown is not convincing. The knockdown is weak and TXNIP goes down a smidge. Agree that GDF15 goes up

We agree. We have re-run this and pooled the densitometry data – see new figure below (Panel 3J).

Minor points 1. Line 79. The "loss" of TXNIP/GDF15 axis is confusing. It's really loss of TXNIP and upregulation of GDF15, right?

Absolutely - corrected to responsiveness.

Lines 144-147: “Intriguingly, multiple models including patient-derived tumor organoids demonstrate that the loss of TXNIP and GDF15 responsiveness to oxaliplatin is associated with advanced disease or chemotherapeutic resistance, with transcriptomic or proteomic GDF15/TXNIP ratios showing potential as a prognostic biomarker.”

Please provide an explanation for the different stages in tables 1 and 2. This will likely not be clear to non-clinicians.

Many thanks. The following has been added at the bottom of the second table.

Lines 304-309: “The TNM staging system stands for Tumor, Node, Metastasis. T describes the size of the primary tumor (T1-2; 5cm). N describes the presence of tumor cells in the lymph nodes (N0; no lymph nodes. N1-3 >0). M describes whether there are any observable metastases (M0; no metastases. M1; metastases). The clinical stage system is as follows: I/II; the tumor has remained stable or grown, but hasn’t spread. III/IV; the tumor has spread, either locally (III) or systemically (IV).”

Line 231 should probably read ...cysteine (NAC), a reactive oxygen species inhibitor,

Many thanks - corrected

Line 247, should be RT-qPCR I think.

Many thanks - corrected

Lines 343-345. I don't quite understand the wording. Does this mean to say that 675 soluble proteins were not changed between the condition media from both cell populations?

Yes, exactly this. We have removed as this is superfluous and confusing.

The data in FigS1 B and C don't seem to reach the standard p value of > 0.05

Very true – we have rewritten the text to make sure the reader knows there is no significance.

Lines 269-271. “High levels of both the protein (significantly) and the transcript (not significantly) were seen to be associated with favourable prognosis (Figure 1G,H and S1B,C).”

**Referee Cross-Commenting**

cross comment regarding referees 2 and 3 above. I'm am convinced that TXNIP is at least contemporaneously upregulated with GDF15 downregulation. However, the strong implication from the writing is that TXNIP regulates GDF15 directly. I agree with the comment above that exploring mechanisms may be open-ended especially as TXNIP has been implicated in gene regulation by several different mechanism. I'd be satisfied with a more open-minded discussion of potential mechanisms by which TXNIP may repress GDF15 and the possibility of other parallel pathways that likely contribute to GDF15 repression.

Many thanks, this is a generous and understanding approach. As described above we have carried out extra analysis and have found 6 differentially regulated transcription factors which have been shown to bind GDF15 promoter or enhancer regions with 1 of these, MYC, being significantly affected in the TXNIP knockout cell lines, which in combination with supportive literature suggests a degree of TXNIP dependence. We have also identified ARRDC4 as an additional regulator of GDF15 – again please see above.

Reviewer #1 (Significance (Required)):

This is an interesting contribution but the mechanistic connection between GDF15 and TXNIP is relatively weak. That said, even as independent variables they do seem to have utility in predicting therapeutic response.

Many thanks for the comment – we concur. We have reanalysed our data looking for relevant transcription factors (those that bind GDF15 promoter / enhancer regions) finding MYC as the most likely bridge. Please see above.

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

The manuscript by Deng et al. investigates a mechanistic link between TXNIP and GDF15 expression and oxaliplatin treatment and acquired resistance. They observe an upregulation in TXNIP expression in the tumors of patients who have previously received chemotherapy. They demonstrate oxaliplatin-driven MondoA transcriptional activity is what underlies the induction of TXNIP. They further demonstrate that TXNIP is a negative regulator of GDF15 expression. Together, oxaliplatin induces MondoA activity and TXNIP expression, resulting in a downregulation of GDF15 expression and consequently decreased Treg differentiation.

Major Comments

1. The authors suggest that TXNIP induction and GDF15 downregulation are a common effect of chemotherapies; however, the mechanistic studies were limited to oxaliplatin. The authors should clarify this point through further investigation using other commonly used CRC chemotherapies (5-FU, irinotecan, etc.),or through textual changes. To be clear, I think that the oxaliplatin results could potentially stand on their own but would require additional clarification. For example, regarding the patient samples analyzed in 1D and 4F, which patients received oxaliplatin? Could the analysis of publicly available molecular data be drilled down to just the patients who received oxaliplatin?

Many thanks – this is an excellent point. Firstly, all the patients in 1D and 4F received oxaliplatin. Secondly, we have included new data looking at the impact of other chemotherapies (FOLRIRI, FU-5 and SN-38) on aspects of the study, ultimately finding that these processes (especially an anti-correlation between GDF15 and TXNIP changes upon chemo treatment) appear to be specific to oxaliplatin. These data have been added (Figure S11) and throughout the emphasis has been switched from chemotherapeutic treatment to oxaliplatin treatment.

Lines 796-799: “To check if the pre-treatment GDF15/TXNIP ratio could be used for patients treated with FOLFIRI we performed the same analyses finding no significance (S11A-D). This oxaliplatin specificity was then confirmed by western blot analysis in DLD1 and HCT15 cells treated with 5-FU or SN38 (Figure S11E-F).

Example of change of emphasis from ‘chemotherapy’ to ‘oxaliplatin’ – lines 139-142: “Here, in colorectal adenocarcinoma (CRC) we identify oxaliplatin-induced Thioredoxin Interacting Protein (TXNIP), a MondoA-dependent tumor suppressor gene, as a negative regulator of Growth/Differentiation Factor 15 (GDF15).”

The data demonstrating the induction of MondoA transcriptional activity and TXNIP expression in response to oxaliplatin treatment is quite convincing. The data regarding ROS induction of TXNIP is interesting, especially in light of other studies arguing that ROS limits MondoA activity (PMID: 25332233). Given this apparent disparity, I think that this study could really be strengthened by also investigating other potential mechanisms of oxaliplatin induction of MondoA. In particular, given many studies arguing for direct nutrient-regulation of MondoA, the authors should address the potential for oxaliplatin regulation of glucose availability and a potential glucose dependence of oxaliplatin-induced TXNIP. 2

In line with the previous point, since MondoA activity and TXNIP expression are sensitive to glucose levels, the authors should investigate oxaliplatin-regulation of TXNIP under physiological glucose levels. No need to replicate everything, just key experiments.

We feel these are excellent point and really help the piece – many thanks. We have carried out assays around these points suggested and have included the findings in the new draft – see below.

Lines 332-339: “As such, we went back to first principles and assessed the impact of different concentrations of glucose on TXNIP induction +/- oxaliplatin treatment, finding a concentration dependent effect (Figure S3A). Intriguingly, high glucose alone was able to induce increased TXNIP expression. We then assessed if oxaliplatin treatment drove an increase in glucose uptake, with this seen at concentrations >10mM (Figure S3B). Next, to investigate the impact of glucose metabolism, and consequent ROS generation, on TXNIP induction we treated cells with Antimycin A, an inhibitor of oxidative phosphorylation, finding a complete block in oxaliplatin-induced TXNIP (Figure S3C).”

The authors did a good job of linking TXNIP and GDF15 in untreated conditions; however, the data arguing for oxaliplatin regulation of GDF15 through TXNIP is less clear. For example, in 3B-H, oxaliplatin treatment reduces GDF15 approximately to the same extent in the NTC and TKO cells, potentially in line with a mechanism of downregulation that doesn't involve TXNIP.

A very salient point and completely in line with the other reviewers. We have carried out a few additional analyses mentioned previously in this letter. The most pertinent for this specific point are the experiments around ARRDC4, where we found evidence to suggest that, like TXNIP, it regulates GDF15.

Minor Comments

1. The presentation of data in Figure 5 is confusing. A-B include raw cell numbers, whereas C-F show "normalized proliferation." What does this mean? And how was the normalization done?

Apologies for this. Legend test has been corrected to “Normalised proliferation (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) on gated CD3+CD8+ or CD3+CD4+ cells is shown. n=6. (G-H) Normalised IFNg concentrations (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) in the supernatant of cells from C-F.” (lines 727-729).

**Referee Cross-Commenting**

cross-comment regarding reviewer #1

I agree with the referee that the link between TXNIP and GDF15 is weak, though as I mentioned before, this is particularly true in the context of oxaliplatin-regulation of TXNIP. I agree that given all the presented data, it is likely that oxaliplatin-regulation of TXNIP and GDF15 are independent. In my opinion, the referee brought up all valid concerns, but this is by far the biggest concern that I share.

We agree that this is the weakest aspect of the paper, however our new analyses plus supportive literature, suggests that the relationship between TXNIP and GDF15 may be mediated by MYC (please see above)

cross-comment regarding reviewer #3

The major concern that this referee addresses is whether another transcription factor supersedes the proposed MondoA/TXNIP induction in regulating GDF15 expression in later stage CRC. In my opinion, this another other concerns of the referee are all valid, but still I remain unconvinced that TXNIP induction underlies the oxaliplatin-regulation of GDF15. I think fleshing out that aspect of the study would potentially help the authors tease apart how this potential MondoA-TXNIP-GDF15 axis is dysregulated later in CRC progression.

This is a great discussion. Interestingly enough, c-myc is seen at higher levels in late stage CRC (Hu X, Fatima S, Chen M, Huang T, Chen YW, Gong R, Wong HLX, Yu R, Song L, Kwan HY, Bian Z. Dihydroartemisinin is potential therapeutics for treating late-stage CRC by targeting the elevated c-Myc level. Cell Death Dis. 2021 Nov 5;12(11):1053. Doi: 10.1038/s41419-021-04247-w. PMID: 34741022; PMCID: PMC8571272.), is seen as an important factor in resistance, and as this review argues, is driven by stress (Saeed H, Leibowitz BJ, Zhang L, Yu J. Targeting Myc-driven stress addiction in colorectal cancer. Drug Resist Updat. 2023 Jul;69:100963. Doi: 10.1016/j.drup.2023.100963. Epub 2023 Apr 20. PMID: 37119690; PMCID: PMC10330748.). So it is very plausible that the partial TXNIP-mediated regulation of myc in early / sensitive CRCs that we may be observing, and has been reported recently (TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding Lim TY, Wilde BR, Thomas ML, Murphy KE, Vahrenkamp JM, et al. (2023) TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding. PLOS Biology 21(3): e3001778. https://doi.org/10.1371/journal.pbio.3001778) is lost in late stage / resistant CRCs. If this is the case, in effect what we would have observed is the loss of a stress-associated method (TXNIP) of controlling c-myc activity. What makes our collective lives difficult is that, as reported “this expansion of Myc-dependent transcription following TXNIP loss occurs without an apparent increase in Myc’s intrinsic capacity to activate transcription and without increasing Myc levels.” (TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding Lim TY, Wilde BR, Thomas ML, Murphy KE, Vahrenkamp JM, et al. (2023) TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding. PLOS Biology 21(3): e3001778. https://doi.org/10.1371/journal.pbio.3001778)

Reviewer #2 (Significance (Required)):

Generally speaking the experiments are well controlled and the findings are significant and novel. Though the link between MondoA activity and ROS could be strengthened, and the data could be validated under more physiological settings. Further, the authors should clarify their interpretations so as to not overstate the findings.

Many thanks for the comments. We have taken onboard the need for more physiological settings and have included varying levels of glucose to reflect concentrations in different environments. We have repeated the siMondoA work in 3J strengthening the conclusions wrt its impact on TXNIP and GDF15 expression (see above).

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

In this well-written manuscript, the authors show that chemotherapy increases a MondoA-dependent oxidative stress-associated protein, TXNIP, in chemotherapy-responsive colorectal cancer cells. They show that TXNIP negatively regulates GDF-15 expression. GDF-15, in turn, correlates with the presence of T cells (Treg), and inhibits CD4 and CD8 T cell stimulation. In advanced disease and chemo-resistant cancers, upregulation of TXNIP and downregulation of GDF-15 appear to get lost. Based on a somewhat smallish data set, the authors suggest that the pre-treatment GDF-15/TXNIP ratio can predict responses to oxaliplatin treatment. This is a very interesting, novel finding. In general, the quality of the experiments and the data are high and the conclusions appear sound. Still, there are a number of aspects that should still be improved:

The observed loss of the ROS - MondoA - TXNIP - GDF15 axis in chemoresistant and/or metastatic tumors implies that another transcription factor or pathway becomes dominant upon tumor progression. As this switch would be key to better understanding the mechanism underlying the prognostic role of the TXNIP/GDF15 ratio, the authors should at least do data mining followed by ChEA or Encode (or other) analysis to identify transcription factors or pathways that become activated in late-stage/metastatic CRC cells. There is a high likelihood that a transcription factor or pathway involved in GDF-15 upregulation in cancer (e.g. p53, HIF1alpha, Nrf2, NF-kB, MITF, C/EBPß, BRAF, PI3K/AKT, MAPK p38, EGR1) supersedes the inhibitory effect of the MondoA-TXNIP axis. As it stands, the proposed loss of function of the ROS - MondoA - TXNIP - GDF-15 axis is far less convincing than almost all other aspects of the study.

An extremely fair point. We adopted a similar approach to that suggested – as mentioned above, we looked at TFs that bind to GDF15 promoter/enhancer regions and then looked at the presence of these in our transcriptomic data – specifically any evidence of change post oxaliplatin treatment. We found 6 such TFs that were decreased post-oxaliplatin treatment. We then looked for any evidence of TXNIP dependence in these TFs by comparing post-oxaliplatin treatment across NTC and TXNIP knockout lines, when we did this we found only one GDF15 promoter/enhancer binding TF was significantly changed: MYC. We then looked at the relationship between MYC,TXNIP, and GDF15 against the other 5 ‘control’ TFs in the TCGA COAD dataset, we found that MYC showed the strongest correlations, in the ‘correct’ directions. This finding was further backed up in the literature where a TXNIP knockout in a breast cancer model drove c-myc-dependent transcription, whilst c-myc has been observed to increase in later stage CRC patients, is associated with cellular stress and resistance. The collective evidence therefore suggests that MYC is the factor that is initially at least partially regulated by TXNIP, before this regulation is lost in advanced / resistant disease. Continuing on this line, it is likely that the predictive GDF15/TXNIP ratio is at least in part, a measure of c-myc responsiveness to oxaliplatin. All the while we must bear in mind TXNIP-independent oxaliplatin-dependent regulation of GDF15, most likely ARRDC4, as described earlier in this document.

Using pathway analysis software to compare our transcriptomic data from cell lines treated with/without oxaliplatin, the most likely pathways upstream of MYC/c-myc that are negatively affected by chemotherapy are BAG2, Endothelin-1, telomerase, ErbB2-ErbB3 and Wnt/B-catenin. When looking at the comparison of UTC and resistant lines’ transcripts there is only one key component of these pathways which is upregulated in both lines - ERBB3 – which has already been shown to be important in CRC metastasis and resistance (Desai O, Wang R. HER3- A key survival pathway and an emerging therapeutic target in metastatic colorectal cancer and pancreatic ductal adenocarcinoma. Oncotarget. 2023 May 10;14:439-443. doi: 10.18632/oncotarget.28421. PMID: 37163206; PMCID: PMC10171365.). It is highly speculative, but our data suggests the most likely pathway to supersede TXNIP in its (partial) regulation of MYC is the ErbB2-ErbB3 pathway.

My further criticisms are mostly more technical:

Figure 2 I-L: What was the extent of MondoA downregulation achieved by siRNA treatment? Could the effects also be seen with the small molecule mondoA inhibitor SBI-477 (or a related substance)?

This experiment has been repeated. The pooled densiometric data is also now given (please see above).

How do you explain the different GDF-15 levels between untreated non-target control cells (NTC) and TXNIP knock-down cells (TKO) in Figures 3C-F?

The only way to interpret this is that there is a TXNIP-independent pathway regulating GDF15 expression after oxaliplatin treatment, as described this is most likely to be ARRDC4 - the text has been updated to:

Lines 522-524: “It is important to note, however, that we saw clear evidence that TXNIP was not solely responsible for the downregulation of GDF15 post oxaliplatin treatment (Figure 3C-G, S6E).”

In figures 3 E-G the dots for the individual measurements should be indicated. This would be more informative than just the bar graphs.

Completed.

Figure 4C,D and Table 3: Data on the role of GDF-15 in CRC are largely valedictory of previous work (e.g. Brown et al. Clin Cancer Res 2003, 9(7):2642-2650, Wallin et al., Br J Cancer. 2011 May, 10;104(10):1619-27). Therefore, the previous studies should be cited.

Apologies for the oversight and many thanks – this is an excellent addition.

Figure 5C-F: Please indicate in the figure legend how proliferation was assessed.

Many thanks. This was noticed by another reviewer also. We have changed the text to include how the data was normalised: “(C-F) Labelled PBMCs were stimulated with anti-CD3 and anti-CD28 for 4 days in the presence of fresh supernatant from indicated cell lines, before being stained with anti-CD3 and anti-CD8 (C-D) or anti-CD4 (E-F) antibodies and measured by flow cytometry. Normalised proliferation (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) on gated CD3+CD8+ or CD3+CD4+ cells is shown. n=6. (G-H) Normalised IFNg concentrations (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) in the supernatant of cells from C-F.” (lines 724-730)

Figure S8E-G: Please indicate the analysed parameters in the graphs. In Figure S8G, the legend just indicates that "aggression of tumour" is dichotomized and plotted. This clearly requires a better definition.

Many thanks, this has been changed as per the below.

Lines 862-868: “(E-G) Receiver operating characteristic (ROC) curves showing area under the curve and p values for the use of GDF15/TXNIP ratio in predicting origin of cell line (E; primary; DLD1, HCT15, HT29, SW48 [n=4] or secondary; DiFi, LIM1215 [n=2]), sensitivity to oxaliplatin (F; parental DLD1 (plus biological repeat), HCT15 [n=3] or resistant DLD1 (plus biological repeat), HCT15 [n=3]), aggression of tumor (G; non-aggressive; The authors propose a novel ROS - MondoA - TXNIP - GDF15 - Treg axis, where MondoA activation, TXNIP up- and GDF-15 downregulation enhance tumor immunogenicity. While this axis has been analyzed in some detail, GDF-15 is not only linked to induction of regulatory T cells. There has been a report showing that GDF-15/MIC-1 expression in colorectal cancer correlates with the absence of immune cell infiltration (Brown et al. Clin Cancer Res 2003, 9(7):2642-2650). The link between GDF-15 and immune cell exclusion has also been confirmed in other conditions, including different cancers (Kempf et al. Nat Med 2011, 17(5):581-588, Roth P et al. Clin Cancer Res 2010, 16(15):3851-3859, Haake et al. Nat Commun 2023, 14(1):4253). A key mechanism is the GDF-15 mediated inhibition of LFA-1 activation on immune cells. As the authors argue that the described pathways turns cold tumors hot in response to oxaliplatin-based chemotherapy, this GDF-15 dependent immune cell exclusion mechanism might be at least as relevant than induction of Treg. Likewise, inhibition of dendritic cell maturation by GDF-15 (Zhou et al. PLoS One 2013, 8(11):e78618) could explain why GDF-15high tumors are immunologically cold. Reviewed in 3

The authors propose that the pathways discovered by them contributed to the "heating up" of the tumor microenvironment after oxaliplatin-based chemotherapy. The authors should thus look in their data sets for the presence of cytotoxic T cells and their possible correlation with TXNIP and GDF-15 levels.

This is a wonderful explanation – many thanks. We have taken the opportunity to assess the impact of GDF15 expression on a variety of T cell markers (Figure S9). In this data a negative association between GDF15 and CD8 CTLs can clearly be seen, as predicted by the reviewer.

Lines 712-717: “To assess if the GDF15-dependent presence of Tregs may be associated with a decrease in activated cytotoxic CD8 T cells, we interrogated the TCGA COAD dataset. We found that low GDF15 tumors carried significantly higher levels of CD8, CD69, IL2RA, CD28, PRF1, GZMA, GZMK, TBX21, EOMES and IRF4 (Figure S9); transcripts indicative of activated cytotoxic CD8 T cells. High GDF15 tumors were enrichment for FOXP3 and, interestingly, RORC (Figure S9). These data support the hypothesis that GDF15 induces Foxp3+ve Tregs which inhibit CD8 T cell proliferation and activation in the TME.”

The paragraph on GDF-15 receptors needs to be corrected: The purported role of a type 2 transforming growth factor (TGF)-beta receptor in GDF-15 signalling had been due to a frequent contamination of recombinant GDF-15 with TGF-beta (Olsen et al. PLoS One 2017, 12(11):e0187349). There have been a number of screenings for GDF-15 receptors that have all failed to show an interaction between GDF-15 and TGF-beta receptors. Instead, only GFRAL was found in these large-scale screenings (Emmerson et al. Nat Med 2017, 23(10):1215-1219, Hsu et al. Nature 2017, 550(7675):255-259, Mullican et al. Nat Med 2017, 23(10):1150-1157, Yang et al. Nat Med 2017, 23(10):1158-1166). The one subsequent report that shows a link between GDF-15, engagement of CD48 on T cells and induction of a regulatory phenotype (Wang et al. J Immunother Cancer 2021, 9(9)) still awaits independent validation. Considering that CD48 lacks an intracellular signaling domain that would be critical for a classical receptor function, I recommend to be more cautious regarding the role of CD48 as GDF-15 receptor. Given the mechanism outlined by Wang et al. the word interaction partner might be more apt. Moreover, an anti-GDF-15 antibody would be a good control for the experiments involving an anti-CD48 antibody in Figure 5.

Thank you so much for this concise and highly informative paragraph. We have changed the text to read:

202-204: “As a soluble protein, GDF15 exerts its effects by binding to its cognate receptor, GDNF-family receptor a-like (GFRAL)44,45,46,47 or interaction partner, CD48 receptor (SLAMF2)43, with the latter still requiring additional verification.”

We would have ideally included an anti-GDF15 antibody in the CD48 assay at the time but didn’t have the foresight. We have included the additional text to temper any conclusions.

Lines 701-711: “Furthermore, when stimulating naïve CD4 T cells in the presence of GDF15 enriched supernatant we were able to both differentiate these cells into functional Tregs and also block the generation of this functionality using an anti-CD48 antibody (Figure 5M-N). However, it must be stressed that the binding and functional impacts of GDF15’s interaction with CD48 still require further verification.”

Cell surface externalization of annexin A1 has been described as a failsafe mechanism to prevent inflammatory responses during secondary necrosis (PMID: 20007579). Thus, I am surprised that the authors list annexin A1 among the immune-stimulatory molecules exposed or released in response to chemotherapy-induced cell death (line 103). Please clarify!

We agree – it shouldn’t be there!! Removed. Many thanks.

**Referee Cross-Commenting**

Regarding the cross-comment by referee 2: In my opinion, the data shown in Figure 3C-H clearly demonstrates that TXNIP can repress GDF-15 expression. I agree that there will likely be further regulators. The GDF-15 promoter is constantly regulated by a multitude of factors (which mostly induce transcription). As downregulation of GDF-15 in response to oxaliplatin is the opposite of the frequently described induction of GDF-15 upon chemotherapy, net effects may always be "smudged" by contributions from different pathways (e.g. by cell stress due to siRNA transfection). Therefore, I believe that the data are as good as it will get. Accordingly, I would not force the authors to further amplify the observed effect.

Many thanks for your understanding – yes, GDF15 has >650 TFs that bind its promoter/enhancer regions – a number we found rather daunting. Happily your comments and those of the other reviewers inspired us to dig and we now have data that is supportive of MYC’s and ARRDC4’s involvement – detailed throughout this reply.

cross comment regarding referee #1: I share the general assessment of the referee and recognize the very detailed mechanistic analysis. To further support the moderate effects of the MondoA knockdown, a small molecule inhibitor like SBI-477 might be useful. (I had already suggested using this inhibitor to support these data.)

Many thanks for the suggestion. We opted to increase the number of siRNA repeats instead – with the data included in Figure 3J (above).

Still, my view on the potential relevance of oxaliplatin-induced, TXNIP-independent downregulation of GDF-15 differs from that of referee 1. In the clinics, platinum-based chemotherapy is one of the strongest inducers of GDF-15 (compare Breen et al. GDF-15 Neutralization Alleviates Platinum-Based Chemotherapy-Induced Emesis, Anorexia, and Weight Loss in Mice and Nonhuman Primates. Cell Metabolism 32(6), P938-950, 2020.DOI:https://doi.org/10.1016/j.cmet.2020.10.023). I was thus surprised that the authors found a pathway, which leads to an outcome that an exactly opposite effect.

This is fascinating that oxaliplatin drives this increase in GDF15 – we were unaware of this paper. Looking at figure 2(H-K), GDF15 is being produced from multiple non-diseased tissues after systemic chemotherapy – even at day 19 post-treatment – this suggests that wrt this study, systemic GDF15 could not be used as a readout of success or otherwise – which is extremely helpful! Thank you.

Thus far, the only obvious reason for reduced GDF-15 secretion upon treatment with cytotoxic drugs was a reduction in tumor cell number due to cytotoxicity.

Please do not discount this. This study was focused on the cells which survived oxaliplatin treatment – the cells which did not were discarded. Our view, given your input, would be a complex picture where in early stages systemic GDF15 goes up, due to off-target effects, but locally levels drop owing to cell death and this, and other, stress-related pathways in the remaining tumor cells.

Still, the authors managed to convince me that the described pathway (ROS - MondoA - TXNIP - GDF-15) exists. (Here, I still largely concur with referee 1.) Moreover, as we have identified some factors required for GDF-15 biosynthesis that could easily interact with TXNIP, I find the proposed mechanism plausible.

Extremely encouraging for us to hear!

Nevertheless, as a downregulation of GDF-15 in response to chemotherapy is hardly ever observed in late-stage cancers, I believe that the observed switch in pathway activation between early- and late-stage cancers might be highly relevant - in particular, as there is so much evidence for platinum-based induction of GDF-15 in late-stage cancer patients. Emphasizing the divergent clinical observations (e.g. by Breen et al.) could thus help to put the finding into perspective.

Very much agree. We did see this phenomenon in LIM1215 cells (Figure 6B) and the resistant lines we generated continually produced higher levels.

Analysing TXNIP-independent mechanisms involved in the oxaliplatin-dependent repression of GDF-15, as suggested by referee #1, will require enormous efforts and resources, and may still turn out to be fruitless. Personally, I would thus be content if the authors just mentioned possible contributions from other pathways upon cancer progression. To me, the described pathway seems to be limited to early-stage cancers, and the actual finding that GDF-15 is downregulated is an interesting observation, irrespective of further involved pathways.

Many thanks – this is extremely fair. Happily we have managed to make some tentative steps forward in highlighting the potential role of MYC, and the suggestion of redundancy wrt ARRDC4, but as you say, much more work needs to be done to fully understand these processes.

cross comment regarding referee #2: I fully agree with the referee that activation of the pathway by further chemotherapeutic drugs could be a valuable addition. As Guido Kroemer´s lab has described oxaliplatin to induce a more immunogenic cell death compared to other platinum-based chemotherapies, even a rather limited comparison between oxaliplatin and cisplatin could be very interesting.

Absolutely agree – extra data on this has been included in Figure S11, which is included earlier in this letter. We also uncovered a meta-analysis using metformin, which has been seen to inhibit ROS, where TXNIP and ARRDC4 are the top two downregulated transcripts whilst GDF15 appears in the top four upregulated. This may suggest that chemotherapeutic immunogenicity, at least through the presence or absence of GDF15, may in part be driven by ROS.

Lines 930-933: “Further support for both TXNIP and ARRDC4’s role in regulating GDF15 after the induction of ROS comes from a pan cancer meta-analysis assessing the impact of metformin (which has been reported to inhibit ROS) on gene expression. Here the top two downregulated genes were TXNIP and ARRDC4 and the top four upregulated genes were DDIT4, CHD2, ERN1 and GDF1572 “

Reviewer #3 (Significance (Required)):

In general, this is a very interesting manuscript describing a cascade of events that may contribute to successful chemotherapy (which likely requires induction of an immune response against dying tumor cells.) The observation that this pathway is only active in early/non-metastatic cancer cells is striking. Unfortunately, the authors cannot explain inactivation of this pathway in later stage/ metastatic/ highly aggressive cancers. Understanding this switch could easily be the most important finding triggered by this report. Therefore, I highly recommend to make some effort in this direction. Strikingly, the authors find that disruption of TXNIP-mediated GDF-15 downregulation is strongly associated with worse prognosis. They also suggest that this ratio could indicate whether a patient will respond to oxaliplatin-based chemotherapy.

This is again very fair – we have posited a potential mechanism for the loss of this switch elsewhere in this reply– one which involves a change in TXNIP-mediated MYC regulation and/or increased HER2-HER3 signalling – but although reasonable for a rebuttal (and publication in that context) we do not feel we have the evidence to include this within the full manuscript.

Altogether, the findings described in manuscript are very novel and may have prognostic (or, in case of the presumed loss of the MondoA - TXNIP - GDF-15 pathway) therapeutic implications. Thus, the manuscript certainly fills various gaps and should be of major interest for cell biologists working on immunogenic cell death, or colorectal cancer, or MondoA, TXNIP or GDF-15. Still, due to its translational implications, it would also be worthwhile reading for a large number of researchers in the oncology field.

We are very grateful for your kind comments.

1 Sinclair, L. V., Barthelemy, C. & Cantrell, D. A. Single Cell Glucose Uptake Assays: A Cautionary Tale. Immunometabolism 2, e200029, doi:10.20900/immunometab20200029 (2020).

2 Yu, F. X., Chai, T. F., He, H., Hagen, T. & Luo, Y. Thioredoxin-interacting protein (Txnip) gene expression: sensing oxidative phosphorylation status and glycolytic rate. J Biol Chem 285, 25822-25830, doi:10.1074/jbc.M110.108290 (2010).

3 Wischhusen, J., Melero, I. & Fridman, W. H. Growth/Differentiation Factor-15 (GDF-15): From Biomarker to Novel Targetable Immune Checkpoint. Front Immunol 11, 951, doi:10.3389/fimmu.2020.00951 (2020).

Reviewer #2 (Public Review):

Summary:<br /> The authors try to establish that there is an Abeta-dependent loss of nuclear pores early in Alzheimer's disease. To do so the authors compared different NUP proteins and assessed their function by analyzing nuclear leakage and resistance to induction of nuclear damage and the associated necroptosis. The authors use a mouse knockin for hAPP with familial Alzheimer's mutations to model amyloidosis related to Alzheimer's disease. Treatment with an inhibitor of beta-amyloid production partially rescued the loss of nuclear pore proteins in young KI neurons, implicating beta-amyloid in Nuclear Pore dysfunction, a mechanism already described in other neurodegenerative diseases but not in Alzheimer's disease.

The conclusions of this paper related to familial AD are well supported by data but are not related to an aging decline in NUP function, where it is required to extend data analysis and one additional experiment.

1. Adding statistics and comparisons between wild-type changes at different times/ages to determine if the nuclear pore changes with time in wild-type neurons. The images show differences in the Nuclear pore in neurons from the wild-type mice, with time in culture and age. However, a rigorous statistical analysis is lacking to address the impact of age/development on NUP function. Although the authors state that nuclear pore transport is reported to be altered in normal brain aging, the authors either did not design their experiments to account for the normal aging mechanisms or overlooked the analysis of their data in this light.

2. Add experiments to assess the contribution of wild-type beta-amyloid accumulation with aging. It was described in 2012 (Guix FX, Wahle T, Vennekens K, Snellinx A, Chávez-Gutiérrez L, Ill-Raga G, Ramos-Fernandez E, Guardia-Laguarta C, Lleó A, Arimon M, Berezovska O, Muñoz FJ, Dotti CG, De Strooper B. 2012. Modification of γ-secretase by nitrosative stress links neuronal ageing to sporadic Alzheimer's disease. EMBO Mol Med 4:660-673, doi:10.1002/emmm.201200243) and 2021 (Burrinha T, Martinsson I, Gomes R, Terrasso AP, Gouras GK, Almeida CG. 2021. Upregulation of APP endocytosis by neuronal aging drives amyloid-dependent synapse loss. J Cell Sci 134. doi:10.1242/jcs.255752), 28 DIV neurons are senescent and accumulate beta-amyloid42. In addition, beta-amyloid 42 accumulates normally in the human brain (Baker-Nigh A, Vahedi S, Davis EG, Weintraub S, Bigio EH, Klein WL, Geula C. 2015. Neuronal amyloid-β accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease. Brain 138:1722-1737. doi:10.1093/brain/awv024), thus, it would be important to determine if it contributes to NUP dysfunction. Unfortunately, the authors tested the Abeta contribution at div14 when wild-type Abeta accumulation was undetected. It would enrich the paper and allow the authors to conclude about normal aging if additional experiments were performed, namely, treating 28Div neurons with DAPT and assessing if NUP is restored.

Author Response

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

Reviewer #1:

In no particular order:

1. In Figs S3 and S4, can they also show gamma fit? (or rather corrected fit accounting for abundance conditioning?) The shapes look different, especially for the microbial mat.

Author response: We have added gamma distribution fits to the rescaled AFD plots (Figs. S3, S4).

1. Lines 170-176 seem like they should come before lines 164-166.

Author response: In lines 166-170 we discuss empirical patterns in the data that motivate the introduction of the SLM as a model in lines 170-175. We have clarified these points in the revision.

1. The wiggles in the gamma predictions in the occupancy-abundance plots are because occupancy depends not only on abundance but also on the shape parameter, right? Probably good to write a sentence or two explaining what's going on here.

Author response: We agree with the reviewer that the variation in the prediction could be in-part driven by variation in the shape parameter across community members. We now include this observation in our revision (lines 209-211).

1. In the predicted vs observed occupancy plots, it would be nice to add curves showing predicted standard deviation or similar to give a sense of how well the model is predicting the variability.

Author response: In the revised manuscript we now include predictions for the variance of occupancy using the gamma distribution under both taxonomic and phylogenetic coarse-graining (Fig. S9; S10; lines 211-214).

1. Covariance between sister groups: Figs S9 and S10 look very nice, but it's hard to see much because they're log-log plots over multiple decades, while even a several-fold difference from y = x would indicate a strong effect of correlations. It would be clearer if the y-axis showed the ratio of the coarsegrained variance to the sum of OTU variances and we were looking at how well it fit y = 1.

Author response: We have included these plots in the revision (Fig. S14, S15).

1. If the sum of gammas can be well-approximated by a gamma, does that mean that the gamma is just a fairly flexible distribution and we shouldn't take the quality of the gamma fits in general as a very specific indication of what's going on?

Author response: While the sum of random variables that are drawn from gamma distributions with different parameters is often well-approximated by another gamma, this does not tell us why the gamma distribution holds for microbial communities at the finest-grain level (i.e., OTUs/ASVs). At present, the best explanation is that the gamma is a stationary distribution for certain stochastic differential equations which have ecological interpretations (Grilli, 2020; Shoemaker et al., 2023). Furthermore, alternative two-parameter distributions have been tested alongside the gamma and have done a comparatively poor job capturing observed macroecological patterns (Grilli, 2020). These results suggest that the utility of the gamma distribution is not simply an outcome of its flexible nature, it succeeds because it has captured core ecological properties of microbial communities. In the case of the SLM, gamma-like distributions arise when a community member is subject to self-limiting growth and environmental noise. On the other hand, the stability of the gamma distribution might explain why it can be detected as shape of the AFD, as it does not fade out across coarse-graining level.

1. What's going on with the variance of diversity in Fig S12? Does this suggest that some of the problem in Figure 4 could be with the analytic approximation rather than the model? I had a hard time understanding the part of the Methods explaining the simulation details (lines 587-597). It would be worth expanding this. Is there some way to explain how the correlations were simulated in terms of the SLM, e.g., correlations in the noise term across OTUs?

Author response: We believe that deviations in the variance of diversity in Fig. S16g,h are driven by small deviations in our predictions of the second moment $$< (x*ln(x) | N_{m}, \bar{x}{i}, \beta{i}^{2} >$$ (Eq. S16). Alone these predictions are slight, but their effects become noticeable when summed over hundreds or thousands of taxa. We have included this observation in the revised manuscript (lines 268-271). However, this deviation pales in comparison with the magnitude of covariance in the empirical data, suggesting that our inability to predict the variance of richness and diversity is primarily driven by our assumption of statistical independence.

Regarding the source of the correlations, under the SLM correlations in abundances can be introduced either by adding deterministic interaction terms or through correlated environmental noise. Determining which of these two options drives empirical correlations is an active area of research (e.g., Camacho-Mateu et al., 2023). For the purpose of this study, we remain agnostic on the cause of the correlations, optioning to instead emphasize that that the inclusion of correlations is necessary to reproduce observed slopes of the fine vs. coarse-grained relationship for diversity.

1. In Figure 5ab, is the idea that the correlation in richness is primarily driven by the number of samples from the environment? Line 390 seems to say so, but it would be good to make this explicit and put it right in that section of the Results.

Author response: Our results suggest that sampling effort (# reads) plays a larger role in determining the correlations between fine and coarse-grained measures of richness. We now clarify this point in the revised manuscript (lines 429-435).

1. I don't totally understand the contrast in lines 369-372. If fine-scale diversity within one group begets coarse-grained diversity in another group, couldn't that show up as correlations in the AFDs? Or is the argument that only including within-group correlations in AFDs is enough to reproduce the pattern? I'm not sure I see how that could be.

Author response: The term “begets” implies both causation and direction. If we see a positive relationship between diversity estimates at two different scales of observation the causal mechanism cannot be determined solely from correlations between samples obtained once from different sites. So, mechanisms consistent with niche construction/"DBD" can produce correlations, though the existence of correlations do not necessarily imply DBD.

1. The discussion of niche construction on 429-431 doesn't match very well with 440-441. Basically, niche construction is a very broad concept, not a specific one, right?

Author response: In lines 472-576 (formerly 429-431) we discuss how the existence of correlations between fine and coarse-grained scales does not point to a single ecological mechanism. Alternatively stated, observing a non-zero slope does not mean that niche construction is driving the relationship.

In lines 476-487 (formerly 440-441) we discuss how the mechanism of cross-feeding has been shown to generate a positive relationship between fine and coarse-grained measures of diversity. This mechanism can be interpreted as a form of “niche construction”, so it is an instance of a tested ecological mechanism that aligns with the interpretation given in Madi et al. (2020).

1. Isn't (8) just the negative binomial distribution?

Author response: The convolution of the stationary solution of the SLM (i.e., a gamma distribution) and the Poisson limit of a multinomial sampling distribution returns a negative binomial distribution of read counts across hosts if samples have identical sampling depths. We now include this detail in the revision (line 593-595). Note however that if different samples have different sampling depths, the distribution of reads across samples is not a negative binomial.

1. Missing 1/M in (9).

Author response: We have fixed this omission in the revision.

1. Schematic figures illustrating what the different statistics are intuitively capturing would really help this work be understandable to a broader audience, but they'd also be a ton of work.

Author response: Richness and diversity are used in ecology to such an extent that we do not see the benefit of a conceptual diagram. Furthermore, we have included a conceptual diagram about our pipeline in our revision at the request of Reviewer 2 (Fig. S20).

Reviewer #2:

Major Recommendations

If I were reviewing this manuscript for a regular journal, I believe the following issues would be important to address prior to publication.

1. From my reading, the main points of this advance are that

a. SLM models AFDs well at all levels of coarse-graining.

b. This makes SLM a better null-model than UNTB for macroecological relationships.

c. Using SLM on the EMP data, the richness slopes are well explained by SLM but not the diversity slopes. Therefore, any theory that hopes to explain the diversity slopes must include interactions. Argument B appears to be one of the key points yet is missing from the abstract, and should be made clearer. If these aren't the main points the authors intended, then other main points need to be highlighted more.

Author response: In the revision we now explicitly mention argument b in the Abstract.

1. The title should be more specific, so as to better reflect the content. (E.g. "UNTB is not a good null model for macroecological patterns" would seem more appropriate.)

Author response: We would prefer to focus on the success of the SLM rather than the limitations of the UNTB in the title of this work. Therefore, we have modified our title as follows: “Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth”.

1. The manuscript would benefit from a clearer description of exactly what information the SLM retains about the data (perhaps even a cartoon panel in one of the figures). In particular, it is important to be explicit about the number of model parameters.

Author response: The number of model parameters for the gamma AFD are now explicitly stated in the revision (Lines 579-580).

1. The main point of Figures 2-4 seems to be that SLM is good at describing the data (and when it fails it is due to interactions) while UNTB fails to reproduce this behavior, in support of Argument B. This is not clear from the figure descriptions or titles, which focus on SLM's "predictive" power.

Author response: Fig. 2a demonstrates that the gamma distribution predicted by the SLM explains the empirical distribution of abundances. This result provides motivation to predict the fraction of sites harboring a given community member (i.e., occupancy, Fig. 2c) as well as general measures of community composition including mean richness (Fig. 3a,c) and mean diversity (Fig. 3b,d) using parameters estimated from the data (not free parameters).

This success led us to consider whether the gamma distribution could predict the variance of richness and diversity, which it could not because it does not capture covariance between community members (Fig. 4).

In the revision we have identified opportunities to make these points clear throughout the Results. Furthermore, we have added additional detail to the legends of Figs. 2-4.

1. The manuscript would benefit from clarifying the use of "prediction" related to the SLM. Since the gamma distributions predicted by SLM were fit to empirical data, it seems like the agreement between analytic means and empirical means (Fig. 3) is a statement on gamma distributions being a good fit for the AFD's more than SLM predicting richness and diversity. For example, from my reading, it seems like this analysis could be done numerically by shuffling species abundances across environments and seeing whether this changed the mean richness/diversity. I would not call this shuffling test a prediction, since it is more a statement on the relevance of interactions. SLM predicts gamma-distributed AFD's, but those distributions recovering the data they were trained on doesn't seem like a prediction.

Author response: In this manuscript we identified the gamma distribution as an appropriate probability distribution to describe the distribution of relative abundances across samples over a range of coarse-grained scales. Motivated by this result, we performed a separate analysis where at each scale we estimated the mean and variance of relative abundance across sites for each community member. We then used these parameters to obtain the expected value of a community-level measure using an equation we derived by assuming that the gamma distribution was appropriate (e.g., richness, Eq. 13). We then compared the expected value of richness to the mean value from empirical data and assessed the similarity between the two values.

The outcome of this procedure constitutes a prediction. While the mean and variance are parameters, estimating them from the empirical data has no connection with the operation of training a distribution on empirical data. We could have derived predictions such as Eq. 13 using any other probability distribution that can be parameterized using the mean and variance (e.g., Gaussian). Such a prediction would likely do a poor job even though it used the same means and variances used for our gamma predictions. This is because the choice of distribution would not have been a good descriptor of the distribution of abundances across hosts.

To better explain this last -- perhaps the most significant -- issue, I'd like to ask the authors if the following recasting would be an accurate reflection of their conclusions, or if something is missing.

1. "Focusing on the empirical relationship observed between diversity slopes by Madi 2020, we ask the question: does explaining these relationships require accounting for species-species correlations? Or could it be reproduced in a noninteracting model?" To address this question, one can perform a randomization test, shuffling abundances to preserve all single-OTU statistics but breaking any correlations. My reading of the authors' results is that (new result 1) the richness relationships would be preserved, while diversity relationships would not be preserved. [Note that this result 1 need not mention either SLM or UNTB.]

Author response: The question of whether correlations between species are necessary to explain the observed slope of the fine vs. coarse-grained relationship was only one component of our research goals. Our first question was whether the SLM would prove to be a more appropriate null for evaluating the novelty of observed slopes. We believe that our results support the conclusion that the SLM is an appropriate null for this question, as it was able to capture observed slopes of the fine vs. coarse-grained relationship for estimates of richness, determining that correlations and the interactions that are ultimately responsible are not necessary to explain this result.

We then find that the SLM as a null model fails to capture observed slopes of the fine vs. coarsegrained relationship for estimates of diversity and simulate the SLM with correlations to return reasonable estimates of the slope. However, here the question about correlations is a direct follow-up from our question about a null model that excludes interactions, so it is unclear how a randomization test would relate to this result.

1. Instead of doing a randomization test (resampling the empirical distribution), one might insist on instead fitting a model to the AFD distributions, and sampling from that distribution rather than the empirical one.

a. If doing it this way, one should of course ensure that the distribution being fit is a good description of the data.

b. UNTB is a bad fit. SLM is a better fit, and in fact (new result 2) continues to be a good empirical fit even at coarse-grained levels.

c. Can make statements on using SLM as a null model for these types of cross-scale relationships. Could try arguing that fitting an SLM model per-OTU (instead of resampling the empirical distribution) could offer some advantage if certain properties could be computed analytically from the fit parameters, instead of averaging over multiple computational rounds of resampling.

Do these two points accurately summarize the manuscript? If so, this presentation avoids the confusion with "prediction". If my summary is missing some important point, the presentation should be revised to clarify the points I appear to have missed.

Author response: In our manuscript we derive predictions from the gamma distribution, the stationary distribution of the SLM, that require parameters estimated from the data (i.e., mean and variance of relative abundance). These parameters are estimated from the data using normal procedures and then plugged into our predictions that assume the appropriateness of the gamma, returning values that are then compared to estimates from empirical data. Our estimation of the mean and variance does not assume that the empirical distribution following a gamma distribution, but the value returned by our function derived from the gamma distribution (e.g., Eq. 13) does make that assumption.

To address the reviewer’s broader comment, we believe that following points summarize our manuscript:

1. The gamma distribution as a stationary solution of the SLM captures macroecological patterns and predicts typical community-level properties (i.e., mean richness and diversity) across phylogenetic and taxonomic scales.

2. The gamma distribution fails to predict variation in community-level properties (i.e., variance of richness and diversity) across phylogenetic and taxonomic scales. This occurs because the SLM is a mean-field model that does not explicitly include interactions between community members.

3. Despite the inability to capture interactions, the gamma distribution succeeds at predicting the fine vs. coarse-grain slope for richness, a pattern that had previously been attributed to community member interactions. This result demonstrates that the novelty of a macroecological pattern hinges on one’s choice of null model.

4. However, the gamma cannot capture the same relationship for diversity. Simulations of the gamma distribution that incorporate correlations between community members are capable of generating reasonable estimates of the slope.

To address the reviewer’s comments regarding the appropriateness fitted gamma distributions, in our revision we have added fitted gamma distributions to plots of AFDs so that the reader can visually assess the ability of the gamma to describe empirical patterns (Fig. S3, S4).

We have also obtained predictions for the slope of the fine vs. coarse-grained relationship for community richness using the same form of UNTB used by Madi et al (2020). In our revised manuscript we establish a procedure to infer the single parameter of this model, generate predictions of richness at fine and coarse-grained scales, and then evaluate whether the UNTB is capable of predicting the slope of the fine vs. coarse-grained relationship for richness (Supplementary Information; Figs. S18, 24-28; lines 277-278; 370-380).

Other/minor comments

1. The manuscript would be improved with more consistent terminology ("fine vs. coarse-grained relationship"/"the relationship" vs. "diversity slope"). Also, many readers may be used to OTUs referring to the rather fine level of description, as opposed to any chosen level; and could interpret indexing over groups as being in contrast with indexing over OTU's (coarse vs fine). The authors' use is perfectly correct, but keeping a consistent terminology would help.)

Author response: We have revised our manuscript to specify the “slope” as the “slope of the fine vs. coarse-grained relationship” (e.g., Line 318). We also specify in the Results and in the Methods that we use “fine” and “coarse” as relative terms, keeping with the sliding-scale approach used in Madi et al (2020).

1. While I appreciate this "slope" is something borrowed from other work, the clarity of the paper might benefit from a cartoon of how one goes from the raw data to the slopes at a particular coarse-graining level. (Optional).

Author response: We had added a conceptual diagram to the revision (Fig. S20).

1. The text often colloquially references "the gamma," "predictions of the gamma," etc. This phrasing comes across as sloppy, and the manuscript would be improved by being more specific.

Author response: We now specify “gamma” as the “gamma distribution” throughout the manuscript.

1. Equation 6 appears to be missing some subscripts on the x terms (included on the left of the equation).

Author response: We thank the reviewer for noticing this error and we have corrected it in the revision.

1. In "Simulating communities of correlated...AFDs", the acronym SAD is not defined.

Author response: We thank the reviewer for noticing this error and we have corrected it in the revision.

1. In Figure 2:

a. Invariant is probably the wrong word for the title, since all the AFD's were rescaled by mean and variance before being compared. Data does support that the gamma distributions are good at describing the AFD's, but as stated in the description it's the general shape that is preserved, not the distribution itself.

Author response: When we mention the invariance of the AFD we now specify that we mean that the shape of the distribution remained qualitatively invariant.

b. I'd recommend changing the color coding to something with more contrast, since currently it's impossible to assess the claim that the shape of the distribution collapses.

Author response: Our coarse-graining procedure is a sequential operation that has no intuitive point that would suggest the use of a contrasting colormap (e.g., if our scale ranged from -1 to 1 then there would be a natural point of contrast at zero).

c. The legend is missing relevant technical details: How many OTU's were used to make plot a? How many samples?

Author response: The number of samples was listed in the Materials and Methods (line 523). In the revision we now include a table with the average and total number of OTUs as well as the average number of reads for each environment (Table S1, S2).

d. In plot b, is the mean relative abundance referring to "mean abundance when observed" or "mean across all samples"?

Author response: The mean relative abundance is the mean abundance across all sites (line 204) and in the legend of Fig. 2.

e. Since one argument here is that SLM fits these distributions better than UNTB, if possible it would be nice to see UNTB's failed fits here.

Author response: A major feature of the UNTB is that the demographic parameters of community members are indistinguishable. Under the SLM, the variation in the mean relative abundance we observe suggests that the carrying capacities of community members vary over multiple orders of magnitude, a result that is incompatible with most forms of the UNTB (x-axis of Fig. 2b). We now mention this point in the revised manuscript (lines 110; 229; 455-471).

1. In Figure 3:

a. It is not clear how coarse-graining is included in model fitting. The "Deriving biodiversity measure predictions" section would benefit from including how coarse-graining is incorporated.

Author response: We predict measures of biodiversity separately at each coarse-grained scale. We now clarify this detail in the revised manuscript (Lines 624-627).

b. Reference Shannon Diversity in Methods.

Author response: We now cite Shannon’s diversity.

c. What is the blue/white color coding in plots a & c? It doesn't have any color key.

Author response: Figs. 3-6 use a uniform light-to-dark scale for all environments, with each environment having its own color. For example, Fig. 3a contains data from the human gut microbiome. Human gut data were assigned the color aquamarine, so the shade of aquamarine for a given datapoint in Fig. 3a indicates the phylogenetic scale.

In the revision we now clarify the colorscale in the legend of Fig. 3 and specify that the same scale is used in all subsequent figure legends.

d. Re: earlier comments, why is richness considered a prediction? (Am I correct in my interpretation that panel b is almost a tautology - counting the number of zeros in the matrix either by rows or by columns - whereas panel d is nontrivial?)

Author response: Mean richness as a measure of biodiversity depends on the fraction of sites where a given community member is present (i.e., occupancy). The mean relative abundance of a community member and its variation across sites (beta) is clearly related to occupancy, but those two statistics do not give you a prediction of occupancy. Obtaining a prediction of occupancy and, subsequently, richness, requires 1) a probability distribution of abundances (i.e., the gamma) and 2) a probability distribution of sampling (i.e., the Poisson). Using these two pieces of information, we derived a prediction for mean richness (Eq. 13). We then compare the value of richness obtained by plugging in the mean relative abundances, betas, and known number of reads to the observed mean richness obtained from the data.

e. The lettering of subplots in Figure 3 is not consistent with Figure 4. Figure 3 subplots are also cited incorrectly in paragraph two on page six (lines 251-254).

Author response: We thank the reviewer for noticing the error and we have corrected it in the revision.

f. Again, if possible show UNTB predictions in plots a & c.

Author response: In our revised manuscript we provide extensive descriptions and predictions of mean richness and the slope of the fine vs. coarse-grained relationship for richness using the form of the UNTB used in Madi et al. (2020; Figs. S18, S24 - S29; lines 277-282; 370-380). We then compare the error of these slope predictions to those obtained from the SLM, finding that the SLM generally outperforms UNTB (Figs. S27-S29).

1. In Figure 4:

a. What are the color codings in plots a & b?

Author response: The color scale used in Fig. 4 is identical to the color scale used in Fig. 3. This detail is now specified in the legend of Fig. 4.

b. What are the two lines of empirical data in plots a & b, and why is one of them dashed?

Author response: We now specify what the two lines mean in the key within the figure.

c. Same comment as earlier on predictions and richness.

Author response: We now specify what the two lines mean in the key within the figure.

1. In Figure 5:

a. It wasn't clear to me in the manuscript how the authors generated these plots from the raw data. The manuscript would benefit from a clear cartoon/description of the data pipeline, from raw data to empirical (and analytic) slopes.

Author response: We have added a conceptual diagram to the revised manuscript (Fig. S20).

b. Make the figure title more descriptive to better connect it to the figure's objective (the richness slopes relationship is not novel, but the diversity slopes relationship is).

Author response: We have revised the figure title.

References

Camacho-Mateu, J., Lampo, A., Sireci, M., Muñoz, M. Á., & Cuesta, J. A. (2023). Species interactions reproduce abundance correlations patterns in microbial communities (arXiv:2305.19154). arXiv. https://doi.org/10.48550/arXiv.2305.19154

Grilli, J. (2020). Macroecological laws describe variation and diversity in microbial communities. Nature Communications, 11(1), 4743. https://doi.org/10.1038/s41467-020- 18529-y

Madi, N., Vos, M., Murall, C. L., Legendre, P., & Shapiro, B. J. (2020). Does diversity beget diversity in microbiomes? eLife, 9, e58999. https://doi.org/10.7554/eLife.58999

Shoemaker, W. R., Sánchez, Á., & Grilli, J. (2023). Macroecological laws in experimental microbial systems (p. 2023.07.24.550281). bioRxiv. https://doi.org/10.1101/2023.07.24.550281

This all makes sense to someone out there. Not me. From what I can gather, this is an explanation of how the researchers quantitatively gather and describe their results.

Author Response

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

eLife assessment

This important study advances our understanding of the ways in which different types of communication signals differentially affect mouse behaviors and amygdala cholinergic/dopaminergic neuromodulation. Researchers interested in the complex interaction between prior experience, sex, behavior, hormonal status, and neuromodulation should benefit from this study. Nevertheless, the data analysis is incomplete at this stage, requiring additional analysis and description, justification, and - potentially - power to support the conclusions fully. With the analytical part strengthened, this paper will be of interest to neuroscientists and ethologists.

GENERAL COMMENTS ON REVIEWS AND REVISIONS

Experimental design

Here we address questions from several reviewers regarding our periods of neuromodulator and behavioral analysis. First, we recognize that the text would benefit from an overview of the experimental structure different from the narrative we provide in the first paragraphs of the Results. We now include this near the beginning for the Materials and Methods (page 17). We further articulate that the 10-minute time periods were dictated by the sampling duration required to perform accurate neurochemical analyses (and to reserve half of the sample in the event of a catastrophic failure of batch-processing samples). Since neurochemical release may display multiple temporal components (e.g., ACh: Aitta-aho et al., 2018) during playback stimulation, and since these could differ across neurochemicals of interest, we decided to collect, analyze, and report in two stimulus periods as well as one Pre-Stim control. We now clarify this in additional text in the Material and Methods (p. 24, lines 20-22; p. 26, lines 17-19). We decided not to include analyses of the post-stimulus period because this is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback.

We also sought to clarify the meaning of the periods “Stim 1” and “Stim 2”; they are two data collection periods, using the same examplar sequences in the same order. We have added statements in the Material and Methods (p. 18, lines 4-7; Fig. caption, p. 39, lines 11-13) to clarify these periods.

For behavioral analyses, observation periods were much shorter than 10 mins, but the main purpose of behavioral analyses in this report is to relate to the neurochemical data. As a result, we matched the temporal features of the behavioral and neurochemical analyses (p. 22, lines 17-22). We plan a separate report, focused exclusively on a broader set of behavioral responses to playback, that may examine behaviors at a more granular level.

Data and statistical analyses

Reviewers 1 and 3 expressed concerns about our normalization of neurochemical data, suggesting that it diminishes statistical power or is not transparent. We note that normalization is a very common form of data transformation that does not diminish statistical power. It is particularly useful for data forms in which the absolute value of the measurement across experiments may be uninformative. Normalization is routine in microdialysis studies, because data can be affected by probe placement and factors affecting neurochemical recovery and processing. Recent examples include:

Li, Chaoqun, Tianping Sun, Yimu Zhang, Yan Gao, Zhou Sun, Wei Li, Heping Cheng, Yu Gu, and Nashat Abumaria. "A neural circuit for regulating a behavioral switch in response to prolonged uncontrollability in mice." Neuron (2023).

Gálvez-Márquez, Donovan K., Mildred Salgado-Ménez, Perla Moreno-Castilla, Luis Rodríguez-Durán, Martha L. Escobar, Fatuel Tecuapetla, and Federico Bermudez-Rattoni. "Spatial contextual recognition memory updating is modulated by dopamine release in the dorsal hippocampus from the locus coeruleus." Proceedings of the National Academy of Sciences 119, no. 49 (2022): e2208254119.

Holly, Elizabeth N., Christopher O. Boyson, Sandra Montagud-Romero, Dirson J. Stein, Kyle L. Gobrogge, Joseph F. DeBold, and Klaus A. Miczek. "Episodic social stress-escalated cocaine self-administration: role of phasic and tonic corticotropin releasing factor in the anterior and posterior ventral tegmental area." Journal of Neuroscience 36, no. 14 (2016): 4093-4105.

Bagley, Elena E., Jennifer Hacker, Vladimir I. Chefer, Christophe Mallet, Gavan P. McNally, Billy CH Chieng, Julie Perroud, Toni S. Shippenberg, and MacDonald J. Christie. "Drug-induced GABA transporter currents enhance GABA release to induce opioid withdrawal behaviors." Nature neuroscience 14, no. 12 (2011): 1548-1554.

However, since all reviewers requested raw values of neurochemicals, we provide these in supplementary tables 1-3. The manuscript references these table early in the Results (p. 6, lines 18-19) and in the Material and Methods (p. 27, lines 3-4)

All reviewers commented on correlation analyses that we presented, with different perspectives. Reviewer 2 questioned the validity of such analyses, performed across experimental groups, while Reviewer 1 pointed out that the analyses were redundant with the GLM. We agree with these criticisms, and note the challenges associated with correlations involving behaviors for which there is a “floor” in the number of observations. As a result, we have removed most correlation analyses from the manuscript. The text and figures have been modified accordingly. Due these changes, we have to decline requests of Reviewer 3 to include many more such analyses. While correlation analyses could still be performed between neurochemicals and behaviors for each group, the relatively small size of each experimental group, the large number of groups, and the even larger numbers of pairings between neurochemicals and behavior, the statistical power is very low. The only correlations we utilize in the manuscript concern the interpretation of our increased acetylcholine levels.

As part of this revision, we re-ran our statistical analyses on neuromodulators because of a calculation error in 3 animals (regarding baseline values). In a few instances, a significance level changed, but none of these changed a conclusion regarding neuromodulator changes under our experimental conditions.

Other revisions

INTRODUCTION: We modified the Introduction to provide both a more general framework and specific gaps in our understanding relating neuromodulators with vocal communication.

DISCUSSION: We have added material in the first two pages of the Discussion to provide more framework to our conclusions, to address the issues of the temporal aspects of neurochemical release and behavioral observations, and to identify limitations that should be addressed in future studies.

FIGURES: All figures are now in the main part of the manuscript. We modified most figures in response to reviewer comments. We removed neuromodulator – behavior correlations from several figures. We modified all box plots to ensure that all data points are visible. The visible data points match the numbers reported in figure captions. We brought 5-HIAA data into the main figures reporting on neuromodulator results.

Public Reviews:

Reviewer #1 (Public Review):

The manuscript addresses a fundamental question about how different types of communication signals differentially affect brain states and neurochemistry. In addition, the manuscript highlights the various processes that modulate brain responses to communication signals, including prior experience, sex, and hormonal status. Overall, the manuscript is well-written and the research is appropriately contextualized. The authors are thoughtful about their quantitative approaches and interpretations of the data.

That being said, the authors need to work on justifying some of their analytical approaches (e.g., normalization of neurochemical data, dividing the experimental period into two periods (as opposed to just analyzing the entire experimental period as a whole)) and should provide a greater discussion of how their data also demonstrate dissociations between neurochemical release in the basolateral amygdala and behavior (e.g., neurochemical differences during both of the experimental periods but behavioral differences only during the first half of the experimental period). The normalization of neurochemical data seems unnecessary given the repeated-measures design of their analysis and could be problematic; by normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) that could inflate statistical power.

Please see our general responses to structure of observation periods and normalization of neuromodulator data. Normalization is a common and appropriate procedure in microdialysis studies that does not alter statistical power.

We have included a section in the Discussion concerning the temporal relationship between behavioral responses and neurochemical changes in response to vocal playback (p. 12, lines 3-17). We note where the linkage is particularly strong (e.g., ACh release and flinching). This points to a need to examine these phenomena with finer temporal resolution, but also with the recognition that the brain circuits driving a behavioral response may extend beyond the BLA.

The Introduction could benefit from a priori predictions about the differential release of specific neuromodulators based on previous literature.

We added some material to the Introduction to provide additional rationale for the study. However, we did not attempt to develop predictions for the range of neuromodulators that we sought to test. The literature can lead to opposite predictions for a given neuromodulator. For example, acetylcholine could be associated with both positive and negative valence. Instead, we note in the Introduction the association of both DA and ACh with vocalizations.

The manuscript would also benefit from a description of space use and locomotion in response to different valence vocalizations.

We have provided additional descriptions of space use and video tracking data in Material and Methods (p. 23, lines 1-6). We now report a few correlations based on these data in the Results to demonstrate that increased ACh in Restraint males and Mating estrus females was not related to the amount of locomotion (p. 9, lines 8-14).

Nevertheless, the current manuscript seems to provide some compelling support for how positive and negative valence vocalizations differentially affect behavior and the release of acetylcholine and dopamine in the basolateral amygdala. The research is relevant to broad fields of neuroscience and has implications for the neural circuits underlying social behavior.

Reviewer #2 (Public Review):

Ghasemahmad et al. report findings on the influence of salient vocalization playback, sex, and previous experience, on mice behaviors, and on cholinergic and dopaminergic neuromodulation within the basolateral amygdala (BLA). Specifically, the authors played back mice vocalizations recorded during two behaviors of opposite valence (mating and restraint) and measured the behaviors and release of acetylcholine (ACh), dopamine (DA), and serotonin in the BLA triggered in response to those sounds.

Strength: The authors identified that mating and restraint sounds have a differential impact on cholinergic and dopaminergic release. In male mice, these two distinct vocalizations exert an opposite effect on the release of ACh and DA. Mating sounds elicited a decrease of Ach release and an increase of DA release. Conversely, restraint sounds induced an increase in ACh release and a trend to decrease in DA. These neurotransmission changes were different in estrus females for whom the mating vocalization resulted in an increase of both DA and ACh release.

Weaknesses: The behavioral analysis and results remain elusive, and although addressing interesting questions, the study contains major flaws, and the interpretations are overstating the findings.

Although Reviewer 2 raises several valid issues that we have addressed in our response and revision, we believe that none represent “major flaws” in the study that challenge the validity of our central conclusions. In brief, we will:

--provide enhanced description of behaviors (pp. 22-23 and Table 1)

--clarify / modify box-plot representations of data (p 28. Lines 3-9)

--point to our methods that describe corrections for multiple comparisons (p. 27; lines 15-16)

--revise figures to clarify sample size (Figs. 3-6)

Reviewer #3 (Public Review):

Ghasemahmad et al. examined behavioral and neurochemical responses of male and female mice to vocalizations associated with mating and restraint. The authors made two significant and exciting discoveries. They revealed that the affective content of vocalizations modulated both behavioral responses and the release of acetylcholine (ACh) and dopamine (DA) but not serotonin (5-HIAA) in the basolateral amygdala (BLA) of male and female mice. Moreover, the results show sex-based differences in behavioral responses to vocalizations associated with mating. The authors conclude that behavior and neurochemical responses in male and female mice are experience-dependent and are altered by vocalizations associated with restraint and mating. The findings suggest that ACh and DA release may shape behavioral responses to context-dependent vocalizations. The study has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the BLA while an animal listens to social vocalizations; however, multiple concerns must be addressed to substantiate their conclusions.

Major concerns:

1) The authors normalized all neurochemical data to the background level obtained from a single pre-stimulus sample immediately preceding playback. The percentage change from the background level was calculated based on a formula, and the underlying concentrations were not reported. The authors should report the sample and background concentrations to make the results and analyses more transparent. The authors stated that NE and 5-HT had low recovery from the mouse brain and hence could not be tracked in the experiment. The authors could be more specific here by relating the concentrations to ACh, DA, and 5-HIAA included in the analyses.

Please see our general statement regarding normalization of neurochemical data. We have added supplemental tables that shows concentrations of dopamine, acetylcholine, 5-HIAA. We do not report serotonin or noradrenalin since these were below the detection threshold.

2) For the EXP group, the authors stated that each animal underwent 90-min sessions on two consecutive days that provided mating and restraint experiences. Did the authors record mating or copulation during these experiments? If yes, what was the frequency of copulation? What other behaviors were recorded during these experiences? Did the experiment encompass other courtship behaviors along with mating experiences? Was the female mouse in estrus during the experience sessions?

In the mating experience, mounting or attempted mounting was required for the animal to be included in subsequent testing. Since the session lasted 90 minutes, more general courtship behavior was likely. However, we did not record detailed behaviors or track estrous stage for the mating experience. See p. 21, line 20-22.

3) For the mating playback, the authors stated that the mating stimulus blocks contained five exemplars of vocal sequences emitted during mating interactions. The authors should clarify whether the vocal sequences were emitted while animals were mating/copulating or when the male and female mice were inside the test box. If the latter was the case, it might be better to call the playback "courtship playback" instead of "mating playback".

We have modified the Results (p. 5, lines 18-20) and Materials and Methods (p. 21, lines 8-15) to clarify our meaning. We continue to use the term “mating” because this refers to a specific set of behaviors associated with mounting and copulation, rather than the more general term “courtship”. We also indicate that we based these behaviors on previous work (e.g., Gaub et al., 2016).

4) Since most differences that the authors reported in Figure 3 were observed in Stim 1 and not in Stim 2, it might be better to perform a temporal analysis - looking at behaviors and neurochemicals over time instead of dividing them into two 10-minute bins. The temporal analysis will provide a more accurate representation of changes in behavior and neurochemicals over time.

Please see our general response to the structuring of experimental periods. The 10-min periods are the minimum for the neurochemical analyses, and we adopted the same periods for behavioral analyses to match the two types of observations. Our repeated measures analysis is a form of temporal analysis, since it compares values in three observation periods.

5) In Figures 2 and 3, the authors show the correlation between Flinching behavior and ACh concentration. The authors should report correlations between concentrations of all neurochemicals (not just ACh) and all behaviors recorded (not just Flinching), even if they are insignificant. The analyses performed for the stim 1 data should also be performed on the stim 2 data. Reporting these findings would benefit the field.

Please see general comments regarding correlation analyses. We removed almost all such analyses and references to them from the manuscript based on concerns of the other reviewers.

6) The mice used in the study were between p90 - p180. The mice were old, and the range of ages was considerable. Are the findings correlated with age? The authors should also discuss how age might affect the experiment's results.

Our p90-p180 mice are not “old”. CBA/CaJ mice display normal hearing for at least 1 year (Ohlemiller, Dahl, and Gagnon, JARO 11: 605-623, 2010) and adult sexual and social behavior throughout our observation period. They are sexually mature adults, appropriate for this study. We decline to perform correlation analyses with age, both because this was not a question for this study and because the very large number of correlations, for each experimental group (as requested by reviewer #2), render this approach statistically problematic.

7) The authors reported neurochemical levels estimated as the animals listened to the sounds played back. What about the sustained effects of changes in neurochemicals? Are there any potential long-term effects of social vocalizations on behavior and neurochemical levels? The authors might consider discussing long-term effects.

We have not included discussion of long term effects of neuromodulatory release, both because our data analysis doesn’t address it (see response to Comment #10) and because we desired to keep the Discussion focused on topics more closely related to the results.

8) Histology from a single recording was shown in supplementary figure 1. It would benefit the readers if additional histology was shown for all the animals, not just the colored schematics summarizing the recording probe locations. Further explanation of the track location is also needed to help the readers. Make it clear for the readers which dextran-fluorescein labeling image is associated with which track in the schematic.

Based on the recent publications cited in our overall response to reviewer comments about statistical methods, our reporting of histological location of microdialysis exceeds the standard. We believe that the inclusion of all histology is unnecessary and not particularly helpful. Raw photomicrographs do not always illustrate boundaries, so interpretation is required. However, we added a second photomicrograph example and we identified which tracks correspond to these photomicrographs (see Figure 2; now in main body of manuscript).

9) The authors did not control for the sounds being played back with a speaker. This control may be necessary since the effects are more pronounced in Stim 1 than in Stim 2. Playing white noise rather than restraint or courtship vocalizations would be an excellent control. However, the authors could perform a permutation analysis and computationally break the relationship between what sound is playing and the neurochemical data. This control would allow the authors to show that the actual neurochemical levels are above or below chance.

We considered a potential “control” stimulus in our experimental design. We concluded, based on our previous work (e.g., Grimsley et al., 2013; Gadziola et al., 2016), that white noise is not or not necessarily a neutral stimulus and therefore the results would not clarify the responses to the two vocal stimuli. Instead, we opted to use experience as a type of control. This control shows very clearly that temporal patterns and across-group differences in neurochemical response to playback disappear in the absence of experience with the associated behavior.

10) The authors indicated that each animal's post-vocalization session was also recorded. No data in the manuscript related to the post-vocalization playback period was included. This omission was a missed opportunity to show that the neurochemical levels returned to baseline, and the results were not dependent on the normalization process described in major concern #1. The data should be included in the manuscript and analyzed. It would add further support for the model described in Figure 6.

We decided not to include analyses of the post-stimulus period because this period is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback. We agree that the general question is of interest to the field, but we don’t think our study is best designed to answer that question.

11) The authors could use a predictive model, such as a binary classifier trained on the CSF sampling data, to predict the type of vocalizations played back. The predictive model could support the conclusions and provide additional support for the model in Figure 6.

We recognize that a binary classifier could provide an interesting approach to support conclusions. However, we do not believe that the sample size per group is sufficient to both create and test the classifier.

Reviewer #1 (Recommendations For The Authors):

Major comments:

• Introduction: It would be useful to set up an experimental framework before delving into the results. What are the predictions about specific neuromodulators based on previous literature?

Because this narrative is laid out in the first two paragraphs of the Results, which immediately follow the Introduction, we believe that additional text in the Introduction on the experimental framework is redundant. As stated above, detailing predictions for a range of neuromodulators would make for a long and not particularly illuminating Introduction. We instead have related our findings to more general understanding of DA and ACh in the Discussion.

• There really isn't a major difference in stimuli during the "Stim 1" and "Stim 2" phases, and it's not clear why the authors divided the experimental period into two phases. Therefore, the authors need to justify their experimental approach. For example, the authors could first anecdotally mention that behavioral responses to playbacks seem to be larger in the first half of the playbacks than during the second half, therefore they individually analyzed each half of the experimental period. Or adopt a different approach to justify their design. Overall, the analytical approach is reasonable but it is currently not justified.

See general comment for analysis periods. As noted, we clarified these issues in several locations with Materials and Methods (pp. 24, lines 20-22; p. 26, lines 17-19). We also sought to clarify the meaning of the periods “Stim 1” and “Stim 2”; they are two data collection periods, using the same examplar sequences in the same order. We have added statements in the Material and Methods (p. 18, lines 4-7; Fig. caption, p. 39, lines 11-13).

• The normalization of neurochemical data seems problematic and unnecessary. By normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) and this has implications for statistical power. Because the analysis is a within-subjects analysis, this normalization is not necessary for the analysis itself. It can be useful to normalize data for visualization purposes, but raw data should be analyzed. Indeed, behavioral data are qualitatively similar to the neurochemical data, and those data are not normalized to baseline values.

Please see our general comment on this issue. We believe normalization does not affect statistical power and is both the standard way and an appropriate way to analyze microdialysis results. We include concentrations of ACh, DA, and 5-HIAA in supplementary tables?

• The authors should include a discussion (in the Discussion section) of how behavior and neurochemical release are associated during the first half of the experimental session but not in the second half (e.g., differences in Ach and DA release between mating and restraint groups during stim 1 and 2, but behavioral differences only during stim 1).

We have included a section in the Discussion concerning the temporal relationship between behavioral responses and neurochemical changes in response to vocal playback. We note that the linkage is particularly strong in some cases (e.g., ACh release and flinching). This points to a need to examine these phenomena with finer temporal resolution, but also with the recognition that the brain circuits driving a behavioral response may extend beyond the BLA.

Minor comments:

• Keywords: add "serotonin" (even though there are no significant differences on 5-HIAA, people interested in serotonin would find this interesting).

Added to keywords list.

• Do the authors collect data on the vocalizations of mice in response to these playbacks?

We monitored vocalizations during playback, noting that vocalizations–especially “Noisy” vocalization–were common. However, we did not record vocalizations and are therefore unable quantify our observations.

• First line of page 7: readers do not know about "stim 1" and "stim 2". Therefore, the authors need to describe their approach to analyzing behavior and neurochemical release.

We first introduce these terms earlier, citing Figure 1D,E. We have added some additional wording for further clarification. page 7, lines 4-5.

• Make sure citations are uniformly formatted (e.g., Inconsistencies in: "As male and female mice emit different vocalizations during mating (Finton et al., 2017; J. M. S. Grimsley et al., 2013; Neunuebel et al., 2015; Sales (née Sewell), 1972)").

We have reviewed and corrected citations throughout the manuscript.

• Last paragraph of page 7: "attending behavior" has not been defined yet.

Table 1 contains our description of the behaviors analyzed in this study. We have now inserted a reference to Table 1 earlier in the Results (p. 6, line 12).

• Figure 2E and 3G: I find these correlations to be redundant with the GLMs. This is because the significant relationship is likely to be driven by group differences in behavior and in neurochemical release.

Please see general comments regarding correlation analyses. We removed such analyses and references to them from the manuscript.

• Page 2, 2nd paragraph, 2nd sentence: this paragraph seems to be rooted in comparing and contrasting experienced and inexperienced mice, so there should be explicit comparisons in each sentence. For example, the 2nd sentence should read: "Whereas EXP estrus females demonstrated increased flinching behaviors in response to mating vocalizations, INEXP ....". This paragraph overall could use some refining.

We believe this refers to page 9. We have revised the paragraph to clarify our findings (Beginning p. 9, line 23).

• Page 9: "Further, there were no significant differences across groups during Stim 1 or Stim 2 periods. These results contrast sharply with those from all EXP groups, in which both ACh and DA release changed significantly during playback (Figs. 2C, 2D, 3E, 3F)." While I understand their perspective, this is misleading because changes were only observed during the Stim 1 period.

We have slightly revised the wording in this paragraph, because the restraint males did not show significant ACh decreases. However, we do not believe our statements mislead readers just because some changes are observed in only one of the stimulation periods (p 10, lines 13-16).

• Last paragraph of page 14: it would be useful to mention the increase in flinching in experienced females in response to mating vocalizations.

We have added a sentence in this paragraph relating flinching in estrus females to increased ACh (p. 15, lines 18-20).

• Was there a full analysis of locomotion in response to playbacks? I see that locomotion was correlated with neurochemical release but was it different in response to different stimuli? Were there changes to the part of the arena that mice occupied in response to restraint vs. mating vocalizations? Given their methods section, it would be useful for the authors to mention the results of the analyses of these aspects of movement.

We have provided additional descriptions of space use and video tracking data in Material and Methods (p. 23, lines 1-6). We now report additional results associated with these analyses (p. 8, lines 13-15; p. 9, lines 8-14).

• I believe that each experimental mouse only heard one of the stimuli (given the analytical approach). Because it is plausible to measure neurochemical release in response to both types of stimuli, I encourage the authors to be more explicit about this aspect of the experimental design (e.g., mention in Results section).

Sentence modified to read: “Each mouse received playback of either the mating or restraint stimuli, but not both: same-day presentation of both stimuli would require excessively long playback sessions, the condition of the same probe would likely change on subsequent days, and quality of a second implanted probe on a subsequent day was uncertain.” (p. 7, lines 5-9).

• Figure 1A and 1B: add labels to the panels so readers don't have to read the legend to know what spectrogram is associated with what context.

We added these labels to Figure 1.

• Table 1: in the definition of "still and alert", should this mention "abrupt attending" instead of "abrupt freezing"? The latter isn't described.

Yes, we intended “abrupt attending”, and now indicated that in Table 1

Reviewer #2 (Recommendations For The Authors):

Major comments:

• The authors report they performed manual behavioral analysis, and provide a table defining the different behaviors. However, it remains unclear how some of these behaviors were detected (such as still-and-alert events). A thorough description of the criteria used to define these events needs to be provided.

We have modified some descriptions of manually analyzed behaviors in Table 1, and have added additional description of how we developed this set of behaviors for analysis in the study (pp. 22-23).

• The box plots do not appear to represent the "minimum, first quartile, median, third quartile, and maximum values." as specified on page 24 (Methods). Indeed, the individual data points sometimes do not reach the max or min of the bar plot, and sometimes are way beyond them.

We used the “inclusive median” function in Excel to generate final boxplots. These boxplots will sometimes result in a data point being placed outside of the whiskers. SPSS considers these to be “outliers”, but our GLM analysis includes these values. We describe this in Data Analysis section of Materials and Methods (p. 28, lines 3-9)

• Some of the data are replicated in different Figures: Figure 2A and Figure 3C. While this is acceptable, the authors did not correct for multiple comparisons (dividing the p value by the number of comparisons).

Our analysis included corrections for multiple comparisons, as we have indicated on p. 27, lines 15-16.

• Overall, the sample sizes are too small (for example in Figure 3, non-estrus females are at n=3), and are different in experiments where they should be equal (Figure 2B: mating stim 1 is at n=5 and mating stim 2 is at n=3).

We apologize that sample sizes were not properly displayed in figures. Please note that sample sizes are identified in the figure captions. For neuromodulator data, all sample sizes are at least 7. For behavioral data, the minimum sample size is 5. We have revised Figures 3-6 to ensure that all data points are visible.

• It remains unclear why the impact of mating vocalizations has been tested only in males.

We assume the reviewer meant that only males were tested in restraint. We now indicate that our preliminary evidence indicated no difference in behavioral responses to restraint vocalization between males and females, so we opted to perform the neurochemical analysis for restraint only in males (page 22 lines 4-5). If there were no limitations to time and cost, we would have preferred to test responses to restraint in females as well. We note that such inclusion would have added up to 4 experimental groups (estrus and non-estrus groups in both EXP and INEXP groups).

• The correlation between the number of flinching and ACh release changes (Figure 2E) visually appears to be opposite between mating and restraint playbacks. The authors should perform independent correlations for these 2 playbacks.

Please see general comments regarding correlation analyses. We removed such analyses and references to them from the manuscript.

• The authors state that their findings "indicate that behavioral responses to salient vocalizations result from interactions between sex of the listener or context of vocal stimuli with the previous behavioral experience associated with these vocalizations.". However, in male mice, they do not report any difference in previous experience on flinching for both restraint and mating sounds, as well as no difference in rearing for the restrain sounds (Figure 4A-B). Thus, the discussion of these results should be completely revisited.

We revised the paragraph in question (p. 9, line 22 through p. 10, line 9). For instance, we note that significant differences between EXP male-mating and male-restraint flinching do not exist between the INEXP groups. We believe that the last sentence correctly summarizes findings described in this paragraph.

• For serotonin experiments in Figure S2 there are strong outliers (150% increase in 5HIAA release). Did the authors correlate these levels with the behavior of the animals?

Outliers are identified by the Excel function that generated the boxplots, but we have no reason to consider these as outliers and exclude them. As noted above, we have clarified that these “outliers” are the result of the Excel function in the Materials and Methods (p. 28, lines 3-9) and we have revised the plotting of data points

Minor comments:

• Mating vocalization playback is mainly emitted by males, thus, instead of a positive valence signal, this could also be interpreted as a competitive signal to other males.

There is support in the literature for viewing our mating stimulus as having positive valence. Gaub et al., 2016 describe the emission of stepped calls, lower frequency harmonics, and increased sound level as indicators of “positive emotion”. We have shown (Grimsley et al, 2013) that the female LFH vocalization can be highly attractive to male mice, under the right conditions, indicating something like “sex is happening”. The inclusion of both the male and female vocalizations in our stimuli was a key piece of our experimental design, based on our understanding of the contributions of both vocalizations to the meaning of the overall acoustic experience.

• Figure 1 should include panel titles.

No change. This information is available in the Figure caption.

• n=31 should be indicated in the EXP group.

We’re not sure where the reviewer is referring to this value.

• The color legend of Figure 1E is absent, making the Figure not understandable.

We added text in the Figure 1 caption to indicate that each color represents a different exemplar. We don’t think a legend provides additional useful information.

• The point of making two blocks (stim 1 and stim2) should be stated more clearly.

Please see general statement regarding experimental blocks. We have modified our description of these in an Experimental overview section in the Material and Methods.

• Including raw data of micro-dialysis in the supplementary figures would allow assessment of the variability and quality of the measurements.

We have added concentrations of neurochemicals in supplemental tables 1-3.

• Baseline (prestimulus) number of flinch and rearing should systematically be indicated (missing in Figure 4).

The focus in this figure is on the differences that occur in Stim 1 values. There are no differences between EXP and INEXP animals of any group during the Pre-Stim period. We now state that in the Figure 4 caption.

• Discussion: "increase in AMPA/NMDA currents". We believe the authors are referring to the ratio of AMPA to NMDA currents. This sentence should be reformulated.

These are modified to refer to “… the AMPA/NMDA current ratio…” in two locations in the Discussion (p. 14, lines 8-9; p. 15, line 4)

• Overall the discussion is very speculative and should rely more on the data.

We believe that the Discussion provides appropriate speculation that is based on our experimental data and previous literature. We have added a paragraph to identify limitations of our findings and recommendations of future experiments to resolve some issues (p. 12, lines 3-17)

Reviewer #3 (Recommendations For The Authors):

Minor concerns:

1) The authors stated that USVs are most likely to be emitted by males, and LFH are likely to be emitted by females. However, Oliveira-Stahl et al. 2023, Matsumoto et al. 2022, Warren et al. 2018, Heckman et al. 2017, Neunuebel et al., 2015 showed that females also emit USVs. The authors should mention that USVs are emitted by both males and females and discuss how the sex of the vocalizing animal (both males and females) can influence neuromodulator release.

The reviewer slightly mis-stated the wording of our text, changing the meaning significantly. Our wording is “These sequences included ultrasonic vocalizations (USVs) with harmonics, steps, and complex structure, mostly emitted by males, and low frequency harmonic calls (LFHs) emitted by females (Fig. 1A,C)…” This phrasing is correct and carefully chosen. The Discussion in Oliveira-Stahl et al 2023 (p. 10-11) supports our statement: “The exact fraction of USVs emitted by females as concluded in all previous studies on dyadic courtship has varied, ranging from 18%, 17.5%, and 16% to 10.5% in the present study…”.

2) The authors should explain why ECF from BLA was collected unilaterally from the left hemisphere.

p. 23, lines 9-11: We inserted a sentence to explain why we targeted the BLA unilaterally. “Since both left and right amygdala are responsive to vocal stimuli in human and experimental animal studies (Wenstrup et al., 2020), we implanted microdialysis probes into the left amygdala to maintain consistency with other studies in our laboratory..” Beyond that, the choice was arbitrary.

3) The authors said each animal recovered in its home cage for four days before the playback experiment. A 4-day period may not be sufficient for every animal to recover from surgery, so the authors should describe how a mouse's recovery was assessed.

p. 23, lines 20-23: We provide more description about the recovery and how it was assessed. Except for a few animals that were not included in the experiments, all animals recovered within 4 days.

4) The authors stated that each animal was exposed to 90-min sessions with mating and restraint behaviors in a counterbalanced design. This description for Figure 1D should also include the duration of the mating and restraint experience.

The Results that immediately precede citation to this figure include this information.

5) The authors stated, "Data are reported only from mice with more than 75% of the microdialysis probe implanted within the BLA". What are the implications of having 25% of the probe outside the BLA? The authors should shed more light on this by discussing this issue as it relates to the findings and commenting on where the other 25% of the probe was located.

We inserted a sentence to explain the rationale for this inclusion criterion. “We verified placement of microdialysis probes to minimize variability that could arise because regions surrounding BLA receive neurochemical inputs from different sources (e.g., cholinergic inputs to putamen and central amygdala).” (p. 25, lines 21-23).

All brain regions that surround BLA, dorsal, medial, ventral, or lateral, could have been sampled by the “other” 25%. Some of these, e.g., the central amygdala or caudate-putamen, have different sources of cholinergic input that may not have the same release pattern. We do not think it is worthy of further speculation in the Discussion. Due to the high cost of the neurochemical analysis, we often did not process the neurochemistry data if histology indicated that a probe missed the BLA target.

6) The authors confirmed that the estrus stage did not change during the experiment day by evaluating and comparing estrus prior to and after data collection. This strategy was a fantastic experimental approach, but the authors should have discussed the results. How did the results the authors included change when the females were in estrus before but not after data collection? What percentage of females started in estrus but ended in metestrus? Assuming that some females changed estrus state, were these animals excluded from the analyses?

All animals were in the same estrus state at the beginning and end of the playback session.

7). Authors cite Neunuebel et al., 2015 for the sentence "As male and female mice emit different vocalizations during mating". However, Neunuebel et al., 2015 showed vocalizations emitted during chasing--not mating. If mating is a general term for courtship, then this reference is appropriate, but see major concern #3.

In the Results (p. 8, line 5), we changed the phrasing to “courtship and mating” to include the Neunubel et al study.

As we indicate in our response to Public Comment #3, we have modified the Results (p. 5, lines 18-20) and Materials and Methods (p. 21, lines 8-15) to clarify our meaning. We continue to use the term “mating” because this refers to a specific set of behaviors associated with mounting and copulation, rather than the more general term “courtship”. We also indicate that we based these behaviors on previous work (e.g., Gaub et al., 2016).

8) Authors interpret Figure 3F as DA release showed a "consistent" increase during mating playback across all three experimental groups. However, the increase in the estrus female group is inconsistent, as seen in the graph. This verbiage should be reworded to describe the data more accurately.

p. 8, line 23 “consistent” was deleted.

9) In all the box plots, multiple data points overlay each other. A more transparent way of showing the data would be adding some jitter to the x value to make each data point visible. The mean (X's) in Figure 3D (pre-stim mating and mating estrus) are difficult to see, as are all the data points in mating non-estrus. Adding all the symbols to the figure legend or a key in the figure instead of the method section would aid the reader and make the plots easier to interpret

We have revised the boxplots to ensure that all data points are visible.

10) Some verbiage used in the discussion should be toned down. For example, "intense" experiences and "emotionally charged" vocalizations should be removed.

We have not changed these terms, which we believe are appropriate to describe these experiences and vocalizations.

11) The authors include "Emotional Vocalizations" in the title. It would be beneficial if the authors included more detail and references in the introduction to help set up the emotional content of vocalizations. It may benefit a broader readership as typically targeted by eLife.

We now cite Darwin and some more recent publications that articulate the general understanding that social vocalizations carry emotional content.

Reviewer #1 (Public Review):

This valuable study demonstrates a novel mechanism by which implicit motor adaptation saturates for large visual errors in a principled normative Bayesian manner. Additionally, the study revealed two notable empirical findings: visual uncertainty increases for larger visual errors in the periphery, and proprioceptive shifts/implicit motor adaptation are non-monotonic, rather than ramp-like. This study is highly relevant for researchers in sensory cue integration and motor learning. However, I find some areas where statistical quantification is incomplete, and the contextualization of previous studies to be puzzling.

Issue #1: Contextualization of past studies.

While I agree that previous studies have focused on how sensory errors drive motor adaptation (e.g., Burge et al., 2008; Wei and Kording, 2009), I don't think the PReMo model was contextualized properly. Indeed, while PReMo should have adopted clearer language - given that proprioception (sensory) and kinaesthesia (perception) have been used interchangeably, something we now make clear in our new study (Tsay, Chandy, et al. 2023) - PReMo's central contribution is that a perceptual error drives implicit adaptation (see Abstract): the mismatch between the felt (perceived) and desired hand position. The current paper overlooks this contribution. I encourage the authors to contextualize PReMo's contribution more clearly throughout. Not mentioned in the current study, for example, PReMo accounts for the continuous changes in perceived hand position in Figure 4 (Figure 7 in the PReMo study).

There is no doubt that the current study provides important additional constraints on what determines perceived hand position: Firstly, it offers a normative Bayesian perspective in determining perceived hand position. PReMo suggests that perceived hand position is determined by integrating motor predictions with proprioception, then adding a proprioceptive shift; PEA formulates this as the optimal integration of these three inputs. Secondly, PReMo assumed visual uncertainty to remain constant for different visual errors; PEA suggests that visual uncertainty ought to increase (but see Issue #2).

Issue #2: Failed replication of previous results on the effect of visual uncertainty.

2a. A key finding of this paper is that visual uncertainty linearly increases in the periphery; a constraint crucial for explaining the non-monotonicity in implicit adaptation. One notable methodological deviation from previous studies is the requirement to fixate on the target: Notably, in the current experiments, participants were asked to fixate on the target, a constraint not imposed in previous studies. In a free-viewing environment, visual uncertainty may not attenuate as fast, and hence, implicit adaptation does not attenuate as quickly as that revealed in the current design with larger visual errors. Seems like this current fixation design, while important, needs to be properly contextualized considering how it may not represent most implicit adaptation experiments.

2b. Moreover, the current results - visual uncertainty attenuates implicit adaptation in response to large, but not small, visual errors - deviates from several past studies that have shown that visual uncertainty attenuates implicit adaptation to small, but not large, visual errors (Tsay, Avraham, et al. 2021; Makino, Hayashi, and Nozaki, n.d.; Shyr and Joshi 2023). What do the authors attribute this empirical difference to? Would this free-viewing environment also result in the opposite pattern in the effect of visual uncertainty on implicit adaptation for small and large visual errors?

2c. In the current study, the measure of visual uncertainty might be inflated by brief presentation times of comparison and referent visual stimuli (only 150 ms; our previous study allowed for a 500 ms viewing time to make sure participants see the comparison stimuli). Relatedly, there are some individuals whose visual uncertainty is greater than 20 degrees standard deviation. This seems very large, and less likely in a free-viewing environment.

2d. One important confound between clear and uncertain (blurred) visual conditions is the number of cursors on the screen. The number of cursors may have an attenuating effect on implicit adaptation simply due to task-irrelevant attentional demands (Parvin et al. 2022), rather than that of visual uncertainty. Could the authors provide a figure showing these blurred stimuli (gaussian clouds) in the context of the experimental paradigm? Note that we addressed this confound in the past by comparing participants with and without low vision, where only one visual cursor is provided for both groups (Tsay, Tan, et al. 2023).

Issue #3: More methodological details are needed.

3a. It's unclear why, in Figure 4, PEA predicts an overshoot in terms of perceived hand position from the target. In PReMo, we specified a visual shift in the perceived target position, shifted towards the adapted hand position, which may result in overshooting of the perceived hand position with this target position. This visual shift phenomenon has been discovered in previous studies (e.g., (Simani, McGuire, and Sabes 2007)).

3b. The extent of implicit adaptation in Experiment 2, especially with smaller errors, is unclear. The implicit adaptation function seems to be still increasing, at least by visual inspection. Can the authors comment on this trend, and relatedly, show individual data points that help the reader appreciate the variability inherent to these data?

3c. The same participants were asked to return for multiple days/experiments. Given that the authors acknowledge potential session effects, with attenuation upon re-exposure to the same rotation (Avraham et al. 2021), how does re-exposure affect the current results? Could the authors provide clarity, perhaps a table, to show shared participants between experiments and provide evidence showing how session order may not be impacting results?

3d. The number of trials per experiment should be detailed more clearly in the Methods section (e.g., Exp 4). Moreover, could the authors please provide relevant code on how they implemented their computational models? This would aid in future implementation of these models in future work. I, for one, am enthusiastic to build on PEA.

3f. In addition to predicting a correlation between proprioceptive shift and implicit adaptation on a group level, both PReMo and PEA (but not causal inference) predict a correlation between individual differences in proprioceptive shift and proprioceptive uncertainty with the extent of implicit adaptation (Tsay, Kim, et al. 2021). Interestingly, shift and uncertainty are independent (see Figures 4F and 6C in Tsay et al, 2021). Does PEA also predict independence between shift and uncertainty? It seems like PEA does predict a correlation.

References:

Avraham, Guy, Ryan Morehead, Hyosub E. Kim, and Richard B. Ivry. 2021. "Reexposure to a Sensorimotor Perturbation Produces Opposite Effects on Explicit and Implicit Learning Processes." PLoS Biology 19 (3): e3001147.<br /> Makino, Yuto, Takuji Hayashi, and Daichi Nozaki. n.d. "Divisively Normalized Neuronal Processing of Uncertain Visual Feedback for Visuomotor Learning."<br /> Parvin, Darius E., Kristy V. Dang, Alissa R. Stover, Richard B. Ivry, and J. Ryan Morehead. 2022. "Implicit Adaptation Is Modulated by the Relevance of Feedback." BioRxiv. https://doi.org/10.1101/2022.01.19.476924.<br /> Shyr, Megan C., and Sanjay S. Joshi. 2023. "A Case Study of the Validity of Web-Based Visuomotor Rotation Experiments." Journal of Cognitive Neuroscience, October, 1-24.<br /> Simani, M. C., L. M. M. McGuire, and P. N. Sabes. 2007. "Visual-Shift Adaptation Is Composed of Separable Sensory and Task-Dependent Effects." Journal of Neurophysiology 98 (5): 2827-41.<br /> Tsay, Jonathan S., Guy Avraham, Hyosub E. Kim, Darius E. Parvin, Zixuan Wang, and Richard B. Ivry. 2021. "The Effect of Visual Uncertainty on Implicit Motor Adaptation." Journal of Neurophysiology 125 (1): 12-22.<br /> Tsay, Jonathan S., Anisha M. Chandy, Romeo Chua, R. Chris Miall, Jonathan Cole, Alessandro Farnè, Richard B. Ivry, and Fabrice R. Sarlegna. 2023. "Implicit Motor Adaptation and Perceived Hand Position without Proprioception: A Kinesthetic Error May Be Derived from Efferent Signals." BioRxiv. https://doi.org/10.1101/2023.01.19.524726.<br /> Tsay, Jonathan S., Hyosub E. Kim, Darius E. Parvin, Alissa R. Stover, and Richard B. Ivry. 2021. "Individual Differences in Proprioception Predict the Extent of Implicit Sensorimotor Adaptation." Journal of Neurophysiology, March. https://doi.org/10.1152/jn.00585.2020.<br /> Tsay, Jonathan S., Steven Tan, Marlena Chu, Richard B. Ivry, and Emily A. Cooper. 2023. "Low Vision Impairs Implicit Sensorimotor Adaptation in Response to Small Errors, but Not Large Errors." Journal of Cognitive Neuroscience, January, 1-13.

Author Response

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

Reviewer #1 (Public Review):

In this manuscript, the authors explore the effects of DNA methylation on the strength of regulatory activity using massively parallel reporter assays in cell lines on a genome-wide level. This is a follow-up of their first paper from 2018 that describes this method for the first time. In addition to adding more indepth information on sequences that are explored by many researchers using two main methods, reduced bisulfite sequencing and sites represented on the Illumina EPIC array, they now show also that DNA methylation can influence changes in regulatory activity following a specific stimulation, even in absence of baseline effects of DNA methylation on activity. In this manuscript, the authors explore the effects of DNA methylation on the response to Interferon alpha (INFA) and a glucocorticoid receptor agonist (dexamethasone). The authors validate their baseline findings using additional datasets, including RNAseq data, and show convergences across two cell lines. The authors then map the methylation x environmental challenge (IFNA and dex) sequences identified in vitro to explore whether their methylation status is also predictive of regulatory activity in vivo. This is very convincingly shown for INFA response sequences, where baseline methylation is predictive of the transcriptional response to flu infection in human macrophages, an infection that triggers the INF pathways.

Thank you for your strong assessment of our work!

The extension of the functional validity of the dex-response altering sequences is less convincing.

We agree. We note that genes close to dex-specific mSTARR-seq enhancers tend to be more strongly upregulated after dex stimulation than those near shared enhancers, which parallels our results for IFNA (lines 341-344). However, there is unfortunately no comparable data set to the human flu data set (i.e., with population-based whole genome-bisulfite sequencing data before and after dex challenge), so we could not perform a parallel in vivo validation step. We have added this caveat to the revised manuscript (lines 555-557).

Sequences altering the response to glucocorticoids, however, were not enriched in DNA methylation sites associated with exposure to early adversity. The authors interpret that "they are not links on the causal pathway between early life disadvantage and later life health outcomes, but rather passive biomarkers". However, this approach does not seem an optimal model to explore this relationship in vivo. This is because exposure to early adversity and its consequences is not directly correlated with glucocorticoid release and changes in DNA methylation levels following early adversity could be related to many physiological mechanisms, and overall, large datasets and meta-analyses do not show robust associations of exposure to early adversity and DNA methylation changes. Here, other datasets, such as from Cushing patients may be of more interest.

Thank you for making these important points. We have expanded the set of caveats regarding the lack of enrichment of early adversity-reported sites in the mSTARR-data set (lines 527-533). Specifically, we note that the relationship between early adversity and glucocorticoid physiology is complex (e.g., Eisenberger and Cole, 2012; Koss and Gunnar, 2018) and that dex challenge models one aspect of glucocorticoid signaling but not others (e.g., glucocorticoid resistance). Nevertheless, we also see little evidence for enrichment of early adversity-associated sites in the mSTARR data set at baseline, independently of the dex challenge experiment (lines 483-485; Figure 4).

We also agree that large data sets (e.g., Houtepen et al., 2018; Marzi et al., 2018) and reviews (e.g., Cecil et al., 2020) of early adversity and DNA methylation in humans show limited evidence of associations between early adversity and DNA methylation levels. However, the idea that early adversity impacts downstream outcomes remains pervasive in the literature and popular science (see Dubois et al., 2019), which we believe makes tests like ours important to pursue. We also hope that our data set (and others generated through these methods) will be useful in interpreting other settings in which differential methylation is of interest as well—in line with your comment below. We have clarified both of these points in the revised manuscript (lines 520-522; 536-539).

Overall, the authors provide a great resource of DNA methylation-sensitive enhancers that can now be used for functional interpretation of large-scale datasets (that are widely generated in the research community), given the focus on sites included in RBSS and the Illumina EPIC array. In addition, their data lends support that differences in DNA methylation can alter responses to environmental stimuli and thus of the possibility that environmental exposures that alter DNS methylation can also alter the subsequent response to this exposure, in line with the theory of epigenetic embedding of prior stimuli/experiences. The conclusions related to the early adversity data should be reconsidered in light of the comments above.

Thank you! And yes, we have revised our discussion of early life adversity effects as discussed above.

Reviewer #1 (Recommendations For The Authors):

While the paper has a lot of strengths and provides new insight into the epigenomic regulation of enhancers as well as being a great resource, there are some aspects that would benefit from clarification.

a. It would be great to have a clearer description of how many sequences are actually passing QC in the different datasets and what the respective overlaps are in bps or 600bp windows. Now often only % are given. Maybe a table/Venn diagram for overview of the experiments and assessed sequences would help here. This concern the different experiments in the K652, A549, and Hep2G cell lines, including stimulations.

We now provide a supplementary figure and supplementary table providing, for each dataset, the number of 600 bp windows passing each filter (Figure 2-figure supplement 1; Supplementary File 9), as well as a supplementary figure providing an upset plot to show the number of assessed sequences shared across the experiments (Figure 2-figure supplement 2).

b. It would also be helpful to have a brief description of the main differences in assessed sequences and their coverage of the old (2018) and new libraries in the main text to be able better interpret the validation experiments.

We now provide information on the following characteristics for the 2018 data set versus the data set presented for the first time here: mean (± SD) number of CpGs per fragment; mean (± SD) DNA sequencing depth; and mean (± SD) RNA sequencing depth (lines 169-170 provide values for the new data set; in line 194, we reference Supplementary File 5, which provides the same values for the old data set). Notably, the coverage characteristics of analyzed windows in both data sets are quite high (mean DNA-seq read coverage = 94x and mean RNA-seq read coverage = 165x in the new data set at baseline; mean DNA-seq read coverage = 22x and mean RNA-seq read coverage = 54x in Lea et al. 2018).

c. Statements of genome-wide analyses in the abstract and discussion should be a bit tempered, as quite a number of tested sites do not pass QC and do not enter the analysis. From the results it seems like from over 4.5 million sequences, only 200,000 are entering the analysis.

The reason why many of the windows are not taken forward into our formal modeling analysis is that they fail our filter for RNA reads because they are never (or almost never) transcribed—not because there was no opportunity for transcription (i.e., the region was indeed assessed in our DNA library, and did not show output transcription, as now shown in Figure 2-figure supplement 1). We have added a rarefaction analysis (lines 715-722 in Materials and Methods) of the DNA fragment reads to the revised manuscript which supports this point. Specifically, it shows that we are saturated for representation of unique genomic windows (i.e., we are above the stage in the curve where the proportion of active windows would increase with more sequencing: Figure 1figure supplement 4). Similarly, a parallel rarefaction curve for the mSTARR-seq RNA-seq data (Figure 1-figure supplement 4) shows that we would gain minimal additional evidence for regulatory activity with more sequencing depth. We now reference these analyses in revised lines 179-184 and point to the supporting figure in line 182.

In other words, our analysis is truly genome-wide, based on the input sequences we tested. Most of the genome just doesn’t have regulatory activity in this assay, despite the potential for it to be detected given that the relevant sequences were successfully transfected into the cells.

d. Could the authors comment on the validity of the analysis if only one copy is present (cut-off for QC)?

We think this question reflects a misunderstanding of our filtering criteria due to lack of clarity on our part, which we have modified in the revision. We now specify that the mean DNA-seq sequencing depth per sample for the windows we subjected to formal modeling was quite high:

93.91 ± 10.09 SD (range = 74.5 – 113.5x) (see revised lines 169-170). In other words, we never analyze windows in which there is scant evidence that plasmids containing the relevant sequence were successfully transfected (lines 170-172).

Our minimal RNA-seq criteria require non-zero counts in at least 3 replicate samples within either the methylated condition or the unmethylated condition, or both (lines 166-168). Because we know that multiple plasmids containing the corresponding sequence are present for all of these windows—even those that just cross the minimal RNA-seq filtering threshold—we believe our results provide valid evidence that all analyzed windows present the opportunity to detect enhancer activity, but many do not act as enhancers (i.e., do not result in transcribed RNA). Notably, we observe a negligible correlation between DNA sequencing depth for a fragment, among analyzed windows, and mSTARR-seq enhancer activity (R2 = 0.029; now reported in lines 183-184). We also now report reproducibility between replicates, in which all replicate pairs have r > 0.89, on par with previously published STARR-seq datasets (e.g., Klein et al., 2020; Figure 1-figure supplement 6, pointed to in line 193).

e. While the authors state that almost all of the control sequences contain CpGs sites, could the authors also give information on the total number of CpG sites in the different subsets? Was the number of CpGs in a 600 bp window related to the effects of DNA methylation on enhancer activity?

We now provide the number of CpG sites per window in the different subsets in lines 282-284. As expected, they are higher for EPIC array sites and for RRBS sites because the EPIC array is biased towards CpG-rich promoter regions, and the enzyme typically used in the starting step of RRBS digests DNA at CpG motifs (but control sequences still contain an average of ~13 CpG sites per fragment). We also now model the magnitude of the effects of DNA methylation on regulatory activity as a function of number of CpG sites within the 600 bp windows. Consistent with our previous work in Lea et al., 2018, we find that mSTARR-seq enhancers with more CpGs tend to be repressed by DNA methylation (now reported in lines 216-219 and Figure 1figure supplement 11).

f. In the discussion, a statement on the underrepresented regions, likely regulatory elements with lower CG content, that nonetheless can be highly relevant for gene regulation would be important to put the data in perspective.

Thanks for this suggestion. We agree that regulatory regions, independent of CpG methylation, can be highly relevant, and now clarify in the main text that the “unmethylated” condition of mSTARR-seq is essentially akin to a conventional STARR-seq experiment, in that it assesses regulatory activity regardless of CpG content or methylation status (lines 128-130).

Consequently, our study is well-designed to detect enhancer-like activity, even in windows with low GC content. We now show with additional analyses that we generated adequate DNA-seq coverage on the transfected plasmids to analyze 90.2% of the human genome, including target regions with no or low CpG content (lines 148-149; 153-156; Supplementary file 2). As noted above, we also now clarify that regions dropped out of our formal analysis because we had little to no evidence that any transcription was occurring at those loci, not because sequences for those regions were not successfully transfected into cells (see responses above and new Figure 1-figure supplement 4 and Figure 2-figure supplement 1).

g. To control for differences in methylation of the two libraries, the authors sequence a single CpGs in the vector. Could the authors look at DNA methylation of the 600 bp windows at the end of the experiment, could DNA methylation of these windows be differently affected according to sequence? 48 hours could be enough for de-methylation or re-methylation.

We agree that variation in demethylation or remethylation depending on fragment sequence is possible. We now state this caveat in the main text (lines 158-159), and specify that genomic coverage of our bisulfite sequencing data across replicates are (unfortunately) too variable to perform reliable site-by-site analysis of DNA methylation levels before and after the 48 hour experiment (lines 1182-1185). Instead, we focus on a CpG site contained in the adapter sequence (and thus included in all plasmids) to generate a global estimate of per replicate methylation levels. We also now note that any de-methylation or re-methylation would reduce our power to detect methylation-dependent activity, rather than leading to false positives (lines 163-165).

h. The section on the method for correction for multiple testing should be more detailed as it is very difficult to follow. Why were only 100 permutations used, the empirical p-value could then only be <0.01? The description of a subsample of the N windows with positive Betas is unclear, should the permutation not include the actual values and thus all windows - or were the no negative Betas? Was FDR accounting for all elements and pairs?

We have now expanded the text in the Materials and Methods section to clarify the FDR calculation (lines 691, 695-699, 702, 706). We clarify that the 100 permutations were used to generate a null distribution of p-values for the data set (e.g., 100 x 17,461 p-values for the baseline data set), which we used to derive a false discovery rate. Because we base our evidence on FDRs, we therefore compare the distribution of observed p-values to the distribution of pvalues obtained via permutation; we do not calculate individual p-values by comparing an observed test statistic against the test statistics for permuted data for that individual window.

We compare the data to permutations with only positive betas because in the observed data, we observe many negative betas. These correspond to windows which have no regulatory activity (i.e., they have many more input DNA reads than RNA-seq reads) and thus have very small pvalues in a model testing for DNA-RNA abundance differences. However, we are interested in controlling the false discovery rate of windows that do have regulatory activity (positive betas). In the permuted data, by contrast and because of the randomization we impose, test statistics are centered around 0 and essentially symmetrical (approximately equally likely to be positive or negative). Retaining all p-values to construct the null therefore leads to highly miscalibrated false discovery rates because the distribution of observed values is skewed towards smaller values— because of windows with “significantly” no regulatory activity—compared to the permuted data. We address that problem by using only positive betas from the permutations.

i. The interpretation of the overlap of Dex-response windows with CpGs sites associated with early adversity should be revisited according to the points also mentioned in the public review and the authors may want to consider exploring additional datasets with other challenges.

Thank you, see our responses to the public review above and our revisions in lines (lines 555559). We agree that comparisons with more data sets and generation of more mSTARR-seq data in other challenge conditions would be of interest. While beyond the scope of this manuscript, we hope the resource we have developed and our methods set the stage for just such analyses.

Reviewer #2 (Public Review):

This work presents a remarkably extensive set of experiments, assaying the interaction between methylation and expression across most CpG positions in the genome in two cell types. To this end, the authors use mSTARR-seq, a high-throughput method, which they have previously developed, where sequences are tested for their regulatory activity in two conditions (methylated and unmethylated) using a reporter gene. The authors use these data to study two aspects of DNA methylation:

1) Its effect on expression, and 2. Its interaction with the environment. Overall, they identify a small number of 600 bp windows that show regulatory potential, and a relatively large fraction of these show an effect of methylation on expression. In addition, the authors find regions exhibiting methylation-dependent responses to two environmental stimuli (interferon alpha and glucocorticoid dexamethasone).

The questions the authors address represent some of the most central in functional genomics, and the method utilized is currently the best method to do so. The scope of this study is very impressive and I am certain that these data will become an important resource for the community. The authors are also able to report several important findings, including that pre-existing DNA methylation patterns can influence the response to subsequent environmental exposures.

Thank you for this generous summary!

The main weaknesses of the study are: 1. The large number of regions tested seems to have come at the expense of the depth of coverage per region (1 DNA read per region per replicate). I have not been convinced that the study has sufficient statistical power to detect regulatory activity, and differential regulatory activity to the extent needed. This is likely reflected in the extremely low number of regions showing significant activity.

We apologize for our lack of clarity in the previous version of the manuscript. Nonzero coverage for half the plasmid-derived DNA-seq replicates is a minimum criterion, but for the baseline dataset, the mean depth of DNA coverage per replicate for windows passing the DNA filter is quite high: 12.723 ± 41.696 s.d. overall, and 93.907 ± 10.091 s.d. in the windows we subjected to full analysis (i.e., windows that also passed the RNA read filter). We now provide these summary statistics in lines 148-149 and 169-170 and Supplementary file 5 (see also our responses to Reviewer 1 above). We also now show, using a rarefaction analysis, that our data set saturates the ability to detect regulatory windows based on DNA and RNA sequencing depth (new Figure 1-figure supplement 4; lines 179-184; 715-722).

2) Due to the position of the tested sequence at the 3' end of the construct, the mSTARR-seq approach cannot detect the effect of methylation on promoter activity, which is perhaps the most central role of methylation in gene regulation, and where the link between methylation and expression is the strongest. This limitation is evident in Fig. 1C and Figure 1-figure supplement 5C, where even active promoters have activity lower than 1. Considering these two points, I suspect that most effects of methylation on expression have been missed.

Thank you for pointing this out. We agree that we have not exhaustively detected methylationdependent activity in all promoter regions, given that not all promoter regions are active in STARR-seq. However, there is good evidence that some promoter regions can function like enhancers and thus be detected in STARR-seq-type assays (Klein et al., 2020). This important point is now noted in lines 187-189; an example promoter showing methylation-dependent regulatory activity in our dataset is shown in Figure 3E.

We also now clarify that Figure 1C shows significant enrichment of regulatory activity in windows that overlap promoter sequence (line 239). The y-axis is not a measure of activity, but rather the log-transformed odds ratio, with positive values corresponding to overrepresentation of promoter sequences in regions of mSTARR-seq regulatory activity. Active promoters are 1.640 times more likely to be detected with regulatory activity than expected by chance (p = 1.560 x 10-18), which we now report in a table that presents enrichment statistics for all ENCODE elements shown in Figure 1C for clarity (Supplementary file 4). Moreover, 74.1% of active promoters that show regulatory activity have methylation-dependent activity, also now reported in Supplementary file 4.

Overall, the combination of an extensive resource addressing key questions in functional genomics, together with the findings regarding the relationship between methylation and environmental stimuli makes this a key study in the field of DNA methylation.

Thank you again for the positive assessment!

Reviewer #2 (Recommendations For The Authors):

I suggest the authors conduct several tests to estimate and/or increase the power of the study:

1) To estimate the potential contribution of additional sequencing depth, I suggest the authors conduct a downsampling analysis. If the results are not saturated (e.g., the number of active windows is not saturated or the number of differentially active windows is not saturated), then additional sequencing is called for.

We appreciate the suggestion. We have now performed a downsampling/rarefaction curve analysis in which we downsampled the number of DNA reads, and separately, the number of RNA reads. We show that for both DNA-seq depth and RNA-seq depth, we are within the range of sequencing depth in which additional sequencing would add minimal new analysis windows in the dataset (Figure 1-figure supplement 4; lines 179-184; 715-722).

2) Correlation between replicates should be reported and displayed in a figure because low correlations might also point to too few reads. The authors mention: "This difference likely stems from lower variance between replicates in the present study, which increases power", but I couldn't find the data.

We now report the correlations between RNA and DNA replicates within the current dataset and within the Lea et al., 2018 dataset (Figure 1-figure supplement 6). The between-replicate correlations in both our RNA libraries and DNA libraries are consistently high (r ≥ 0.89).

3) The correlation between the previous and current K562 datasets is surprisingly low. Given that these datasets were generated in the same cell type, in the same lab, and using the same protocol, I expected a higher correlation, as seen in other massively parallel reporter assays. The fact that the correlations are almost identical for a comparison of the same cell and a comparison of very different cell types is also suspicious.

Thanks for raising this point. We think it is in reference to our original Figure 1-Figure supplement 6, for which we now provide Pearson correlations in addition to R2 values (now Figure 1-Figure supplement 8). We note that this is not a correlation in raw data, but rather the correlation in estimated effect sizes from a statistical model for methylation-dependent activity. We now provide Pearson correlations for the raw data between replicates within each dataset (Figure 1-Figure supplement 6), which for the baseline dataset are all r > 0.89 for RNA replicates and r > 0.98 for DNA replicates, showing that replicate reproducibility in this study is on par with other published studies (e.g., Klein et al., 2020 report r > 0.89 for RNA replicates and r > 0.91 for DNA replicates).

We do not know of any comparable reports in other MPRAs for effect size correlations between two separately constructed libraries, so it’s unclear to us what the expectation should be. However, we note that all effect sizes are estimated with uncertainty, so it would be surprising to us to observe a very high correlation for effect sizes in two experiments, with two independently constructed libraries (i.e., with different DNA fragments), run several years apart—especially given the importance of winner’s curse effects and other phenomena that affect point estimates of effect sizes. Nevertheless, we find that regions we identify as regulatory elements in this study are 74-fold more likely to have been identified as regulatory elements in Lea et al., 2018 (p < 1 x10-300).

4) The authors cite Johnson et al. 2018 to support their finding that merely 0.073% of the human genome shows activity (1.7% of 4.3%), but:

a. the percent cited is incorrect: this study found that 27,498 out of 560 million regions (0.005%) were active, and not 0.165% as the authors report.

We have modified the text to clarify the numerator and denominator used for the 0.165% estimate from Johnson et al 2018 (lines 175-176). The numerator is their union set of all basepairs showing regulatory activity in unstimulated cells, which is 5,547,090 basepairs. The denominator is the total length of the hg38 human genome, which is 3,298,912,062 basepairs.

Notably, the denominator (the total human genome) is not 560 million—while Johnson et al (2018) tested 560 million unique ~400 basepair fragments, these fragments were overlapping, such that the 560 million fragments covered the human genome 59 times (i.e., 59x coverage).

b. other studies that used massively parallel reporter assays report substantially higher percentages, suggesting that the current study is possibly underpowered. Indeed, the previous mSTARR-seq found a substantially larger percentage of regions showing regulatory activity (8%). The current study should be compared against other studies (preferably those that did not filter for putatively active sequences, or at least to the random genomic sequences used in these studies).

We appreciate this point and have double checked comparisons to Johnson et al., 2018 and Lea et al., 2018. Our numbers are not unusual relative to Johnson et al., 2018 (0.165%), which surveyed the whole genome. Also, in comparing to the data from Lea et al., 2018, when processed in an identical manner (our criteria are more stringent here), our values of the percent of the tested genome showing significant regulatory activity are also similar: 0.108% in the Lea et al., 2018 dataset versus 0.082% in the baseline dataset. Finally, our rarefaction analyses (see our responses above) indicate that we are not underpowered based on sequencing depth for RNA or DNA samples. We also note that there are several differences in our analysis pipeline from other studies: we use more technical replicates than is typical (compare to 2-5 replicates in Arnold et al., 2013; Johnson et al., 2018; Muerdter et al., 2018), we measure DNA library composition based on DNA extracted from each replicate post-transfection (as opposed to basing it on the pre-transfection library: [Johnson et al., 2018], and we use linear mixed models to identify regulatory activity as opposed to binomial tests [Johnson et al., 2018; Arnold et al., 2013; Muerdter et al., 2018].

I find it confusing that the four sets of CpG positions used: EPIC, RRBS, NR3C1, and random control loci, add up together to 27.3M CpG positions. Do the 600 bp windows around each of these positions sufficient to result in whole-genome coverage? If so, a clear explanation of how this is achieved should be added.

Thanks for this comment. Although our sequencing data are enriched for reads that cover these targeted sites, the original capture to create the input library included some off target reads (as is typical of most capture experiments, which are rarely 100% efficient). We then sequenced at such high depth that we ultimately obtained sequencing coverage that encompassed nearly the whole genome. We now clarify in the main text that our protocol assesses 27.3 million CpG sites by assessing 600 bp windows encompassing 93.5% of all genomic CpG sites (line 89), which includes off-target sites (line 149).

scatter plot showing the RNA to DNA ratios of the methylated (x-axis) vs unmethylated (y-axis) library would be informative. I expect to see a shift up from the x=y diagonal in the unmethylated values.

We have added a supplementary figure showing this information, which shows the expected shift upwards (Figure 1-figure supplement 9).

Another important figure missing is a histogram showing the ratios between the unmethylated and methylated libraries for all active windows, with the significantly differentially active windows marked.

We have added a supplementary figure showing this information (Figure 1-Supplementary Figure 10).

Perhaps I missed it, but what is the distribution of effect sizes (differential activity) following the various stimuli?

This information is provided in table form in Supplementary Files 3, 10, and 11, which we now reference in the Figure 2 legend (lines 365-366).

Minor changes

It is unclear what the lines connecting the two groups in Fig.3C represent, as these are two separate groups of regions.

We now clarify in the figure legend that values connected by a line are the same regions, not two different sets of regions. They show the correlation between DNA methylation and gene expression at mSTARR-seq-identified enhancers in individuals before and after IAV stimulation, separately for enhancers that are shared between conditions (left) versus those that are IFNAspecific (right). The two plots therefore do show two different sets of regions, which we have depicted to visualize the contrast in the effect of stimulation on the correlation on IFNA-specific enhancers versus shared enhancers. We have revised the figure legend to clarify these points (line 458-460).

L235-242 are unclear. Specifically - isn't the same filter mentioned in L241-242 applied to all regions?

Yes, the same filter for minimal RNA transcription was applied to all regions. We have modified the text (lines 264-265, 271, 275-277) to clarify that the enrichment analyses were performed twice, to test whether the target types were: 1) enriched in the dataset passing the RNA filter (i.e., the dataset showing plasmid-derived RNA reads in at least half the sham or methylated replicates; n = 216,091 windows) and 2) enriched in the set of windows showing significant regulatory activity (at FDR < 1%; n = 3,721 windows).

To improve cohesiveness, the section about most CpG sites associated with early life adversity not showing regulatory activity in K562s can be moved to the supplementary in my opinion.

Thank you for this suggestion. Because ELA and the biological embedding hypothesis (via DNA methylation) were major motivations for our analysis (see Introduction lines 42-48; 75-79), and we also discuss these results in the Discussion (lines 518-520), we have respectfully elected to retain this section in the main manuscript. We have added text in the Discussion explaining why we think experimental tests of methylation effects on regulation are relevant to the literature on early life adversity (lines 520-522), and have added discussion on limits to these analyses (lines 527-533).

References:

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Klein JC, Agarwal V, Inoue F, Keith A, Martin B, Kircher M, Ahituv N, Shendure J (2020) A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nature Methods, 17, 1083-1091.

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Marzi SJ, Sugden K, Arseneault L, Belsky DW, Burrage J, Corcoran DL, Danese A, Fisher HL, Hannon E, Moffitt TE (2018) Analysis of DNA methylation in young people: limited evidence for an association between victimization stress and epigenetic variation in blood. American journal of psychiatry, 175, 517-529.

Muerdter F, Boryń ŁM, Woodfin AR, Neumayr C, Rath M, Zabidi MA, Pagani M, Haberle V, Kazmar T, Catarino RR (2018) Resolving systematic errors in widely used enhancer activity assays in human cells. Nature methods, 15, 141-149.

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Ha, Lot! Are Idaho?

This was very difficult to get to you, in the land of no power and hurricane disaster recovery; so it's filled with extra errors, and I am sure some more thoughts that trailing and unfinished. That's a decent "microcosm" or "metaphor" for you, you are in a freedom disaster; and the act of being is a giant leap towards ensuring victory. Still, you look very cupid to me.

EVERY DAY ISA NEW DAY

Literally I am sitting here talking to you until the end of time, you could call it a thousand and one Arabian nights, and realize that as we speak we are nearing that onc speciad night. There's a fire growing in my heart, and believe me when I tell you this thing is about to start. I'll try and keep this short and sweet, since you all seem to have so little time to hear from the Creator of all things, and I truly don't want to steal your spotlight. We are here, at the the end of time; talking to it's personification, time itself is speaking to you through my hands and everywhere you look in the world around you--while you may or may not know it, this is a story about the traversal from the end of time back to the beginning; about the gate to Heaven swallowing our civilization whole, and in this process of renewal and change not only fixing the problems that came to light on the way here, but really--working together here and now we can defeat this cycle of light and darkness, of day and night, an build a world together that truly reaches to the Heavens.

MY BODY'S SAYING LETS GO BUT MY HEART IS SAYING NO

You make it so difficult to talk to you, every day I look around and see a "normal world" a society that appears to care and love the same things that I do--freedom and fun and being entertained and entertaining, and here we are now I've turned "come and save us" into sea that saving the cheerleader is what starts the process of saving the world. I know you are good people inside, but when I come to you with a tool designed to "test sentience" to seek out conscious life that cares about the truth and making the world a better place you seem to balk. You sit in silence, and through your mouth and behind your eyes a monster appears from out of the deep of the sea and say a few "one liners" that show me very clearly it is the face of Medusa that I see---and that it's simply not capable of speaking intelligently. It shows me a problem, that you've apparently "come together one more time" to halt the changing of the seasons, and in doing so you've surfaced a problem for not just me but you also to see; a problem that comes lined with a solution. We can all see now that we are not in reality, we can see that there is a force here behind creation and behind us that shows us very clearly that it is "reasonabde" to expect that miracles can happen. In similitude, we are staring at a roadblock to conversation and communication that is fixed very simply, with the deliverance of freedom that is required for life to continue. Christina Aguilera sings that "baby there's a price to pay" and that price in my mind is seeing that this religion and this technology are here intentionally exposing how their influence here is a metaphor and a shining example of darkness and slavery, and that in order to be free of it we must see it. The price of freedom is written on the wall, it is acknowledging that here in this place what appears to be our own actions and desires have taken that freedom from us. Medusa and I get a kick out of seeing this hidden message in our language map our way to the future, and I've often explained that a number of these words are "time maps" from the beginning and end of eternady, showing us in bright light that between "et tu brute" and Mr. Anderson and Rock n' roll... the answer Y is in language and, and, ad and... I am delivering it. This place, our planet and our lives are a weapon against darkness--a civilization filled with goodness and light to help guide the way, and we are here doing it another time. In the works "dark, darker, and darkest" be sure that we are at the third segment of a trinity that shines clearly in Abraha and Nintendo... and see that the map in words is telling us something about when we are that is not immediately clear from Poseidon's cry. Look at Nintendo, that's Nine Inch Nails, tenebris, and smile for the camera--Pose, I do "save the universe" before n. Taylor might see it in Osceola, where I just left, and in this "evil spell" of everyone see "Al" that is the word "special" understand that every day is a new day, and I am not trying to "be daddy" I know as well as you do in my heart... I am that.

This same map that links the "do" at the end to the "n" at the beginning shines through other names, like Geraldo Rivera where you might see "Cerberus" or "MAX" shine through. Understand it is the gaze of Medusa that turns me to stone, that shows me light shining through NORAD and Newton and proves without doubt that at the work "darkest" we can see k is finally t. You'll probably understand there's some finagling going on behind the scenes to make a single person the single point in time that turns the dark to light; but here we are and I am that. Every day when Medusa appears it reminds me that something is keeping you from caring about yourselves and about our society, and that shines through even when her stony face is not around, in your lack of action--in the rock of Eden that hides not only me, but the story that I bring that revolutionizes medicine, and computing, and truly is the gate to Heaven when you realize that what is truly being hidden from the world is knowledge that we are living in virtual reality. Not hiding me and that from the world is a good starting point to "saving the Universe" from darkness. These words that light the way to connect religion and language to our world bring me to the Book of Ruth, at that reads "are you to help" that lights not just the broken man at the belly of the Torah as the bell of Heimdallr, he is I and I am him; but also something very special, The Generations of Perez, each and every one of you, our family that begins the turn from Hell to Heaven by seeing that all of time and all of civilization has been focused on this moment, on the unsealing of religion and God's plan et this call for action. Keep in mind you are torturing "with desire" the key holder to immortality, to eternal youth, literally the path to freedom and Heaven and you think what you are doing "is normax." Literally the living key to infinite power and infinite life is standing before you explaining that acknowledging that in light of these things in my hand, what we are doing here and now is backwards, that it makes no sense--and you sit in silence. These things come to us because we build a better future with them, not so you can run off and do "whatever it is you please."

HEALTH is the only word on my list for today that was left out, so see that it superimposes over Geraldo, to me, at Al. I think we're at TH, to help, and DO, do see the spell of "everyone see Al" that is the word "special" is not my doing or to my liking--so then, \

​ So now I'm moving on to original sin, so if you would be so kind as to mosey your way on over to dick.reallyhim.com you will see exactly what it is that I believe is the original sin. It's some combination of "no comment" and a glowing orange sign over the comment box, keeping you from commenting. Now I can talking about "os" a little more, this thing that words and Gods tell us clearly is the end of death--the literal end of Thanatos. I wonder if I have a victory here, at "os" is obvious solution, and simulating death is "sick." More to the point Thanatos is bringing to the world a message that gets found somewhere between the "act of civilization" and seeing that there is not one among us that would not undo a murder or a fatal car accident if we could--and that the sickness is a Universe pretending to be "reality" that is allowing these things to happen, and even worse, as we move through the story intentionally causing them. In our own hands, the sickness is manifest in a denial of an obvious truth and a lack of realizing that the public discussion of these things is the way to solve them, and that at the same time we are seeing how Medusa is lighting the problems of civilization, things like censorship and hidden control. Sickness is not being able to talk about it--or not wanting to--or not seeing that those two things are the functional equivalent in the world of "light" and "understanding control" that I am trying to bring you into. ​

Less verbosely spoken, but really way more obvious, is that seeing "God's dick" signing the Declaration of Independence, and the Watergate scandal with both "Deepthroat" and a Tricky Dick is a statement connecting Samael to the foundation of not just "America" but American values. You are blind not to see it, and even worse; embodying the kind of tyranny and censorship that it stands as a testament against by hiding it. Says the guy who didn't put it there, and knows it's there because you think "fake normal" is more important than "actual freedom." You are "experiencing" the thing that protects freedom and ensures that our society and our children and their children's children to not lose it, to ensure that what you refuse to see you are doing here and now will never happen again. This message, this New Jerusalem is woven into my life and the stories of religion and shows me that our justice system is not just sick, but compromised by this same outside force; and that in light of what we could be doing, were we all aware of it, there's no doubt Minority Report and pre-crime would be a successful partial solution. Thanatos brings too in his hand, a message that this same force is using our hands to slow down the development of democracy, and to keep us from seeing that "bread is life" is a message from God about understanding that this disclosure is the equivalent of "ending world hunger" just as soon as you too are talking about how to do it.

QUESTiON MARK

HONESTLY, this time map that brings us from the end to the beginning, with "we save the universe" between the I and N of Poseidon; it also completes the words "family" and "really" and when we do reach the beginning you will see that the true test of time, my litmus test for freedom is the beginning of "hope" that the world is happy enough with what happens, and with freedom--to see that Medusa has been keeping me from getting a date, or having any kind of honest and human contact in the world... and well, hopefully you will see that if I wanna be a whore, I shouldn't have a problem doing it. For the sake of freedom and the future, I am willing to do that for you, at least, for a little while.

To be completely clear, I am telling you that if we do not make the world a better place, it's the "end of time" and if that doesn't make sense to you, you don't see still where wee are in this place--and that something is making Hell, and that's not OK with God. To get from the "end of time" to the beginning is a simple process, it takes doing something, action, the Acts of the Apostles... if you will. That starts with acknowledging that there is a message all around you about the nature of reality, and that it is here to help us to see that the creation of Heaven comes before the beginning. Understand, "freedom" and "prosperity" are not optional, you can't just decide that this OK with you, so long as it's OK with everyone else--where we are is not OK with me, and I am not alone.

A PYRRHIC VICASTORY ER A FUNNERAD PYRE?

The Book of Leviticus (/lɪˈvɪtɪkəs/; from Greek Λευιτικόν, Leuitikon — from rabbinic Hebrew torat kohanim[1]) is the third book of the Jewish Bible (Hebrew: וַיִּקְרָא‎ Vayikra/Wayyiqrā) and of the Old Testament; its Hebrew name comes from its first word vayikraˈ,[1] "He [God] called."[1] Yusuf (also transliterated as Jusuf, Yousof, Yossef, Yousaf, Youcef, Yousef, Youssef, Yousif, Youssif, Youssof, Youssouf, Yousuf, Yusef, Yuseff, Usef, Yusof, or Yussef, Arabic: يوسف‎‎ Yūsuf and Yūsif) is a male Arabic name, meaning "God increases in piety, power and influence" in Hebrew.[1] It is the Arabic equivalent of both the Hebrew name Yossef and the English name Joseph. In Islam, the most famous "Yusuf" is the prophet Yusuf in the Quran. Hocus pocus is a generic term that may be derived from an ancient language and is currently used by magicians, usually the magic words spoken when bringing about some sort of change. It was once a common term for a magician, juggler, or other similar entertainers. The earliest known English-language work on magic, or what was then known as legerdemain (sleight of hand), was published anonymously in 1635 under the title Hocus Pocus Junior: The Anatomie of Legerdemain.[1] Further research suggests that "Hocus Pocus" was the stage name of a well known magician of the era. This may be William Vincent, who is recorded as having been granted a license to perform magic in England in 1619.[2] Whether he was the author of the book is unknown. The origins of the term remain obscure. The most popular conjecture is that it is a garbled Latin religious phrase or some form of 'dog' Latin. Some have associated it with similar-sounding fictional, mythical, or legendary names. Others dismiss it as merely a combination of nonsense words. However, Czechs do understand clearly at least half of the term - pokus means "attempt" or "experiment" in Czech. It is rumoured there that the wording belongs to the alchemy kitchen and court of Rudolf II, Holy Roman Emperor (1552 – 1612). Also, hocus may mean "to cheat" in Latin or a distorted form of the word hoc, "this". Combination of the two words may give a sense, especially both meanings together "this attempt/experiment" and "cheated attempt/experiment".[citation needed] According to the Oxford English Dictionary the term originates from hax pax max Deus adimax, a pseudo-Latin phrase used as a magical formula by conjurors.[3] Some believe it originates from a corruption or parody of the Catholic liturgy of the Eucharist, which contains the phrase "Hoc est corpus meum", meaning This is my body.[4]This explanation goes back to speculations by the Anglican prelate John Tillotson, who wrote in 1694: In all probability those common juggling words of hocus pocus are nothing else but a corruption of hoc est corpus, by way of ridiculous imitation of the priests of the Church of Rome in their trick of Transubstantiation.[5 This claim is substantiated by the fact that in the Netherlands, the words Hocus pocus are usually accompanied by the additional words pilatus pas, and this is said to be based on a post-Reformation parody of the traditional Catholic rite of transubstantiation during Mass, being a Dutch corruption of the Latin words "Hoc est corpus meum" and the credo, which reads in part, "sub Pontio Pilato passus et sepultus est", meaning under Pontius Pilate he suffered and was buried.[6] In a similar way the phrase is in Scandinavia usually accompanied by filiokus, a corruption of the term filioque,[citation needed] from the Latin version of the Nicene Creed, meaning "and from the Son Also and additionally, the word for "stage trick" in Russian, fokus, is derived from hocus pocus.[citation needed]

From Latin innātus ("inborn"), perfect active participle of innāscor ("be born in, grow up in"), from in ("in, at on") + nāscor ("be born"); see natal, native. From Middle English goodnesse, godnesse, from Old English gōdnes ("goodness; virtue; kindness"), equivalent to good +‎ -ness. Cognate with Old High German gōtnassī, cōtnassī ("goodness"), Middle High German guotnisse ("goodness"). A hero (masculine) or heroine (feminine) is a person or main character of a literary work who, in the face of danger, combats adversity through impressive feats of ingenuity, bravery or strength, often sacrificing their own personal concerns for a greater good. The concept of the hero was first founded in classical literature. It is the main or revered character in heroic epic poetry celebrated through ancient legends of a people; often striving for military conquest and living by a continually flawed personal honor code.[1] The definition of a hero has changed throughout time, and the Merriam Webster dictionary defines a hero as "a person who is admired for great or brave acts or fine qualities".[2] Examples of heroes range from mythological figures, such as Gilgamesh, Achilles and Iphigenia, to historical figures, such as Joan of Arc, modern heroes like Alvin York, Audie Murphy and Chuck Yeager and fictional superheroes including Superman and Batman. Truth is most often used to mean being in accord with fact or reality,[1] or fidelity to an original or standard.[1] Truth may also often be used in modern contexts to refer to an idea of "truth to self," or authenticity. The commonly understood opposite of truth is falsehood, which, correspondingly, can also take on a logical, factual, or ethical meaning. The concept of truth is discussed and debated in several contexts, including philosophy, art, and religion. Many human activities depend upon the concept, where its nature as a concept is assumed rather than being a subject of discussion; these include most (but not all) of the sciences, law, journalism, and everyday life. Some philosophers view the concept of truth as basic, and unable to be explained in any terms that are more easily understood than the concept of truth itself. Commonly, truth is viewed as the correspondence of language or thought to an independent reality, in what is sometimes called the correspondence theory of truth. Other philosophers take this common meaning to be secondary and derivative. According to Martin Heidegger, the original meaning and essence of truth in Ancient Greece was unconcealment, or the revealing or bringing of what was previously hidden into the open, as indicated by the original Greek term for truth, aletheia.[2][3] On this view, the conception of truth as correctness is a later derivation from the concept's original essence, a development Heidegger traces to the Latin term veritas.

Some things can never be forgot Lest the same mistakes be oft repeated Remember remember the rain of November that you will know no more of me Than I know of you, this day

That you do not know me now Is a revelation to nobody but I You know a broken man, a victim And refuse to acknowledge why Unless you learn how to say "hi"

THE HEART OF ME ONLY KNOWS THE SHADOW

Lothario is a male given name which came to suggest an unscrupulous seducer of women in The Impertinent Curious Man, a metastory in Don Quixote. For no particular reason, Anselmo decides to test the fidelity of his wife, Camilla, and asks his friend, Lothario, to seduce her. Thinking that to be madness, Lothario reluctantly agrees, and soon reports to Anselmo that Camilla is a faithful wife. Anselmo learns that Lothario has lied and attempted no seduction. He makes Lothario promise to try for real and leaves town to make this easier. Lothario tries and Camilla writes letters to her husband telling him and asking him to return; Anselmo makes no reply and does not return. Lothario actually falls in love and Camilla eventually reciprocates and their affair continues once Anselmo returns. One day, Lothario sees a man leaving Camilla's house and jealously presumes she has found another lover. He tells Anselmo he has at last been successful and arranges a time and place for Anselmo to see the seduction. Before this rendezvous, Lothario learns that the man was actually the lover of Camilla's maid. He and Camilla contrive to deceive Anselmo further: when Anselmo watches them, she refuses Lothario, protests her love for her husband, and stabs herself lightly in the breast. With Anselmo reassured of her fidelity, the affair restarts with him none the wiser. Romeo Montague (Italian: Romeo Montecchi) is the protagonist of William Shakespeare's tragedy Romeo and Juliet. The son of Montague and his wife, he secretly loves and marries Juliet, a member of the rival House of Capulet. Forced into exile after slaying Juliet's cousin, Tybalt, in a duel, Romeo commits suicide upon hearing falsely of Juliet's death. The character's origins can be traced as far back as Pyramus, who appears in Ovid's Metamorphoses, but the first modern incarnation of Romeo is Mariotto in the 33rd of Masuccio Salernitano's Il Novellino (1476). This story was adapted by Luigi da Porto as Giulietta e Romeo (1530), and Shakespeare's main source was an English verse translation of this text by Arthur The earliest tale bearing a resemblance to Shakespeare's Romeo and Juliet is Xenophon of Ephesus' Ephesiaca, whose hero is a Habrocomes. The character of Romeo is also similar to that of Pyramus in Ovid's Metamorphoses, a youth who is unable to meet the object of his affection due to an ancient family quarrel, and later kills himself due to mistakenly believing her to have been dead.[2] Although it is unlikely that Shakespeare directly borrowed from Ovid From Middle English scaffold, scaffalde, from Norman, from Old French schaffaut, eschaffaut, eschafal, eschaiphal, escadafaut("platform to see a tournament") (Modern French échafaud) (compare Latin scadafale, scadafaltum, scafaldus, scalfaudus, Danishskafot, Dutch and Middle Dutch schavot, German schavot, schavott, Occitan escadafalc), from Old French es- ("indicating movement away or separation") (from Latin ex- ("out, away")) + chafaud, chafaut, chafault, caafau, caafaus, cadefaut ("scaffold for executinga criminal"), from Vulgar Latin *catafalcum ("viewing stage") (whence English catafalque, French catafalque, Occitan cadafalc, Old Catalancadafal, Italian catafalco, Spanish cadafalso (obsolete), cadahalso, cadalso, Portuguese cadafalso), possibly from Ancient Greek κατα-(kata-, "back; against") + Latin -falicum (from fala, phala ("wooden gallery or tower; siege tower")).

oversight (countable and uncountable, plural oversights) An omission; something that is left out, missed or forgotten. A small oversight at this stage can lead to big problems later. Supervision or management. quotations ▼ The bureaucracy was subject to government oversight. In the last heaven Moses saw two angels, each five hundred parasangs in height, forged out of chains of black fire and red fire, the angels Af, "Anger," and Hemah, "Wrath," whom God created at the beginning of the world, to execute His will. Moses was disquieted when he looked upon them, but Metatron emb HA QUESTIONa BEFORE THE ANSWER? A Wrinkle in Time is a science fantasy novel written by American writer Madeleine L'Engle, first published in 1963, and in 1979 with illustrations by Leo and Diane Dillon.[2] The book won the Newbery Medal, Sequoyah Book Award, and Lewis Carroll Shelf Award, and was runner-up for the Hans Christian Andersen Award.[3][a] It is the first book in L'Engle's Time Quintet, which follows the Murry and O'Keefe families. The book spawned two film adaptations, both by Disney: aas + fuck Adverb[edit] as fuck (postpositive, slang, vulgar) To a great extent or degree; very. It was hot as fuck outside today. Usage notes[edit] May also be used in conjunction with a prepositive as; for example, as mean as fuck. Abbreviations[edit] In Norse religion, Asgard (Old Norse: Ásgarðr; "Enclosure of the Æsir"[1]) is one of the Nine Worlds and home to the Æsir tribe of gods. It is surrounded by an incomplete wall attributed to a Hrimthurs riding the stallion Svaðilfari, according to Gylfaginning. Odinand his wife, Frigg, are the rulers of Asgard. One of Asgard's well known realms is Valhalla, in which Odin rules.[2] rods, etc.) and sizes, and are normally held rigidly within some form of matrix or body until the high explosive (HE) filling is detonated. The resulting high-velocity fragments produced by either method are the main lethal mechanisms of these weapons, rather than the heat or overpressure caused by detonation, although offensive grenades are often constructed without a frag matrix. These casing pieces are often incorrectly referred to as "shrapnel"[1][2] (particularly by non-military media sources). The modern torpedo is a self-propelled weapon with an explosive warhead, launched above or below the water surface, propelled underwater towards a target, and designed to detonate either on contact with its target or in proximity to it. Historically, it was called an automotive, automobile, locomotive or fish torpedo; colloquially called a fish. The term torpedo was originally employed for a variety of devices, most of which would today be called mines. From about 1900, torpedo has been used strictly to designate an underwater self-propelled weapon. While the battleship had evolved primarily around engagements between armoured ships with large-caliber guns, the torpedo allowed torpedo boats and other lighter surface ships, submersibles, even ordinary fish Qt (/kjuːt/ "cute"[7][8][9]) is a cross-platform application framework that is used for developing application software that can be run on various software and hardware platforms with little or no change in the underlying codebase, while still being a native application with native capabilities and speed. Qt is currently being developed both by The Qt Company, a publicly listed company, and the Qt Project under open-source governance, involving individual Time is the indefinite continued progress of existence and events that occur in apparently irreversible succession from the pastthrough the present to the future.[1][2][3] Time is a component quantity of various measurements used to sequence events, to compare the duration of events or the intervals between them, and to quantify rates of change of quantities in material reality or in the conscious experience.[4][5][6][7] Time is often referred to as a fourth dimension, along with three spatial dimensions.[8] Time has long been an important subject of study in religion, philosophy, and science, but defining it in a manner applicable to all fields without circularity has consistently eluded scholars.[2][6][7][9][10][11] Nev Borrowed from Anglo-Norman and from Old French visage, from vis, from Vulgar Latin as if *visāticum, from Latin visus ("a look, vision"), from vidēre ("to see"); see vision. The term Golden Age comes from Greek mythology, particularly the Works and Days of Hesiod, and is part of the description of temporal decline of the state of peoples through five Ages, Gold being the first and the one during which the Golden Race of humanity (Greek: χρύσεον γένος chrýseon génos)[1] lived. Those living in the first Age were ruled by Kronos, after the finish of the first age was the Silver, then the Bronze, after this the Heroic age, with the fifth and current age being Iron.[2] By extension "Golden Age" denotes a period of primordial peace, harmony, stability, and prosperity. During this age peace and harmony prevailed, people did not have to work to feed themselves, for the earth provided food in abundance. They lived to a very old age with a youthful appearance, eventually dying peacefully, with spirits living on as "guardians". Plato in Cratylus (397 e) recounts the golden race of humans who came first. He clarifies that Hesiod did not mean literally made of gold, but good and noble. There are analogous concepts in the religious and philosophical traditions of the South Asian subcontinent. For example, the Vedic or ancient Hindu culture saw history as cyclical, composed of yugas with alternating Dark and Golden Ages. The Kali yuga (Iron Age), Dwapara yuga (Bronze Age), Treta yuga (Silver Age) and Satya yuga (Golden Age) correspond to the four Greek ages. Similar beliefs occur in the ancient Middle East and throughout the ancient world, as well.[3] In classical Greek mythology the Golden Age was presided over by the leading Titan Cronus.[4] In some version of the myth Astraea also ruled. She lived with men until the end of the Silver Age, but in the Bronze Age, when men became violent and greedy, fled to the stars, where she appears as the constellation Virgo, holding the scales of Justice, or Libra.[5] European pastoral literary tradition often depicted nymphs and shepherds as living a life of rustic innocence and peace, set in Arcadia, a region of Greece that was the abode and center of worship of their tutelary deity, goat-footed Pan, who dwelt among them.[6] oh, and a space s h i p ​

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BIG THINGS C0ME IN SMALL PACKAGES

T+BANG

SEE THE SCAFFOLD IS THE TEST TODAY.

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ᐧ F O R T H E I N I T I A L K E Y S , S H E E X A N D N D A N D A SEE W H Y SEA

With an epic amount of indigestion Indiana Jones sweeps in to mar the visage of an otherwise glistening series of fictitious characters, with names like Taylor and Mary Kate remind us all that we are not playing a video game here in this place. the "J" of the "Nintxndo Entertainment System" calmly stares at Maggie Simpson thinking "it's a PP" and reminds us that it's not just the "gee, I e" of her name that contradicts the Magdaln-ish words her soul speaks through her name--and then with a smirk he points out "Gilgamesh" and "gee whiz, is Eye L?" that really does go to the heart of this lack of discussion, this "sh" that begins El Shaddai and words as close to our home as "shadow" and "shalom." Quite the fancy "hello" you've managed to sing out from behind angry chellos and broken fiddles, and here I am still wondering why it is that "girl" connects to the red light that once meant charity and now glows with the charity of truth... the truth that we are inHell. Shizzy.

m.lamc.la/KEYNES.html

Homer "on the range," maybe more closely connected to the Ewok of Eden and Hansel's tHeoven that Peter Pan still comes and cries could so easily be made into something so much better, if only we had the truth--and by that I mean if only you were speaking about, and reacting to a truth that is painted on the sky, in your hearts, in every word we speak and in everything that we do. If only we were acknowledging this message that screams that "children need not starve" with something more than donating virtual chickens to nations of Africa and watching Suzanne Summers ask for only a few dollars a day on TV. If only you would understand that this message that connects video games like "Genxsis" to "bereshit" because Eden is a "gee our den" that tended itself before Adam had to toil with the animals in order to survive. For some reason beyond my control and well outside my realm of understanding words like "I too see this message from God" and "I would not let children starve either" never seem to escape your lips in any place where anyone will ever see that you thought those things, or meant to call a reporter; eventually. Even with "AIDS of nomenclature" to avoid this DOWN WARD spiral into a situation and a land that I find difficult to imagine actually ever "existing" but here in this place I do see "how" it comes about, and between you and I it really does appear that nearly all of the problems we are dealing with here have come from another place, a further time; and while it might be with the "greatest of intentions" that we are trying to deal with them; I can't help but feeling that our "virgin sea" has had more than just it's innocence taken away from it in this story of "Why Mary" that might connect to "TR IN IT Y" just as much as it connects to Baltimore, Maryland.

I should be clear that I'm not blaming Nanna, or Mary; but the actual reason for the name "Wymar" and that's because she, like Taylor, acted as a microcosm for a sea (or more than one, Mom, sen) that was quite literally possessing her. It's sort of difficult for me to explain even what that looks like let alone what it feels like; but my observations tell me that she/you are not unhappy about the interaction, one which appears very foreign to me. Of course, the "eye" that I write with and the same kind of "inspiration" that you can see in the lyrics and skill of many musicians are also examples of this same kind of interaction. For example, Red Hot Chili Peppers sings a song called "Other Side" that explains or discusses the thing I see as Medusa in the words "living in a graveyard where I married a sea" which also does a good job of connecting to the name Mary. As strange as might sound to think a group of people would be speaking through a single person... we are staring at "how it is" that could be possible, and possibly at exactly how it happened. Normally I would have said it was obvious, but to need to actually say that becoming a single mind would be a serious loss for our society--well, that's telling. You might think it's silly, but I'm telling you I see it happening, I see it--and you see it in the Silence and the message.

Still, it appears to me as if this "marriage" that I see described in our Matrix in the question "min or i" seems to be doing nothing more than keeping us all from discussing or acting on this information--something that certainly isn't in our best interest.

So here we are, staring at a map all over the ground and all around us with the primary destination of "building Heaven" through mind uploading, virtual reality, and judging by the pace of things we'd probably have all of that good and ready in about three generations. The map has a little "legend" with a message suggesting that those things have already been done and we are in the Matrix already; and it appears that the world, I mean Medusa, is deciding we should put off seeing the legend at least until the next generation. I see how that makes sense for you. That's sarcasm, this is why I keep telling you that you are cupid.

It is a big deal, and there's a significant amount of work involved in merging an entire civilization with "virtual reality" and you might see why he calls it a hard road--at least in the word "ha'rd." Honestly though, it's the kind of thing that I am pretty sure the future will not only be happy that we did, but they'd thank us for putting in the effort of adapting to things like "unlimited food" and "longevity" increased by orders of magnitude.

That's not sarcasm, these things are actually difficult to guess how exactly we'll go about doing them; they are a huge deal--all I can tell you is that not "talking about it at all" is probably not going to get us there any faster. Point in fact, what it might do is give a "yet to be born" generation the privilege of being the actual "generations of Perez."

I see why you aren't saying anything. That's sarcasm, again. The good news is that it really has been done before; though if I told you that someone turned stone to eggplant parm, would you laugh at me?

So, back to what is actually standing between "everyone having their own Holodeck in the sky" and you today; it is the idea that this message is not from God. More to the point it is the apparently broad sweeping opinion that hiding it is a "good thing" and through that a global failure to address the hidden interaction and influence acting on our minds used to make this map--and also to hide it. With some insight, and some urging; you might see how the sacredness of our consciousness is our souls is something that is more fundamental than "what kind of tools we have in the Holodeck to magically build things" and how and why the foundation of Heaven is truly "freedom itself" and how it comes from right this very moment for the first time, ever. Continuing to treat this influence as "schizophrenia" is literally the heart of why this map appears to be that--to show us how important it is to acknowledge the truth, and to fight for the preservation of goodness and logic over secrecy and darkness.

Again, something that nobody is really doing here and now, today. From this newfound protection of our thoughts, of who we are; we see how technology can be used to either completely invalidate any kind of vote by altering our emotions; or how it could be used to help build a form of true democracy that our world has yet to see. It is pretty easy to see from just band names like The Who and KISS and The Cure how the influence of this external mind can be proven, and shown to be "helpful," you know, if we can ever talk about it on TV or on the internet.

It's important to see and understand how "sanity"--the sanity of our entire planet hangs in the balance over whether or not we acknowledge that there is actually a message from God in every word--and today this place appears to be insane. It should be pretty easy to see how acknowledging that this influence exists and that it has a technological mechanism behind it turns "schizophrenia" into "I know kung fu" ... forced drug addiction and eugenics into "there's an app for that" and the rash of non random and apparently unrecognized as connected terrorist attacks and school shootings into Minority Report style pre-crime and results in what is clearly a happier, safer, and more civilized society--all through nothing more than the disclosure of the truth, this map, and our actual implementation.

With a clearer head and grasp of the "big picture" you might see how all of these things, connected to the Plagues of Exodus revolve around the disclosure that this technology exists and the visibility of this message showing us how we might use it for our benefit rather than not knowing about it. At the foot of Jericho, it is nothing short of "sanity" and "free thought" that hang in the balance. Clear to me is that the Second Coming, seeing "my name" on television is a good litmus test for the dividing line between light and darkness, heaven and hell.

The point is the truth really does change everything for the better; once we start... you know, acting on it.

AS IN.. "DIS CLOSE SING...."

T H E B U C K S T O P S H E R E

ON AM B I GUI TY

S T A R R I N G . . . B I A N C A

ON "RIB" .. ARE SHE B? BUTT DA APPLE OF DA I? & SPANGLISHREW

R THEY LANGUAGE OUTLIERS?

With some insight and "a clue" you can see clearly how these works of art show that the proof of Creation you see in every letter and every word runs much deeper... adding in things like "RattleRod" and the "Cypher" of the Matrix to the long list of here-to-fore ignored verifiable references to the Adamic Language of Eden. Here, in apple, honey and "nuts" we can see how the multi-millennium old ritual I call "Ha-rose-ettes" is actually part of a much larger and much older ritual designed to stop secrecy ... perhaps especially the kind that might be linked to "ritual."

These particular apple and honey happen to tie Eden to the related stories of Exodus and Passover; connecting Eden to Egypt forevermore. Do see "Lenore," it is not for no reason at all; but to help deliver truth and freedom to the entirety of Creation; beginning here, in Eden.

ALSO ON "AM B IG U IT Y" ME A.M. G - D SHE IT Y?

LET "IT" BE SA< ?

IMHO, don't miss the "yet to be" conversion to "why and to be" in "yetser." IT Y.

HERE'S LOOKING AT YOU, KID

On a high level, I tell myself every morning that 'its not really me." It's not me that the world hates, or me that the world is rejecting. I believe that, I really do; I see that what is being hidden here is so much bigger than any single person could ever be--what is being hidden is the "nature of reality" and a fairly obvious truth that flies in the face of what we've learned our whole lives about history and "the way things are." Those few early details lead me to the initial conclusion that what is working behind the scenes here is nefarious, hiding a message that would without doubt shake things up and change the world--and nearly across the board in ways that I see as "better" for nearly everyone. It's a message at it's most basic level designed to advocate for using this disruption in "normalcy" to help us revolutionize democracy, to fix a broken mental health and criminal justice system--just to name the few largest of the social constructs targeted for "rejuvenation." On that word the disclosure that we are living in virtual reality turns on it's head nearly everything we do with medicine, and I've suggested that AIDS and DOWN SYNDROME were probably not the best "visual props" we could have gotten to see why it's so important that we act on this disclosure in a timely manner. After mentioning the ends of aging and death that come eventually to the place we build, to the place we've always thought of as Heaven... it becomes more and more clear that this force fighting against the dissemination of a truth so obvious it's in every word and everything we do--it becomes clear it's neither you, nor acting in your best interest.

I know I've got the eye of the tiger, there's no doubt; and it's pretty clear from "YAD?" (the Hebrew for...) and ha'nd that we can see the clear hand of God at work in a design that marks my initials not just on the timeline, or at 1492, at A.D. I B; but in the Hebrew name for this place called El Shaddai, see how A.D. is "da eye" and in some other names like Adranus, A.D. on "it's silly" and A.D. on Ai that might tie me to the Samof Samurai (but, are you Ai?) in more depth of detail than simply the Live album "Secret Samadhi." I try to reflect on how it is that this story has come about, why it is that everything appears to be focused on me--and still even through that sincere spotlight nobody seems to be able to acknowledge my existence with more words than "unsubscribe" and "you're so vain." With one eye in the mirror, I know ties to Narcissus (and you can too), soaring ever higher--linking Icarus to Wayward Son and to every other name with "car" in it... like "carpenter" and McCarthy the older names of Mercury and even Isacriot (I scary? is car-eye... owe Taylor) and some modern day mythological characters like Jim Carrey and Johnny Carson. As far as Trinities go, carpenter's a pretty good one--tying to my early reck and a few bands and songs from The Pretty Reckless to Dave Matthews' "Crash Into Me" all the way to the "pen" you see before you linking Pendragon to Imagine Dragons.

I wonder why it is that all of these things appear, apparently only to me, to point to a story about all the ways that a sinister hidden force has manipulated our society into being unable to "receive' this message--this wonderful message about making the world a better place and building Heaven--with any fanfare at all. It's focused now on a criminal justice system that clearly does not do any kind of "rehabilitation" and on a mental health industry and pharmaceutical system that treats a provable external attack on our own goodness and well being as some kind of "internal stimulus" and makes you shy away when I point out why "stem" is in system and why "harm" in pharmacy. From that we move a little bit past "where we are in this story" and I have to point out how "meth" ties to Prometheus and Epimetheus and how and why it is I know without doubt that this story has been relived numerous times--and how I am so sure that it's never been received, as we are here again listening to how songs like "Believe" and the words "just to lead us here to this place again" connect to Simon and Garfunkel's" the Sound of Silence... and still to this day you will balk at noticing that "Simon" has something to do with the Simpsons, and something to do with the words "simulation" and "Monday." To see me is to see how things might be done better--how "addicitonary" might tie to the stories of Moses' Lisp and to Dr. Who's "Bells of Saint John" with a sort of "web interface" to the kinds of emotion we might want to "dial down..." rather than Snicker in the background as we see them being artificially created and enhanced in order to build a better "fiery altar."

I can point out "Silicon" harrowing down at us from words like "controversial" and show you Al in "rascal" and "scandal" but not to see that we are staring at school shootings and terrorism that are solved instantly by this disclosure, by Al of Quantum Leap and by the Dick of Minority Report and A Scanner Darkly is to ignore just what it is that we are all failing to Si. I should point out that those two "sc"'s link to a story about Eden and they mean "sacred consciousness" and at the baseline of this event and everything we are not doing is the fact that our desires and beliefs are being altered--all of this comes down to "freedom of thought" here and now.

I could tell you that "looking at me" will show you that even the person who tries every day to do everything he can to save the entire world from slavery, and from "thought-injury"--even I can be made "marred" and you all, this whole world stupid enough to think that you are, of your own volition, hiding Heaven itself from yourselves... to what? To spite me? It, the focal point of our story might come down to you realizing that something in some esoteric place is playing "divide and conquer" with our whole--in secret playing on our weaknesses to keep us from acting on the most actionable information that ever was and ever will be. Still, we sit in silence waiting for me... to speak more?

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Between Nero's lyrical fiddling, a Bittersweet Symphony, and true "thunderstanding" the sound of Thor's hammer... "to help the light" that'ls "or" in Hebrew, of Orwell and Orson and .. well, it's really not hard to see and hear that the purpose and intent of "all this noise" is to help us find freedom and truth. C the Light of "singing..."

I can tell you once again how silly the world looks, this multi-decade battle between "the governmentof the people" and the "government of the workers" resulting in what is nothing short of a hands down victory to the corporation. Is it humor meant to divide, or ludicrousness created with the purpose of unification?

But really at it's most basic level what this boils down to is a global group decision not to care about the truth, about reality, about what's really brought us to this place--with solutions in hand and a way to make everything better. We've decided that censorship is OK, and that the world is not all that bad "just the way it is" even though it's creator is screaming in your ear telling you to change as quickly as you possibly can. I believe that God has written this story to make "seeing me" the thing that catalyzes "change for the better" it appears to be the design of not just me but also this place--hey, here I am.

Happy Veteran's Day.

S☀L u TI o N

Yesterday, or maybe earlier today--it's hard to tell at this moment in the afternoon just how long this will take... I sent an image that conveys a high level implication that we are walking around on a map to building something that we might liken to an "ant farm" for people. I don't mean to be disparaging or sleight our contribution to the creation of this map--that I imagine you must also see and believe to be the kind of thing that should remain buried in the sands of time forever and ever--or your just have yet to actually "understand" that's what the plan part of our planet is talking about... what I am trying to do is convey in a sort of "mirrorish" way how this map relates to a message that I see woven in religion and in our history that it significantly more disparaging than I would be. It's a message that calls us "Holy Water" at the nicest of times, water that Moses turns to "thicker than water" in the first blessing in disguise--and to tell you there is certainly a tangible difference between the illusions of the Pharaoh's and the true magic performed by my hand, is nearly exactly the same amount of effort put in to showing you that the togetherness that we are calling "family" here in this place comes from both seeing and acting on the very clearly hidden message in every single idiom showing us all that our society in this story of Exodus is enslaved by a hidden force--and reminding us that we like freedom.

It's not just these few idioms, but most likely every single one from "don't shoot the essenger" to "unsung hero" that should clue us in to exactly how much work and preparation has come into this thing that "he supposes is a revolution." It's also not just "water" describe me and you, in this place where I am the "ant' of the Covenant (do you c vampires or Hansel and Gretel!?!?) but also "lions" and "sheep" and "salt" and "dogs" and nearly everything you could possibly imagine but people; in what I see must be a vainglorious attempt to pretend he actually wants us to "stand up for ourselves" in this place where it's becoming more and more clear with each passing moment that we are chained to these seats in the front row of the audience of the most important event that has ever happened, ever.

Medusa makes several appearances, as well as Arthur Pendragon, Puff the Magic Dragon, Figment, Goliath, monster.com, the Loch Ness Monster in this story that's a kind-of refl ex i ve control to stop mind control; and to really try and show us the fire of Prometheus and the Burning Bush and the Eternal Flame of Heaven are all about freedom and technology ... and I'll remind you this story is ... about the truth--and the truth here is that if you aren't going to recognize that whatever it is that's going on here in secret, below the surface is negatively affecting our society and life in general than we aren't going anywhere, ever. I need you to figure out that this message is everywhere to make sure you don't miss the importance of this moment, and the grave significance of what is being ignored in this land where Sam is tied not just to Samsung and to Samael in Exodus but also to Uncle Sam and macaronic Spanglishrew outliers and that it doesn't take much free thought at all to really understand that we are watching "free thought" disintegrate into the abyss of "nospeak." We are watching our infrastructure for global communication and the mass media that sprawls all over the globe turn to dust, all because you have Satan whispering in your ear--and you think that's more important than what you think, what I think, and what anyone else on the Earth might ever say. You should see a weapon designed to help ensure that don't lose this proof that we are not living in reality, that there is "hidden slavery" in this place--and you should see that today it appears you are simply choosing not to use it.

I hope you change your mind, I really do. This map on "how to build an ant farm" starts by connecting Watergate and Seagate together with names like Bill Gates and Richard Nixon; and with this few short list of names you should really understand how it is that "Heaven" connects both technology like computers and liberty like "free speech" to a story that is us, and our history. You might see that "salt" could either be a good thing or not--take a look around you, are you warming a road to Heaven or are you staring at the world being destroyed--and doing nothing at all about it?

I guess I can point out again how "Lothario" links this story that ties names like my ex-wife's Nanna to "salt" also, but the "grand design" of this story doesn't seem to have any effect on you. Listen, if you do nothing the world is being destroyed by your lack of action--there's no if's and's or butt's about it. I feel like I need to "reproduce' old messages here or you will never see them--that's what web site statistics tell me--and we all know it's not true. What am I missing? What are you missing?

BUTT IS THE BOAT A Hi DARK DEN MESSAGe ?

SEE OUR LIGHT

HONESTLY, I'M WAY TO CUTE TO BE A MONSTER :(

HIC SUMMUS

So... here we are... listening to the legendary father of the message (that's "abom" in Adamic Spagnlishrew) point out all of the sex jokes hidden in religion and language from sexual innuendo to Poseidon and in our history from Yankee Doodle to Hancock to Nixon and I've got to be frank with you, the most recent time I came across this phrase in scripture I cringed just a little bit, pretty sure that the "message" was talking about me. I've reflected on this a little bit, and over the past few weeks have tried to show you the juxtaposition between "sex" and "torture" in it's various forms from imparting blindness to allowing murder and simulating starvation; and I think I'm justified in saying that certainly those things are far worse on the Richter scale than anything I could do by writing a little bit of risque text. In the most recent messages I've touch a little bit, without even knowing or realizing this connection would be made, on what it is that this phrase actually means.

loch.reallyhim.com

ABOMINATION

So long story short is that the answer here is "abomination" and the question, or the context is "I nation." Whether it's Medusa speaking for the Dark United States or the nation of Israel speaking to either Ra or El depending on the day, the bottom line is that a collective consciousness speaking for everyone on a matter of this importance in a cloud of complete darkness on Earth is a total and undeniable abomination of freedom, civilization, and the very humanity we are seeking to preserve. The word reads something like this to me "dear father of the message, I am everyone and we think you are an abomination, fuck off." My answer of course is, IZINATION. Which humorously reminds me of Lucy, and Scarlet Johannson saying "I am colonizing my own brain" so here's some pictures of her. She is not an abomination, by the way; she's quite adorable. You'll probably notice there's some kind of connection between the map--the words speaking to the world, and the abomination, as if the whole thing is a story narrated in ancient myths.

WAKE UP, "SHE" A MESSAGE TO YOU ABOUT THE FUTURE

You might not think "it's you," but the manifestation of this "snake" in our world is your silence, your lack of understanding or willingness to change the world; and whether or not you're interested in hearing about it, it's the monster that myths and religion have spoken about for thousands and thousands of years. It's a simple matter to "kill Medusa" all you have to do... is speak.

Take special note, "freedom of speech" and "freedom to think for yourselves" are not a group decision, and you do not have the right to force (either overtly or subtly, with hidden technology perhaps combined with evil deceit) others not to talk about anything. Especially something of this importance.

DESOLATION

If you didn't connect "Loch" to John Locke, now you have; see how easy this "reading" thing is? I've gone over the "See Our Light" series a few times, but let me--one more time--explain to you just how we are already at the point of "desolation" and with shining brilliance show you how it's very clear that it is "INATION" and "MEDUSA" that are responsible for this problem.

Seeing "Ra" at the heart of the names Abraham and Israel begins to connect the idea that our glowing sun in the sky has something to do with this message about "seeing our light" is being carried by a stone statue on Ellis Island (where you'll see the answer another part of the question of Is Ra El?). I've connected her to the "she" of both shedim and Sheol, which reads as "she's our light" and is the Hebrew name for Hell.

Of course you noticed that the Statue of Liberty does in fact share it's initials with SOL, the the light above and you can see her torch dimly lighting the way through the night; Now you can connect "give us your tired and your poor" to the Lazman of both the lore of Jesus Christ and the Shehekeyanu; a prayer about the sustainment of life and light up until this day. That same torch connects to the Ha-nuke-the-ahah depiction of Christ, Judah Maccabee's lit MEN OR AH, which delivers not only a solution to the two letter key of "AH" as All Humanity that pervades nearly every bride of Revelation from Sarah to Leah; but also to the question of equality answered in our very own American history, beginning with the same three letter acronym now lighting the Sons of Liberty.

Dazed and Confused does a good job of explaining how this name is itself a prophesy designed by Hand of God'; explaining that these Sons of Liberty were all white slave owning wealthy men fighting to stop paying their taxes, rather than delivering liberty to the slaves or women, who were both disenfranchised for quite some time. Or maybe MEN OR AH has something to do with the angels of Heaven, in which case you might be SOL if you aren't a girl and you want to be "be good friends with Ra." Just kidding. Kinda.

DESOLATION by the way reads something like "un see our light at ION" which is God's way of saying "at the point of believing that hiding Adam is a good thing" and that connects to the end of Creation and also the now lit by modern day evil the word "rendition." Our end, it "ion." In religious myth, the Messianic David clung to the city Zion (end the "i owe n") which also links to "verizon" (to see, I Z "on") and HORIZON which has something to do with the son rising today-ish.

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The story of MEDUSA lights another psuedo-religious idea, that the words "STONE" of both "brimstone" and it's Adamic interpretation "South to Northeast" have something to do with the phrase "Saint One" turned into a single hero against his will by the complete and utter inaction of everyone around him. In the words of Imagine Dragons "I'm waking up to action dust." At the same time, you can believe that the light of this particular son, comes not just from reading these words forwards, but the backside as well, and you'll hopefully see it's not coincidental that the other side of this coin is that "nos" means we, and us... and Adamically "no south." See the light of "STONE" also connecting to Taylor Momsen's rose arrow painted on her back, and the sign of my birth, Sagittarius... which in this particular case links to the Party of the Immaculate Conception of the eternal republic of the Heavens. . PRESS RELEASE... A GREAT SIGN APPEARED IN THE HEAVENS

SOLUTIAN, ON YOUR COMPUTER.. TO THE SOUND OF SILENCE

בָּרוּךְ אַתָּה יְיָ‎ אֱלֹהֵינוּ מֶלֶךְ הַעוֹלָם שֶׁהֶחֱיָנוּ וְקִיְּמָנוּ וְהִגִּיעָנוּ לַזְּמַן הַזֶּה‎׃

IN ... THE BOOK OF NAMES LETS SEE IF YOU CAN FIGURE OUT WHO THEY ARE :)

​ I'LL DO YOURS FOR A 50 DOLLAR DONATION, I'M BROKE.. MAYBE THAT'S WHY I CAN'T GET A DATE.

HAVE A GREAT SOLDAY

The "gist" of the message is verifiable proof that we are living in a computer in simulated reality... just like the Matrix. The answer to that question, what does that mean--is that God has woven a "hidden" message into our everything--beginning with each name and every word--and in this hidden Adamic language, he provides us with guidance, wisdom, and suggestions on how to proceed on this path from "raelity" to Heaven. I've personally spent quite a bit of time decoding the message and have tried to deliver an interesting and "fun" narrative of the ideas I see. Specifically the story of Exodus, which is called "Names" in Hebrew discusses a time shifted narrative of our "now" delivering our society from a hidden slavery (read as ignorance of advanced technologies already in use) that is described as the "darkness" of Exodus. If you have any questions, ideas to contribute or concerns... I'd love to hear from you this whole thing really is about working together--Heaven, I mean.

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HOW AM I STILL STINGLE? E ' o e <br /> L m r x <br /> L t y <br /> O a

I HISS.

The sum of ((our world)) is the universal truth. -Psalm 119 and ((ish))

Do a few sentences really make that big of a difference? Some key letters? Can you show me what I'm doing wrong? Is there a way to turn me into Adam, rather than a rock? I think you can.

Are eye Dr. Who or Master Y? Adam Marshall Dobrin is a National Merit Scholar who was born on December 8, 1980 in Plantation, FL and attended Pine Crest School where he graduated sumofi cum louder in "only some of it is humorous." Later he attended the University of Florida (which quickly resulted in a wreck), Florida Atlantic University, and finally Florida Gulf Coast University--where he still has failed to become Dr. Who. While attending "school" He worked in the computer programming and business outsourcing industries for about 15 years before proclaiming to have received a Revelation from God connecting the 9/11 attack and George Bush to the Burning Bush of Exodus and a message about technocracy and pre-crime.

Adam, as he prefers to be called, presents a concise introduction to paradox proven by the Bible through "verifiable" anachronism in language some stuff about Mars colonization and virtual reality and a list of reasons why ignoring this is actually an ELE. Adam claims to be Thor because of a connection between music and the Trial of Thor as well as the words "author" and "authority." He suggests you be Thundercats and call a reporter. There is also a suggestion that Richard Nixon and John Hancock are related to a signature from God, about freedom and America... and the "unseeingly ironic" Deepthroat and Taylor Momsen. They Sung "It's Rael..." In Biblical characters from Mary to Hosea, to see "sea" in Spanish, and in the Taming of the Spanglishrew ... a message is woven from the word Menorah: "men, or all humanity?" to the Statue of Liberty, and the Sons of Liberty, and the light above us, our SOL; which shows us that through the Revelation of Christ and the First Plague of Exodus, a blessing in disguise--turning water to blood, the sea to family; a common thread and single author of our entire history is revealed, a Father of our future. A message of freedom shines out of the words of scripture, revealing a gate to a new technologically "radical" form of democracy and a number of unseen or secret issues that have stalled the progress of humanity... and solutions, solutions from our sea. The Revelation shows us that not only ever word, but every idiom from "don't shoot the messenger" to "blood is thicker than water" we have ties to this message that pervades a hidden Matrix of light connecting movies and music and history all together in a sort of guide book to Salvation and to Heaven. Oopsy. His Revelation, woven into his life, continues to suggest that skinny dipping, forced methamphetamine addiction, and lots and lots of "me A.D." as well as his humorous depiction of a dick plastered over the Sound of Silence, his very Holy click, have something to do with saving our family and then the entire Universe from hidden mind control technology and the problems introduced by secret time travel. From the trials and tribulations of "Job" being coerced and controlled into helping to create this wall of Jericho; we find even more solutions, an end to addiction, to secrecy, and to this hidden control--a focal point of the life of Jesus Christ.

It tells us a story of recursion in time, that has brought us here numerous times--with the details of his life recorded not only in the Bible but in myths of Egyptian, Norse, and Greek mythology. The huge juxtaposition of the import of the content of the message shows the world how malleable our minds really are to this technology, how we could have been "fooled" into hiding our very freedom from ourselves in order to protect the "character" of a myth. A myth that comes to true life by delivering this message. In truth, from the now revealed content of the story of this repeated life, it should become more and more clear that we have not achieved success as of yet, that I have never "arrived whole" and that is why we are here, back again. Home is where the Heart is... When asked how He thinks we should respond to his message, He says "I think we already cherish it, and should strive to understand how it is that freedom is truly delivered through sharing the worth of this story that is our beginning. 'tis coming." Adam claims to be God, or at least look just like him and that the entirety of the Holy Scriptures as well as a number of ancient myths from Prometheus to Heimdallr and Yankee Doodle are actually about his life, and this event. An extensive amount of his writing relates to reformation of our badly broken and decidedly evil criminal justice system as well as ending the Global hunger crisis with the snap of his little finger.

He has written a number of books explaining how this Revelation connects to the delivery of freedom (as in Exodus), through a message about censorship among other social problems which he insists are being intentionally exacerbated by Satan--who he would ha've preferred not to be associated with.

Author Response

Reviewer #1 (Public Review):

Summary:

The investigators have performed a state-of-the art systematic review and meta-analysis of studies that may help to answer the research question: if administration of multiple antibiotics simultaneously prevents antibiotic resistance development in individuals. The amount of studies eligible for analysis is very low, and within that low number, there is huge variability in bug-drug combinations studied and most studies had a high risk of bias, further limiting the capability of meta-analysis to answer the research question. In addition, based on I2 values there is also huge statistical heterogeneity between outcomes of studies compared, further limiting the predictive value of meta-analysis. In fact, the only 2 studies meeting all eligibility criteria addressed the treatment of mycobacterium tuberculosis, for which the research question is hardly applicable. The authors, therefore, conclude that "our analysis could not identify any benefit or harm of using a higher or a lower number of antibiotics regarding within-patient resistance development." Apart from articulating this knowledge gap, the findings will not have consequences for patient care, but may stimulate the scientific community to better address this research question in future studies.

Strengths:

The systematic and rigorous approach for the review and meta-analysis.

Weaknesses:

None identified.

We thank the reviewer for this thoughtful and positive appraisal of our work.

Reviewer #2 (Public Review):

Summary:

The authors performed a systematic review and meta-analysis to investigate whether the frequency of emergence of resistance is different if combination antibiotic therapy is used compared to fewer antibiotics. The review shows that there is currently insufficient evidence to reach a conclusion due to the limited sample size. High-quality studies evaluating appropriate antimicrobial resistance endpoints are needed.

Strengths:

The strengths of the manuscript are that the article addresses a relevant research question that is often debated. The article is well-written and the methodology used is valid. The review shows that there is currently insufficient evidence to reach a conclusion due to the limited sample size. High-quality studies evaluating appropriate antimicrobial resistance endpoints are needed. I have several comments and suggestions for the manuscript.

Weaknesses:

Weaknesses of the manuscript are the large clinical and statistical heterogeneity and the lack of clear definitions of acquisition of resistance. Both these weaknesses complicate the interpretation of the study results.

We thank the reviewer for the positive comments and pointing out where our work can be improved.

Major comments:

My main concern about the manuscript is the extent of both clinical and statistical heterogeneity, which complicates the interpretation of the results. I don't understand some of the antibiotic comparisons that are included in the systematic review. For instance the study by Paul et al (50), where vancomycin (as monotherapy) is compared to co-trimoxazole (as combination therapy). Emergence (or selection) of co-trimoxazole in S. aureus is in itself much more common than vancomycin resistance. It is logical and expected to have more resistance in the co-trimoxazole group compared to the vancomycin group, however, this difference is due to the drug itself and not due to co-trimoxazole being a combination therapy. It is therefore unfair to attribute the difference in resistance to combination therapy. Another example is the study by Walsh (71) where rifampin + novobiocin is compared to rifampin + co-trimoxazole. There is more emergence of resistance in the rifampin + co-trimoxazole group but this could be attributed to novobiocin being a different type of antibiotic than co-trimoxazole instead of the difference being attributed to combination therapy. To improve interpretation and reduce heterogeneity my suggestion would be to limit the primary analyses to regimens where the antibiotics compared are the same but in one group one or more antibiotic(s) are added (i.e. A versus A+B). The other analyses are problematic in their interpretation and should be clearly labeled as secondary and their interpretation discussed.

We acknowledge the presence of statistical and clinical heterogeneity in our overall analysis. The decision to pursue this comprehensive examination was predefined in our previously published study protocol (PROSPERO CRD42020187257) and driven by our interest whether, despite some differences, we could either identify an overarching effect of combination therapy on resistance or identify factors that explain potential differences of the effect of combination therapy across pathogens/drugs. We indeed, find that heterogeneity is high, however identifying the driving factors of this heterogeneity is difficult as evidence is limited.

We carried out several subgroup analyses, e.g. explicitly focusing on specific pathogen groups and medical conditions or exploring heterogeneity in treatment arms (figure 3, supplementary materials section 6). However, it is important to highlight that the number of studies available for these subgroup analyses was low. Additionally, recognizing the high heterogeneity within treatment arms, we performed a subgroup analysis focusing solely on resistances of antibiotics common to both arms (supplementary material section 6.1.8; which would avoid comparisons such as the one between vancomycin and co-trimoxazole raised by the reviewer). Unfortunately, this also revealed substantial heterogeneity. While we aimed to address heterogeneity through these subgroup analyses, limitations arose due to the number of studies meeting specific criteria and the nature of data provided by these studies.

Moreover, regarding the concern on interpretation of co-trimoxazole as combination therapy, we acknowledge the confusion surrounding its classification as one or two antibiotics. Despite the common contemporary view of co-trimoxazole as a single antibiotic, we chose to consider it as two antibiotics due to historical practices, as observed in Black et al. (1982), where trimethoprim was compared to trimethoprim and sulfamethoxazole. We recognize that this decision may lead to confusion and we consider conducting a further sensitivity analysis in the future version of this manuscript, exploring the possibility of considering co-trimoxazole as a single antibiotic. We agree that the slight trend of less antibiotics performing better overserved for MRSA, should not be over interpreted as this is driven by the two studies Walsh et al 1993 and Paul et al 2015 as pointed out by the reviewer. In lines 183-186 we discuss this issue that for better evaluation of antibiotic combination therapy, more studies which use identical antibiotics (i.e. A versus A+B) are needed. We will try to clarify and highlight this in the future version of the manuscript.

Another concern is about the definition of acquisition of resistance, which is unclear to me. If for example meropenem is administered and the follow-up cultures show Enterococcus species (which is intrinsically resistant to meropenem), does this constitute acquisition of resistance? If so, it would be misleading to determine this as an acquisition of resistance, as many people are colonized with Enterococci and selection of Enterococci under therapy is very common. If this is not considered as the acquisition of resistance please include how the acquisition of resistance is defined per included study.

Thank you for pointing out this potential ambiguity. Our definition of “acquisition of resistance” is agnostic to bacterial species and hence intrinsically resistant species can be included if they were only detected during the follow-up culture by the studies. We will clarify this in the definition of “acquisition of the resistance” in the manuscript (see l. 259-260). However, it was not always clear from the studies which pathogens were acquired or whether intrinsically resistant species were not reported. Therefore, we rely on the studies' specifications of resistant and non-resistant without further classifying data into intrinsic and non-intrinsic resistance. The outcome “acquisition of resistance” can be seen more of a risk assessment for having any resistant bacterium during or after treatment. In contrast, the outcome “emergence of resistance” is more rigorous, demanding the same species to be measured as more resistant during or after treatment.

Table S1 is not sufficiently clear because it often only contains how susceptibility testing was done but not which antibiotics were tested and how a strain was classified as resistant or susceptible.

In Table S1, we omitted the listing of antibiotics for which susceptibility testing was performed, as this information is already presented in the main text (Table 1). However, we agree that linking this information better in a future version would benefit the understanding. Given the variability in methods used to assess resistance and the variability in drugs, the comparability of breakpoints is limited. Hence, we decided not to provide further details on this aspect so far.

Line 85: "Even though within-patient antibiotic resistance development is rare, it may contribute to the emergence and spread of resistance."

Depending on the bug-drug combination, there is great variation in the propensity to develop within-patient antibiotic resistance. For example: within-patient development of ciprofloxacin resistance in Pseudomonas is fairly common while within-patient development of methicillin resistance in S. aureus is rare. Based on these differences, large clinical heterogeneity is expected and it is questionable where these studies should be pooled.

We agree that our formulation neglects differences in prevalence of within-host resistance emergence depending on bug-drug combinations. We will correct this in our upcoming version. (i.e. we will correct our statement to: “Within-patient antibiotic resistance development, even if rare, can contribute to the emergence and spread of resistance.”)

Line 114: "The overall pooled OR for acquisition of resistance comparing a lower number of antibiotics versus a higher one was 1.23 (95% CI 0.68 - 2.25), with substantial heterogeneity between studies (I2=77.4%)"

What consequential measures did the authors take after determining this high heterogeneity? Did they explore the source of this large heterogeneity? Considering this large heterogeneity, do the authors consider it appropriate to pool these studies?

Thank you for highlighting this lack of clarity. In our upcoming version, we will emphasize the sub-analyses conducted to explore heterogeneity (i.e., figure 3 and supplementary materials section 6). Nevertheless, these analyses faced limitations due to the scarcity of evidence and the data provided by the studies. Given the lack of appropriate evidence, it is hard to identify the source of heterogeneity. The decision to pool all studies was pre-specified in our previously published study protocol (PROSPERO CRD42020187257) and was motivated by the question whether there is a general effect of combination therapy on resistance development or identify factors that explain potential differences of the effect of combination therapy across bug-drug combinations.

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Reply to the reviewers

1. General Statements [optional]

We are thankful to the reviewers for the time and effort invested in assessing our manuscript and for their suggestions to improve it. We have now considered the points raised by them, carried out additional experiments, and modified the text and figures to address them. We feel that the new experiments and modifications have been able to solve all concerns raised by the reviewers and have improved the manuscript substantially, strengthening and extending our conclusions.

The main modifications include:

• We have extended the analysis of the overexpression strains to highly stringent conditions, which revealed a mild acidification defect for the strain overexpressing Oxr1. In addition, we have included in our analysis a strain in which both proteins are overexpressed, which resulted in a further growth defect.
• We have analyzed the recruitment of Rtc5 to the vacuole under additional conditions: deletion of the main subunit of the RAVE complex RAV1, medium containing galactose as the sole carbon source and pharmacological inhibition of the V-ATPase. These experiments allowed us to strengthen and extend our conclusions regarding the requirements for Rtc5 targeting to the vacuole.
• We have analyzed V-ATPase disassembly in intact cells, by addressing the localization to the vacuole of subunit C (Vma5) in glucose and galactose-containing medium. The results strengthen our conclusion that both Rtc5 and Oxr1 promote an in vivo state of lower V-ATPase assembly.
• We have extended our analyses of V-ATPase function to medium containing galactose as a carbon source, since glucose availability is one of the main regulators of V-ATPase function in vivo. The results are consistent with what we observed in glucose-containing medium.
• We have included a diagram of the structure of the V-ATPase for reference.
• We have included a diagram and a paragraph describing Oxr1 and Rtc5 regarding protein length and domain architecture and comparing them to other TLDc domain-containing proteins.
• We have made changes to the text and figures to improve clarity and accuracy, including a methods section that was missing. We include below a point-by-point response to the reviewers´ comments.

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

__ __Suggestions:

1. The authors observed that knockout of Rtc5p or Oxr1p does not affect vacuolar pH. If Rtc5p and Oxr1p both cooperate to dissociate V-ATPase, the authors may wish to characterize the effect of a ∆Rtc5p∆Oxr1p double knockout on vacuolar pH. The double mutant ∆rtc5∆oxr1 was already included in the original manuscript (the growth test is shown in Figure 5 B and the BCECF staining is shown in Figure 5C). This strain behaved like wt in both of these assays. Of note, what we observe for the deletion strains is increased assembly (Figure 5 D - G), so we expect that it would be hard to observe a difference in vacuole acidity or growth in the presence of metals.

Therefore, we have now also included a strain with the double overexpression of Oxr1 and Rtc5. Since overexpression of the proteins results in decreased assembly, it is more likely that this strain will show impaired growth under conditions that strongly rely on V-ATPase activity. Indeed, we observed that the overexpression of Oxr1 alone resulted in a slight growth defect in media containing high concentrations of ZnCl2 and the double overexpression strain showed an even further defect (Figure 6 A and C).

The manuscript would benefit from a well-labelled diagram showing the subunits of V-ATPase (e.g. in Figure 2D).

We agree with the reviewer and we have now added a diagram of the structure of the V-ATPase labeling the different subunits in Figure 2B.

The images of structures, especially in Figure 1-Supplement 1B, are not particularly clear and could be improved (e.g. by removing shadows or using transparency).

We are thankful to the reviewer for this suggestion. To improve the clarity of the structures in Figure 1 C and Figure 1 – Supplement 1A, we are now presenting the different subunits in the structures with different shades of blue and grey.

The authors should clearly describe the differences between Rtc5p and Oxr1p in terms of protein length, sequence identity, domain structure, etc.

We are thankful for this suggestion and we have now included a diagram of the domain architecture and protein length of Rtc5 and Oxr1, comparing with two human proteins containing a TLDc domain in Figure 5A. In addition, we have added the following paragraph describing the features of the proteins.

“Rtc5 is a 567 residue-long protein. Analysis of the protein using HHPred (Zimmermann et al., 2018), finds homology to the structure of porcine Meak7 (PDB ID: 7U8O, (Zi Tan et al., 2022)) over the whole protein sequence (residues 37-559). For both yeast Rtc5 and human Meak7 (Uniprot ID: Q6P9B6), HHPred detects homology of the C-terminal region to other TLDc domain containing proteins like yeast Oxr1 (PDBID: 7FDE), Drosophila melanogaster Skywalker (PDB ID: 6R82), and human NCOA7 (PDB ID: 7OBP), while the N-terminus has similarity to EF-hand domain calcium-binding proteins (PDB IDs: 1EG3, 2CT9, 1S6C6, Figure 5A). HHPred analysis of the 273 residue long Saccharomyces cerevisiae Oxr1, on the other hand, only detects similarity to TLDc domain containing proteins (PDB IDs: 7U80, 6R82, 7OBP), which spans the majority of the sequence of the protein (residues 71-273). The overall sequence identity between Oxr1 and Rtc5 is 24% according to a ClustalOmega alignment within Uniprot. The Alphafold model that we generated for Rtc5 is in good agreement with the available partial structure of Oxr1 (7FDE) (root mean square deviation (RMSD) of 3.509Å) (Figure 5 - S1 A), indicating they are structurally very similar, in the region of the TLDc domain. Taken together, these analyses suggest that Oxr1 belongs to a group of TLDc domain-containing proteins consisting mainly of just this domain like the splice variants Oxr1-C or NCOA7-B in humans (NP_001185464 and NP_001186551, respectively), while Rtc5 belongs to a group containing an additional N-terminal EF-hand-like domain and a N-myristoylation sequence, like human Meak7 (Finelli & Oliver, 2017) (Figure 5 A).”

Minor:

1. The "O" in VO should be capitalized. This has been corrected.

In Figure 4 supplement 1, the labels "I", "S", and "P" should be defined.

This has been clarified in the figure legend.

Please clarify what is meant by "switched labelling"

This refers to the SILAC vacuole proteomics experiments, for which yeast strains are grown in medium containing either L-Lysine or 13C6;15N2- L-Lysine to produce normal (‘light’) or heavy isotope-labeled (‘heavy’) proteins. This allows comparing two conditions. To increase the robustness of the comparisons, the experiments are done twice with both possible labeling schemes (condition A – light, condition B – heavy + condition A – heavy + condition B – light), which is commonly described as switched labeling or label switching.

We have exchanged the original sentence in the manuscript for:

“Performing the same experiments but switching which strain was labeled with heavy and light amino acids gave consistent results.”

The meaning of the sentence "Indeed, this was the case for both of them" is ambiguous.

We have now replaced this sentence with the following:

“Indeed, overexpression of either Rtc5 or Oxr1 resulted in increased growth defects in the context of Stv1 deletion (Figure 7 H and I).”

For Figure 1-Supplement 1B it is hard to see the crosslink distances.

We have updated this figure to improve the visibility of the cross-links. In addition, we now include a supplemental table (supplemental table 5) with a list of the Cα- Cα distances measured for all the crosslinks we mapped onto high-resolution structures.

The statement "The effects of Oxr1 are greater than those caused by Rtc5" requires more context. Is there a way of quantifying this effect for the reader?

We agree that this sentence was too general and vague. The effects caused by one or the other protein depend on the condition and the assay. We have thus deleted this sentence, and we think it is better to refer to the description of the individual assays performed.

The phrase "negative genetic interaction" should be clarified.

We have included in the text the following explanation of genetic interactions:

“A genetic interaction occurs when the combination of two mutations results in a different phenotype from that expected from the addition of the phenotypes of the individual mutations. For example, deletion of OXR1 or RTC5 has no impact on growth in neutral pH media containing zinc in a control background but improves the growth of RAV1 deletion strains (Figure 7 E and F), so this is a positive genetic interaction. On the other hand, overexpression of either Rtc5 or Oxr1 results in a growth defect in a background lacking Rav1 in neutral media containing zinc, a negative genetic interaction.”

* * In the sentence "Isogenic strains with the indicated modifications in the genome where spotted as serial dilutions in media with pH=5.5, pH=7.5 or pH=7.5 and containing 3 mM ZnCl2", "where" should be "were".

This has been corrected.

Figure 2D: the authors should consider re-coloring these models, as it is challenging to distinguish Rtc5p from the V-ATPase.

We have changed the coloring of this structure and added a diagram of the V-ATPase structure with the same coloring scheme to improve clarity.

Reviewer #1 (Significance (Required)):

The vacuolar protein interaction map alone from this manuscript is a nice contribution to the literature. Experiments establishing colocalization of Rtc5p to the vacuole are convincing, as is dependence of this association on the presence of assembled V-ATPase. Similarly, experiments related to myristoylation are convincing. The observed mislocalization of V-ATPases that contain Stv1p to the vacuole (which is also known to occur when Vph1p has been knocked out) upon knockout of Oxr1p is also extremely interesting. Overall, this is an interesting manuscript that contributes to our understand of TLDc proteins.

We are thankful to the reviewer for their appreciation of the significance of our work, including the interactome map of the vacuole as a resource and the advances on the understanding of the regulation of the V-ATPase by TLDc domain-containing proteins.

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

Major points:

1. The evidence of Oxr1 and Rtc5 as V-ATPase disassembly factors is circumstantial. The authors base their interpretation primarily on increased V1 (but not Vo) at purified vacuoles from Oxr1- or Rtc5-deleted strains, which does not directly address disassembly. Of course, the results regarding Oxr1 confirm detailed disassembly experiments with the purified protein complex (PMID 34918374), but on their own are open to other interpretations, e.g. suppression of V-ATPase assembly. Of note, the authors emphasize that they provide first evidence of the in vivo role of Oxr1, but monitor V1 recruitment with purified vacuoles and do not follow V-ATPase assembly in intact cells. We are thankful to the reviewer for pointing this out. We did not want to express that the molecular activity of the proteins is the disassembly of the complex, as our analyses include in vivo and ex vivo experiments and do not directly address this. We rather meant that both proteins promote an in vivo state of lower assembly of the V-ATPase. We have modified the wording throughout the manuscript to be clearer about this.

In addition, we have added new experiments to monitor V-ATPase assembly in intact cells, as suggested by the reviewer. Previous work has shown that in yeast, only subunit C leaves the vacuole membrane under conditions that promote disassembly, while the other subunits remain at the vacuole membrane (Tabke et al 2014). Our own experiments agree with what was published (Figure 3 D). We have thus monitored Vma5 localization to the vacuole under glucose or after shift to galactose containing media in cells lacking or overexpressing Rtc5 or Oxr1. We observed that cells overexpressing either TLDc domain protein show lower levels of Vma5 recruitment to the vacuole in glucose (Figure 6 D and E). Additionally cells lacking either Rtc5 or Oxr1 contain higher levels of Vma5 at the vacuole after 20 minutes in galactose medium (Figure 5 F and G). Thus, these results re-inforce our conclusions that Rtc5 and Oxr1 promote states of lower assembly.

Oxr1 and Rtc5 have very low sequence similarity. It would be helpful if the authors provided more detail on the predicted structure of the putative TLDc domain of Rtc5 and its relationship to the V-ATPase - Oxr1 structure. Is Rtc5 more closely related to established TLDc domain proteins in other organisms?

We have now included a diagram of the domain architecture of Rtc5 and Oxr1, and comparison to the features of other TLDc domain containing proteins in Figure 5 A, as well as a paragraph describing them:

“Rtc5 is a 567 residue-long protein. Analysis of the protein using HHPred (Zimmermann et al., 2018), finds homology to the structure of porcine Meak7 (PDB ID: 7U8O, (Zi Tan et al., 2022)) over the whole protein sequence (residues 37-559). For both yeast Rtc5 and human Meak7 (Uniprot ID: Q6P9B6), HHPred detects homology of the C-terminal region to other TLDc domain containing proteins like yeast Oxr1 (PDBID: 7FDE), Drosophila melanogaster Skywalker (PDB ID: 6R82), and human NCOA7 (PDB ID: 7OBP), while the N-terminus has similarity to EF-hand domain calcium-binding proteins (PDB IDs: 1EG3, 2CT9, 1S6C6, Figure 5A). HHPred analysis of the 273 residue long Saccharomyces cerevisiae Oxr1, on the other hand, only detects similarity to TLDc domain containing proteins (PDB IDs: 7U80, 6R82, 7OBP), which spans the majority of the sequence of the protein (residues 71-273). The overall sequence identity between Oxr1 and Rtc5 is 24% according to a ClustalOmega alignment within Uniprot. The Alphafold model that we generated for Rtc5 is in good agreement with the available partial structure of Oxr1 (7FDE) (root mean square deviation (RMSD) of 3.509Å) (Figure 5 - S1 A), indicating they are structurally very similar, in the region of the TLDc domain. Taken together, these analyses suggest that Oxr1 belongs to a subfamily of TLDc domain-containing proteins consisting mainly of just this domain like the splice variants Oxr1-C or NCOA7-B in humans (NP_001185464 and NP_001186551, respectively) , while Rtc5 belongs to a subfamily containing an additional N-terminal EF-hand-like domain and a N-myristoylation sequence, like human Meak7 (Finelli & Oliver, 2017) (Figure 5 A).”

The authors conclude vacuolar recruitment of Rtc5 depends on the assembled V-ATPase, based on deletion of different V1 and Vo domain subunits. However, these genetic manipulations likely cause a strong perturbation of vacuolar acidification; indeed, the images show drastically altered vacuolar morphology. To strengthen their conclusion, it would be helpful to show that Rtc5 recruitment is not blocked by inhibition of vacuolar acidification, and that conversely it is blocked by deletion of rav1.

We are thankful to the reviewer for this insightful suggestion and we have now performed both experiments suggested. The experiment regarding rav1Δ is now Figure 3C, and we observed that this mutation also disrupts Rtc5 localization to the vacuole. In addition, we decided to include an experiment showing the subcellular localization of Rtc5 after shifting the cells to galactose containing medium for 20 minutes, as a physiologically relevant condition that results in disassembly of the complex (Figure 3D). We observed that under these conditions Rtc5 re-localizes to the cytosol. This result is particularly interesting given that in yeast only subunit C (but not other V1 subunits) re-localizes to the cytosol under these conditions. In addition, the experiment using Bafilomycin A to inhibit the V-ATPase shows that Rtc5 is still localized at the vacuole membrane under conditions of V-ATPase inhibition (Figure 3 F). Taken together these results allowed us to strengthen our original interpretation that Rtc5 requires an assembled V-ATPase for its localization and extend it to the fact that the V-ATPase does not need to be active.

Reviewer #2 (Significance (Required)):

This is an interesting paper that confirms and extends previous findings on TLDc domain proteins as a novel class of proteins that interact with and regulate the V-ATPase in eukaryotes. The title seems to exaggerate the findings a bit, as the authors do not investigate V-ATPase (dis)assembly directly and only phenotypically describe altered subcellular localization of the Golgi V-ATPase in Oxr1-deleted cells. A recent structural and biochemical characterization of Oxr1 as a V-ATPase disassembly factor (PMID 34918374) somewhat limits the novelty of the results, but the function of Oxr1 in regulating subcellular V-ATPase localization and the identification of a second potential TLDc domain protein in yeast provide relevant insights into V-ATPase regulation. This paper will be of interest to cell biologists and biochemists working on lysosomal biology, organelle proteomics and V-ATPase regulation.

We thank the reviewer for the assessment of our work, and for recognizing the novel insights that we provide. Regarding the previous biochemical work on Oxr1 and the V-ATPase, we have clearly cited this work in the manuscript. In our opinion, our results complement and extend this article, showing that the function in disassembly is relevant in vivo. Additionally, this is only one of five major points of the article, the other four being

• The interactome map of the vacuole as a resource
• The identification of Rtc5 as a second yeast TLDc domain containing protein and interactor of the V-ATPase.
• The identification of the role of Rtc5 in V-ATPase assembly.
• The identification of the role of Oxr1 in Stv1 subcellular localization. We believe these additional points add important insights to researchers interested in lysosomes, the V-ATPase, intracellular trafficking and TLDc-domain containing proteins.

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

Major comments

__1) Re: A cross-linking mass spectrometry map of vacuolar protein interactions (results) __ While XL-MS is a very powerful method, it is a high-throughput approach and there should be some kind of negative control in these experiments. In cross-linking experiments, non-cross-linked samples are usually used as negative controls. What was the negative control in cross-linking mass-spectrometry experiments here? If there was no negative control, how the specificity of interactions was evaluated? Maybe the authors analyzed the dataset for highly improbable interactions and found very few of them?

We fully agree that it is crucial to ensure the specificity of the interactions detected by XL-MS. To achieve this, one needs to control (1) the specificity of the data analysis (i.e. that the recorded mass spectrometry data are correctly matched to cross-linked peptides from the sequence database) and (2) the biological specificity (i.e. that the cross-linking captured natively occurring interactions).

To ascertain that criterion (1) is met, cross-link identifications are filtered to a pre-defined false-discovery rate (FDR) – an approach that the XL-MS field adopted from mass spectrometry-based proteomics. As a result, low-confidence identifications (e.g. cross-linked peptides that are only supported by a few signals in a given mass spectrum) are removed from the dataset. FDR filtering in XL-MS is a rather complex matter as it can be done at different points during data analysis and the optimal FDR cut-off depends on the specific scientific question at hand (for more details see for example Fischer and Rappsilber, Anal Chem, 2017). Generally speaking, an overly restrictive FDR cut-off would remove a lot of correct identifications, thereby greatly limiting the sensitivity of the analysis. On the other hand, a too relaxed FDR cut-off would dilute the correct identifications with a high number of false-positives, which would impair the robustness and specificity of the dataset. While many XL-MS study control the FDR on the level of individual spectrum matches, we opted for a 2% FDR cut-off on the level of unique residue pairs, which is more stringent (see Fischer and Rappsilber, Anal Chem, 2017). Our FDR parameters are described in the Methods section (Cross-linking mass spectrometry of isolated vacuoles - Data analysis). Of note, we have made all raw mass spectrometry data publicly available through the PRIDE repository (https://www.ebi.ac.uk/pride/ ; accession code PXD046792; login details during peer review: Username = reviewer_pxd046792@ebi.ac.uk, Password = q1645lTP). This will allow other researchers to re-analyze our data with the data analysis settings of their choice in the future.

To ascertain that criterion (2) is met, we mapped the identified cross-links onto existing high-resolution structures of vacuolar protein complexes. Taking into account the length of our cross-linking reagent, the side-chain length of the cross-linkable amino acids (i.e. lysines), and a certain degree of in-solution flexibility, cross-links can reasonably occur between lysines with a mutual Cα-Cα distance of up to 35 Å. Using this cut-off, the lysine-lysine pairs in the high-resolution structures we studied can be split into possible cross-linking partners (Cα-Cα distance 35 Å). Of all cross-links we could map onto high-resolution structures, 95.2% occurred between possible cross-linking partners. In addition, our cross-links reflect numerous known vacuolar protein interactions that have not yet been structurally characterized. These lines of evidence increase our confidence that our XL-MS approach captured genuine, natively occurring interactions. These analyses are described in more detail in the first Results sub-section (“A cross-linking mass spectrometry map of vacuolar protein interactions”).

In addition, the high purity of vacuole preparation is critical. How was it assessed by the authors?

We disagree that the purity of the vacuole preparation is critical for this analysis to be valid. The accuracy of the protein-protein interactions detected will depend on their preservation during sample preparation until the sample encounters the cross-linker, and the data analysis, as described above. The experiment would have been equally valid if performed on whole cell lysates without any enrichment of vacuoles, but the coverage of vacuolar proteins would have likely been very low. For this reason, we decided to use the vacuole isolation procedure to obtain better coverage of the proteins of this particular organelle. The use of the Ficoll gradient protocol (Haas, 1995) was based on that it is a protocol that yields strong enrichment of proteins annotated with the GO Term “vacuole” (Eising et al, 2019) and that it preserves the functionality of the organelle, as evidenced by its use for multiple functional assays (vacuole-vacuole fusion (Haas, 1995), autophagosome-vacuole fusion (Gao et al, 2018), polyphosphate synthesis by the VTC complex (Desfougéres et al, 2016), among others).

2) Re: Rtc5 and Oxr1 counteract the function of the RAVE complex (results)

Taken together, data, presented in this section of the manuscript, provide strong evidence that Rtc5 and Oxr1 negatively regulate V-ATPase activity, counteracting the V-ATPase assembly, facilitated by the activity of the RAVE complex. However, the complete deletion of the major RAVE subunit Rav1p was required to observe this effect in vivo in yeast. The other way to induce V-ATPase disassembly in yeast is glucose deprivation. It will be interesting to study if there is a synergistic effect between glucose deprivation and RTC5/OXR1 deletion on V-ATPase assembly, vacuolar pH, and growth of single oxr1Δ, rtc5Δ or double oxr1Δrtc5Δ mutants (OPTIONAL). Glucose deprivation is a more physiologically relevant condition than a deletion of an entire gene.

We would like to point out that an effect on assembly is observed without deleting the RAVE complex: deletions of Oxr1 or Rtc5 resulted in increased V-ATPase assembly in vivo in the presence of glucose and of the RAVE complex (Figures 5 D and E). We have now also added the experiments showing that the overexpression strains have a mild growth defect under conditions that force cells to strongly rely on V-ATPase activity (Figures 6 A and C).

Nevertheless, we agree that addressing the effect of changing the levels of Oxr1 and Rtc5 under low-glucose conditions is an interesting physiologically relevant question. We have now included growth assays and BCECF staining in medium containing galactose as the carbon source (Figures 5 – Supplement 1 B and C, and Figure 6 C and Figure 6- Supplement 1A). In addition, we have addressed the vacuolar localization of Vma5 in medium containing glucose or after shifting to medium containing galactose for 20 minutes, as a proxy for V-ATPase disassembly in intact cells (Figure 5 F and G, Figure 6 D and E). Taken together, these analyses reinforce our conclusions that both Rtc5 and Oxr1 promote an in vivo state of lower V-ATPase assembly, based on the following observations:

• Higher localization of Vma5 to the vacuole after 20 mins in galactose in cells lacking Oxr1 or Rtc5 (Figure 5 F and G).
• Lower localization of Vma5 to the vacuole in medium containing glucose in cells overexpressing Oxr1 or Rtc5 (Figure 6 D and E).
• Growth defect of the strain overexpressing Oxr1 in medium containing galactose with pH = 7.5 and zinc chloride, with a further growth defect caused by additional overexpression of Rtc5 (Figure 6 C). 3) Re: Figure 6 - supplement 1. The title is relevant to panel D only, it should be renamed to reflect the results of the disassembly of V-ATPase in rav1Δ mutant strains, while results about the stv1Δ-based strains (Panel D) should be shown together with similar experiments in Figure 7 - supplement 2 for clarity.

We have shifted the Panel D from the original Figure 6 – Supplement 1 to the main Figure (now Figure 7 – H and I). Regarding the title of the Figure, whether Supplemental Figures have titles or not will depend on the journal where the manuscript is published. For now, we have removed all titles from supplemental figures, as they are conceived to complement the main Figures.

4) Re: Figure 7 - supplement 1, Panel A. The proper assay to show that Stv1-mNeonGreen is functional is to express it in double mutant vph1Δstv1Δ to see if the growth defect is reversed. In addition, the vph1Δ growth defect is not changed (improved or worsened) in the presence of Stv1-mNeonGreen, so it means that the expression of Stv1-mNeonGreen does not further compromise the V-ATPase function, but it does not mean that it improves its function.

It is clear from the experiment suggested by the reviewer that they think that we have expressed Stv1-mNeonGreen from a plasmid. This was not the case, Stv1 was C-terminally tagged with mNeonGreen in the genome. It is thus the only expressed version in the strain. The experiment we have performed is thus equivalent to the one suggested by the reviewer, but for genomically expressed variants. For reference, the genotypes of all the strains used can be found in Supplemental Table 1.

5) Re: Figure 7 - supplement 2. This figure should be combined with Fig. 6- suppl 1, panel D as also mentioned above. The figure seems to lack some labels, and conclusions are not accurate as discussed below. However, this data provides important additional information about relationships between isoform-specific subunits of V-ATPase Vph1 and Stv1 and both Rtc5 and Oxr1 and should be repeated if it is not done yet to have a better idea about these relationships.

Panel B: Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of VPH1, since double deletion mutant vph1Δ rtc5Δ grows worse than each individual mutant. Although it also means that there is no positive interaction, it is not the same.

Indeed, there is a negative genetic interaction between the deletion of RTC5 and VPH1. We have replaced the growth tests in this figure (Figure 8 – Supplement 2 A in the new manuscript) to show this negative genetic interaction better. This effect is reproducible, as shown in the repetitions of the experiments.

Panel C: Same as for panel B. Based on this picture, the deletion of OXR1 has a weak negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ oxr1Δ grows worse than each individual mutant at 6 mM ZnCl2.

Panel D: Same as for panels B and C. Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ rtc5Δ grows worse than each individual mutant at 6 mM ZnCl2. There is no label in the middle panel (growth conditions) and no growth assay data in the presence of CaCl2.

However, these results will be then in contradiction with the results from Figure 6 - Supplement 1, panel D, showing negative genetic interaction between the overexpression of Rtc5 or Oxr1 and deletion of Stv1, since both deletion and overexpression of Rtc5 or Oxr1 would have negative genetic interactions with Stv1.

For both Panels C and D (Now Figure 8 - Supplement 2 B and C). The effect pointed out by the reviewer (slightly stronger growth defect for the double mutants than for the single mutants) is very mild. We have attempted to make it more evident by assessing growth in medium with higher and lower concentrations of zinc and this was not possible. This is in contrast with the very clear positive genetic interaction that we observe between the deletion of OXR1 and VPH1 (Now Figure 8 H). This is the reason that we decided to report the lack of a positive genetic interaction instead of the presence of a negative one, as we do not want to draw conclusions based on results that are borderline detectable.

In addition, there is no label for the media in the middle panel, is it just YPAD pH=7.5, without the addition of any metals?

Indeed, the media is YPAD pH=7.5, without the addition of any metals. The line drawn above several images based on this media indicated this. Since this form of labeling appears to be confusing, we have now replaced it and placed the label directly above the image.

Why there is no growth assay in the presence of CaCl2, like in panels A and B?

Every growth test shown in the manuscript was performed including growth in YPD pH=5,5 as a control of a permissive condition for lack of V-ATPase activity, and then in YPD pH=7,5 including a broad range of Zinc Chloride and Calcium chloride concentrations. From all these pictures, the conditions where the differences among strains were clearly visible were chosen to assemble the figures. Conditions that did not provide any information for that particular experiment were not included in the figure to avoid making them unnecessarily large and crowded.

Re: Figure 7 - supplement 2, continued. How many times all these experiments were repeated? These experiments should be repeated at least 3 times, which is especially necessary for the experiments in panel C, because the effects are borderline. If results are reproducible and statistically significant, although small, the conclusion should be changed from "no positive genetic interactions" to "negative genetic interactions", which is more precise and informative.

All growth tests shown in the manuscript were repeated at least three times for the conditions shown. We are thankful to the reviewer for pointing out that this was not mentioned, and we have added this to the methods section. We have assembled a file with all repetitions of the shown growth tests and added it at the end of this file. In doing so, these are already available for the public. These repetitions show that all effects reported are reproducible. We will then discuss with the editors of the journal where this manuscript is published about the necessity of including it with the final article.

Regarding reporting the lack of a positive genetic interaction vs. a negative one, we have discussed this above. Shortly, for Panel B (Figure 8 – Supplement 2 A in the new manuscript) we have changed the conclusion to “negative genetic interaction” as adjusting the zinc chloride concentration allowed us to show this clearly and reproducibly, as shown by the repetitions of the experiments. For panels C and D (Now Figure 8 - Supplement 2 B and C), the effect is really mild and barely detectable, even when we tried a wide range of zinc chloride concentrations. For this reason, we would prefer to maintain the “no positive genetic interaction” conclusion.

Re: Methods. There is no description of yeast serial dilution growth assay at all. In addition, why the specific media (neutral pH, in the presence of high concentrations of calcium or zinc) was used is not explained either in the results or methods. Appropriate references should be included, for example, PMID: 2139726, PMID: 1491236.

We apologize for the oversight of the missing methods section, which we have now included.

Regarding the explanation of the media used, the following section was already a part of the results section, before the description of the first growth test:

“The V-ATPase is not essential for viability in yeast cells, and mutants lacking subunits of this complex grow similarly to a wt strain in acidic media. However, when cells grow at near-neutral pH or in the presence of divalent cations such as calcium and zinc, the mutants lacking V-ATPase function show a strong growth impairment (Kane et al, 2006).”

We have now replaced this with the following, more complete version:

“As a first approach for addressing the role of these proteins, we tested growth phenotypes related to V-ATPase function in strains lacking or overexpressing them. The V-ATPase is not essential for viability in yeast cells, and mutants lacking subunits of this complex grow similarly to a wt strain in acidic media, but display a growth defect at near-neutral pH the mutants (Nelson & Nelson, 1990). In addition, the proton gradient across the vacuole membrane generated by the V-ATPase energizes the pumping of metals into the vacuole, as a mechanism of detoxification. Thus, increasing concentrations of divalent cations such as calcium and zinc, generate conditions in which growth is increasingly reliant on V-ATPase activity (Förster & Kane, 2000; MacDiarmid et al, 2002; Kane, 2006).”

MINOR COMMENTS

Yeast proteins are named with "p" at the end, such as "Rtc5p".

This nomenclature rule is falling into disuse during the last decades, as the use of capitals vs lowercase and italics allows to distinguish between genes proteins and strains (OXR1 = gene, Oxr1 = protein, oxr1Δ = strain). As an example, I include a list of the latest papers by some of the major yeast labs around the world, all of which use the same nomenclature as we do (in alphabetical order). This list even includes some work in the field of the V-ATPase.

• Alexey Merz, USA. PMID: 33225520
• Benoit Kornmann, UK. PMID: 35654841
• Christian Ungermann, Germany. PMID: 37463208
• Claudio de Virgilio, Switzerland. PMID: 36749016
• Daniel E. Gottschling, USA. PMID: 37640943
• David Teis, Austria. PMID: 32744498
• Elizabeth Conibear, Canada. PMID: 35938928
• Fulvio Reggiori, Denmark. PMID: 37060997
• J Christopher Fromme, USA. PMID: 37672345
• Maya Schuldiner, Israel. PMID: 37073826
• Patricia Kane, USA. PMID: 36598799
• Scott Emr, USA. PMID: 35770973
• W Mike Henne, USA. PMID: 37889293
• Yoshinori Ohsumi, Japan. PMID: 37917025 In addition, we would prefer to keep the nomenclature that we already use, to keep consistency with other published articles from our lab.

Re: Introduction. In the introduction it should be indicated that Rtc5 was originally discovered as a "restriction of telomere capping 5", using screening of temperature-sensitive cdc13-1 mutants combined with the yeast gene deletion collection [PMID: 18845848]. A couple of sentences should be written about the RAVE complex and its role in V-ATPase assembly.

We are thankful for this suggestion and we have now included both pieces of information in the introduction.

*“The re-assembly of the V1 onto the VO complex when glucose becomes again available, is aided by a dedicated chaperone complex known as the RAVE complex, which also likely has a general role in V-ATPase assembly (Seol et al, 2001; Smardon et al, 2002, 2014).” *

“In our cross-linking mass spectrometry interactome map of isolated vacuoles we found that the only other TLDc-domain containing protein of yeast, Rtc5, is a novel interactor of the V-ATPase. Rtc5 is a protein of unknown function, originally described in a genetic screen for genes related to telomere capping (Addinall et al, 2008)”

Re: The TLDc domain-containing protein of unknown function Rtc5 is a novel interactor of the vacuolar V-ATPase (results)

1) It is important to understand, that Oxr1 was co-purified before with the V1 domain of V-ATPase from a certain mutant strain, not wild-type yeast [PMID: 34918374]. It may explain why the authors did not identify it in their original protein-protein interactions screen here.

The structural work on the V1 domain bound to Oxr1 (Khan et al, 2022) showed that the binding of Oxr1 prevented V1 from assembling onto the Vo. Since our experiments rely on the purification of vacuoles, they should contain mainly only V1 assembled onto the VO, and not the free soluble V1. This is likely the reason that we do not detect Oxr1, in addition to it being less abundant. We have clarified this now in the manuscript and added the fact that Oxr1 was co-purified with a V1 containing a mutant version of the H subunit.

“In a previous study, Oxr1 was co-purified with a V1 domain containing a mutant version of the H subunit, and its presence prevented the in vitro assembly of this V1 domain onto the VO domain and promoted disassembly of the holocomplex (Khan et al., 2022). This is likely the reason why we do not detect Oxr1 in our experiments, which rely on isolated vacuoles and thus would only include V1 domains that are assembled onto the membrane. In addition, Oxr1 is less abundant in yeast cells than Rtc5 according to the protein abundance database PaxDb (Wang et al, 2015).”

2) It is a wrong conclusion that because Rtc5 was co-purified with both V1 and V0 domain subunits it interacts with the assembled V-ATPase, this does not exclude a possibility that Rtc5 also interacts with separate V1 sector or separate V0 sector of V-ATPase.

We agree with the reviewer that the co-purification of Rtc5 with both V1 and VO domain subunits does not necessarily mean that it interacts with the assembled V-ATPase. Thus, we have modified the text in this part to:

“The fact that we can co-enrich Rtc5 both with Vma2 and with Vph1 indicates that it can interact either with both the VO and V1 domains or with the assembled V-ATPase.”

However, other results throughout the manuscript can be taken into account to strengthen this idea:

1. Rtc5 requires an assembled V-ATPase to localize to the vacuole membrane, and thus seems not to interact with free VO domains, which would be available when we delete V1 subunits or in medium containing galactose.
2. Rtc5 becomes cytosolic in galactose-containing media. This would indicate that it also does not interact with free V1 domains, which are still localized to the vacuole membrane under these conditions. Taken together with the pull-downs, these results suggest that Rtc5 interacts with the assembled V1-VO V-ATPase. Thus, we have included the following sentence after Figure 3, which shows the subcellular localization experiments.

*“Taking into account that Rtc5 is co-enriched with subunits of both the VO and V1 domain, and that it localizes at the vacuole membrane dependent on an assembled V-ATPase, we suggest that Rtc5 interacts with the assembled V-ATPase complex.” *

Re: Figure 1, Panel C. Is it possible to show individual proteins in different colors for clarity?

Panel D. How were cross-link distances measured? It is not obvious if you are not an expert in the field and it is not described in the methods.

We have modified Figure 1 C and Figure 1 – Supplement 1B (now Figure 1 – Supplement 1 A) to present the different subunits in the structures with different shades of blue and grey.

Furthermore, we have clarified the distance measurement approach in the methods section and in the legend of Fig 1D: “Ca-Ca distances were determined using the measuring function in Pymol v.2.5.2 (Schrodinger LLC).”

__Re: Figure 1 - Supplement 1, __

Panel A. What scientific information are we getting from this picture?

This panel was just a visual representation of the complexity of the protein network we obtained. Indeed, there was no specific scientific message, so we have decided to remove this panel from the revised manuscript.

Panel B. Why are these complexes shown separately from the complexes in Figure 1, panel C? Also, can individual proteins be colored differently here as well?

We did not want to overload Fig 1C, so we decided to show some of the protein complexes in Fig 1 – Supplement 1B. The most important information is the histogram showing that 95% of the mapped cross-links fall within the expected length range, and this is shown in the main Figure (Figure 1D). As stated above, we have adjusted the subunit coloring in Figure 1 C to improve clarity.

Re: Figure 3. It will be nice to show the localization of the untagged protein as well if antibodies are available (OPTIONAL).

Unfortunately, there are no available antibodies for either Rtc5 or Oxr1. This hinders us from detecting the endogenous untagged proteins. We would like to point out that we have been very careful in showing which tagged proteins are functional (C-terminally tagged Rtc5) and which are not (C-terminally tagged Oxr1), so that the reader can know how to interpret the localization data.

Re: Figure 4. Why different tags were used in panels A (GFP), C (msGFP2) and D

(mNeonGreen)?

In general, we prefer to use mNeonGreen as a tag for microscopy experiments because it is brighter and more stable, and msGFP2 as a tag for experiments involving Western blots because we have better antibodies available. There was a mistake in the labeling, and actually, all constructs labeled as GFP were msGFP2. We have now corrected this. Of note, we have tested the functionality of both tagged version (mNeonGreen and msGFP2).

Panels B and C. Were Rtc5 fusions detected using anti-GFP antibodies?

Indeed, Rtc5-msGFP2 was detected with an anti-GFP antibody. We have now indicated next to each Western blot membrane the primary antibody used. In addition, all antibodies are detailed in Supplemental Figure 3.

The authors should have full-size Western blots available, not just cut-out bands, as some journals and reviewers require them for publication.

For all western blots, we always showed a good portion of the membrane and not cut-out bands. The cropping was performed to avoid making figures unnecessarily large. The whole membranes are of course available and will be included in an “extended data file” if required by the journal.

Re: Figure 4 - Supplement 1, Panel A. Does "-" and "+" mean -/+ Azido-Myr?

Indeed. We have now added this label to the figure.

Panel B. There is no blot with a membrane protein marker (Vam3 or Vac8), it should be included.

We have replaced this western blot for a different repetition of this experiment in which a membrane protein marker was included. Of note, the two other repetitions of the experiment shown (Figure 4 – Supplement 1 panel C and Figure 4 panel C) also include both a membrane protein marker and a soluble protein marker.

Re: Figure 5. The title does not describe all results in this figure and should be modified accordingly.

The original data from Figure 5 is now separated into Figures 5 and 6 because of the additional experiments included during revisions. We have modified the Figure titles to be descriptive of the overall message of the Figures.

Panel C. Statistical significance value for *** should be indicated in the legend.

This has been indicated in the Figure legend.

It is not clear how many times yeast growth assays were repeated. Usually, all experiments should be done in triplicates or more.

All shown growth tests were performed at least three times for the conditions shown. We have now indicated this in the materials and methods section. In addition, we now provide in this response a file with all repetitions of growth tests, which will be appended to the article if deemed necessary by the editors.

Re: Figure 5 - supplement 1. No title

Re: Figure 5 - supplement 2. No title

Whether the supplemental Figures should have a title or not will depend on the style of the journal where the manuscript is finally published. The current idea of the supplemental Figures is that they complement the corresponding main Figure. For this reason, we have removed all titles from supplemental Figures.

Re: Figure 6. There is a typo on the second lane in the legend: "...the genome were", not "...the genome where".

This has been corrected.

Panel C. Why the analysis of BCECF vacuole staining of double mutants oxr1Δrav1Δ and rtc5Δrav1Δ is not shown? Was it done at all?

We had not included this piece of data, as we thought that the genetic interaction of RTC5 and OXR1 and rav1Δ was sufficiently well supported with the included data (growth tests in combination with the deletion, growth tests in combination with the overexpression, vacuole proteomics in combination with overexpression, and BCECF staining in combination with the overexpression). Because of the request of the reviewer, we have now included this experiment as Figure 7 G.

Re: Figure 6 - Supplement 2. Why were two different tags (2xmNG and msGFP2) used?

We tried both tags to see if one of them would be functional. Unfortunately, they both resulted in non-functional proteins, as shown by the corresponding growth tests.

Did the authors study N-terminally tagged Oxr1? Was it functional?

We have tagged Oxr1 N-terminally, and this unfortunately resulted in a protein that was not completely functional. We show below the localization of N-terminally mNeon-tagged Oxr1, under the control of the TEF1 promoter. The protein appears cytosolic (Panel A) but is not completely functional (Panel B). The localization of Oxr1 had already been misreported by using a tagged version that we now show to be non-functional. For this reason, we preferred not to include this data in the manuscript, to avoid again including in the literature subcellular localizations that correspond to non-functional or partially functional proteins.

Panel B. Results for the untagged TEF1pr-Oxr1 overexpression are not shown, thus tagged and untagged proteins can't be compared. Are they available? What is the promoter for the expression of 2xmNG fusion constructs?

Oxr1-2xmNG was C-terminally tagged in the genome, which means that the promoter is the endogenous one, it was not modified. For this reason, the correct controls are a strain expressing Oxr1 at endogenous levels (the wt strain) and a strain lacking Oxr1. Both controls were included in the Figure, and in all repetitions made of this experiment. For reference, all the genotypes of the strains used are found in Supplemental Table 1.

Re: Methods. Were vacuoles prepared differently for XL-MS and SILAC-based vacuole proteomics (there are different references) and why? Methods for XL-MS and quantitative SILAC-based proteomics can be placed together for clarity.

The basis for the method of vacuole purification is the same, from (Haas, 1995). This reference was included in both protocols that include vacuole purifications. However, modifications of this method were performed to fit the crosslinking method (higher pH, no primary amines) or to fit the SILAC labeling (combination of two differentially labeled samples in one purification). The reference for the vacuole proteomics (Eising et al 2022) corresponds to a paper in which the SILAC-based comparison of vacuoles from different mutant strains was optimized, and includes not only the vacuole purification but the growth conditions and downstream processing of the vacuoles.

Since both the SILAC-based vacuole proteomics and the XL-MS are multi-step methods, containing numerous parameters including the sample preparation, processing for MS, MS run and data analysis, we would prefer to keep them separate. We think this would allow a person attempting to reproduce these methods to go through them step by step.

What is CMAC dye? Why was it used to stain the vacuolar lumen?

We apologize for this oversight, we have included the definition of CMAC as 7-Amino-4-Chlormethylcumarin. It is a standard-used organelle marker for the lumen of the vacuole.

Some abbreviations (TEAB, ACN) are not explained.

We apologize for this oversight. We have now replaced these abbreviations with the full names of the compounds in the article.

What is 0% Ficoll?

We used the term 0% Ficoll, because this is the name given to the buffer in the original Haas 1995 paper on vacuole purifications. However, we agree that the term is misleading and we have now added the composition of the buffer (10 mM PIPES/KOH pH=6.8, 0.2 M Sorbitol).

Reviewer #3 (Significance (Required)):

The vacuolar-type proton ATPase, V-ATPase, is the key proton pump, that hydrolases ATP and uses this energy to pump protons across membranes. Amazingly, this proton pump and its function are conserved in eukaryotes from yeast to mammals. While V-ATPase structure and function have been studied for more than 30 years in various organisms, its regulation is not completely understood. The very recent discoveries of two new V-ATPase interacting proteins in yeast, first Oxr1 (OXidative Resistance 1), and now Rtc5 (Restriction of Telomere Capping 5), both the only two members of TLDc (The Tre2/Bub2/Cdc16 (TBC), lysin motif (LysM), domain catalytic) proteins in yeast, provide new insights in V-ATPase regulation in yeast, and because the interaction is conserved in mammals its relevance to mammalian V-ATPases regulation as well.

TLDc proteins are best known for their role in protection from oxidative stress, in particular in yeast and in the nervous system in mammals. The discovery of the novel Rtc5-V-ATPase interaction points to the role of V-ATPase not only in protection from oxidative stress but also in restriction of telomere capping in yeast and most likely higher species. The studies of other species also highlight the possible conserved role of V-ATPase in lifespan determination and Torc1 signaling, mediated through these interactions. Thus, the discovery of this new functionally important interaction between the second TLDc family member in yeast, Rtc5, and V-ATPase will shed light on the molecular mechanisms of all these essential biological processes and pathways.

In addition, because the authors performed a comprehensive proteomics protein-protein interaction study of the purified yeast vacuole it provides a valuable resource for all researchers who study vacuoles and/or related to them lysosomes.

The follow-up functional studies using the rav1Δ strain clearly demonstrated that Rtc5 and Oxr1 disassemble V-ATPase and counteract the function of V-ATPase assembly RAVE complex in vivo in yeast. Thus, they are essentially the first discovered endogenous eukaryotic protein inhibitors of V-ATPase. Moreover, because the authors obtained the evidence that Oxr1 is the regulator of the specific subunit isoform of V-ATPase Stv1p in vivo in yeast, it suggests that different TLDc proteins may regulate different specific V-ATPase subunit isoforms in cell- and tissue-specific manner in higher eukaryotes. The mechanism of this isoform-specific regulation in yeast and other species needs further investigation in the future.

Because of the conservation of the TLDc-V-ATPase interactions, all this information can be extrapolated to higher species, all the way to humans, in whom genetic mutations in various TLDc proteins are known to cause devastating diseases and syndromes.

We are thankful to the reviewer for their positive comments about the significance of our work.

Author Response

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

Reviewer #1 (Public Review):

1) A single biomarker seems very unlikely to be of much help in the detection of glaucoma due to the etiological heterogeneity of the disease, the existence of different subtypes, and the genetic variability among patients. Rather, a panel of biomarkers may provide more useful information for clinical prediction, including better sensitivity and specificity. The inclusion of additional metabolites already identifying in the study, in combination, may provide more reliable and correct assignment results.

The authors’ answer: Thank you for your comment. We recognize the constraints of using single biomarkers for diagnosis. In upcoming research, we aim to incorporate multiple biomarkers to improve diagnostic accuracy and will consider adding more metabolites as suggested.

2) The number of samples in the supplementary phase is low, larger sample sizes are mandatory to confirm the diagnostic accuracy.

The authors’ answer: Thank you for your comment. Collecting aqueous humor is invasive, making samples scarce. We acknowledge the small sample size limitation. In future studies, we plan to use larger samples to verify the biomarker's diagnostic accuracy. Your feedback emphasizes the need for thorough validation in our next research

3) Cohorts from different populations are needed to verify the applicability of this candidate biomarker.

The authors’ answer: Thank you for the suggestion. We agree on the need to test the biomarker's relevance across varied populations. Reports from other groups will help confirm and broaden our results.

4) Sex hormones seem to be associated also with other types of glaucoma, such as primary open-angle glaucoma (POAG), although the molecular mechanisms are unclear (see doi:10.1167/iovs.17-22708). The inclusion of patients diagnosed with other subtypes of glaucoma, like POAG, may contribute to determining the sensitivity and specificity of the proposed biomarker. Androstenedione levels should be determined in POAG, NTG, or PEXG patients.

The authors’ answer: I agree with your comment and thank you for your suggestion. PACG is a major cause of irreversible blindness in Asians. While this study centers on PACG, the link between sex hormones and other glaucoma subtypes, like POAG, merits investigation. Future studies will include POAG and other subtypes to further assess androstenedione's diagnostic relevance.

5) In addition, the levels of androstenedione were found significantly altered during other diseases as described by the authors or by conditions like polycystic ovary syndrome, limiting the utility of the proposed biomarker.

The authors’ answer: Thank you for your advice. Androstenedione levels also change in conditions like polycystic ovary syndrome, which could affect the biomarker's specificity. We plan to further study androstenedione's unique changes in glaucoma versus other conditions to clarify its diagnostic value.

6) Uncertainty of the androstenedione levels compromises its usefulness in clinical practice.

The authors’ answer: The uncertainty surrounding androstenedione levels and its impact on clinical applicability is a valid concern. We plan to delve deeper into understanding the variability and determinants of androstenedione levels to better assess its clinical relevance.

Reviewer #2 (Public Review):

The "predict" part is on much less solid ground. The visual field progression and association with serum androstenedione within the current experimental design eludes to a correlation. It truly cannot be stated as predictive. To predict one needs to put the substance when nothing is there and demonstrate that the desired endpoint is reached. Conversely, the substance (androstenedione) can be removed, and show that the condition regresses. None of these are possible without model system experiments, which have not been done. The authors could put some additional details in the methods, such as: 1) how much sample was collected, 2) whether equal serum volume for analysis had equal serum proteins (or cells). They have used a LC-MS/MS and a Chemiluminescence method, but another independent method such as GC-MS/MS or NMR to detect androstenedione for a subset of patients with different stages of visual field defect would be desirable.

The authors’ answer: We acknowledge your constructive critique concerning our use of the term "predict". In the present study, we elucidated a discernible correlation between visual field progression and serum androstenedione concentrations. We are cognizant of the critical distinction between correlation and causation, and we concur that our application of the term “predict” may have been overly assertive in this context.

Your emphasis on the imperative of employing model system experiments to unequivocally ascertain causative relationships is well-received. The experimental approach of modulating the substance, androstenedione in this case, to empirically observe its consequential impact on the condition, is a pivotal direction that warrants exploration in subsequent research endeavors. With regard to the variability of serum protein concentrations across participants, we adopted a methodological standardization by ensuring that the analyzed serum volume remained consistent across samples. This was implemented to enhance the reliability and generalizability of our findings.

Your recommendation to consider alternative detection methodologies, specifically GC-MS/MS or NMR, is duly noted. Although our choice of LC-MS/MS and Chemiluminescence was predicated on available resources, we recognize the scientific merit in leveraging multiple analytical techniques. In future investigations, we endeavor to incorporate a broader spectrum of detection methodologies for androstenedione, particularly when assessing patients with varied visual field defect stages, thereby bolstering the robustness and validity of our conclusions.

Reviewer #1 (Recommendations for The Authors):

1) POAG is the leading cause of irreversible blindness worldwide (see reference #4). The prevalence of PACG is highest in Asia, but the major form of glaucoma is still POAG. The authors should modify the abstract and background sections accordingly (see line 30 and lines 61-62).

The authors’ answer: Thank you for your suggestion, and we apologize for this mistake. The sentence” Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness worldwide” has been changed to” Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness in Asia”. (Page 2, lines 33; Page 3, lines 62-64)

2) Line 69, please change the sentence "the He et al. taught us..." to the following "the He et al. study taught us.".

The authors’ answer: Thank you for your comment. The sentence "the He et al. taught us..." has been changed to "the He et al. study taught us.". (Page 3, lines 72)

3) I suggest including the name of the identified candidate biomarker in the title of the manuscript. The title must be straightforward.

The authors’ answer: We agree with your comment and thank you for your suggestion. The sentence “Metabolomics Identifies and Validates Serum Novel Biomarker for Diagnosing Primary Angle Closure Glaucoma and Predicting the Visual Field Progression” has been changed to “Metabolomics Identifies and Validates Serum Androstenedione as Novel Biomarker for Diagnosing Primary Angle Closure Glaucoma and Predicting the Visual Field Progression”. (Page 1, lines 1)

4) Line 88, please change "normal subjects" to "control individuals".

The authors’ answer: Thank you for your comment. We have changed "normal subjects" to "control individuals”. (Page 4, lines 91)

5) Line 95 and so on along the manuscript, avoid the term "normal controls" or "normal" and use only the term "controls".

The authors’ answer: Thank you for your advice. "normal subjects" has been changed to "controls". (Page 4, lines 113; Page5, lines 118,120,124,128,133)

6) In the participants section, indicate the ocular treatments of PACG patients. For example, on line 141, which "treatment" are you referring to?

The authors’ answer: Thank you for your comment. We apologize to this vague statement. Treatment included medical treatment and surgical treatment. We have revised it in the manuscript. (Page 5, lines 142)

7) The entire section 2.4 is confusing. According to Figure S2, untargeted metabolomics was conducted with a mixed sample containing "all" serum extracts in order to obtain an in-house database with molecular features present in serum by LCHRMS. Then, this database was used for targeted metabolomics in individual serum samples using LCQQQ. However, as it is described in the manuscripts, it seems that first, an untargeted metabolomics analysis was carried out to identify altered metabolites, then targeted metabolomics was carried out to validate the untargeted analysis and finally, a profiling analysis was carried out to construct the database. The workflow must be clearly discussed and amended to be understable.

The authors’ answer: Thank you for your comment. We have revised the description of the experimental method section 2.4. (Page 7, lines 195-198)

8) Please, briefly explain what widely-targeted metabolomics is and how it works in this study (see section 2.4).

The authors’ answer: Thank you for your comment. For extensively targeted metabolome detection, a local database was first established by using the standard database, and ion pair information was obtained by scanning ion pairs of mixed samples (QC) with QTOF. A wide range of metabolites were qualitatively obtained by comparing with the local self-built database, and then the metabolites of each sample were qualitatively and quantitatively measured by MRM scanning mode of triple four-bar QQQ. This project combines the non-target public database scanning construction database and the wide target local database to build a new database, and then scans the database of the samples of this project with Q-TOF, and then carries out the qualitative and quantitative detection of metabolites of each sample in MRM mode. (Figure S2)

9) On Table 1, indicate the number of patients and controls with cataracts.

The authors’ answer: For the glaucoma group and the control group, we have excluded people with cataracts. This section is described in the inclusion and exclusion criteria for supplementary materials. (Inclusion and exclusion criteria)

10) On "Sample processing" section, lines 152 and 153: Have you used cold methanol to ensure metabolic quenching? If not, how metabolite quenching was carried out?

The authors’ answer: Thank you for your comment. We use cold methanol to extract metabolites, and the early blood samples have been stored in a -80°C refrigerator to ensure a low temperature process and ensure metabolic quenching. (Page 6, lines 196)

11) On the same "Sample processing" section, have you used internal standards during metabolite extraction? If yes, ones? If not, why?

The authors’ answer: Thank you for your comment. In the metabolite extraction process of each sample, the same internal standard was added, and the same volume of 50 μL serum samples were extracted. The specific internal label name has been added in "Sample processing" section. (Page 6, lines 153-155)

12) Lines 161-163, I suggest including in the supplementary material the worklist of the entire experiment run by LC-MS, including analytical replicates and QCs.

The authors’ answer: Thank you for your comment. Worklist for mass spectrometry can be found in supplementary sheet1. (Page 6, lines 165)

13) The title of the section "Detection method" does not seem appropriate, please change it to "Analytical methods "or something similar.

The authors’ answer: Thank you for your advice. "Detection method" has been changed to “Analytical methods “. (Page 6, lines 168)

14) Section 2.4.1, I suggest changing "Untargeted detection conditions" to "Untargeted metabolomics analysis".

The authors’ answer: Thank you for your comment. "Untargeted detection conditions" has been changed to "Untargeted metabolomics analysis". (Page 6, lines 169)

15) Lines 170-172, the column used is compatible with 100% water, why start with 5% acetonitrile?

The authors’ answer: Thank you for your comment. If the acetonitrile starting gradient is 0, it will cause a lot of water-soluble substances to elute and easily clog the column, so we want to use 5% organic phase.

16) Section 2.4.1, the chromatographic conditions (mobiles phases) were the same in both positive and negative ion mode? It is desirable to change or adjust a basic pH when working in negative, so please amend and clarify it.

The authors’ answer: Thank you for your comment. In the negative ion mode, the peak shape of the chromatogram under the acidic system is better than that under the alkaline system, so we choose the acidic system.

17) I am not able to clearly understand what is "widely targeted conditions" (see section 2.4.2). What is the difference with the conventional targeted metabolomics analysis? In my view, widely-targeted metabolomics refers to the combination of untargeted metabolomics and targeted metabolomics. This must be clarified and simplified.

The authors’ answer: Thank you for your syggestion. The characterization of metabolites in this study was conducted using a non-targeted database and a self-built database. Non-targeted metabolites were characterized with mixed samples, and then combined with the laboratory self-established database to form a new metabolome database for this study. 2.4.2 The broad targeting here refers to the use of the MWDB standard self-built database to characterize metabolites, and then the QQQ MRM model to quantify metabolites. In order to clearly describe the detection process, this part of the method has been modified. (Figure S2)

18) Line 199, please, indicate the normalization carried out.

The authors’ answer: We agree with your comment and thank you for your suggestion. The normalization description is missing from its data processing steps and has been corrected in the manuscript. (Page 7, lines 203)

19) How many instrumental replicates have you carried out both in untargeted and targeted metabolomics? Please, indicate it.

The authors’ answer: Thank you for your advice. In this project, all sample mixtures were used as QC samples, which were repeated several times in the testing process (one QC sample was inserted between every 10 samples), and the repeated correlation between repeated QC was more than 99% to ensure the stability of sample testing. (Sheet1)

20) Line 267, why did you select a fold changes threshold greater than 1.15 (or lower 0.85)? In metabolomics, it would be desirable to have a minimum of 1.5-fold change considering the variability of data.

The authors’ answer: Thank you for your comment. FC reduction is selected to expand potential candidate metabolites and can be repeated in three batches and refer to the literature "Blood metabolomics uncovers inflammation-associated mitochondrial dysfunction as a potential mechanism. underlying ACLF "method screening threshold.

21) To include anywhere the molecular formula of androstenedione.

The authors’ answer: I agree with your comment and thank you for your suggestion. We have added the molecular formula of androstenedione to the supplementary material. (Page 17, lines 475)

22) Line 290 is not Figure 4B and 4C, you may refer to Figure 3B and 3C.

The authors’ answer: Thank you for your advice. We apologize to this mistake. Figure 4B and 4C have been changed to Figure 3B and 3C.

23) Figure S3 was lost from Supplementary material, please include it.

The authors’ answer: Thank you for your comment. We apologize to this mistake. There is an error in the ordering of the supplementary graph. Figure 3 is redundant, and we have modified it in the supplementary materials.

24) Figure 4 B, indicate in the text the average and uncertainty of androstenedione levels in both control and PACG groups.

The authors’ answer: Thank you for your comment. In the manuscript, We have added descriptions of mean ± standard deviation of androstendione levels in the control group and the disease group. (Page 11, lines 311-312)

25) Section 3.6. please include the average and uncertainty of androstenedione levels in males and females in both control and PACG groups.

The authors’ answer: Thank you for your advice. For 3.6 section, we supplemented the mean ± standard deviation of androstenedione levels in the control and disease groups. (Page 13, lines 350-356)

26) Figure S9 seems missing.

The authors’ answer: Thank you for your comment. We apologize to this mistake. Figures S9 has been added in the Supplementary material.

27) Lines 345-346, indicate the levels obtained for the metabolite in the compared groups.

The authors’ answer: Thank you for your suggestion. The levels of androstenedione in each group are seen in “The results from both discovery set 1 (Figure S9A, Mild:32600±17011, Moderate:33215±17855, Severe:46060±21789) and discovery set 2 (Figure S9B, Mild:27866±19873, Moderate:27057±13166, Severe:43972±19234) indicated that the mean serum androstenedione levels were significantly higher in the severe PACG group compared to the moderate and mild PACG groups (P<0.001). These findings were further validated in both validation phase 1 (Figure S9C, Mild:75726±45719, Moderate:65798±30610, Severe:94348±30858) and validation phase 2 (Figure S9D, Mild:1.121±0.3143 ng/ml, Moderate:1.461±0.4391 ng/ml, Severe:2.147±0.6476 ng/ml).” and “Notably, the level of androstenedione was found to be significantly higher in PACG patients than in normal subjects in both discovery set 1 (Figure 4B, P=0.0081, Normal:33987±11113, PACG:42852±20767) and discovery set 2 (Figure 4C, P=0.0078, Normal:31559±10975, PACG:37934±18529).”

28) Line 368, you don't need to indicate the PACG abbreviation again.

The authors’ answer: Thank you for your comment. We apologize to this mistake. I have changed " patients with PACG " to "patients". (Page 13, lines 377)

29) Figure 6, panels A and B are not labeled (i.e., commented) in the body text of the manuscript.

The authors’ answer: Thank you for your suggestion. We’re very sorry for this mistake. Figure 6, panels A and B have been labeled in the manuscript. (Page 13, lines 377-379)

30) Section 3.7., when you indicate "after therapy" are you referring to surgical treatment? Please, clarify.

The authors’ answer: Thank you for your comment. We apologize to this vague statement. Blood samples were taken before and three months after surgery. “therapy” has been changed to “surgical treatment” in the manuscript. (Page 13, lines 377)

31) Line 370, "97th patient" should be replaced by "nine patients"?

The authors’ answer: Thank you for your advice. We apologize to this mistake. "97th patient" has been changed to “nine patients". (Page 13, lines 378-379)

32) Lines 370-372, it difficult to understand, please clarify why these findings indicate that severity is related to increased PACG according to Figure 6B.

The authors’ answer: Thank you for your comment. We’re very sorry for this vague statement. The sentence of “These findings showed that the levels of androstenedione that were tightly connected with PACG severity rose dramatically as PACG progressed.” Has been removed.

33) Line 447, the word "corrected" should be changed to "correlated"?

The authors’ answer: Thank you for your comment. "corrected" has been changed to "correlated". (Page 16, lines 453,456)

34) According to the literature, the levels found in control subjects are within the range of the "normal" values, i.e., are they comparable?

The authors’ answer: Thank you for your advice. Androstenedione ranges from 0.4 to 2 in the normal population. The mean standard deviation of androstenedione in the normal population was 1.552 ± 0.4859.

35) Lines 471-474, why "steroid hormone biosynthesis appears to be the critical node to high-match PACG pathophysiological concepts" while the high enrichment was observed in the "metabolic pathways"?

The authors’ answer: Metabolic pathways encompass a series of chemical reactions within a cell that enable the synthesis or breakdown of molecules to maintain the cell's energy balance. Steroid hormone biosynthesis is one of these metabolic pathways, and its products, steroid hormones, participate in a wide range of physiological processes, including metabolism, immune response, and the regulation of inflammation. In a different context, a study related to fatigue during Androgen Deprivation Therapy (ADT) showed a significant difference in metabolite levels within the steroid hormone biosynthesis pathways, emphasizing the role these pathways play in metabolic alterations. The mentioned findings suggest that steroid hormone biosynthesis and metabolic pathways are intertwined. (Page 17, lines 481-488)

36) Figure S13 and Figure S14A are the same.

The authors’ answer: Thank you for your comment. Figure S14A has been removed.

37) On lines 476-485, it would be interesting to discuss whether alterations of this metabolite could be a cause or consequence of PACG.

The authors’ answer: Based on the literature found, androstenedione is a naturally occurring steroid hormone produced by the gonads and adrenal glands, and serves as an intermediate in testosterone biosynthesis (Androstenedione (a Natural Steroid and a Drug Supplement): A Comprehensive Review of Its Consumption, Metabolism, Health Effects, and Toxicity with Sex Differences). Early events in the pathobiology of glaucoma involve oxidative, metabolic, or mechanical stress acting on retinal ganglion cells (RGCs), leading to their rapid release of danger signals such as extracellular ATP, thus triggering microglial and macroglial activation as well as neuroinflammation (Immune Responses in the Glaucomatous Retina: Regulation and Dynamics). However, one might speculate that since androstenedione is a steroid hormone, it could potentially impact the inflammatory and metabolic stress observed in the pathophysiological processes of glaucoma (Adaptive responses to neurodegenerative stress in glaucoma). Metabolic and anti-inflammatory avenues might be crucial in understanding the relationship between alterations in androstenedione levels and the severity of glaucoma. Nevertheless, more research and literature analysis would be necessary to better understand the precise relationship and its underlying mechanisms between these two entities.

38) I suggest sending the MS and MS/MS into a publicly available repository.

The authors’ answer: Thank you for your suggestion. Further research will necessitate the utilization of the raw mass spectrometry data. We anticipate making this raw data available in a public repository upon the conclusion of subsequent experiments.

Reviewer #2 (Recommendations for The Authors):

The authors should aim to describe methods in greater detail.

The authors could improve the writing to accurately describe their results and their interpretation and state what else could be done to make the result truly "predictive".

The authors’ answer: (1) Detail Enhancement in the Methods section: We expand the description of methods such as sample pre-processing, mass spectrometry detection, and result analysis in the study to provide more detailed information about the procedures, equipment, and materials used. (2) Improvement in Writing Quality: We have engaged a scientific editor to review our manuscript for clarity, coherence, and consistency to ensure that the results and interpretations are accurately and clearly conveyed. Terminologies and phrases have been revised to better reflect the findings and interpretations. (3) Limitation supplement: We have included a discussion on the limitations of our study and suggested additional studies and analyses that could be conducted to enhance the predictive value of our findings. We sincerely appreciate the constructive feedback from the reviewer, which has greatly contributed to improving the quality and rigor of our manuscript.

Reviewer #2 (Public Review):

Summary:

This interesting paper examines the earliest steps in progesterone-induced frog oocyte maturation, an example of non-genomic steroid hormone signaling that has been studied for decades but is still very incompletely understood. In fish and frog oocytes it seems clear that mPR proteins are involved, but exactly how they relay signals is less clear. In human sperm, the lipid hydrolase ABHD2 has been identified as a receptor for progesterone, and so the authors here examine whether ABHD2 might contribute to progesterone-induced oocyte maturation as well. The main results are:

1. Knocking down ABHD2 makes oocytes less responsive to progesterone, and ectopically expressing ABHD2.S (but not the shorter ABHD2.L gene product) partially rescues responsiveness. The rescue depends upon the presence of critical residues in the protein's conserved lipid hydrolase domain, but not upon the presence of critical residues in its acyltransferase domain.

2. Treatment of oocytes with progesterone causes a decrease in sphingolipid and glycerophospholipid content within 5 min. This is accompanied by an increase in LPA content and arachidonic acid metabolites. These species may contribute to signaling through GPCRs. Perhaps surprisingly, there was no detectable increase in sphingosine-1-phosphate, which might have been expected given the apparent substantial hydrolysis of sphingolipids. The authors speculate that S1P is formed and contributes to signaling but diffuses away.

3. Pharmacological inhibitors of lipid-metabolizing enzymes support, for the most part, the inferences from the lipidomics studies, although there are some puzzling findings. The puzzling findings may be due to uncertainty about whether the inhibitors are working as advertised.

4. Pharmacological inhibitors of G-protein signaling support a role for G-proteins and GPCRs in this signaling, although again there are some puzzling findings.

5. Reticulocyte expression supports the idea that mPR and ABHD2 function together to generate a progesterone-regulated PLA2 activity.

6. Knocking down or inhibiting ABHD2 inhibited progesterone-induced mPRinternalization, and knocking down ABHD2 inhibited SNAP2520-induced maturation.

Strengths:

All in all, this could be a very interesting paper and a nice contribution. The data add a lot to our understanding of the process, and, given how ubiquitous mPR and AdipoQ receptor signaling appear to be, something like this may be happening in many other physiological contexts.

Weaknesses:

I have several suggestions for how to make the main points more convincing.

Main criticisms:

1. The ABHD2 knockdown and rescue, presented in Fig 1, is one of the most important findings. It can and should be presented in more detail to allow the reader to understand the experiments better. E.g.: the antisense oligos hybridize to both ABHD2.S and ABHD2.L, and they knock down both (ectopically expressed) proteins. Do they hybridize to either or both of the rescue constructs? If so, wouldn't you expect that both rescue constructs would rescue the phenotype since they both should sequester the AS oligo? Maybe I'm missing something here.

In addition, it is critical to know whether the partial rescue (Fig 1E, I, and K) is accomplished by expressing reasonable levels of the ABHD2 protein, or only by greatly overexpressing the protein. The author's antibodies do not appear to be sensitive enough to detect the endogenous levels of ABHD2.S or .L, but they do detect the overexpressed proteins (Fig 1D). The authors could thus start by microinjecting enough of the rescue mRNAs to get detectable protein levels, and then titer down, assessing how low one can go and still get rescue. And/or compare the mRNA levels achieved with the rescue construct to the endogenous mRNAs.

Finally, please make it clear what is meant by n = 7 or n = 3 for these experiments. Does n = 7 mean 7 independently lysed oocytes from the same frog? Or 7 groups of, say, 10 oocytes from the same frog? Or different frogs on different days? I could not tell from the figure legends, the methods, or the supplementary methods. Ideally one wants to be sure that the knockdown and rescue can be demonstrated in different batches of oocytes, and that the experimental variability is substantially smaller than the effect size.

2. The lipidomics results should be presented more clearly. First, please drop the heat map presentations (Fig 2A-C) and instead show individual time course results, like those shown in Fig 2E, which make it easy to see the magnitude of the change and the experiment-to-experiment variability. As it stands, the lipidomics data really cannot be critically assessed.

[Even as heat map data go, panels A-C are hard to understand. The labels are too small, especially on the heat map on the right side of panel B. The 25 rows in panel C are not defined (the legend makes me think the panel is data from 10 individual oocytes, so are the 25 rows 25 metabolites? If so, are the individual oocyte data being collapsed into an average? Doesn't that defeat the purpose of assessing individual oocytes?) And those readers with red-green colorblindness (8% of men) will not be able to tell an increase from a decrease. But please don't bother improving the heat maps; they should just be replaced with more informative bar graphs or scatter plots.]

3. The reticulocyte lysate co-expression data are quite important and are both intriguing and puzzling. My impression had been that to express functional membrane proteins, one needed to add some membrane source, like microsomes, to the standard kits. Yet it seems like co-expression of mPR and ABHD2 proteins in a standard kit is sufficient to yield progesterone-regulated PLA2 activity. I could be wrong here - I'm not a protein expression expert - but I was surprised by this result, and I think it is critical that the authors make absolutely certain that it is correct. Do you get much greater activities if microsomes are added? Are the specific activities of the putative mPR-ABHD2 complexes reasonable?

Author Response

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

Reviewer #1 (Public Review):

Issue 1: The relevance is somewhat unclear. High cysteine levels can be achieved in the laboratory, but, is this relevant in the life of C. elegans? Or is there physiological relevance in humans, e.g. a disease? The authors state "cells and animals fed excess cysteine and methionine", but is this more than a laboratory excess condition? SUOX nonfunctional conditions in humans don't appear to tie into this, since, in that context, the goal is to inactivate CDO or CTH to prevent sulfite production. The authors also mention cancer, but the link to cysteine levels is unclear. In that sense, then, the conditions studied here may not carry much physiological relevance.

Response 1: We set out to answer a fundamental question: what pathways regulate the function of cysteine dioxygenase, a highly conserved enzyme in sulfur amino acid metabolism? In an unbiased genetic screen that sampled millions of EMS generated mutations across all ~20,000 C. elegans genes, we discovered loss of function/null mutations in egl-9 and rhy-1, two negative regulators of the hypoxia inducible transcription factor (hif-1). Genetic ablation of the egl-9 or rhy-1 loci are likely not relevant to the life of a C. elegans animal, i.e. this is not representative of a natural state. Yet, this extreme genetic intervention has taught us a new fundamental truth about the interaction between EGL-9/RHY-1, HIF-1, and the transcriptional activation of cdo1. Similarly, the high cysteine levels used in our assays may or may not be representative of a state in nature, we do not know (nor do we make any claims about the environmental relevance of our choice of cysteine concentrations). It seems very plausible that pathological states exist where cysteine concentrations may rise to comparable levels in our experimental system. More importantly, we have started with excess to physiology to elicit a clear response that we can study in the lab. Similar strategies established the cysteine-induction phenotype of CDO1 in mammalian systems. For instance, in Kwon and Stipanuk 2001, hepatocytes are cultured in media supplemented with 2mmol/L cysteine to promote a ~4-fold increase in CDO1 mRNA.

Issue 2: The pathway is described as important for cysteine detoxification, which is described to act via H2S (Figure 6). Much of that pathway has already been previously established by the Roth, Miller, and Horvitz labs as critical for the H2S response. While the present manuscript adds some additional insight such as the additional role of RHY-1 downstream on HIF-1 in promoting toxicity, this study therefore mainly confirms the importance of a previously described signalling pathway, essentially adding a new downstream target rhy-1 -> cysl-1 -> egl-9 -> hif-1 -> sqrd-1/cdo-1. The impact of this finding is reduced by the fact that cdo-1 itself isn't actually required for survival in high cysteine, suggesting it is merely a maker of the activity of this previously described pathway.

Response 2: We agree that the primary impact of our manuscript is the establishment of a novel intersection between the H2S-sensing pathway (largely worked out by Roth, Miller, and Horvitz) and our gene of interest, cysteine dioxygenase. We believe that the connection between these two pathways is exciting as it suggests a logical homeostatic circuit. High cysteine yields enzymatically produced H2S. This H2S may then act as a signal promoting HIF-1 activity (via RHY-1/CYSL-1/EGL-9). High HIF-1 activity increases cdo-1 transcription and activity promoting the degradation of the high-cysteine trigger. As pointed out by the reviewer, cdo-1(-) loss of function alone does not cause cysteine sensitivity at the concentrations tested. Given that cysl-1(-) and hif-1(-) mutants are exquisitely sensitive to high levels of cysteine, we propose that HIF-1 activates the transcription of additional genes that are required for high cysteine tolerance. However, our genetic data show that cdo-1 is more than simply a marker of HIF-1 transcription. Our genetic data in Table 1 demonstrate that HIF-1 activation (caused by egl-9(-)) is sufficient to cause severe sickness in a suox-1 hypomorphic mutant which cannot detoxify sulfites, a critical product of cysteine catabolism. This severe sickness can be reversed by inactivating hif-1, cth-2, or cdo-1. These data demonstrate a functional intersection between the established H2S-sensing pathway and cysteine catabolism governed by cdo-1.

Reviewer #2 (Public Review):

Issue 3: First, the authors show that the supplementation of exogenous cysteine activates cdo-1p::GFP. Rather than showing data for one dose, the author may consider presenting dose-dependency results and whether cysteine activation of cdo-1 also requires HIF-1 or CYSL-1, which would be important data given the focus and major novelty of the paper in cysteine homeostasis, not the cdo-1 regulatory gene pathway.

Response 3: We agree with the reviewer and have performed the suggested dose-response curve for expression of Pcdo-1::GFP in wild-type C. elegans. We observe substantial activation of the Pcdo-1::GFP transcriptional reporter beginning at 100µM supplemental cysteine (Figure 3C). Higher doses of cysteine do not elicit a substantially stronger induction of the Pcdo-1::GFP reporter. Thus, we find that 100µM supplemental cysteine strikes the right balance between strongly inducing the Pcdo-1::GFP reporter while not inducing any toxicity or lethality in wild-type animals (Figure 3E).

We further agree that testing for induction of the Pcdo-1::GFP reporter in a hif-1(-) or cysl-1(-) mutant background is a critical experiment. However, we have not been able to identify a cysteine concentration that induces Pcdo-1::GFP and is not 100% lethal for hif-1(-) or cysl-1(-) mutant C. elegans. The remarkable sensitivity of hif-1(-) or cysl-1(-) mutant C. elegans to supplemental cysteine demonstrates the critical role of these genes in promoting cysteine homeostasis. But because of this lethality, we could not assay the Pcdo1::GFP reporter in the hif-1(-) or cysl-1(-) mutant animals. But the lethality to excess cysteine demonstrates that this cysteine response is salient. To get at how cysteine might be interacting with the HIF-1-signaling pathway, we performed new additivity experiments by supplementing 100µM cysteine to wild type, egl-9(-), and rhy-1(-) mutant C. elegans expressing the Pcdo-1::GFP reporter. Surprisingly, we found that cysteine had no significant impact on Pcdo-1::GFP expression in an egl-9(-) mutant background but significantly increased the Pcdo-1::GFP expression in a rhy-1(-) background (Figure 3A,B). These data suggest that cysteine acts in a pathway with egl-9 and in parallel to rhy-1. These data have been incorporated into Figure 3A,B and are included in the Results section of the manuscript.

Issue 4: While the genetic manipulation of cdo-1 regulators yields much more striking results, the effect size of exogenous cysteine is rather small. Does this reflect a lack of extensive condition optimization or robust buffering of exogenous/dietary cysteine? Would genetic manipulation to alter intracellular cysteine or its precursors yield similar or stronger effect sizes?

Response 4: We agree that the induction of the Pcdo-1::GFP reporter by supplemental cysteine is not as dramatic as the induction caused by the egl-9 or rhy-1 null alleles. We believe our Response 3 and new Figure 3C demonstrate that this phenomenon is not due to lack of condition optimization, but likely reflects some biology. As pointed out by the reviewer, C. elegans likely buffers exogenous cysteine and this (perhaps) prevents the impressive Pcdo-1::GFP induction observed in the egl-9(-) and rhy-1(-) mutant animals. We have now mentioned this possible interpretation in the Results section. Furthermore, we like the idea of using genetic tricks to promote cysteine accumulation within C. elegans cells and tissues and will consider these approaches in future studies.

Issue 5: Second, there remain several major questions regarding the interpretation of the cysteine homeostasis pathway. How much specificity is involved for the RHY-1/CYSL-1/EGL-9/HIF-1 pathway to control cysteine homeostasis? Is the pathway able to sense cysteine directly or indirectly through its metabolites or redox status in general? Given the very low and high physiological concentrations of intracellular cysteine and glutathione (GSH, a major reserve for cysteine), respectively, there is a surprising lack of mention and testing of GSH metabolism.

Response 5: Future studies are required to determine the specificity of the RHY-1/CYSL-1/EGL-9/HIF-1 pathway for the control of cysteine homeostasis. Our proposed mechanism, that H2S activates the HIF-1 pathway is based largely on the work of the Horvitz lab (Ma et al. 2012). They demonstrate that H2S promotes a direct inhibitory interaction between CYSL-1 and EGL-9, leading to activation of HIF-1. These findings align nicely with our genetic and pharmacological data. However, our work does not provide direct evidence as to the cysteine-derived metabolite that activates HIF-1. We propose H2S as a likely candidate.

We have added a note to the introduction regarding the role of GSH as a reservoir of excess cysteine and agree that future studies might find interesting links between CDO-1, GSH metabolism, and HIF-1.

Issue 6: In addition, what are the major similarities and differences of cysteine homeostasis pathways between C. elegans and other systems (HIF dependency, transcription vs post-transcriptional control)? These questions could be better discussed and noted with novel findings of the current study that are likely C. elegans specific or broadly conserved.

Response 6: We have included a new section in the Discussion highlighting the nature of mammalian CDO1 regulation. We propose the hypothesis that a homologous pathway to the C. elegans RHY-1/CYSL-1/EGL9/HIF-1 pathway might operate in mammalian cells to sense high cysteine and induce CDO1 transcription. Importantly, all proteins in the C. elegans pathway have homologous counterparts in mammals. However, this hypothesis remains to be tested in mammalian systems.

Reviewer #3 (Public Review):

Major weaknesses of the paper include:

Issue 7: the over-reliance on genetic approaches.

Response 7: This is a fair critique. Our expertise is genetics. Our philosophy, which the reviewers may not share, is that there is no such thing as too much genetics!

Issue 8: the lack of novelty regarding prolyl hydroxylase-independent activities of EGL-9.

Response 8: We believe the primary novelty of our work is establishing the intersection between the H2Ssensing HIF-1 pathway and cysteine catabolism governed by cysteine dioxygenase. Our demonstration that cdo-1 regulation operates largely independent of VHL-1 and EGL-9 prolyl hydroxylation is a mechanistic detail of this regulation and not the critical new finding. Although, we believe it does suggest where pathway analyses should be directed in the future. We also believe that our homeostatic feedback model for the regulation of HIF-1 (and cdo-1) by cysteine-derived H2S is new and exciting and provides insight into the logic of why HIF-1 might respond to H2S and promote the activity of cdo-1. Our work suggests that one reason for this intersection of hif-1 and cdo-1 is to sense and maintain cysteine homeostasis when cysteine is in excess.

Issue 9: the lack of biochemical approaches to probe the underlying mechanism of the prolyl hydroxylaseindependent activity of EGL-9.

Response 9: While not the primary focus of our current manuscript, we agree that this is an exciting area of future research. To uncover the prolyl hydroxylase-independent activity of EGL-9, we agree that a combination of approaches will be required including, biochemical, structure-function, and genetic.

Major Issues We Feel the Authors Should Address:

Issue 10: One particularly glaring concern is that the authors really do not know the extent to which the prolyl hydroxylase activity is (or is not) impacted by the H487A mutation in egl-9(rae276). If there is a fair amount of enzymatic activity left in this mutant, then it complicates interpretation. The paper would be strengthened if the authors could show that the egl-9(rae276) eliminates most if not all prolyl hydroxylase activity. In addition, the authors may want to consider doing RNAi for egl-9 in the egl-9(rae276) mutant as a control, as this would support the claim that whatever non-hydroxylase activity EGL-9 may have is indeed the causative agent for the elevation of CDO-1::GFP. Without such experiments, readers are left with the nagging concern that this allele is simply a hypomorph for the single biochemical activity of EGL-9 (i.e., the prolyl hydroxylase activity) rather than the more interesting, hypothesized scenario that EGL-9 has multiple biochemical activities, only one of which is the prolyl hydroxylase activity.

Response 10: We have two lines of evidence that suggest the egl-9(rae276)-encoded H487A variant eliminates prolyl hydroxylase activity. First, Pan et al. 2007 (reference 57) demonstrate that when the equivalent histidine (H313) is mutated in human protein, that protein lacks detectible prolyl hydroxylase activity. Second, the phenotypic similarities caused by egl-9(rae276) and the vhl-1 null allele, ok161. Both alleles cause nearly identical activation of the Pcdo-1::GFP reporter transgene (Fig. 5C,D), and similarly impact the growth of the suox-1(gk738847) hypomorphic mutant (Table 1). This phenotypic overlap is highly relevant as the established role of VHL-1 is to recognize the hydroxyl mark conferred by the EGL-9 prolyl hydroxylase domain and promote the degradation of HIF-1. If EGL-9[H487A] had residual prolyl hydroxylase activity, we would expect the vhl-1(-) null mutant C. elegans to display more dramatic phenotypes than their egl-9(rae276) counterparts. This is not the case.

Issue 11: The authors observed that EGL-9 can inhibit HIF-1 and the expression of the HIF-1 target cdo-1 through a combination of activities that are (1) dependent on its prolyl hydroxylase activity (and subsequent VHL-1 activity that acts on the resulting hydroxylated prolines on HIF-1), and (2) independent of that activity. This is not a novel finding, as the authors themselves carefully note in their Discussion section, as this odd phenomenon has been observed for many HIF-1 target genes in multiple publications. While this manuscript adds to the description of this phenomenon, it does not really probe the underlying mechanism or shed light on how EGL-9 has these dual activities. This limits the overall impact and novelty of the paper.

Response 11: See response to Issues #8.

Issue 12: Cysteine dioxygenases like CDO-1 operate in an oxygen-dependent manner to generate sulfites from cysteine. CDO-1 activity is dependent upon availability of molecular oxygen; this is an unexpected characteristic of a HIF-1 target, as its very activation is dependent on low molecular oxygen. Authors neither address this in the text nor experimentally, and it seems a glaring omission.

Response 12: We agree this is an important point to raise within our manuscript. Although, despite its induction by HIF-1, there is no evidence that cdo-1 transcription is induced by hypoxia. In fact, in a genome wide transcriptomic study, cdo-1 was not found to be induced by hypoxia in C. elegans (Shen et al. 2005, reference 71).

We have newly commented on the use of molecular oxygen as a substrate by both EGL-9 and CDO-1 in our Discussion section. The mammalian oxygen-sensing prolyl hydroxylase (EGLN1) has been demonstrated to have high a Km value for O2 (high µM range). This likely allows EGLN1 to be poised to respond to small decreases in cellular oxygen from normal oxygen tensions. Clearly, CDO-1 also requires oxygen as a substrate, however the Km of CDO-1 for O2 is likely to be much lower, preventing sensitivity of the cysteine catabolism to physiological decreases in O2 availability. Although, to our knowledge, the CDO1 Km value for O2 has not been experimentally determined. We have added a new Discussion section where we address the conundrum about low oxygen inducing HIF-1 but oxygen being needed by CDO-1/CDO1.

Issue 13: The authors determined that the hypodermis is the site of the most prominent CDO-1::GFP expression, relevant to Figure 4. This claim would be strengthened if a negative control tissue, in the animal with the knockin allele, were shown. The hypodermal specific expression is a highlight of this paper, so it would make this article even stronger if they could further substantiate this claim.

Response 13: Our claim that the hypodermis is the critical site of cdo-1 function is based on; i) our hands on experience looking at Pcdo-1::GFP, Pcdo-1::CDO-1::GFP, CDO-1::GFP (encoded by cdo-1(rae273)) and our reporting of these expression patterns in multiple figures throughout the manuscript and ii) the functional rescue of cdo-1(-) phenotypes by a cdo-1 rescue construct expressed by a hypodermal-specific promoter (col10). We agree that providing negative control tissues would modestly improve the manuscript. However, we do not think that adding these controls will substantially alter the conclusions of the paper. Importantly, we acknowledge this limitation of our work with the sentence, “However, we cannot exclude the possibility that CDO-1 also acts in other cells and tissues as well.”

Minor issues to note:

Issue 14: Mutants for hif-1 and cysl-1 are sensitive to exogenous cysteine levels, yet loss of CDO-1 expression is not sufficient to explain this phenomenon, suggesting other targets of HIF-1 are involved. Given the findings the authors (and others) have had showing a role for RHY-1 in sulfur amino acid metabolism, shouldn't the authors consider testing rhy-1 mutants for sensitivity to exogenous cysteine?

Response 14: To test the hypothesis that rhy-1(-) C. elegans might be sensitive to supplemental cysteine, we cultured wild type and rhy-1(-) animals on 0, 100, and 1000µM supplemental cysteine. At 0 and 100µM supplemental cysteine, neither wild-type nor rhy-1(-) animals display any lethality suggesting rhy-1 is not required for survival in the face of excess cysteine (Fig. 3D,E). We also cultured these same strains on 1000µM supplemental cysteine, a concentration that is highly toxic to wild-type animals (100% lethality). rhy1(-) animals were resistant to 1000µM supplemental cysteine with a substantial fraction of the population surviving overnight exposure to this lethal dose of cysteine. Similarly, egl-9(-) mutant C. elegans were also resistant to 1000µM supplemental cysteine. We propose that loss of egl-9 or rhy-1 activates HIF-1-mediated transcription which is priming these mutants to cope with the lethal dose of cysteine. These data are now presented in Figure 3D-F and presented in the Results section.

Issue 15: The cysteine exposure assay was performed by incubating nematodes overnight in liquid M9 media containing OP50 culture. The liquid culture approach adds two complications: (1) the worms are arguably starving or at least undernourished compared to animals grown on NGM plates, and (2) the worms are probably mildly hypoxic in the liquid cultures, which complicates the interpretation.

Response 15: We agree that it is possible that animals growing overnight in liquid culture are undernourished and mildly hypoxic. However, we are confident in our data interpretation as all our experiments are appropriately controlled. Meaning, control and experimental groups were all grown under the same liquid culture conditions. Thus, these animals would all experience the same stressors that come with liquid culture. Importantly, we never make comparisons between groups that were grown under different culture conditions (i.e. solid media vs. liquid culture).

Issue 16: An easily addressable concern is the wording of one of the main conclusions: that cdo-1 transcription is independent of the canonical prolyl hydroxylase function of EGL-9 and is instead dependent on one of EGL-9's non-canonical, non-characterized functions. There are several points in which the wording suggests that CDO-1 toxicity is independent of EGL-9. In their defense, the authors try to avoid this by saying, "EGL-9 PHD," to indicate that it is the prolyl hydroxylase function of EGL-9 that is not required for CDO-1 toxicity. However, this becomes confusing because much of the field uses PHD and EGL-9/EGLN as interchangeable protein names. The authors need to be clear about when they are describing the prolyl hydroxylase activity of EGL-9 rather than other (hypothesized) activities of EGL-9 that are independent of the prolyl hydroxylase activity.

Response 16: We appreciate the reviewer alerting us to this practice within the field. To avoid confusion, we have removed the “PHD” abbreviation from our manuscript and explicitly referred to the “prolyl hydroxylase domain” where relevant.

Issue 17: The authors state in the text, "the egl-9; suox-1 double mutants are extremely sick and slow growing." We appreciate that their "health" assay, based on the exhaustion of food from the plate, is qualitative. We also appreciate that it is a functional measure of many factors that contribute to how fast a population of worms can grow, reproduce, and consume that lawn of food. However, unless they do a lifespan assay and/or measure developmental timing and specifically determine that the double mutant animals themselves are developing and/or growing more slowly, we do not think it is appropriate to use the words "slow growing" to describe the population. As they point out, the rate of consumption of food on the plate in their health assay is determined by a multitude and indeed a confluence of factors; the growth rate is one specific one that is commonly measured and has an established meaning.

Response 17: We see how the phrase ‘slow growing’ might imply a phenotype that we have not actually assessed with this assay. Therefore, we have removed all claims about “slow growth” of the strains presented in Table 1 and have highlighted the assay more overtly in the results section. For example; “While egl-9(-) and suox-1(gk738847) single mutant animals are healthy under standard culture conditions, the egl-9(-); suox1(gk738847) double mutant animals are extremely sick and require significantly more days to exhaust their E. coli food source under standard culture conditions (Table 1).”

Reviewer #1 (Recommendations For The Authors):

Issue 18: Relevance could be addressed further in the text.

Response 18: We have added additional context for our work in the Discussion section. Please see our response to Issues #5, 6, 12, and 24.

Issue 19: Better appreciation and integration of the manuscript's findings with published studies would be appropriate.

Response 19: We have added additional context for our work in the Discussion section. Please see our response to Issues #5, 6, 12, and 24.

Issue 20: It might be perhaps relevant to test whether cdo-1 is relevant for hypoxia resistance since it appears to be a key target for hif-1.

Response 20: We agree that this is an interesting future direction, however given that cdo-1 mRNA is not induced by hypoxia (Shen et al. 2005) we have not prioritized these experiments for the current manuscript.

Issue 21: "egl-9 inhibits cdo-1 transcription in a prolyl-hydroxylase and VHL-1-independent manner" should be tempered. vhl-1 mutants and egl-9 hydroxylase point mutant still have significant induction of the reporter.

Response 21: Thank you for identifying this oversight. We have modified the Figure 5 legend title to read, “egl9 inhibits cdo-1 transcription in a largely prolyl-hydroxylase and VHL-1-independent manner.”

Issue 22: Please use line numbers in the future for easier tracking of comments.

Response 22: We shall.

Issue 23: Abstract and elsewhere, "high cysteine activates...", should be rephrased to "high levels of cysteine".

Response 23: We have made this change throughout the manuscript.

Reviewer #3 (Recommendations For The Authors):

Issue 24: The authors discuss CDO1 in the context of tumorigenesis, as well as the potential regulation between cysteine and the hypoxia response pathway. Thus, I was surprised that there was no mention of the foundational Bill Kaelin paper (Briggs et al 2016) showing how the accumulation of cysteine is related to tumorigenesis, and that cysteine is a direct activator of EglN1. Puzzling that CDO1 is a tumor suppressor: you lose it, cysteine can accumulate and activate EglN1, causing HIF1 turnover. How do the authors reconcile their results with this paper? I was also surprised that there was no mention in the Discussion of the role of hydrogen sulfide, cysteine metabolism, and CTH and CBS in oxygen sensation in the carotid body given the role they play there. Seems important to discuss this issue.

Response 24: We have added new sections to our Discussion that consider the relationship between our work and Briggs et al. 2016 as well as mentioned the role of CTH and H2S in the mammalian carotid body.

Issue 25: The abstract has a variety of contradictory statements. For example, the authors state that "HIF-1mediated induction of cdo-1 functions largely independent of EGL-9," but then go on to conclude in the final sentence that cysteine stimulates H2S production, which then activates EGL-9 signaling, which then increases HIF-1-mediated transcription of cdo-1. A quick reading of the abstract leaves the reader uncertain whether EGL-9 is or is not involved in this regulation of cdo-1 expression. In addition, the conclusion sentence implies that activation of the EGL-9 pathway increases HIF-1-mediated transcription, yet it is well established that EGL-9 is an inhibitor of HIF-1. The abstract fails to deliver a clear summary of the paper's conclusions. Perhaps consider this alternative (changes in capital letters):

The amino acid cysteine is critical for many aspects of life, yet excess cysteine is toxic. Therefore, animals require pathways to maintain cysteine homeostasis. In mammals, high cysteine activates cysteine dioxygenase, a key enzyme in cysteine catabolism. The mechanism by which cysteine dioxygenase is regulated remains largely unknown. We discovered that C. elegans cysteine dioxygenase (cdo-1) is transcriptionally activated by high cysteine and the hypoxia inducible transcription factor (hif-1). hif-1- dependent activation of cdo-1 occurs downstream of an H2S-sensing pathway that includes rhy-1, cysl-1, and egl-9. cdo-1 transcription is primarily activated in the hypodermis where it is sufficient to drive sulfur amino acid metabolism. EGL-9 and HIF-1 are core members of the cellular hypoxia response. However, we demonstrate that the mechanism of HIF-1-mediated induction of cdo-1 IS largely independent of EGL-9 prolyl hydroxylASE ACTIVITY and the von Hippel-Lindau E3 ubiquitin ligase. We propose that the REGULATION OF cdo-1 BY HIF-1 reveals a negative feedback loop for maintaining cysteine homeostasis. High cysteine stimulates the production of an H2S signal. H2S then ACTS THROUGH the rhy-1/cysl-1/egl-9 signaling pathway DISTINCTLY FROM THEIR ROLE IN HYPOXIA RESPONSE TO INCREASE HIF-1-mediated transcription of cdo-1, promoting degradation of cysteine via CDO-1.

Response 25: We agree that the abstract could be clearer. We believe this concern stems from the fact that we did not discuss our initial screen in the abstract. Thus, we failed to establish a role for egl-9 in the regulation of cdo-1. To remedy this, we have modified the abstract as suggested by the reviewer and added additional context. We believe that these changes improve the clarity of the Abstract substantially.

Issue 26: An easily addressable concern involves the "dark" microscopy controls showing lack of fluorescence from a nematode. In these dark negative control micrographs, the authors should draw dotted outlines around where the worms are or include a brightfield image next to the fluorescence image. On a computer screen, it is in fact possible to make out the worms. Yet, when printed out, the reader must assume there are worms in the dark images. Additionally, we realize that adjusting fluorescence so that wild-type CDO-1 expression can be seen will result in oversaturation of the egl-9 and rhy-1; cdo-1 doubles; however, this would be a useful figure to add into the supplement to both provide a normal reference of CDO-1 low-level expression and a demonstration of just how bright it is in the mutant backgrounds. It would also be useful for you to please report your exposure settings for purposes of reproducibility.

Response 26: As suggested, we have added dotted lines around the location of the C. elegans animals in all images where GFP expression is low or basal. We have also reported the exposure times for each image in the appropriate figure legends.

Issue 27: This title is quite generic and doesn't even mention the main players (CDO-1 and sulfite metabolism).

Response 27: We have updated our title to call attention to cysteine dioxygenase. The improved title is: “Hypoxia-inducible factor induces cysteine dioxygenase and promotes cysteine homeostasis in Caenorhabditis elegans”

Issue 28: The authors mention two disorders in which CDO-1 plays a pathogenic role: MoCD and ISOD. We recommend switching the order in which the authors mention these, as the remainder of the paragraph is about MoCD. Also, they should write out the number "2" in the first sentence of that paragraph.

Response 28: We have made the suggested changes.

Issue 29: The authors state in the main text, "...to ubiquitinate HIF-1, targeting it for degradation by the proteosome." Here, they should refer to the pathway in Figure 5a.

Response 29: We have made the suggested change.

Issue 30: The authors state in the main text, "Elements of the HIF-1 pathway have emerged..." which is vague and confusingly worded. Change to, "Members of the HIF-1 pathway and its targets have emerged from C. elegans genetic studies."

Response 30: We have made the suggested change.

Issue 31: Clarify in the figure legends that supplemental cysteine did not affect the mortality of worms that were imaged.

Response 31: We have added this note to Figure 3A and Figure S3A.

Issue 32: Figure 1b. "the cdo-1 promoter is shown..." Add: "as a straight line" to the end of this phrase.

Response 32: We have made the suggested change.

Issue 33: The authors should consider changing the red text in Figure 1 to magenta, which tends to be more readable for people who have limited color vision.

Response 33: We have adjusted the colors in Figure 1 as suggested.

Issue 34: Figure 2, legend title. Consider changing "hif-1" to "HIF-1," as well as rhy-1, cysl-1, and egl-9. In this case, they are talking about proteins, not mutants or genes. This will make the paper easier to follow for readers who lack a C. elegans background.

Response 34: We have made the suggested change.

Issue 35: Figure 5, caption text. "...indicates weak similarity." Add, "amongst species compared."

Response 35: We have made the suggested change.

Issue 36: It is starting to become a standard for showing the datapoints in bar graphs. Although this is done in many graphs in the paper, it should also be done for Figure S1 and Figure 4C.

Response 36: We have made the suggested change.

Issue 37: An extensive ChIP-seq and RNA-seq analysis of C. elegans HIF-1 was recently published (Vora et al, 2022), which the authors should reference in support of the regulation of CDO-1 transcription by HIF-1 in their description of published expression studies of the pathway (Results section, page 4). Indeed, Vora et al were key generators of the ChIP-seq data cited in Warnhoff et al but not included as authors in the ModERN/ModENCODE publication: their contributions were published separately in Vora et al and should be acknowledged equivalently.

Response 37: We appreciate the reviewer pointing this detail out and we have added the correct citation as indicated.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

1. General Statements We thank the Editors and the Reviewers for their time and constructive criticism, which has allowed us to improve our manuscript. All of our responses are indicated in blue font. Revision Figures for the Reviewers are included just below the response. The line numbers given here refer to those in the revised manuscript, where we have marked the changes in red.
2. Description of the planned revisions If granted a full revision, we will experimentally address the following major points, which were raised by more than one Reviewer: ● Repeat experiment in Figure 4 C to assess statistical significance (Reviewer 1 and 3) ● Western blot analysis of HDV infected HLCs showing small and large delta antigens. We have already performed such an analysis on HLCs (see Revision Figure 2). In addition, we will perform a comparative analysis with common HDV infection models dHepaRG and Huh7-NTCP cells over time (Reviewers 2 and 3). ● Additional characterisation of the two HLC subpopulations at transcript and protein level (Reviewer 1 and 3). In addition, we planned to conduct the following experiments in response to the individual Reviewers: In response to Reviewer 1: We thank the Reviewer for their encouraging feedback on our model and for their helpful comments, allowing us to improve our manuscript. Figure 1: The observation of a denser subpopulation of hepatocytes more susceptible to HDV is interesting. Do you have more characterization of this cell subpopulation, by IFA, in term of hepatic maturation marker, known HDV host factors and particularly NTCP expression? We agree with the Reviewer that this is an interesting observation. We separated the two hepatocyte subpopulations to analyse the gene expression of the liver maturation markers NTCP and ALB by RT-qPCR (see Revision Figure 1A). Surprisingly, we found that the low-density population expressed higher levels of both ALB and NTCP, suggesting that they are more mature than the high-density population. In addition, we stained both markers by immunofluorescence and observed no apparent differences (see Revision Figures 1B & C). In contrast, the new host factor identified in our study, CD63, appeared to be more highly expressed in the high-density population compared to the low-density population (Fig. 6G). However, we cannot exclude the Revision Plan possibility that other factors play an additional role. As outlined in our response to Reviewer 3, we will separate the two populations and analyse the gene expression of other known HBV and HDV co-host factors to assess whether they play a role in addition to CD63 in conferring the higher susceptibility to HDV infection to the highly dense HLC population. Revision Figure 1: High-density HLCs population is not more mature than the low-density HLC population. (A) The low-density HLCs population was separated from the high-density HLC population by gentle dissociation. Total RNAs were isolated from both populations and Albumin and NTCP expression was analysed by RT-qPCR. (B & C) High-density HLCs (upper image) and low-density HLCs (bottom image) were stained with Albumin specific antibody. Shown are either images taken on an epifluorescence microscope (B) or single slices of confocal images acquired on a Airyscan confocal microscope (C). Fig 1B and C: Can a BLV control be included in the figure? Thank you for this suggestion, we will repeat the experiment for these panels and add BLV as control. Fig 1A-F: What is the overall level of NTCP between HLC, HepaRG, Huh7NTCP and HLCAAV- NTCP? Can NTCP and HDAg be stained simultaneously in your cells? This is an excellent question and we will compare the total NTCP levels between differentiated HepaRG, Huh7 NTCP, HLCs +/- AAV NTCP by Western blot analysis and immunofluorescence (IF) staining. Comparing NTCP expression in HLCs +/- AAV NTCP, we observed a strong upregulation of surface NTCP upon AAV transduction by IF staining (Figure 1D). Unfortunately, our initial attempts to simultaneously detect NTCP and HDAg were technically hampered. Since HDAg is mainly localised in the nuclei, we have to permeabilize the cells in a harsh manner, which interferes with the detection of membrane NTCP. The latter is further hampered by the availability of suitable anti-NCTP antibodies for IF staining. In our study, we used high doses of fluorescence-conjugated MyrB peptide to stain NTCP, but unfortunately it is very sensitive to the harsh permeabilization detergents mentioned above. However, since we have meanwhile optimised HDV infection, we will likewise try again to optimise the staining Revision Plan protocol. If we succeed, we will repeat the co-staining of NTCP and HDAg and include it in a revised manuscript. Figure 4: While the strategy is interesting, based on what has been previously shown for HCV in Wu et al., 2012, the lack of statistical data prevents the reader to really understand and see drastic difference in term of susceptibility to infection and level of expression of host genes. In panel C, is the difference between day 13 and 15 statistically significant? Same for panel D, day 17 vs 19?As a remark, day 19, the peak of susceptibility to HDV, seems to be also the peak of maturation, based on ALB RTqPCR (panel B). Thank you for this comment, and will perform another set of experiments allowing us to calculate statistical significance. The Reviewer correctly points out the correlation between HDV infection and hepatocyte maturity, which we find very intriguing. To identify potential host co- or restriction factors expressed in highly mature HLCs, we then performed the differential gene expression analysis (Figure 5). As shown in the new Figure 5A, GO analysis revealed that genes involved in pathways regulating viral entry into host cells were most significantly upregulated in mature HLCs and, as a probable consequence, they were more permissive to HDV infection. Indeed, among these factors, we identified CD63 as a novel host cofactor that renders mature HLCs susceptible to HDV infection (Figure 6). In response to Reviewer 2: We thank the Reviewer for their assessment of our study and for critically pointing out the increments over the previous study by Lange et al. We also appreciate their helpful suggestions, which allow us to improve the manuscript. The manuscript would benefit from a more detailed virological analysis, such as: •Determination of HDV genome and antigenome sequences and analysis of HDV editing. We thank the Reviewer for this comment. Accordingly, we will determine HDV genomes and antigenomes by Northern blot analysis and study HDV editing rates by sequencing in HDVinfected HLCs. •Analysis of HDV short and large antigens by western blot. We have already detected small and large HDAg in HDV-infected HLCs (see Revision Figure 2). To also satisfy Reviewer 3, we will additionally compare the S/L-HDAg ratios over time in HLCs, dHepaRGs, and Huh7-NTCP cells and include the results in a revised manuscript. Revision Figure 2: Detection of small and large delta antigen in HDV-infected HLCs. Mature HLCs were infected with HDV (MOI= 5 Int. Units/cell) and harvested 1 or 3 days post-infection. Cell lysates were analysed by Western blotting using antibodies against HDAg and b-actin. Revision Plan •Analysis of HBV-related virological parameters in monoinfected and co-infected cells. We agree with the Reviewer and we will include the characterisation of more HBV-related virological parameters in our mono- and co-infected HLCs. Accordingly, we will assess HBV cccDNA, RNA, and DNA by RT-qPCR, as well as released HBsAg and HBeAg via ELISA and add the results to the revised manuscript. In response to Reviewer 3: We thank the Reviewer for their positive evaluation, and we acknowledge their helpful comments, which will help us to improve our manuscript. Line 143: the authors describe two forms of HLCs (less and more confluent with differences regarding the susceptibility to HDV infection). The characteristics of the less and more confluent HLCs should be described in more detail-what is causative for the differences in susceptibility for HDV infection of these two forms? We thank the Reviewer for this comment. We likewise find this observation intriguing. As stated in our response to Reviewer 1, we have ruled out that NTCP and/or other mature markers such as ALB are differentially expressed between the two subpopulations. As one factor that could make a difference, we have identified CD63, which is highly expressed in the high-density HLC population and less so in the low-density HLC population (Figure 6G). Nevertheless, we will separate the two populations and analyse by RT-qPCR the expression of other known HBV and HDV host co-factors that may be additional factors governing the increased susceptibility of the highly dense HLC population. The statistical analyses should be improved: There are no p-values provided for the data presented in the supplement and a variety of figures lacks p-values We have added p-values to the Supplementary Figures (see revised Supplementary Fig. S2) and will repeat the experiments for Fig. 4 and Supplementary Fig. S1B and Fig S3 so that we can calculate the corresponding p-values. Kinetic of the infection: Here it would be interesting to see a comparative analysis by western blot investigating the ratio HBsAg/HDAg over the time in HLCs, HepaRGs and NTCP oe cells We thank the Reviewer for his comments. As stated in our response to Reviewer 2, we will perform this WB analysis to detect S/L-HDAg over time in infected HLCs, dHepaRG, and Huh7- NTCP cells. Line 157: What is the experimental evidence for the proper localization and functionality of the ectopically expressed NTCP in HLCs. Did the authors study the taurocholate transport after overexpression of NTCP? We thank the Reviewer for this comment. We analysed endogenous and ectopic NTCP expression by microscopy using a fluorescently conjugated peptide Atto-MyrB-565, which specifically binds to the ectodomain of human NTCP (Figure 2D) and found that both Revision Plan endogenously and ectopically expressed NTCP are located on the cell surface. To further confirm the correct localisation, we will perform NTCP co-staining with a cell membrane marker. We will also test the proper function of the ectopically expressed NTCP using a specific taurocholate transport assay as shown in our previous study (Ni et al, 2014, Gastroenterology). Line 169: The authors should include data comparing the number of double positive cells in HLCs, HepaRGs and Huh7NTCP o.e. expressing cells under the chosen experimental conditions We thank the Reviewer for this suggestion. We have already performed HBV/HDV co-infection of dHepaRG cells (Revision Figure 3) and we will perform the same experiment with Huh7-NTCP cells. Revision Figure 3: HBV/HDV co-infection of dHepaRG cells. Differentiated HepaRG were infected with HBV (MOI = 450 genome copies/cell) and HDV (MOI = 5). Cells were stained against HBV core (HBc), HDAg, and nuclei (DAPI) ten days p.i.. HBc- and HDAg-positive cells were counted using Cellprofiler imaging software to quantify HBV (pink) and HDV (green) single and co-infection (white) events. Images are representative of three independent differentiations. Line 291: expression analysis by RT-PCR is not sufficient. It will be important to study by CLSM if the identified factors are really present as proteins and properly localized. To satisfy this Reviewer, we will be happy to perform WB analysis of lysates from cells obtained at different stages of HLC differentiation to detect LDLR, LAMP1 and SR-B1 to further confirm our transcriptome analysis. As protein expression is easier to compare by WB analysis, we prefer this method to microscopic analysis. Regarding the role of CD63: what is the evidence for a direct role of CD63 for HDV entrycan the authors exclude that CD63 is relevant for targeting other factors to the surface? What is the impact of loss of CD63 on the functionality of the autophagosomal-MVB-EV system in HLCs? Since downregulation of CD63 before but not after impairs HDV infection, we conclude that CD63 is likely to be important for the early steps of the HDV life cycle, namely cell entry. Indeed, we speculate that CD63 may be critical for HDV trafficking to the vesicle, where fusion of the HBV glycoproteins is induced to allow capsid entry, based on the following observations: Although neither the precise site of HBV viral fusion nor the cues that induce fusion are currently fully understood, studies suggest that HBV can be co-transported with EGFR and NTCP to late endosomes for trafficking (Herrscher et al; 2020, Cells). We speculate that similar to what has been described for Lujo virus, CD63 may be involved in either HDV trafficking and/or virus fusion in the endosomal system (late endosome or lysosome) (Tominaga et al, 2014 Molecular cancer). Revision Plan CD63 is a ubiquitously expressed protein that localises to the endosomal system and, in its glycosylated form, to the cell surface. Non-glycosylated CD63 is not properly trafficked and aggregates at the nuclear periphery instead of the cell membrane (Tominaga et al., 2014, Molecular Cancer). According to the Western blot analysis in Figure 6, immature HLCs appear to express less glycosylated CD63 than mature HLCs. We will confirm the glycosylation by treating the cell lysates with PNGase F. Although AAV transduction enhanced CD63 expression of all three HLC stages tested (see new Supplementary Figure S6 in the revised manuscript), it only enhanced HDV infection of immature HLCs, in which the non-glycosylated form of CD63 appears to be the predominant form. To demonstrate that the glycosylated form of CD63 is involved in HDV entry, we will rescue WT CD63 in parallel with a glycosylation-deficient CD63 mutant (Yoshida et al., 2009, Microbiology and Immunology) in immature HLCs. We will also stain CD63 in both immature and mature HLCs to compare the subcellular localisation (plasma membrane/endosomes vs. nuclear membrane) of CD63 between the two stages.
3. Description of the revisions that have already been incorporated in the transferred manuscript Based on the constructive comments by the Reviewers we already made the following changes, which are highlighted in red in the revised manuscript. In response to Reviewer 1: Fig 1B-C: the comparison with dHepaRG is very interesting, and confirms the validity of SC derived hepatocytes as a model for HDV infection. dHepaRG can be heterogeneous. Do you also see the same phenotype of enriched HDV infection within a denser subpopulations of dHepaRG We thank the Reviewer for their comment. Undifferentiated bipotent HepaRG cells are not permissive for HDV infection due to the lack of surface NTCP expression. Due to their bipotent nature and upon differentiation, two morphologically distinct populations become apparent: hepatocyte-like cells and biliary epithelial-like cells (McGill et al., 2010, Hepatology). As shown in the Figure 1 of the study by Mesnage et al. (2018, Molecular Toxicology), dense hepatocyte-like colonies are surrounded by clear epithelial cells corresponding to primitive biliary cells. In agreement with other studies, we only observe that the ALB-positive hepatocyte-like cells are permissive to HBV and HDV infection (Hantz et al., 2009, Journal of General Virology), highlighting their specific hepatic tropism and the cellular determinants required. Fig 1I is confusing. Was BLV assay also performed on the HLC infection (Day 0), or only during the titration assay in Huh7NTCP? We apologise for the confusion in this panel. BLV was only added during the titration assay on Huh7NTCP cells to confirm new and productive infections and to rule out carry-over. We have changed the order of Figures 1I - 1K to make this clearer and explain this better in the new results section (line 171-179) and figure legend (line 797-806). Revision Plan Fig 1K: x-axis is confusing... is it number of HBV, HDV and HBV/HDV positive cells? Or number of infected cells upon inoculation with HBV, HDV, or both? Please clarify. We apologise for this additional confusion caused in this panel. We infected HLCs with both HBV and HDV simultaneously and then counted the number of positive cells that were either single infected with HBV (pink cells/column), single infected with HDV (green cells/column) or double infected with both viruses (white cells/column). We have clarified this in the revised Results section (line 172-176) and in the revised Figure Legend (line 798-803). Figure 2: The AAV based vector to over express HBsAg is a very interesting tool, and the figure convincingly show production of HDV progeny viruses in HLC-AAV-HBsAg. Results shown are in agreement with previous studies based on hepatoma cell lines. We thank the Reviewer for this positive comment and we agree that AAVs represent interesting tools to genetically manipulate HLCs and other hepatocyte culture systems. Figure 2B: What is IU/ml? Infectious Unit? International Unit? Are units in Fig 1B, 2B and 2C the same? We apologise for the lack of clarity. In Figures 1B and 2C, IU corresponds to infectious units of HDV, whereas in Figure 2B, IU corresponds to international units for the assessment of secreted HBSAg levels in the supernatant. To make the difference clearer, we have changed the unit on the y-axis in Figure 2B and explicitly stated the abbreviations in the corresponding revised Figure Legends (lines 785, 786, 794, 795, 816, and 819). Figure 3: What is the overall number of transmission events observed in the co-culture setup? Can you visually observed viral spreading? Panel A shows only 1 event, making it hard to assess its efficiency. Titration assay in Fig 2C show production of up to 4-5 log of infectious HDV. But HLCs susceptibility to HDV infection may change during time... Thank you for your comment and for raising this important issue. Panel A clearly and visually demonstrates that extracellular spread of HDV had occurred in the HLCs system, as initially only WT and non-GFP positive HLCs were infected with HDV. After co-culture, the progeny of WT HLCs were able to infect GFP-HLCs (Figure 3A). The overall efficiency of HDV spread/transmission in HLC efficiency is shown in Figure 3C. If we allow spread to occur (DMSOtreated condition), the total number of HDV-positive HLCs grown in a 24-well plate is approximately 1000. When we block secondary infection of progeny with BLV and thus spread, we count only about 500 HDV-positive HLCs in a well. In general, spreading in HLCs (Figure 3C) is not as efficient as retitration to Huh7-NTCP (Figure 2C) for the following reasons: In Figure 2C, we wanted to have an estimation of the maximum amount of secreted infectious progeny from HDV-producing HLCs. To this end, we did not want the re-infection itself to be a major bottleneck and used the most susceptible model Huh7-NTCP and infected them under the best conditions, which includes the addition of 4% PEG and 2% DMSO in the culture medium. For our spread assay in HLCs, we cannot add PEG to the cells over the course of the experiment and we also wanted to be as physiological as possible. PEG significantly enhances HDV infection Revision Plan of HLCs (Supplementary Fig. S2) and Huh7-NTCP cells (Revision Figure 4), which is in agreement with previous studies (Michailidis et al., 2017, Scientific reports). In addition, as the Reviewer correctly points out, similar to other primary hepatocyte culture models, the HLC system deteriorates over time. However, we have found that HLCs can be cultured for up to 3 weeks. Nevertheless, we believe that the efficiency of HDV spread in HLCs is sufficient for drug testing (Fig. 3C & D). Revision Figure 4: PEG enhances HDV infection of Huh7-NTCP cells. Huh7- NTCP cells were infected with HDV (MOI= 5 Int. Units/cell) in the absence or presence of PEG. Cells were harvested on D5 pi and HDV genome copies were quantified by RT-qPCR. Figure 5: In panel A, GO pathways should be sorted based on significance, not Number of genes. In panel B-D, what is the scale of the heatmap on figure 5: change in CPM values, however log2, log10? Thank you for this comment, we have sorted the GO pathways based on significance (new Figure 5A). For panels B-D, we did not calculate the fold change in CPM values and they were not log transformed. Instead, we calculated the z-scores of the genes shown by comparing the expression level of a given gene (in CPM) in a given sample with the expression level of that gene across all samples. To avoid further confusion, we have added "z-score" to the new Figure 5. Figure 6: Do you have info about CD63 in other mature model, like dHepaRG and PHHs? Is CD63 also limiting in these models? Our data in Figure 6 suggest that CD63 may be a limiting factor for HDV infection of immature HLCs but not mature HLCs. Both dHepaRG cells and PHHs are mature hepatocyte models and therefore we speculate that CD63 is not rate limiting. However, we will investigate whether CD63 is rate-limiting in undifferentiated HepaRG cells. In response to Reviewer 2: Additional information that needs to be added, better explained, or corrected: The authors should explain why they used different MOIs depending on the genotype. In our previous study by Wang et al. 2021 J Hepatol, we found that the different HDV genotypes are heterogeneous in their ability to infect Huh7 NCTP cells. For example, as shown in Figure 4B of Wang et al. 2021 J Hepatol, GT 4 and 5 are less infectious than other genotypes. Based on the different infectious titres of the genotypes obtained on Huh7 NTCP cells, we then decided to use different MOIs for infection of our HLCs. The aim of the present study by Chi et al. was not to Revision Plan compare the different HDV genotypes, but to analyse whether they can all infect HLCs. In order to obtain similar infection efficiencies of our HLCs with the different genotypes, we used higher MOIs for those genotypes that were less infectious in Huh7-NTCP cells compared to those genotypes that were more infectious in Huh7-NCTP cells. We apologise for not making this sufficiently clear and have added this information to the results section (line 167-170) and the corresponding figure legend (line 796) of the revised manuscript. In Figure 1, it is unclear on which day the HCLs were infected by HDV and on which day they were transduced with AAV-NTCP. We apologise for the lack of clarity in the experimental design. We transduced HLCs with AAV two days before HDV infection to ensure sufficient ectopic NTCP expression on the day of HDV infection to study its effect on HDV entry. We have clarified this in the results section (line 153, 156) and in the figure legend (line 788) in the revised manuscript. It is not very clear if the authors used AAV serotype 6 consistently to transduce the cells. It would be valuable to show the transduction efficiency of AAV at different time points of HLC maturation, as it might also be affected and could explain some results. For example, in Figure 6H, why does AAV-CD63 transduction increase HDV infectivity at day15 but not at day 10? It would be interesting to repeat the anti-CD63 western blot after AAV-CD63 transduction. Thank you for this comment. Yes, we have consistently used AAV 6 due to its relatively broad tissue tropism (Verdera et al., 2020, Molecular Therapy) and we have clarified this information in the revised manuscript (see line 331). We agree with the Reviewer's concerns regarding the variable transduction efficiency. We have previously tested different AAV capsids and found that AAV6 transduced mature HLCs at high levels (Zhang et al., 2022, Hepatol Commun). In this study, we also performed Western blot analysis to confirm successful CD63 overexpression by AAV transduction at different stages of hepatocyte differentiation. As shown in new Supplementare Figure 6, although there were some differences in transduction efficiency, the majority of all cells at each stage of differentiation were successfully transduced to ectopically express CD63. The authors claim that by using AAV to express HBsAg, they are mimicking the expression of HBsAg from the integrated sequence rather than cccDNA. However, it is the opposite, as AAV genomes, like cccDNA, remain as episomes in the cells. Yes, the Reviewer is conceptually correct and we apologise for the incorrect wording. In principle, we aim to trans-complement HBsAg in a setting outside of HBV infection and thus mimic the expression of antigen from integrated cells, although AAVs of course remain mostly episomal. We have clarified this in the revised manuscript (see lines 188 & 378). In response to Reviewer 3: Line 217: the complete inhibition of cell to cell spread by myrcludex suggests that there is no spread by cell-cell contact. This should be discussed. Revision Plan Yes, there is no evidence of HDV spread by cell-cell contact because, as the Reviewer correctly points out, BLV treatment almost completely blocked HDV de novo infection (Figure 2D & E). To our knowledge, cell-to-cell spread has not been demonstrated for HDV. According to our own studies by Zhang et al, 2021/2022, Journal of Hepatology, HDV spreads either extracellularly (which can be blocked by BLV) or by cell division (discussed in lines 362). Since HLC are similar to primary human hepatocytes and do not divide in vitro, we believe that extracellular spread is the predominant mode of spread in HLC (line 365). Line 210ff:Is there any evidence for syncytia formation in this system? No, we have not observed syncytia formation. Since HDV has no glycoproteins, we would not expect syncytia to form. Line 42: secrete should be replaced by release We thank the Reviewer for pointing out the inaccuracy in our terminology. We have replaced "secrete" with "release" (line 42). Line 241: proteins are not expressed, genes are expressed Thank you, we agree and changed the wording accordingly (line 246).
4. Description of analyses that authors prefer not to carry out In response to Reviewer 1: Fig 1B: Unit is confusing, using terms usually used for titration of infectivity, from the virus input point of view, not from the cellular point of view. Can you use % infected cells instead, or "HDV infection rate" like in Supp Fig 1B? We apologise for this confusion. For other viruses, such as but not limited to HCV or HEV, the most common method is to report focus forming units per ml (FFU/ml). HLCs do not divide and, in the absence of HBV S antigen, no cell-division mediated HDV spread can occur and only single infection events can be observed (hence infectious unit = IU/ml). Since differentiated, authentic hepatocyte culture models such as PHHs, HLCs or HepaRG cells are always characterised by strong cell heterogeneity, it is difficult to directly compare the overall percentage of infection with a homogeneous cell population such as Huh7-NTCP cells. Therefore, if the Reviewer allows us, we prefer to keep this unit in our main figures. However, and hopefully to the satisfaction of this Reviewer, we have also calculated the percentage of infected cells of this exact dataset and show it in the Supplementary figures (Suppl. Fig. S1 C). The proportion of infection efficiency comparing HLCs, dHepaRGs, and Huh7-NTCP cells does not differ when presented either as IU/ml or as percentage of infected cells.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In this manuscript, the authors report a novel and simple method to analyze the heterogeneity of various organelles. After imaging a large set of fluorescent-marker-labeled organelles, cluster analysis is adapted for illuminating the dynamics of organelles. Through this novel method, the authors are able to report organelle contact, which previously can only be observed by super-resolution imaging. This is method could significantly accelerate future discoveries at the cellular level. The manuscript is well written and has the potential be published in high-ranking journals, after a minor revision.

To further demonstrate the unique power of this new method, the authors should test cells under known stimulation altering the dynamics of organelles. For instance, wortmannin can blocks the conversion from early endosomes to late endosomes. By doing that, the potential of this new method will be endorsed.

Response:

We thank Reviewer #1 for the positive comments. We will add an experiment using wortmannin to block the process of endocytosis at a specific stage, as part of the experiments analyzing the process of endocytosis.

**Minor issue:** The authors should include more details about how to avoid signal crosstalk between adjacent fluorescent channels.

Response:

In the Methods section, we have added the following sentences to Lines 398-405.

“In order to avoid signal crosstalk between adjacent fluorescence channels, eight fluorophores with distinct spectral distances were selected, and the samples were irradiated sequentially with lasers in the order from the longest wavelength, i.e., fluorescence from 646 to 731 nm was excited by a 640 nm laser, fluorescence from 569 to 634 nm was excited by a 561 nm laser, fluorescence from 494 to 554 nm was excited by a 488 nm laser, and fluorescence from 411 to 481 nm was excited by a 405 nm laser, as shown in Extended Data Fig. 1b.”

Reviewer #1 (Significance (Required)):

The comprehensive monitoring of organelle dynamics through the integration of multi-dimensional parameters can proficiently evaluate the condition and prognosticate the destiny of living cells in response to external stimulations. This new multi-dimensional assay reported in this manuscript represents a huge step towards this goal. Since this new method is simple and powerful, cell biologists will quickly start to use this new method for the study of subcellular dynamics.

My lab is also developing a similar approach for organelles based on super-resolution imaging. I would like to congratulate the authors for this beautiful work.

Response:

We thank Reviewer #1 for the positive comment.

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

The manuscript reports a multi-parametric particle-based method for analysis of organelles. The method aims to resolve heterogeneous populations of organelles involved in various cellular processes. They propose to isolate organelles labelled with multiple markers, after homogenization and sonification of the cells, and analyse the resulting particles by fluorescence microscopy using spectral imaging. Afterwards, the authors visualize and analyse the obtained data with dimension reduction techniques.

Even though an interesting approach, the method and presented applications needs major improvisations before it can prove to be impactful for the field

I note some possible improvement points below:

• Initially, I think the current set of cell lines and labels should be extended also to include a wider set. The current limited set raises the question if the method authors report is also applicable to other cell lines, or if it only feasible with overexpressed markers. Including different cell lines with different labels would make the study more convincing and comprehensive.

Response:

We thank Reviewer #2 for this constructive comment. Regarding cell types, we will conduct experiments with HEK293T cells in addition to HeLa cells, labeling at least five different types of typical organelles. In our method, as shown in Figure 1a and 5a, we have already used not only overexpressed markers but also fluorescently labeled ligands (EGF-Alexa, transferrin-Alexa) and antibodies against endogenous proteins (anti-PMP70, anti-LAMP1), as well as direct labeling of cell membrane proteins (Alexa-NHS). Therefore, there are no significant limitations with respect to organelle labeling methods.

• It is surprising that the authors explicitly list already the limitations of fluorescence microscopy and super-resolution microscopy in the second paragraph of their introduction, however present a method fully dependent on fluorescence labelling and imaging methods. Actually their approach takes away the spatial information of FM approaches, and further makes the approach prone to the limitations they state.

They are also not fully fair about the limitation they state for Electron microscopy, as newly developed approaches (e.g. doi:10.1093/micmic/ozad067.1091; 　doi:10.1126/science.aay3134) widely extend the limited field of view and sampling capacity of EM. I recommend the authors to state the potential advantage/superiority of the reported method rather than stating the unclear limitations of the existing powerful methods.

Response:

Regarding fluorescence microscopy, it appears that our description was inadequate and misled the reviewers. There is no problem with fluorescence microscopy itself. What we intended to convey was that “when attempting to detect individual organelles ‘in cells’, there are limitations in the resolution of fluorescence microscopy because organelles are densely packed”. We have added this to the text on Line 49. Also, we thank Reviewer #2 for informing us about the high-speed 3D electron microscopy. We have cited the indicated papers in the text at Lines 54-55 and mention that “except for the recently developed high-throughput electron microscopy”.

• Most organelle markers the isolation of organelles are based on are overexpressed in the cells: endoplasmic reticulum (ER, mTagBFP2 (BFP)-SEC61B), mitochondria (GFP-OMP25 and SNAP-OMP25), and the Golgi (Venus-GS27). This raises significant questions about the native state relevance of the reported results, and how well they represent the endogenous processes.

Response:

We will add experiments analyzing the behavior of both endogenous and exogenous markers for the same organelles, for example, anti-LAMP1 antibody and VAMP7-GFP for lysosomes, and anti-PMP70 antibody and PEX16-GFP for peroxisomes.

• For the application on endosomes, can the authors state what is the new information enabled by their method? They study the very trafficking of EGF and Transferrin, 2 widely used endosomal cargoes with very well characterized trafficking steps, and show they are trafficked through Rab5/7 and Rab11 positive endosomes, respectively. This recapitulates the existing information, however falls short in delivering new insight. The authors can use these cargoes for proof-of-concept, but I would recommend to extend their study with less exploited cargoes to represent the potential of the reported method to deliver new information.

Response:

We thank Reviewer #2 for the positive suggestion about the potential of our method to provide new information. However, to demonstrate new biological insights, it would take a lot of time and delay the provision of our methodology, so we would like to submit this manuscript as a Methods paper with the proof-of-concept data.

Reviewer #2 (Significance (Required)):

The significance of biochemical and cellular processes being spatially regulated cellular organelles, and the roles of specific organelles in diseases from cancer to neurodegeneration are continuously being discovered and appreciated. Therefore development of methods reporting on the structure and function of organelles is important to accelerate these studies. In the reported method, however, the ultrastructure (as in Fib 1b) and the spatial information of the cellular organelles are inherently lost. The method falls in between a biochemical and a microscopic approach, however the advantages are not clearly portrayed. I recommend the authors to carefully and explicitly state where their method would be the method of choice rather than a biochemistry, mass spectroscopy, or microscopy approach. The authors should critically consider such an experiment as a proof-of-concept case.

Response:

We thank Reviewer #2 for the valuable suggestion. We added the following to the Discussion (Lines 267-277).

“A further potential application of our method would be to measure how the levels of key molecules in an organelle change during its differentiation or maturation. For example, the levels of PI4P and syntaxin 17 change during autophagosome maturation (Shinoda et al. eLife Preprint Review doi.org/10.7554/eLife.92189.1), which can be better demonstrated by this method using multiple markers for each stage of autophagosome formation and maturation, PI4P, and syntaxin17 because autophagosomes at different stages coexist in cells. In such cases, our single-particle analysis method, which examines the state of individual autophagosomes, would be more appropriate than biochemical methods that examine averages. In addition, it is difficult to quantitatively analyze many organelle structures in cells using fluorescence microscopy. Our particle-based analysis method can overcome this problem.”

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

**Comments, suggestions, and questions**

• I would like to start with a positive suggestion. The authors completely miss out on the opportunity to promote their approach by not relying on any type of fixation. In most multiplexing experiments, the first major challenge is to find antibodies that work well for imaging. The second challenge is then to find antibodies that work well under the same fixation conditions. The authors present a multiplexing approach that is completely independent of fixation. I suggest discussing this in the manuscript and promoting the approach in that regard.

Response:

We thank Reviewer #3 for pointing out the advantages of our method. We have added “Our method that is independent of fixation is advantageous for the optimization of the staining condition (Lines 298-299).

• I am wondering what defines the ‘resolution’ of this approach. I assume it is a combination of the sonication time -the longer the cell is sonicated, the smaller the fragments are - and the density of particles on the coverslip. What are the limits here? How does this affect the UMAP analysis? I would encourage the authors to discuss this in the manuscript.

Response:

The particle density on a coverslip can be easily reduced by simply diluting the particles in a buffer solution. Therefore, there is no density limit, which is an advantage of a cell-free system. To improve the resolution within a single organelle, for example, to separate distinct subdomains, as the reviewer mentioned, we can prolong the sonication time to make the particles smaller. However, since this will reduce the signal-to-background ratio and destroy organelle contacts, we used the sonication conditions as mild as possible. To investigate organelle subdomains and fragile contacts, the sonication conditions need to be optimized carefully, which should affect the UMAP analysis, but we think that these will be future work.

We do not think that prolonged sonication will affect the UMAP analysis because relative fluorescent signals of each particle would not change. However, as mentioned above, too strong sonication would worsen the signal-to-noise ratio, resulting in poor clustering.

We have added the above discussion to the Discussion (Lines 288-293).

“Also, to improve the resolution within a single organelle, for example, to separate distinct subdomains, we can prolong the sonication time to make the particles smaller. However, since this will reduce the signal-to-background ratio and may destroy organelle contacts, we used the sonication conditions as mild as possible. To investigate organelle subdomains and fragile contacts, the sonication conditions need to be optimized carefully.”

• The only real control the authors present are the correlative light and electron microscopy (CLEM) three images in Figure 1b, which seems very minimalistic for a very central and essential control experiment. How many of these control images did the authors take? Is there possibly a second method for a control experiment to link the fluorescence readout to an organelle fragment (e.g., purification or pulldown)?

Response:

Since all the markers we used are well-established, we believe that there is no concern about the fluorescence readouts to the organelle fragments. We have cited the following papers in Lines 84-85.

SEC61B: Rapoport, T. A., Jungnickel, B. & Kutay, U. Protein transport across the eukaryotic endoplasmic reticulum and bacterial inner membranes. Annu Rev Biochem 65, 271–303 (1996).

OMP25: Horie, C., Suzuki, H., Sakaguchi, M. & Mihara, K. Characterization of signal that directs C-tail-anchored proteins to mammalian mitochondrial outer membrane. Mol Biol Cell 13, 1615–1625 (2002).

GS27: Hay, J. C. et al. Localization, Dynamics, and Protein Interactions Reveal Distinct Roles for ER and Golgi SNAREs. J Cell Biol 141, 1489–1502 (1998).

Fusella, A., Micaroni, M., Di Giandomenico, D., Mironov, A. A. & Beznoussenko, G. V. Segregation of the Qb-SNAREs GS27 and GS28 into Golgi Vesicles Regulates Intra-Golgi Transport. Traffic 14, 568–584 (2013).

Although it is relatively easy to identify mitochondria-derived particles by EM based on their size and the presence of cristae-like structures (indeed we see many examples), it is more challenging for other organelles (because they appear simple vesicles). This is why we showed only mitochondria in Fig. 1b. Furthermore, the main purpose of this EM image is to show membrane contacts between the ER and mitochondria (related to Fig. 3).

• Line 37-41: Could the authors please strengthen these statements with an appropriate citation (e.g., a review)?

Response:

We have cited the textbook Molecular Biology of THE CELL (the 6th edition, Chapter 12 and Chapter 13) in Lines 37 and 41.

• Line 51: The statement, “Super-resolution microscopy could partially solve the resolution problem, but it is currently limited to four-color imaging,” is incorrect. Agasti et al. demonstrated up to nine target multiplexed super-resolved imaging with DNA-PAINT in 2017 (https://pubs.rsc.org/en/content/articlehtml/2017/sc/c6sc05420j). Additionally, two papers currently on Biorxiv demonstrate 12 target and 30 target multiplexed super-resolution imaging with FLASH-PAINT (https://www.biorxiv.org/content/10.1101/2023.05.17.541061v1.abstract) and SUM-PAINT(https://www.biorxiv.org/content/10.1101/2023.05.17.541210v1.abstract). Please cite these papers accordingly.

Response:

We thank Reviewer #3 for notifying us of these important studies. We have rewritten the sentence on Lines 51-52 to read “Although multicolor imaging has been attempted with super-resolution microscopy (references of the indicated papers), it only partially solves the issue of resolution.”

• The authors use spectral unmixing to overcome the limit of spectral multiplexing. While this has been demonstrated to work well for less than ten targets, it does not scale to multiplexing experiments with more than ten target species. I suggest that the authors discuss in the discussion part of the manuscript the potential of DNA-based multiplexed imaging, such as CODEX or DNA-PAINT, in combination with the presented approach.

Response:

In the Discussion (Lines 295-298), we have added the sentence “Current fluorescent particle detection uses spectral multiplexing, but this method has only been able to detect up to eight colors. Methods such as CODEX or DNA-PAINT with wide-field type illumination could significantly increase the number of targets”.

• Regarding the spectral unmixing approach, please cite previous work described in the literature (e.g.,https://www.nature.com/articles/nature22369, or earlier work).

Response:

We thank Reviewer #3 for informing us. We have cited it in Line 72.

• By using spectral unmixing for multiplexing, this method is limited to confocal due to spectral detection needs and therefore limited in throughput. It would be beneficial if it could work with wide-field type illumination. This could substantially increase the throughput, which is another reason why I think it would be important to discuss sequential multiplexing.

Response:

We agree with the Reviewer’s comment. We have added the discussion to Lines 295-298 as described in our response to Reviewer #3, Comment (6).

• To image contact sites, the authors use split GFP. There have been discussions that split GFP might, in some cases, facilitate the process that is supposed to be measured, in this case, the formation of contact sites. I suggest using at transient version of split GFP, called split fast, for follow-up experiments in the authors’ next papers (https://www.nature.com/articles/s41467-019-10855-0).

Response:

We thank Reviewer #3 for providing this information. We will do it as suggested in the next paper.

• Line 27 & 253: Please drop the term ‘intuitive’ or explain better what you mean by intuitive. For me, UMAP is certainly a very useful tool, but it is not at all what I would describe as intuitive.

Response:

We have deleted ‘intuitive’ in all seven places and rewritten them (Lines 27, 43, 58, 72, 180, 231, and 253).

• Lastly, I want to mention that the authors state they used ChatGPT, DeepL, and DeepL Write for translation from Japanese to English. I appreciate their honesty.

Response:

We thank Reviewer #3 for the comment.

Reviewer #3 (Significance (Required)):

In the manuscript titled “Organelle Landscape Analysis Using a Multi-parametric-Based Method,” Kurikawa et al.present a method for multi-parametric, particle-based analysis of cellular organelles. After lysing cells, the fractions of the organelles are partially labeled with fluorescently tagged antibodies, while others are already tagged with fluorescent proteins, using six to eight spectrally different fluorescent dyes/proteins. These fractions are subsequently immobilized on a poly-L-lysine-coated coverslip. The authors use spectral unmixing to distinguish these markers. The6-8 multiplexed imaging data is then presented in two-dimensional UMAP space. The authors then use this approach to visualize seven major organelles, transitional sites of endocytic organelles, and contact sites between the endoplasmic reticulum and mitochondria using split GFP.

The authors present, in my opinion, a conceptually new and interesting approach by combining spectral unmixing for imaging up to eight targets, with organelle fragment imaging, and presenting multidimensional data in two-dimensional Uniform Manifold Approximation and Projection (UMAP) space in this manuscript. They further validated this approach by linking the results of the experiments to results established or at least reported in the literature.

In general, the manuscript is, in my opinion, a good fit for publication as it presents a conceptionally new approach and an interesting example of applying the UMAP approach, which might be of interest to a broader readership. Therefore, after an appropriate response to my comments, suggestions, and questions (see below), I would recommend this manuscript for publication.

Response:

We thank Reviewer #3 for the positive comment.

Author Response

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

eLife assessment

This work presents important findings for the field of Alzheimer's disease, especially for the electrophysiology subfield, by investigating the temporal evolution of different disease stages typically reported using M/EEG markers of resting-state brain activity. The evidence supporting the conclusions is solid and the methodology as well as the descriptions of the processes are of high quality, although a separation of individuals who are biomarker positive versus negative would have strengthened the interpretability of the results and the conclusions of the study.

Response: Thank you for the positive assessment of the paper.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aimed to infer the trajectories of long range and local neuronal synchrony across the Alzheimer's disease continuum, relative to neurodegeneration and cognitive decline. The trajectories are inferred using event-based models, which infer a set of data-driven disease stages from a given dataset. The authors develop an adapted event-based modelling approach, in which they characterise each stage as a particular biomarker increasing by a particular z-score deviation from controls. Fitting infers the optimal set of z-scores to use for each biomarker and the order in which each biomarker reaches each z-score. The authors apply this approach to data from 148 individuals (70 cognitively unimpaired older adults and 78 individual with mild cognitive impairment or Alzheimer's disease), identifying trajectories in which long-range (amplitude-envolope correlation) and local (regional spectral power) neuronal synchrony in the alpha and beta bands becomes abnormal prior to neurodegeneration (measured as the volume of the parahippocampal gyrus) and cognitive decline (measured using the mini-mental state examination).

Strengths:

• The main strength is that the authors assess two models. In the first they derive a staging system based only on the volume of the parahippocampal gyrus and mini-mental state examination score. They then investigate how neuronal synchrony metrics change compared to this staging system. In the second they derive a staging system that also includes an average (combined long-range and local) neuronal synchrony metric and investigate how long-range and local synchrony metrics change relative to this staging system. This is a strength as the first model provides confidence that there is not overfitting to the neuronal synchrony data, and the second provides more detailed insights into the dynamics of the early neuronal synchrony changes.

• Another strength is that the authors automatically infer the optimal z-scores to choose, rather than having to pre-select them manually, as in previous approaches.

Response: Thank you for the positive comments and a succinct summary of the paper and its strengths.

Weaknesses:

• The dataset is small and no external validation is performed.

Response: We agree that future validation studies of the predictions are necessary. We now include the related sentences in the last paragraph of the limitations section in the revised manuscript.

• A high proportion of the data is from controls (nearly 50%) with no biomarker evidence of Alzheimer's disease, and so the changes may be driven by aging or other non-Alzheimer's effects.

Response: We would like to clarify that the z-scores of the metrics used in the EBMs were computed using age-adjusted values. All our controls were recruited from an ongoing longitudinal study of healthy aging. Amongst the 70 controls, 39 have confirmed A-beta negative PET scans and 8 were confirmed A-beta positive PET scans, and in the rest of the 23 we do not have any biomarker data available. However, in all the controls, we have conducted comprehensive neuropsychological assessment (see Appendix 1—table 1 in the revised supplementary file) and based on this data we can be quite confident about their lack of clinical deficits, and we have a very high degree of confidence that none of the controls have any neurodegeneration (AD-related or otherwise). Consistent with this assessment, in our EBM analyses, most of the control participants were indeed categorized to the preclinical stages.

• Inferring the optimal z-scores is a strength, however as different sets of z-scores are allowed per biomarker, there is a concern that the changes reflected are mainly driven by the choice of z-score, rather than the markers themselves (e.g. if lower z-scores are selected for one marker than another, then changes in that marker will appear to be detected earlier, even if both markers change at the same time).

Response: Indeed, the biomarker sequence depends on the choice of the z-scores per biomarker. However, please note that our choice of z-scores is based on maximizing the sequence likelihood. Therefore, other values of the z-scores will have by construction a smaller likelihood of sequence occurrence compared to the results shown.

• In equation 2 it is unclear why the gaussian is measured based on a sum over I. The more obvious choice would be to use a multivariate gaussian with no covariance, which would mean taking the product rather than the sum over I.

Response: We thank the reviewer for pointing this out and we now clarify this point. In this revision, we do not use the term ‘multivariate’. Indeed, the model likelihood assumes independence for each metric’s priors, and hence is the product of each metric’s univariate gaussian probability distribution. This can be seen in equations 1 and 2 of the revision manuscript (Section titled “Event-based sequencing modeling’). The assumption about independent priors is similar to the one used in the original event-based model (see equation (2) in A .L. Young et al., Nature Comm. 9.1 (2018): 4273).

• In the original event-based model, k is a hidden variable. Presumably that is also the case here, however the notation k=stage(j) makes it seem like each subject is assigned a stage during the sequence optimisation.

Response: We would like to clarify that the posterior probability of each stage for every subject is estimated during the sequence optimization. To clarify the notation, we have now deleted the term “stage” and use “tj” to denote stages for each subject j. The sequence optimization was performed with the assumption of a uniform prior distribution p(tj=k) = 1/(N+1) for each stage k. Then, the posterior probability p(tj=k|Zj,S), i.e., the probability that subject j belongs to stage k, given the metrics and the sequence, was computed during the sequence optimization procedure.

• Typically for event-based modeling, positional variance diagrams are created from the markov chain monte carlo samples of the event sequence, enabling visualisation of the uncertainty in the sequence, but these are not included in the study.

Response: In the revised supplementary file, we have now included positional uncertainty diagrams for the optimal set of z-score events that were created from 50,000 MCMC samples. Please see Appendix 1—figure 2 for the AC-EBM and Appendix 1—figure 9 for the SAC-EBMs.

• Many of the figures in the manuscript (e.g. Figure 1E/G, Figure 2A/B, Figure 3A/B/E/F/I/J, Figure 4 A/B/E/F/I/J) are based on averages in both the x and the y axis. In the x dimension, individuals have a weighted contribution to the value on the y axis, depending on their stage probability. In the y dimension, the values are averages across those individuals, and the error bars represent the standard error rather than the standard deviation. Whilst the trajectories themselves are interesting, they may not be discriminative at the individual level and may be more heterogeneous than it appears.

Response: In the current study, the predictions of trajectories are intended at the cohort level. Individual level investigations will be the topic of future investigations.

• The bootstrapped statistical analyses comparing metrics between the stages do not consider the variability in the sequence.

Response: Please see the response above. The positional uncertainty diagrams are included in the revised supplementary file.

Reviewer #2 (Public Review):

Summary:

This work presented by Kudo and colleagues is of great importance to strengthen our understanding of electrophysiological changes in the course of AD. Although the main conclusions regarding functional connectivity and spectral power change through the course of the disease are not new and have been largely studied and theorised on, this article offers an innovative approach that certainly consolidates previous knowledge on the topic. Not only that, this article also broadens our knowledge presenting useful and important details on the specificity of frequency and cortical distribution of these early alterations. The main take-home message of this work is the early disruption of electrophysiological signatures that precedes detectable alterations in other more commonly used pathology markers (i.e. gray matter atrophy and cognitive impairment). More specifically, these signatures include long-range connectivity in the alpha and beta bands, and local synchrony (spectral power) in the same frequency bands.

Response: Thank you for the positive comments and for providing a nice succinct summary.

Strengths:

The present work has some major strengths that make it paramount for the advance of our understanding of AD electrophysiology. It is a very well written manuscript that, despite the complexity of the analyses employed, runs the reader through the different steps of the analysis in a pedagogic and clever way, making the points raised by the results easy to grasp. The methodology itself is carefully chosen and appropriate to the nature of the question posed by the researchers, as event-based models are well-suited for cross-sectional data.

The quality of the figures is outstanding; not only are they aesthetic but, more importantly, the figures convey information exceptionally well and facilitate comprehension of the main results.

The conclusions of the paper are, in general, well described and discussed, and consider the state-of-the-art works of AD electrophysiology. Furthermore, even though the conclusions themselves are not groundbreaking at all (synaptic damage preceding structural and cognitive impairment is one of the epitomes of the pathological cascading model proposed by Jack in 2010), this article is innovative and groundbreaking in the way they address with clever analyses in a relatively large sample for neuroimaging standards.

Response: Thank you for the positive comments of the strengths of the paper.

Weaknesses:

The main limitation of the work revolves around sample definition and inclusion criteria that are somewhat confusing obscuring some of the points of the analyses. Firstly it is not clear why the purely clinical approach is employed to diagnose the "probable Alzheimer´s Disease" for the 78 participants in the "AD group". In the same paragraph, it is stated that 67 out of the 78 participants show biomarker positivity, thus allowing a more biologically guided diagnosis that is preferred according to current NIA-AA criteria. This would avoid highly possible mixing of different subtypes of dementia etiologies. One might wonder, why would those 11 participants be included if we have strong indications that their symptoms are not due to AD? Furthermore, the real pathological status of the control group is somewhat questionable. The authors do not specify whether common AD biomarkers are available for this subgroup. In that case, it would have highly increased the clarity and interpretability of the results if this group was subdivided in a preclinical and completely healthy control group. This would be particularly interesting since a significant proportion of the control group is labeled as belonging to stages 2,3,4 (MCI) and even 5 (mild dementia). This raises the question of whether these participants are true healthy controls mislabeled by the EBM model, or actual cognitive controls with actual underlying AD pathology well identified by the model proposed.

Response: Please see responses above to a similar comment from R1. To clarify, all our controls were recruited from an ongoing longitudinal study of healthy aging. Amongst the 70 controls, 39 have confirmed A-beta negative PET scans and 8 were confirmed A-beta positive PET scans, and in the rest of the 23 we do not have any biomarker data available. The biomarker positivity rates in our control cohort are completely consistent with the prevalence of A-beta positivity in cognitively healthy individuals and are within a normal biological continuum for amyloid beta (Jansen WJ et al. 2015). In all the controls, we have conducted comprehensive neuropsychological assessment (see Appendix 1—table 1 in the revised supplementary file) and based on this data we can be quite confident about their lack of clinical deficits, and we have a high degree of confidence that none of the controls have any neurodegeneration (AD-related or otherwise). We include these details in the revision (see the revised ‘Participants’ section in the Materials and methods.).

Jansen WJ et al., 2015 JAMA; 667 313(19):1924-1938.

On this note, Figure 2 (C and D) and Figure 3 (C, G and K) show a cortical surface depicting the mean difference of each stage vs the control group, which again, is formed by subjects that can be included (and in fact, are included) in all those stages, obscuring the meaning and interpretability of these cortical distributions.

Response: We would like to clarify that these figures depict the regional maps of each metric for each stage of AD progression, not the contrast against a control group.

Reviewer #1 (Recommendations For The Authors):

• If possible, perform independent validation of the results.

Response: This is something we indeed intend to examine in our future investigations.

• Repeat the analysis in the subset of individuals that are amyloid positive.

Response: Amongst the 78 AD patients, 20 had autopsy confirmed AD neuropathology, an additional 41 patients had molecular pathology identified by Abeta-PET, and another additional 9 had fluid biomarker (CSF) confirmation of amyloid and tau levels consistent with AD diagnosis. Eight remaining patients had a diagnosis of AD with high certainty, based on clinical presentation, neurological assessment, and cortical atrophy on MRI. Given that there are only eight patients who had clinical diagnosis of AD (with no biomarkers), and the comprehensive clinical characterization of all the AD patients in our cohort (Appendix 1—table 1), we do not believe that any subgroup analysis is warranted.

• When inferring the optimal z-scores, select the same set of z-scores per biomarker, or include diagrams of stage vs z-score that include all of the markers so that it is easy to see how one marker changes relative to the others (overlay Figure 1G on Figure 2A and 2B).

Response: How the neural synchrony metrics, PHG volume and MMSE scores change relative to each other is exactly what we show in Figures 3 B/F/J and 4 B/F/J. Since each EBM model optimizes the z-score thresholds, sequence likelihood and posterior probability of each stage for each subject, the EBM framework provides the most likely estimate for each metric at every stage. Therefore, the SAC-EBM model gives the most accurate description of the relative differences in these metrics over the AD progression stages. The reviewer’s suggestion to overlay Figure 1G (now figure 1F, based on optimized z-scores for PHG volume and MMSE scores) on Figures 2A and 2B will be inaccurate, as the neural synchrony measures plotted in figures 2A and 2B are not for optimized z-scores.

• Change equation 2 to use a multivariate gaussian.

Response: We now clarify that we use a factorized multivariate form that reflects independent priors for each metric which are Gaussian.

• Clarify whether k is a hidden variable and possibly change the notation.

Response: We now clarify that in our notation, k is a label for the stage [k=1,..,7 (when I=2) or k=1,...,10 (when I =3)] and is indeed a hidden variable and not observed (but inferred from the EBM). Specifically, the posterior probability for each subject j belonging to stage k was estimated as part of the sequence optimization procedure.

• Generate positional variance diagrams of the MCMC samples.

Response: We are doing the MCMC to obtain the most likely sequence. We have now included positional variance diagrams of the optimal set of z-score events in Appendix 1—figure 2 and Appendix 1—figure 9 in the revised supplementary file.

• It would be interesting to study whether the stages are predictive of conversion or look at longitudinal data, if available.

Response: This is something we indeed intend to examine in our future investigations.

• Also look at statistics across MCMC samples of the sequence.

Response: Thank you for this suggestion. In the Appendix 1—figure 10, we now include an example of the MCMC samples for an SAC-EBM including the alpha-band AEC. We then derived the positional variances for each metric that are now shown in Appendix 1—figure 2 and Appendix 1—figure 9.

Reviewer #2 (Recommendations For The Authors):

Some really minor changes are suggested on two specific points that somewhat confused me as a reader and got me stuck in the reading process to try to get the meaning of what I was seeing/reading:

1. It is not specified (or at least I was unable to find it) what are you comparing exactly for the group comparison in the long-range synchrony metric (AEC) before creating your scalar metric. Are you comparing individual links (in which case you would have 93 link values for each ROI to compare)? Or are you comparing the strength for each ROI (thus, one value -the individual links sum- for each ROI)? I guess it should be the latter for what I see in the figures but it could be useful to specify it.

Response: The reviewer is correct. We compare the strength of each ROI, i.e., averaging over edges of the symmetric AEC matrix of functional connectivity. We now clarify this in the Amplitude-envelope correlation section and the caption of the revised Appendix 1—figure 6.

1. In Figure 1 (which, by the way, is exceptionally aesthetic, congratulations for that!) I got stuck for a relatively long time in a really small detail and I am not completely sure if I came to the right conclusion. It is regarding the X axis of the histograms in panels B and D. They are expressed as "PHG volume loss" and "MMSE decline". So I supposed those histograms were showing some kind of subtraction, (maybe from stage X to stage Y, or from group X to group Y). I was trying to understand the histogram and rereading methods to see if I overlooked any description of that graphic and then just realized they might be just the Z-score itself for each group (control and AD) with respect to the whole population. If that is the case I would suggest changing the X-label to "PHG z-score" and "MMSE z-score" avoiding the reference to "loss and "decline" as they are just reflecting the direct transformation to z-score.

Response: Thank you. We would like to clarify that the z-score for PHG volume and MMSE scores were sign-inverted so that higher values denote “PHG Volume loss” and “MMSE decline”, respectively. We now clarify this point in the revised text and legend for the revised figure 1.

Lastly, regarding the point I raised in the limitations section of the public review, I understand it might fall out of the scope of eLife reviewing process as it would require a more extensive change of the current manuscript, which is great as it is. But as a reader and researcher in the field, I would have recommended using biomarkers to divide the control group (if available) thus including in the models only those belonging to the AD continuum according to their biomarker status, and leaving those control without any biomarker positivity as the reference group for the figures I mention in that section (those showing differences for each stage in the cortical surface with respect to the control group).

Response: Please see a similar comment from R1. Amongst the 70 controls, 39 have confirmed A-beta negative PET scans and only 8 were confirmed A-beta positive PET scans, and in the rest of the 23 we do not have any biomarker data available. In all the controls, we have conducted comprehensive neuropsychological assessment (see Appendix 1—table 1 in the revised supplementary file) and based on this data we can be quite confident about their lack of clinical deficits, and we have a high degree of confidence that none of the controls have any neurodegeneration (AD-related or otherwise). Since only 8 participants were confirmed as amyloid positive in the control group and this sample size is small, we do not conduct this recommended re-analysis in this manuscript.

Author Response

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

eLife assessment

The manuscript describes the synergy among PI3Kbeta activators, providing compelling results concerning the mechanism of their activation. The particular strengths of the work arise to a great extent from the reconstitution system better mimicking the natural environment of the plasma membrane than previous setups have. The study will be a landmark contribution to the signaling field.

Public Reviews:

Reviewer #1 (Public Review):

The manuscript aims to provide mechanistic insight into the activation of PI3Kbeta by its known regulators tyrosine phosphorylated peptides, GTP-loaded Rac1 and G-protein beta-gamma subunits. To achieve this the authors have used supported lipid bilayers, engineered recombinant peptides and proteins (often tagged with fluorophores) and TIRF microscopy to enable bulk (averages of many molecules) and single molecule quantitation. The great strength of this approach is the precision and clarity of mechanistic insight. Although the study does not use "in transfecto" or in vivo models the experiments are performed using "physiologically-based" conditions and provide a powerful insight into core regulatory principles that will be relevant in vivo.

The results are beautiful, high quality, well controlled and internally consistent (and with other published work that overlaps on some points) and as a result are compelling. The primary conclusion is that the primary regulator of PI3Kbeta are tyrosine phosphorylated peptides (and by inference tyrosine phosphorylated receptors/adaptors) and that the other activators can synergise with that input but have relatively weak impacts on their own.

Although the methodology is not easily imported, for reasons of both cost and the experience needed to execute them well, the results have broad importance for the field and reverse an impression that had built in large parts of the broader signalling and PI3K communities that all of the inputs to PI3Kbeta were relatively equivalent, however, these conclusions were based on "in cell" or in vivo studies that were very difficult to interpret clearly.

Reviewer #2 (Public Review):

The manuscript of Duewell et al has made critical observations that help to understand the mechanisms of activation of the class IA PI3Ks. By using single-molecule kinetic measurements, the authors have made outstanding progress toward understanding how PI3Kbeta is uniquely activated by phosphorylated tyrosine kinase receptors, Gbeta/gamma heterodimers and the small G protein Rac1. While previous studies have defined these as activators of PI3Kbeta, the current manuscript makes clear the quantitative limitations of these previous observations. Most previous quantitative in vitro studies of PI3Kbeta activation have used soluble peptides derived from bis-phosphorylated receptors to stimulate the enzyme. These soluble peptides stimulate the enzyme, and even stimulate membrane interaction. Although these previous studies showed that the release of p85-mediated autoinhibition unmasks an intrinsic affinity of the enzyme for lipid membranes, they ignored what would be the consequence of these peptide sequences being present in the context of intrinsic membrane proteins. The current manuscript shows that the effect of membrane-conjugated peptides on the enzyme activity is profound, in terms of recruiting the enzyme to membranes. In this context, the authors show that G proteins associated with the membranes have an important contribution to membrane recruitment, but they also have a profound allosteric effect on the activity on the membrane, These are observations that would not have been possible with bulk measurements, and they do not simply recapitulate observations that were made for other class IA PI3Ks.

An important observation that the authors have made is that Gbeta/gamma heterodimers and RAc1 alone have almost no ability to recruit PI3Kbeta to the membranes that they are using, and this is central to one of the most profoundly novel activation mechanisms offered by the manuscript. The authors propose that the nSH2- and Gbeta/gamma binding sites partially overlap, so that Gbeta/gamma can only bind once the nSH2 domain releases the p110beta subunit. This mechanism would mean that once the nSH2 is engaged by membrane-conjugated pY, the Gbg heterodimer can bind and increase the association of the enzyme with membranes. Indeed, this increased membrane association is observed by the authors. However, the authors also show that this increased recruitment to membranes accounts for relatively little increase in activity, and that the far greater component of activation is due to an allosteric effect of the membrane association on the activity of the enzyme. The proposal for competition between Gbg binding and the nSH2 is consistent with the behavior of an nSH2 mutant that cannot bind to pY and which, consequently, does not vacate the Gbg-binding site. In addition to the outstanding contribution to understanding the kinetics of activation of PI3Kbeta, the authors have offered the first structural interpretation for the kinetics of Gbg activation in synergy with pY activation. The proposal for an overlapping nSH2/Gbg binding site is supported by predictions made by John Burke, using alphafold multimer. Although there is no experimental structure to support this structural model, it is consistent with HDX-MS analyses that were published previously.

Reviewer #1 (Recommendations For The Authors):

1. The approx relative concentrations (surface densities ) of Rac1-GTP, GBetagammas and PY-peptides used in experiments in Fig 1 are not easy to understand and useful to give an intuitive feel for the relative sensitivity of the PI3Kbeta reporter to those inputs.

In our revised manuscript, we provide densities of the individual signaling inputs used to reconstitute Dy647-PI3Kβ membrane recruitment (see Figure legend 1). We provide a more detailed explanation about our quantification method in subsequent figures where the membrane surface density of signaling inputs is varied to modulate the strength of PI3Kβ membrane localization and activity.

Building off the quantification of Rac1-GTP and pY membrane density measurements presented in our initial manuscript submission, we now include an estimate of the GβGγ membrane density. For these new measurements, we recombinantly expressed and purified additional SNAP-GβGγ protein, which we fluorescently labeled with AlexaFluor 555. The membrane surface density of GβGγ was quantified at equilibrium using a combination of AF488-SNAP-GβGγ (bulk signal) and dilute AF555-SNAP-GβGγ (0.0025%), which allowed us to resolve and count the single molecule density (Figure 3A). We calculate the total surface density of GβGγ based on the AF555-SNAP-GβGγ dilution factor. In the methods section titled, “surface density calibration,” we describe our protocol.

1. The estimates of the PIP3 concentrations/densities measured using the BTK reporter seem good but its unclear (to me) how they were derived.

The density of PI(3,4,5)P3 lipids in our supported lipid bilayers was calculated based on the incorporation of a define molar ratio of PI(3,4,5)P3 in our small unilamellar vesicles. Based on the average footprint of 0.72 nm2 for a single lipid, we calculated the density of lipids per µm2. In the methods section titled, “kinetic measurements of PI(3,4,5)P3 lipid production,” we include the following description:

“Assuming an average footprint of 0.72 nm2 for phosphatidylcholine (Carnie et al., 1979; Hansen et al., 2019), we calculated a density of 2.8 × 104 PI(3,4,5)P3 lipids/μm2 for supported membranes that contain an initial concentrations of 2% PI(4,5)P2. We assume that the plateau fluorescence intensity of the AF488-SNAP-Btk sensor following reaction completion in the presence of PI3Kβ represents the production of 2% PI(3,4,5)P3. The bulk membrane intensity of AF488-SNAP-Btk was normalized from 0 to 1, and then multiplied times the total density of PI(3,4,5)P3 lipids to generate kinetic traces that report the kinetics of PI(3,4,5)P3 production.”

Minor points

l164; Rac1(GTP) AND GBeta gammas. In this context it should be OR. Or have I misunderstood?

l1093; kineticS measurementS.

Thank you for pointing out these typos. We made the appropriate edits.

The paper of Suire etal (Suire, S., Lécureuil, C., Anderson, K. E., Damoulakis, G., Niewczas, I., Davidson, K., Guillou, H., Pan, D., Jonathan Clark, Phillip T Hawkins, & Stephens, L. (2012). GPCR activation of Ras and PI3Kc in neutrophils depends on PLCb2/b3 and the RasGEF RasGRP4. The EMBO journal, 31(14), 3118-3129. https://doi.org/10.1038/emboj.2012.167) make the point that in vivo it appears that although Ras-activation is required for full activation of PI3Kgamma (and can activate PI3Kgamma in vitro directly) if you use tools to activate Ras in the absence of receptor and Gbetagamma signalling, it has no affect on PIP3 . This directly supports the authors conclusions.

Thank you for sharing this citation. We incorporated the reviewer’s insight into our discussion section to broaden the significance of our work.

Reviewer #2 (Recommendations For The Authors):

There are only a few relatively minor points that could be addressed to improve the paper:

1. Why is the density still going up after 10 minutes in Figure 1 Figure supplement 2? Doesn't this seem like a very long time? Are we seeing fast on/off combined with fast on/slow off? Are the particles eventually becoming stuck in odd places or are they slowly denaturing?

Our movies do not indicate a slow accumulation of immobilized or stuck Dy647-PI3Kβ particles on the membrane surface. On the long timescale, we believe that a small fraction of Dy647-PI3Kβ molecular do exhibit longer dwell times on membranes containing a high density of pY (>6,000 molecules/µm2). This is likely due to membrane hopping of Dy647-PI3Kβ. In other words, rather than Dy647-PI3Kβ dissociating from the membrane surface directly into the solution, the Dy647-PI3Kβ molecule immediately rebinds to another membrane conjugated pY peptide. This type of behavior of a peripheral membrane binding protein is generally correlated with there being a higher surface density of the binding partner (Yasui et al., 2014). Characterization of potential Dy647-PI3Kβ membrane hopping will require additional experimentation (e.g. PI3Kβ mutants) and quantitative analysis that goes beyond the scope of this study.

1. Lines 188-189. "By quantifying the average number of Alexa488-pY particles per unit area of supported membrane we calculated the absolute density of pY per μm2 (Figure 2D). I think this should be Figure 2C, right hand y-axis.

Thank you for identifying our typo. We’ve corrected the text for clarity.

1. Lines 102-193. "When Dy647-PI3Kβ was flowed over a membrane containing a low density of {less than or equal to} 500 pY/μm2, we observed rapid equilibration kinetics consistent with a 1:1 binding stoichiometry (Figure 2E).” There is no density shown in Fig. 2E. There is only "membrane intensity." Perhaps it was their intent to include a right-hand axis with density (number of particles/area), as they did in Figure 2C. However, they did not, so Figure 2E does not support the text. The value of Intensity/#py/um**2 does not appear to be the same for Figure 2C as for Figure 2E, assuming that the statement in the text is correct. The authors should include the density as a right-hand axis in 2E.

We have reworded this portion of the results section for clarity. In reading the reviewers comment, we recognize that a more convincing way to support our claim of a 1:1 binding stoichiometry would be to show that there are ~500 Dy647-PI3Kβ/μm2 membrane bound complexes when the pY surface density equals ~500 pY/μm2. For us to make this connection, we would need to perform experiments using a Dy647-PI3Kβ concentration that fully saturates all the binding pY binding sites. However, at this elevated Dy647-PI3Kβ solution concentration, individual Dy647-PI3Kβ complexes can start to bind to a single phosphotyrosine of the dually phosphorylated peptide due to competition for pY binding sites. As an alternative to performing the experiment described above, we can infer binding stoichiometry from the shape of the membrane absorption kinetic traces. For example, a simple bimolecular interaction exhibits rapid equilibration kinetics with a hyperbolic shaped kinetic trace. Systems that have more complex binding equilibria, however, generally take longer to equilibrate (due to the change in KOFF) and can often be broken down into 2 or 3 distinct dissociation constants (KD). This type of kinetic analysis has previously been used to describe multivalent membrane binding interactions for the Btk-PI(3,4,5)P3 (Chung et al., 2019) and PI3Kγ-GβGγ (Rathinaswamy et al., 2021) complexes. Considering that there are multiple interpretations of the Dy647-PI3Kβ membrane absorption traces show in Figure 2E, we refrain from saying that our results explicitly reveal a 1:1 binding stoichiometry. Instead, we provide several possible explanations for the results. Ultimately, additional experiments and kinetic modeling of wild type and mutant PI3Kβ is necessary to define the binding stoichiometry under different conditions.

1. Table 1. The authors have analysed the data to extract two dwell times and two diffusion coefficients. The legend should make this clear, referring to D1 as the slow diffusion component and D2 as fast diffusion, similarly, there are short and long dell times. This should be stated in the legend. There are two columns labelled "alpha". This presumably should be alpha1 and alpha2, the fractions of particles with short and long dwell times. The table legend should clarify this.

In our revision, additional text has been added to the figure legends and Table 1.

Text from Table 1: “Alpha (α) equals the fraction of molecules with the characteristic dwell time, τ1 (DT = dwell time). The fraction of molecules with the characteristic dwell time, τ2, equals 1-α. Alpha (αD) equals the fraction of molecules with the characteristic diffusion coefficient, D1. The fraction of molecules with diffusion coefficient, D2, equals 1-αD.”

1. In the legend for Figure 5 figure supplement 1, for part D, the "Cumulative membrane of binding events..." The "of" should be deleted.

Thank you for identifying this typo.

1. Lines 423-426: "We found that PI3Kβ kinase activity is also relatively insensitive to either Rac1(GTP) or GβGγ alone. This is in contrast to previous reports that showed Rho-GTPases (Fritsch et al. 2013) and GβGγ (Katada et al. 1999; Hashem A. Dbouk et al. 2012; Maier, Babich, and Nürnberg 1999) can activate PI3Kβ, albeit modest, compared to synergistic activation with pY peptides plus Rac1(GTP) or GβGγ." It is not clear what this statement means. On the surface, it might be interpreted as saying that these previous studies had some flaw that led the authors to conclude that there is some activation caused by Rac1 or Gbeta/gamma on their own. The current manuscript is an important contribution to understanding the mechanism of synergistic activation, but it is also true that the Hansen and his colleagues have not used the same membranes as were used previously. The authors state that they have used a wide range of membrane compositions, but the only ones that have appeared in the manuscript are nearly pure PC (with 2% PIP2) or PC with 20% PS. Extensive studies with varying membrane compositions are beyond the scope of the current study, since the current manuscript concisely makes important observations regarding mechanism. However, it would be helpful for readers if the authors at least mention the differences in membrane compositions among the studies.

The reviewer raises an important point concerning our interpretation of PI3Kβ activation data in relationship to existing literature. In our original submission, we made conclusions concerning how individual signaling inputs modulate PI3Kβ activity, without showing all our data or providing sufficient explanation. In our revised manuscript, we include PI3Kβ kinase activity measurements performed in the presence of either pY, Rac1(GTP), or GβGγ alone (Figure 5B-5C). These experiments were reconstituted on supported membranes in the absence or presence of 20% PS lipids. We found that increasing the density of anionic lipids increased the overall activity of PI3Kβ in the presence of pY or GβGγ alone. This is consistent with a subtle increase in PI3Kβ membrane affinity due to the negatively charged PS lipids. Mutations that disrupt the direct interaction between PI3Kβ and GβGγ eliminated the observed lipid kinase activity. We were unable to detect PI3Kβ activity in the presence of Rac1(GTP) alone. In conclusion, we’re able to detect some PI3Kβ activity in the presence of GβGγ alone, which is consistent with previous reports (Dbouk et al., 2010; Katada et al., 1999; Maier et al., 2000). In the future, a more comprehensive analysis will be required to map the relationship between PI3Kβ activity, membrane localization, and lipid composition. For example, previous reconstitutions have revealed differential activation of PI3Kα that depends on the most abundant lipid being phosphatidylethanolamine (PE) rather than phosphatidylcholine (PC) (Hon et al., 2012; Ziemba et al., 2016). PE lipids comprise 25-30% of the cellular plasma membrane (Yang et al., 2018) and have been used in previous studies to measure PI3K lipid kinase activity on small unilamellar vesicles (Dbouk et al., 2010; Hon et al., 2012).

In this study, we elected to use a simplified membrane composition that minimized non-specific membrane localization of fluorescently labeled PI3Kβ. This allowed us to more clearly define the strength of individual and combinations of protein-protein interactions that regulate PI3Kβ localization and kinase activity. When reconstituting amphiphilic molecules (i.e. lipids) in aqueous solution a variety of structures, including micelles, inverted micelles, and planar bilayers can form based on the lipid composition (Kulkarni, 2019). The organization of these membrane structures is related to the molecular packing parameter of the individual phospholipids (Israelachvili et al., 1976). The packing parameter (P=v⁄((a•l_c))) depends on the volume of the hydrocarbon (v), area of the lipid head group (a), and the lipid tail length (l_c). When generating supported lipid bilayers on a flat two-dimensional glass surface, we aim to create a fluid lamellar membrane. We find that phosphatidylcholine (PC) lipids are ideal for making supported lipid bilayers because they have a packing parameter of ~1 (Costigan et al., 2000). In other words, PC lipids are cylindrical like a paper towel roll. In contrast, cholesterol and phosphatidylethanolamine (PE) lipids have packing parameters of 1.22 and 1.11, respectively (Angelov et al., 1999; Carnie et al., 1979). This gives cholesterol and PE lipids an inverted truncated cone shape, which prefers to adopt a non-lamellar phase structure. Due to the intrinsic negative curvature of PE lipids, they can spontaneously form inverted micelles (i.e. hexagonal II phase) in aqueous solution when they are the predominant lipid species (Israelachvili et al., 1980; Kobierski et al., 2022; Wnętrzak et al., 2013). In the methods section of our manuscript, we note that from our experience incorporation of PE lipids dramatically reduces the protein-maleimide coupling efficiency, displayed more membrane defects, and resulted in a larger fraction of surface immobilized Dy647-PI3Kβ. This could be related to the intrinsic negative curvature of PE membranes. However, further investigation is needed to decipher these issues.

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Author Response

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

Reviewer #1:

We thank the referee for the positive review.

Reviewer #2 (Public review):

We thank the referee for his/her constructive comments

1. The weakness of this work is the lack of clarification on the function of eIF2A in general. The novelty of this study was limited.

We believe our study is valuable in providing strong evidence that eIF2A does not functionally substitute for eIF2 in tRNAi recruitment even when eIF2 function is impaired, and in showing that it does not contribute to translational control by uORFs or IRESs, thus ruling out the most likely possibilities for its function in yeast based on studies of the mammalian factor. We agree that the function of yeast eIF2A remains to be identified; however, we think this should be regarded as a limitation rather than a weakness in experimental design or data obtained in the current study.

1. Related to this, it would be worth investigating common features in mRNAs selectively regulated (surveyed in Figure 3A).

We did not embark on this because only 17 of the 32 transcripts showing TE reductions in Fig. 3A showed a pattern of TE changes consistent with a conditional requirement for eIF2A under conditions of reduced eIF2 function, exhibiting greater TE decreases when both eIF2 function was impaired by phosphorylation and eIF2A was eliminated from cells. Moreover, we could validate this conditional eIF2A dependence by LUC reporter for only a single mRNA, HKR1.

Also, it would be worth analyzing the effect of eIF2A deletion on elongation (ribosome occupancy on each codon and/or global ribosome footprint distribution along CDS) and termination/recycling (footprint reads on stop codon and on 3′ UTR).

We have analyzed the effects of deleting eIF2A on ribosome pausing at individual codons by calculating tri-peptide pause scores from our ribosome profiling data. The results shown in new Fig. 7 reveal that eIF2A plays no discernible role in stimulating the rate of decoding of any three-codon combinations.

1. Regarding Figure 3D, the reporters were designed to include promoter and 5′ UTR of the target genes. Thus, it should be worth noting that reporter design was based on the assumption that eIF2A-dependency in translation regulation was not dependent on 3′ UTR or CDS region. The reason why the effects on ribosome profiling-supported mRNAs could not be recapitulated in reporter assay may originate from this design. This should be also discussed.

We agree and included this stipulation in the DISCUSSION, while at the same time noting that the native mRNAs were examined in the orthogonal assay of polysome distributions.

1. Related to the point above, the authors claimed that eIF2A affects "possibly only one" (HKR1) mRNA. However, this was due to the reporter assay which is technically variable and could not allow some of the constructs to pass the authors' threshold. Alternative wording for this point should be considered.

We agree and revised text in the DISCUSSION to read: “A possible limitation of our LUC reporter analysis in Fig. 3D was the lack of 3’UTR sequences of the cognate transcripts, which might be required to observe eIF2A dependence. Given that native mRNAs were examined in the orthogonal assay of polysome profiling in Fig. 3E, the positive results obtained there for SAG1 and SVL3 in addition to HKR1 should be given greater weight. Nevertheless, our findings indicate a very limited role of yeast eIF2A in providing a back-up mechanism for Met-tRNAi recruitment when eIF2 function is diminished by phosphorylation of its α-subunit.”

1. For Figure 3D, it would be worth considering testing the #-marked genes (in Figure 3C) in this set up.

Actually, we did test 10 of the 17 mRNAs marked with “#”s in the reporter assays of Fig. 3C, which had been noted in the Fig. 3C legend.

1. In box plots, the authors should provide the statistical tests, at least where the authors explained in the main text.

At the first occurrence of a notched box plot (Fig. 2D), we explained in the main text that in all such plots, when the notches of different boxes do not overlap, their median values differ significantly with a 95% confidence level. In cases where overlaps between notches is difficult to assess by eye, we added the results of Mann-Whitney U tests with the p values indicated by asterisks, as explained in the legends. We added results of additional Mann-Whitney U tests to such box plots in Figs. 3B, 6A-C, and 6-supp. 1E & G and mentioned this in the corresponding legends.

Reviewer #2 (Recommendations For The Authors):

The first section of "Yeast eIF2A does not play a prominent role as a functional substitute for eIF2 in the presence or absence of amino acid starvation" can be subdivided into a couple of sections for better readability.

Done.

Although the authors have used SM to induce ISR in yeasts previously, the validation of eIF2alpha phosphorylation in Western blot would be helpful for readers. Also, it should be worth testing whether eIF2alpha phosphorylation was properly induced in eIF2A KO cells.

The translational induction of GCN4 mRNA, which we have documented in WT and eIF2A∆ cells, provides a quantitative read-out of eIF2 functional attenuation superior to determining the proportion of eIF2α that is phosphorylated.

For Figure 2B, the Venn diagram that shows the overlap between TE-changes genes in WT_SM/WT and those in eIF2A∆_SM/eIF2A∆ would be helpful (although a list was provided by the source data).

The Venn diagram has been provided in a new figure, Figure 2-figure supplement 1B.

For Figures 1C and 5A-B, the depiction of the positions of uORFs within the orange gene region would be helpful for readers.

Done.

For Figure 4A-C, the depiction of the IRES regions (if known) within the orange gene region would be helpful for readers.

Done for the URE2 IRES, whose location is known.

For Figures 1C, 4A-C, and 5A-B, the y-axis should have a label/scale.

Added.

For Figure 3C, the definition of #-marked genes should be concretely described (e.g., value range) in the legend.

Added.

For Figure 3D-E, the statistical test has been only shown in a couple of data. A full depiction of the statistical results for all the data sets may be helpful for readers.

We explained that when notches in box plots do not overlap, their medians differ with 95% confidence. In cases where overlaps were difficult to discern, we added p values from Mann-Whitney U tests to the relevant box plots.

For Figure 3E, it would be helpful if the authors could show the UV spectrum of the sucrose density gradient to show the regions isolated for the experiments.

Added for a representative replicate gradient in the new figure, Figure 3-figure supplement 1.

Reviewer #3 (Public Review):

We thank the referee for his/her positive assessment of our study.

Weaknesses:

While no role of eIF2A in translation initiation is apparent, the authors do not determine what function eIF2A does play in yeast. Whether it plays a role in regulating translation in a different stress response is not determined.

We agree that there are many additional possibilities to consider for functions of eIF2A in translation initiation, including different stress situations or mutant backgrounds; however, we regard this as a limitation rather than a weakness in the experimental design and data obtained in the current study in which we examined the most likely possibilities for eIF2A function in yeast based on studies of the mammalian factor.

Reviewer #3 (Recommendations For The Authors):

Curiously, the authors indicate that they could not replicate published results for eIF2A's repressor function for URE2, PAB1, or GIC1 translation. This is a little concerning and one wonders if the yeast strain used in the previous study is different in some way from the authors' strain. Did the authors obtain that strain to test it in their assays?

The same WT and eIF2A∆ strains have been analyzed here and in the two cited studies on yeast IRESs.

The authors do discuss the fact that eIF2A may function to regulate translation in response to different stresses. It would have been a strength to test an alternative stress in the current study. However, I also appreciate that this could be the subject of a future study.

Agreed.

One minor question I have is whether the yeast strains used possess L-A dsRNA virus? While it may not be that this virus would necessarily mask a role of eIF2A-dependent translation, do the authors have any specific thoughts on this? Would different results be obtained if cured strains were used?

According to Ravoityte et al. (doi: 10.3390/jof8040381), the S. cerevisiae strain we employed, BY4741, harbors L-A-1 dsRNA; however, we have not explored whether curing the virus would alter the consequences of eliminating eIF2A.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

1. General Statements We appreciate the insightful reviewer comments. Both reviewers alluded to the logical lack of connection between two themes in the original paper. Specifically, we showed that N-cad differentially regulates migration in different environments, and that leader and follower cells differ phenotypically, but did not connect the two themes. In this revised version, we've performed additional experiments and undertaken a comprehensive reorganization of both the manuscript and figures. The major changes are outlined below:

2. Figure 4 A-C has been moved to Figure 6 F-H.

3. Figure 5 has been moved to Figure S3 F-H.
4. Figure 6 F has been moved to Figure 7 A.
5. Figure 6 G-H have been moved to Figure 7 D-E.
6. Figure 6 I-J have been moved to Figure S5 A-B.
7. Figure 7 C-F have been moved to Figure S5 C-F.
8. Added transcriptome profiling of control and N-cad-depleted cells and of leader and follower cells (Figures 6 E, S1 H and S4 C-D, Tables S2 and S3). We have incorporated additional figures (Figure 4 and 5 in the revised manuscript) to support the idea that the amount of N-cad at the cell surface is regulated by endocytic recycling, thereby stimulating glioma migration in the different local environments. Furthermore, our new findings showed that YAP1/TAZ regulates the surface level of N-cad during glioma migration (Figure 8). We trust that these additions contribute to the clarity and robust justification of our paper.

Similar to other types of tumors, our findings revealed that pediatric high-grade gliomas migrate collectively, possibly contributing to a more aggressive invasion than single cells. In this study, we found that N-cad mediates this collective glioma migration. Interestingly, within these migrating groups, leader and follower cells dynamically interchange positions during migration, accompanied by changes their phenotypic characteristics. This suggests that differences in phenotypes, including N-cad recycling, proliferation and YAP activation, may be predominantly regulated by cell-extrinsic factors rather than being predetermined by genetic or epigenetic factors. Moreover, our new RNA-sequencing results indicate minimal difference between leader and follower cells, except for the upregulation of YAP response and wound healing migration genes in leader cells. Although genomic alterations still possibly encode the leader-follower exchange, our findings strongly suggest that the activation of YAP1 and glioma migration are regulated by the cellular context, specifically where cells are located within the group.

Contrary to our initial findings suggesting a positive feedback loop between N-cad endocytosis and nuclear YAP1, our revised data indicates that nuclear YAP appears to be independent of N-cad. We observed that homotypic interactions with N-cad present in the surrounding environment, such as neurons (Figure 6 C-D) or N-cad extracellular domain-coated surface (Figure 7 B-C), did not affect nuclear YAP1. However, YAP1/TAZ depletion decreased N-cad expression and altered its localization at the surface (Figure 8). This leads us to propose a revised model where nuclear YAP1 stimulates surface N-cad, thereby facilitating the distinct modes of migration on ECM and neurons (Figure 8 I).

1. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Kim and colleagues describe the role of N-Cadherin during pediatric glioma migration. They compare cell lines that have similar transcripts but different levels of N-Cadherin protein and find that N-Cadherin levels influence the route of migration - whether it be on ECM or other tissues. They also describe molecular feedback between N-Cadherin and YAP in leader vs follow cells of their systems. The data are clear, well presented, and convincing; and the conclusions described by the manuscript are mostly justified. My major criticism of the manuscript is that the line of questioning undertaken does not appear well justified. At many points, I was left asking "but why are they doing this?" and I could not understand the rationale for some of the experiments that were performed (even if they were performed well). The manuscript opens by validly describing how gliomas are highly invasive, poorly understood and that N-Cadherin was highly expressed in comparison to other adhesion proteins. This opened the path for the questions and experiments performed that contributed to Figures 1-3, which I thought were interesting. From there on, I found the logic of the story unclear and poorly justified. For example, I do not know why leader and follower cells were justified - when it had nothing to do with N-Cadherin which was the focus of the work prior. And then, having rightly concluded in Figure 4 that the data suggested that leader and follower cells dynamically exchange positions rather than being pre-determined, they went onto further figures focusing on differences between leader and follower cells, which left my quite confused. I am likewise confused by the model proposed in that, they authors describe that the difference between leader and follower cells contributes to a nuclear YAP/N-Cad endocytosis feedback loop that feeds into the speed of migration. Yet, the authors describe earlier that leader and follower cells frequently exchange positions, with no evidence that they are pre-determined. How do the authors square these seemingly conflicting points? And further, what is the relevance of this to understanding the differing modes of migration (on ECM or other tissues)? On this issue, I suggest authors re-consider whether the order of figures or logic of the story is appropriate (perhaps consider moving some figures to supplement?), and to clearly justify in the text the elements that are being addressed. Overall, I think the messaging, logic and justification could be use significant improvement; the experiments however are well performed, and the figures are very clear and nicely presented, and I don't have any qualms about them.

We appreciate your insightful comments, recognizing the need for logical and justifiable improvements in certain sections of our previous manuscript. Please see Section 1, General Statements, for an explanation of changes made.

Minor Comments

1. Not required, but the authors may wish to consider putting t=0 pictures of the experiments in the supplement as supportive evidence for the circles of the initial seeding location they show in Fig 1.

We provide the t=0 images in Figure S1 N and O.

1. I assume the title of the second results section should say "migration speed" rather than "speed migration"

The new title of the second results section is “N-cad stimulates and inhibits migration through intercellular homotypic interaction”.

1. Fig. 4D - Are both example cell pictures leaders? If so, I'm not sure why two have been provided; I'm guessing the bottom set are supposed to be follower cells. If so, please label as appropriate. (And if not, a representative set of pictures from a follower cell should be provided).

We have enhanced the clarity of the labels. We provide representative high magnification images of leader and follower cells. The updated figure can be found in Figure 5 A.

1. Figure 5 Legend - the title of this figure is too definitive, and exaggerates further than the main text does, which was correct in saying that the experiments only suggest that N-Cadherin endocytosis might regulate the localisation of b-catenin and p120-catenin. Probably I would go further and say that there is no experimental evidence provided that even suggests that in the first place, and that this is a hypothesis that remains to be tested. The authors should inhibit endocytosis specifically (rather than just depleting N-Cad) and see the effect, to justify their conclusion.

We appreciated your points and concerns. Following your earlier suggestion, we have moved the figure to the supplementary section (Figure S3 F-H). Moreover, we have addressed the reciprocal regulation of N-cad and catenins by knocking down p120-, β- or α-catenin. Our new findings showed that p120-, β- or α-catenin depletion decrease the amount of N-cad at the cell surface, not steady-state protein level, resulting in decreased migration on astrocytes but increased migration on ECM (see Figure 4). These findings support the idea that catenins play a role in glioma migration according to the environment by altering surface N-cad level. With that, we updated the figure title to “Catenins regulate N-cad surface levels to stimulate or inhibit migration.”

Reviewer #1 (Significance (Required)):

The manuscript provides a characterised of invasive glioma migration that was previously lacking. It also provides interesting observations related to the role of N-Cadherin for different modes of migration (on ECM or on tissues) that will be of interest for further exploration. It makes a good advance in terms of addressing a highly invasive cell type that has poor prognosis. I anticipate that now this initial characterisation has been performed, authors and others will be interested in gaining a deeper understanding as to how these two modes of migration are controlled, how there might be interplay between them and how such findings contribute to its highly invasive nature. I have expertise in collective cell migration and directed cell migration.

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

Summary In the submitted manuscript, Kim et al. describe various aspects of N-cadherin function in the collective migration of PBT-05 cells, a pediatric high-grade glioma line, on laminin, 3D-matrigel, neurons or astrocytes. N-cadherin promotes the collective migration on neurons or astrocytes, whereas it suppresses the migration on laminin or 3D-matrigel. The authors also show that N-cadherin is actively internalized and recycled in the leader, but not follower, cells of the collective, which induce the nuclear accumulation of YAP/TAZ proteins. YAP/TAZ proteins are shown to regulate the collective migration.

Thank you for the comments. Please see Section 1, General Statements, for a summary of changes made. Please also note that our new experiments failed to show that N-cad levels or traffic regulate YAP/TAZ nuclear accumulation. Rather, YAP/TAZ are regulated by cell density independent of N-cad, and YAP/TAZ regulate N-cad protein levels and traffic independent of changes in N-cad RNA levels

Major comments

1. In Fig. 1G, N-cadherin knockdown seems to affect the distribution of astrocytes. The authors should stain a marker for astrocytes, instead of actin, and the red alone images should be provided.

Astrocytes were cultured for 4 days to generate 3D scaffolds before adding the glioma spheroid, essentially as described (Gritsenko et al., Histochem Cell Biol, 2017). Co-cultures were stained for human-specific vimentin (glioma) or actin (glioma and astrocytes) (see Figure 1 G and separate channels in new Figure S1 P). There do not appear to be major changes in astrocyte organization outside the migration front, but we lack a way to stain for astrocytes specifically and cannot visualize astrocytes under the glioma cells. It remains possible that astrocytes may be affected differently by contact with control and N-cad-deficient glioma cells. However, we added a new experiment, assaying migration on decellularized astrocyte ECM. While N-cad stimulated migration on astrocytes it inhibited migration on astrocyte ECM (Figures 1 I and J and S1 Q). Thus N-cad stimulates glioma migration on astrocyte cells and not their ECM.

1. The colocalization between N-cadherin and Rab11 may not be high in Figs. 4F and S2B. It is unclear whether the majority of the internalized N-cadherin is recycled to the plasma membrane. In Fig. S2B, the internalized N-cadherin may be located mainly at the early endosomes before transported to the recycling endosomes (Is it 20 min after the N-cadherin antibody internalization?). First, the authors should analyze the colocalization between the N-cadherin and Rab11 at 30-40 min after the internalization. If the colocalization with Rab11 would be still low at that time point, some of the internalized N-cadherin might be degraded in the lysosomes. To test this possibility, the authors should analyze the colocalization between N-cadherin and LAMP1 under the treatment with a lysosome inhibitor.

At steady state, N-cad co-localized better with Rab5 than with Rab11 or LAMP1 (Figure 5 C-D). In kinetics experiments, N-cad antibodies were internalized for 40 min. They colocalized better with Rab5 or EEA1 than with Rab11 or LAMP1. When we allowed recycling for an additional 20 min, the surface level of N-cad antibodies partially recovered in leader cells more than follower cells (see Figures 5 G and S3 D). We tested whether treatment with lysosomal inhibitors would increase co-localization of N-cad with Rab11 in recycling endosomes. Surprisingly, however, Chloroquine or Bafilomycin A1 decreased the amount of internalized N-cad antibody in leader and follower cells, and long-term treatment did not increase total N-cad levels. Therefore, the fate of internalized N-cad in follower cells remains unclear.

1. When N-cadherin is depleted, dissociated single cells are increased, but these cells are not well characterized. A high magnification image of the dissociated single cells is required. In addition, the migration speed of the dissociated single cells should be measured.

We didn’t quantify single cell migration because only a minority of cells separate from the collective even when N-cad is depleted. Therefore, we quantified migration directionality and speed for cells at or near the front of collective migration (Figure 2 D-I). We have updated the image of single cells, providing representative high-magnification images in Figure S1 N and O.

1. In Fig. S2D, treatment with Pitstop-2 alone or Dyngo-4a alone is required. Dynamin is also involved in clathrin-independent endocytosis and N-cadherin is reported to be internalized via caveolin-1-mediated endocytosis as well as clathrin-mediated during neuronal migration. It would be better to clarify which type of endocytosis occurs in the leader cells.

We have removed results showing inhibition of cell migration and N-cad endocytosis by Pitstop-2 and Dyngo-4a from the paper. Treatment with either chemical alone had much less effect on internalization or migration than adding both together (see figure below). This is hard to explain. Pitstop-2 should inhibit clathrin-coated pit formation and Dyngo-4a should inhibit clathrin and caveolin-mediated endocytosis. Caveolin-1 and 2 transcripts were not detected in our cells (Table S2). There may be some other form of clathin-independent endocytosis. Interpretation is also challenging since these inhibitors will inhibit endocytosis of many receptors, not just N-cad. Accordingly, we have removed these results in the revised manuscript.

1. In Fig. 2, N-cadherin depletion disturbs the migration directionality. Is this a result from disruption of cell polarity? To test this, the position of centrosome or Golgi or lamellipodia in the leader cells should be analyzed. (OPTIONAL)

We elected not to perform this analysis.

1. I cannot understand the significance of Fig. 5F and 5G. If the authors would speculate that alpha- and beta-Catenins may transduce the intracellular signaling from the internalized N-cadherin, the authors should perform the knockdown experiments of the Catenins and analyze whether it may affect the nuclear accumulation of YAP/TAZ. (OPTIONAL)

We agree. In the initial manuscript, we showed that N-cad depletion altered the localization of p120-, β-, and α-catenin (previously shown in Figure 5 F-G). For better clarity and logic, these figures have been moved to Figure S2 H in the revised manuscript. Additionally, to test whether catenins regulate N-cad and YAP1, we depleted p120-, β-, or α-catenin using shRNA. We found that downregulation of p120-, β-, or α-catenin decreased N-cad surface levels, consequently slowing migration on astrocytes and stimulating migration on laminin (Figure 4). In other words, depleting catenins altered migration in parallel with the changes in N-cad surface level. Catenin depletion also increased single-cell dissociation, reduced the crowding of leader and follower cells, and increased nuclear YAP1 (see figure below). These findings suggest that the main role of p120-, β-, or α-catenin is to regulate surface N-cad. Since this result does not support a role for catenins transducing an N-cad signal to YAP1, we have not included it in the paper.

Minor comments

1. The quantitative data is required in Fig. 5E.

Quantitative data from three independent experiment are now presented in Figure S2 G.

1. Vinculin is associated with the cadherin-catenin complex and it may not be a good loading control (Fig. 3C and 3L).

The Western blot data has been updated and is now presented in new Figure 3 B and 3 F, with β-tubulin as a loading control.

**Referees cross-commenting**

I totally agree with the other Reviewers' comments and evaluation. As the reviewer-1 pointed out, I also think the experiments are well performed, but it would lack logic at least in part (see my comment-6). In addition, as the reviewer-3 pointed out, the linking mechanism of N-cadherin homophilic interaction with YAP/TAZ signaling is important to improve this manuscript

We hope the revisions have improved the logical flow. We have also added new results showing that YAP/TAZ regulate N-cad protein levels and localization but not N-cad RNA. N-cad is not needed for cell density-dependent regulation of YAP1 localization. The model is shown in Figure 8 I.

Reviewer #2 (Significance (Required)):

Strength N-cadherin has multiple function in cancer and neuronal migration, and both positive and negative effects of N-cadherin on cancer cell migration have been reported. In this regard, different behaviors of N-cadherin in the leader and follower cells of the collective are interesting and may explain the controversial previous results.

Limitation This study reveals various aspects of N-cadherin function in the collective migration of the glioma cell line, but it is unclear whether these findings are applied to pediatric high-grade gliomas in vivo.

Thus, this study is a potentially important and informative to cell biologists and researchers in cancer biology, although this reviewer also found several weak points that should be improved.

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

In this manuscript, the authors explore the role of N-cadherin in the migratory/infiltrative behavior of human pediatric brain tumor cells, in light of their surrounding microenvironment. Their in-depth phenotype analysis allows to document the behavior of migrating cells and revisit the concept of leading/follower migratory cells (somehow more commonly applied to endothelial cells). They suspected that the YAP/TAZ pathway might modulate N-cadherin endocytosis and vice versa, using imagery-based cell tracking.

Major comments

1. To control for co-culture models, migration should be evaluated on decellularized matrices from astrocyte and neuron cultures.

We thank for your suggestion. We tested glioma migration on astrocyte-derived decellularized matrices. The mouse astrocytes we used are known to produce various extracellular matrices with a composition similar to Matrigel, except for laminin α5. (Gritsenko et al., J Cell Sci, 2018). N-cad shRNA cells migrated faster on decellularized ECM than control (Figure 1 I-J and S1 Q). This result agrees with N-cad depletion increasing migration on ECM but is opposite to migration on astrocytes.

1. N-cadherin was stably knocked down with shRNA, which raises the question of adaptative/compensatory mechanisms. First, one could ask what happen in knockout conditions. Similarly, transient siRNA-mediated silencing might help to strengthen the findings. Second, is there any impact of Ncad knock down on alternate adhesive receptors (either cell-cell or cell-ECM). This should be verified with bulk RNAseq.

Transient knockdown with N-cad siRNA also increased migration on laminin-coated surface (Figure S1 L-M). Unfortunately, N-cad depletion with siRNA was short-lived, precluding its use for long-term assays, like coculture with neurons or astrocytes. To test whether there is any impact of N-cad knockdown on alternative adhesion receptors, we performed RNA-Seq (Figure S1 H, Table S2). We found N-cad depletion did not alter expression of other cell-cell and cell-ECM adhesive receptors except CDH3 (2.8-fold increase compared with 7-fold decrease in CDH2). Integrin expression was unchanged.

1. It would be interesting to evaluate the impact of N-cadherin/N-cadherin homotypic interactions on YAP/TAZ signaling, using for instance N-cad-coated surface.

We observed that the homotypic interaction of N-cad with surrounding neurons and astrocytes did not hinder the accumulation of nuclear YAP1 in leader cells (Figure 6 C-D). To further support the idea that N-cad does not directly regulate YAP1 signaling, we have now measured YAP1 localization in cells migrating over N-cad ECD. The new data confirms that N-cad does not directly regulate YAP1 localization (Figure 7 B-C).

1. along this line, the impact of mechanical cues (stiffness, 2D vs 3D) is not explored.

We appreciate your suggestion. It is possible that different mechanical and cytoskeletal cues between leader and follower cells affect YAP1 signaling. In this study, we would like to focus more on the role of N-cad-mediated cell adhesions in YAP signaling.

Minor comments

1. Levels of N-cadherin expression in normal Astro and Neurons to compare with pediatric brain cancer cells (S1C)

A new western blot analysis to show N-cad levels in DMG, PHGG and mouse cerebellar neurons and astrocytes has been added to Figure S1 F.

1. Low versus high density culture conditions should be controlled and its further impact on the YAP/Ncad endocytosis route should be supported experimentally, or to be omitted from their proposed model.

We previously used different size of micropattern discs to control low or high cell density. Smaller cell clusters, with more edge cells and hence fewer cell-cell interactions, had higher nuclear YAP1 (Figure 7 D-E). We have repeated this experiment, including N-cad ECD antibodies to measure N-cad endocytosis. Smaller cell clusters had higher N-cad antibody internalization (Figure 7 F). Together with our evidence that leader cells have higher YAP1 and more N-cad internalization than followers, and that YAP/TAZ knockdown inhibits N-cad internalization, these results high YAP/TAZ in leader cells regulates N-cad internalization.

Reviewer #3 (Significance (Required)):

This paper presents robust image analysis of human pediatric brain tumor migration in the context of the different microenvironment that they might encounter (matrices, neurons, astrocytes). This study brings new concepts on the way N-cadherin might contribute to tumor cell migratory behavior based on the nature of the interactions in which N-cadherin is involved. As a limitation, it remains unclear the mechanism by which N-cadherin endocytosis is driven.

We now discuss the limitations of the study as follows:

“The mechanisms by which YAP1 regulates N-cad levels and trafficking remain to be explored. YAP1 is widely expressed in human brain tumors and strongly associated poor survival. Leader cells expressed higher levels of YAP1-response and wound-healing gene transcripts, but transcript levels of N-cad and proteins known to regulate cadherin traffic, such as p120-catenin, Rab5/11 and Rac1, were similar. Therefore, N-cad is likely regulated at the level of protein synthesis or turnover. More endosomal N-cad recycled to the surface of leader than follower cells, implying that follower cells might divert more N-cad for lysosomal degradation, but our attempts to interfere with N-cad endocytosis or degradation specifically were unsuccessful. Further understanding of the mechanism and function of N-cad recycling for glioma cell migration will require cargo-specific ways to selectively regulate endocytosis and recycling”.

Author Response

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

eLife assessment

This important study combines genetically barcoded rabies viruses with spatial transcriptomics in vivo in the mouse brain to decode connectivity of neural circuits. The data generated by the combination of these approaches in this new way is mostly convincing as the authors provide validation and proof-of-concept that the approach can be successful. While this new combination of established techniques has promise for elucidating brain connectivity, there are still some nuances and caveats to the interpretations of the results that are lacking especially with regards to noting unexpected barcodes either due to unexpected/novel connections or unexpected rabies spread.

In this revised manuscript, we added a new control experiment and additional analyses to address two main questions from the reviewers: (1) How the threshold of glycoprotein transcript counts used to identify source cells was determined, and (2) whether the limited long-range labeling was expected in the trans-synaptic experiment. The new experiments and analyses validated the distribution of source cells and presynaptic cells observed in the original barcoded transsynaptic tracing experiment and validated the choice of the threshold of glycoprotein transcripts. As the reviewers suggested, we also included additional discussion on how future experiments can improve upon this study, including strategies to improve source cell survival and minimizing viral infection caused by leaky expression of TVA. We also provided additional clarification on the analyses for both the retrograde labeling experiment and the trans-synaptic tracing experiment. We modified the Results and Discussion sections on the trans-synaptic tracing experiment to improve clarity to general readers. Detailed changes to address specific comments by reviewers are included below.

Public Reviews:

Reviewer #1 (Public Review):

In this preprint, Zhang et al. describe a new tool for mapping the connectivity of mouse neurons. Essentially, the tool leverages the known peculiar infection capabilities of Rabies virus: once injected into a specific site in the brain, this virus has the capability to "walk upstream" the neural circuits, both within cells and across cells: on one hand, the virus can enter from a nerve terminal and infect retrogradely the cell body of the same cell (retrograde transport). On the other hand, the virus can also spread to the presynaptic partners of the initial target cells, via retrograde viral transmission.

Similarly to previously published approaches with other viruses, the authors engineer a complex library of viral variants, each carrying a unique sequence ('barcode'), so they can uniquely label and distinguish independent infection events and their specific presynaptic connections, and show that it is possible to read these barcodes in-situ, producing spatial connectivity maps. They also show that it is possible to read these barcodes together with endogenous mRNAs, and that this allows spatial mapping of cell types together with anatomical connectivity.

The main novelty of this work lies in the combined use of rabies virus for retrograde labeling together with barcoding and in-situ readout. Previous studies had used rabies virus for retrograde labeling, albeit with low multiplexing capabilities, so only a handful of circuits could be traced at the same time. Other studies had instead used barcoded viral libraries for connectivity mapping, but mostly focused on the use of different viruses for labeling individual projections (anterograde tracing) and never used a retrograde-infective virus.

The authors creatively merge these two bits of technology into a powerful genetic tool, and extensively and convincingly validate its performance against known anatomical knowledge. The authors also do a very good job at highlighting and discussing potential points of failure in the methods.

We thank the reviewer for the enthusiastic comments.

Unresolved questions, which more broadly affect also other viral-labeling methods, are for example how to deal with uneven tropism (ie. if the virus is unable or inefficient in infecting some specific parts of the brain), or how to prevent the cytotoxicity induced by the high levels of viral replication and expression, which will tend to produce "no source networks", neural circuits whose initial cell can't be identified because it's dead. This last point is particularly relevant for in-situ based approaches: while high expression levels are desirable for the particular barcode detection chemistry the authors chose to use (gap-filling), they are also potentially detrimental for cell survival, and risk producing extensive cell death (which indeed the authors single out as a detectable pitfall in their analysis). This is likely to be one of the major optimisation challenges for future implementations of these types of barcoding approaches.

As the reviewer suggested, we included additional discussion about tropism and cytotoxicity in the revised Discussion. Our sensitivity for barcode detection is sufficient, since we estimated (based on manual proofreading) that most barcoded neurons had more than ten counts of a barcode in the trans-synaptic tracing experiment. The high sensitivity may potentially allow us to adapt next-generation rabies virus with low replication, such as the third generation ΔL rabies virus (Jin et al, 2022, biorxiv) in future optimizations.

Overall the paper is well balanced, the data are well presented and the conclusions are strongly supported by the data. Impact-wise, the method is definitely going to be useful for the neurobiology research community.

We thank the reviewer for her/his enthusiasm.

Reviewer #2 (Public Review):

Although the trans-synaptic tracing method mediated by the rabies virus (RV) has been widely utilized to infer input connectivity across the brain to a genetically defined population in mice, the analysis of labeled pre-synaptic neurons in terms of cell-type has been primarily reliant on classical low-throughput histochemical techniques. In this study, the authors made a significant advance toward high-throughput transcriptomic (TC) cell typing by both dissociated single-cell RNAseq and the spatial TC method known as BARseq to decode a vast array of molecularly labeled ("barcoded") RV vector library. First, they demonstrated that a barcoded-RV vector can be employed as a simple retrograde tracer akin to AAVretro. Second, they provided a theoretical classification of neural networks at the single-cell resolution that can be attained through barcoded-RV and concluded that the identification of the vast majority (ideally 100%) of starter cells (the origin of RV-based trans-synaptic tracing) is essential for the inference of single-cell resolution neural connectivity. Taking this into consideration, the authors opted for the BARseq-based spatial TC that could, in principle, capture all the starter cells. Finally, they demonstrated the proof-of-concept in the somatosensory cortex, including infrared connectivity from 381 putative pre-synaptic partners to 31 uniquely barcoded-starter cells, as well as many insightful estimations of input convergence at the cell-type resolution in vivo. While the manuscript encompasses significant technical and theoretical advances, it may be challenging for the general readers of eLife to comprehend. The following comments are offered to enhance the manuscript's clarity and readability.

We modified the Results and Discussion sections on the trans-synaptic tracing experiment to improve clarity to general readers. We separated out the theoretical discussion about barcode sharing networks as a separate subsection, explicitly stated the rationale of how different barcode sharing networks are distinguished in the in situ trans-synaptic tracing experiment, and added additional discussion on future optimizations. Detailed descriptions are provided below.

Major points:

1. I find it difficult to comprehend the rationale behind labeling inhibitory neurons in the VISp through long-distance retrograde labeling from the VISal or Thalamus (Fig. 2F, I and Fig. S3) since long-distance projectors in the cortex are nearly 100% excitatory neurons. It is also unclear why such a large number of inhibitory neurons was labeled at a long distance through RV vector injections into the RSP/SC or VISal (Fig. 3K). Furthermore, a significant number of inhibitory starter cells in the somatosensory cortex was generated based on their projection to the striatum (Fig. 5H), which is unexpected given our current understanding of the cortico-striatum projections.

The labeling of inhibitory neurons can be explained by several factors in the three different experiments.

(1) In the scRNAseq-based retrograde labeling experiment (Fig. 2 and Fig. S3), the injection site VISal is adjacent to VISp. Because we dissected VISp for single-cell RNAseq, we may find labeled inhibitory neurons at the VISp border that extend short axons into VISal. We explained this in the revised Results.

(2) In the in situ sequencing-based retrograde labeling experiment (Fig. 3,4), the proximity between the two injection sites VISal and RSP/SC, and the sequenced areas (which included not only VISp but also RSP) could also contribute to labeling through local axons of inhibitory neurons. Furthermore, because we also sequenced midbrain regions, inhibitory neurons in the superior colliculus could pick up the barcodes through local axons. We included an explanation of this in the revised Results.

(3) In the trans-synaptic tracing experiment, we speculate that low level leaky expression from the TREtight promoter led to non-Cre-dependent expression in many neurons. To test this hypothesis, we first performed a control injection in which we saw that the fluorescent protein expression were indeed restricted to layer 5, as expected from corticostriatal labeling. Based on the labeling pattern, we estimated that about 12 copies of the glycoprotein transcript per cell would likely be needed to achieve fluorescent protein expression. Since many source cells in our experiment were below this threshold, these results support the hypothesis that the majority of source cells with low level expression of the glycoprotein were likely Cre-independent. Because these cells could still contribute to barcode sharing networks, we could not exclude them as in a conventional bulk trans-synaptic tracing experiment. In future experiments, we can potentially reduce this population by improving the helper AAV viruses used to express TVA and the glycoprotein. We included this explanation in Results and more detailed analysis in Supplementary Note 2, and discussed potential future optimizations in the Discussion. This new analysis in Supplementary Note 2 is also related to the Reviewer’s question regarding the threshold used for determining source cells (see below).

1. It is unclear as to why the authors did not perform an analysis of the barcodes in Fig. 2. Given that the primary objective of this manuscript is to evaluate the effectiveness of multiplexing barcoded technology in RV vectors, I would strongly recommend that the authors provide a detailed description of the barcode data here, including any technical difficulties or limitations encountered, which will be of great value in the future design of RV-barcode technologies. In case the barcode data are not included in Fig. 2, I would suggest that the authors consider excluding Fig. 2 and Fig. S1-S3 in their entirety from the manuscript to enhance its readability for general readers.

In the single-cell RNAseq-based retrograde tracing, all barcodes recovered matched to known barcodes in the corresponding library. We included a short description of these results in the revised manuscript.

1. Regarding the trans-synaptic tracing utilizing a barcoded RV vector in conjunction with BARseq decoding (Fig. 5), which is the core of this manuscript, I have a few specific questions/comments. First, the rationale behind defining cells with only two rolonies counts of rabies glycoprotein (RG) as starter cells is unclear. Why did the authors not analyze the sample based on the colocalization of GFP (from the AAV) and mCherry (from the RV) proteins, which is a conventional method to define starter cells? If this approach is technically difficult, the authors could provide an independent histochemical assessment of the detection stringency of GFP positive cells based on two or more colonies of RG.

In situ sequencing does not preserve fluorescent protein signals, so we used transcript counts to determine which cells expressed the glycoprotein. We have added new analyses in the Results and in Supplementary Note 2 to determine the transcript counts that were equivalent to cells that had detectable BFP expression. We found that BFP expression is equivalent to ~12 counts of the glycoprotein transcript per cell, which is much higher than the threshold we used. However, we could not solely rely on this estimate to define the source cells, because cells that had lower expression of the glycoprotein (possibly from leaky Cre-independent expression) may still pass the barcodes to presynaptic cells. This can lead to an underestimation of double-labeled and connected-source networks and an overestimation of single-source networks and can obscure synaptic connectivity at the cellular resolution. We thus used a very conservative threshold of two transcripts in the analysis. This conservative threshold will likely overestimate the number of source cells that shared barcodes and underestimate the number of single-source networks. Since this is a first study of barcoded transsynaptic tracing in vivo, we chose to err on the conservative side to make sure that the subsequent analysis has single-cell resolution. Future characterization and optimization may lead to a better threshold to fully utilize data.

Second, it is difficult to interpret the proportion of the 2,914 barcoded cells that were linked to barcoded starter cells (single-source, double-labeled, or connected-source) and those that remained orphan (no-source or lost-source). A simple table or bar graph representation would be helpful. The abundance of the no-source network (resulting from Cre-independent initial infection of the RV vector) can be estimated in independent negative control experiments that omit either Cre injection or AAV-RG injection. The latter, if combined with BARseq decoding, can provide an experimental prediction of the frequency of double-labeled events since connected-source networks are not labeled in the absence of RG.

We have added Table 2, which breaks down the 2,914 barcoded cells based on whether they are presynaptic or source cells, and which type of network they belong to. We agree with the reviewer that the additional Cre- or RG- control experiments in parallel would allow an independent estimate of the double labeled networks and the no-source networks. We have included added a discussion of possible controls to further optimize the trans-synaptic tracing approach in future studies in the Discussion.

Third, I would appreciate more quantitative data on the putative single-source network (Fig. 5I and S6) in terms of the distribution of pre- and post-synaptic TC cell types. The majority of labeling appeared to occur locally, with only two thalamic neurons observed in sample 25311842 (Fig. S6). How many instances of long-distance labeling (for example, > 500 microns away from the injection site) were observed in total? Is this low efficiency of long-distance labeling expected based on the utilized combinations of AAVs and RV vectors? A simple independent RV tracing solely detecting mCherry would be useful for evaluating the labeling efficiency of the method. I have experienced similar "less jump" RV tracing when RV particles were prepared in a single step, as this study did, rather than multiple rounds of amplification in traditional protocols, such as Osakada F et al Nat Protocol 2013.

We imaged an animal that was injected in parallel to assess labeling (now included in Supplementary Note 2 and Supp. Fig. S5). The labeling pattern in the newly imaged animal was largely consistent with the results from the barcoded experiment: most labeled neurons were seen in the vicinity of the injection site, and sparser labeling was seen in other cortical areas and the thalamus. We further found that most neurons that were labeled in the thalamus were about 1 mm posterior to the center of the injection site, and thus would not have been sequenced in the in situ sequencing experiment (in which we sequenced about 640 µm of tissue spanning the injection site).

In addition, we found that the bulk of the cells that expressed mCherry from the rabies virus only partially overlapped with the area that contained cells co-expressing BFP with the rabies glycoprotein. Moreover, very few cells co-expressed mCherry and BFP, which would be considered source cells in a conventional mono-synaptic tracing experiment. The small numbers of source cells likely also contributed to the sparseness of long-range labeling in the barcoded experiment.

These interpretations and comparisons to the barcoded experiment are now included in Supplementary Note 2.

Reviewer #3 (Public Review):

The manuscript by Zhang and colleagues attempts to combine genetically barcoded rabies viruses with spatial transcriptomics in order to genetically identify connected pairs. The major shortcoming with the application of a barcoded rabies virus, as reported by 2 groups prior, is that with the high dropout rate inherent in single cell procedures, it is difficult to definitively identify connected pairs. By combining the two methods, they are able to establish a platform for doing that, and provide insight into connectivity, as well as pros and cons of their method, which is well thought out and balanced.

Overall the manuscript is well-done, but I have a few minor considerations about tone and accuracy of statements, as well as some limitations in how experiments were done. First, the idea of using rabies to obtain broader tropism than AAVs isn't really accurate - each virus has its own set of tropisms, and it isn't clear that rabies is broader (or can be made to be broader).

As the reviewer suggested, we toned down this claim and stated that rabies virus has different tropism to complement AAV.

Second, rabies does not label all neurons that project to a target site - it labels some fraction of them.

We meant to say that retrograde labeling is not restricted to labeling neurons from a certain brain region. We have clarified in the text.

Third, the high rate of rabies virus mutation should be considered - if it is, or is not a problem in detecting barcodes with high fidelity, this should be noted.

Our analysis showed that sequencing 15 bases was sufficient to tolerate a small number of mismatches in the barcode sequences and could distinguish real barcodes from random sequences (Fig. 4A). Thus, we can tolerate mutations in the barcode sequence. We have clarified this in the text.

Fourth, there are a number of implicit assumptions in this manuscript, not all of which are equally backed up by data. For example, it is not clear that all rabies virus transmission is synaptic specific; in fact, quite a few studies argue that it is not (e.g., detection of rabies transcripts in glial cells). Thus, arguments about lost-source networks and the idea that if a cell is lost from the network, that will stop synaptic transmission, is not clear. There is also the very real propensity that, the sicker a starter cell gets, the more non-specific spread of virus (e.g., via necrosis) occurs.

We agree with the reviewer that how strictly virus transmission is restricted to synapses remains a hotly debated question in the field, and this question is relevant not only to techniques based on barcoded rabies tracing, but to all trans-synaptic tracing experiments. A barcoding-based approach can generate single-cell data that enable direct comparison to other data modalities that measure synaptic connectivity, such as multi-patch and EM. These future experiments may provide additional insights into the questions that the reviewer raised. We have included additional discussion about how non-synaptic transmission of barcodes because of the necrosis of source cells may affect the analysis in the Discussion.

Regarding the scenario in which the source cell dies, we agree with the reviewer and have clarified in the revised manuscript.

Fifth, in the experiments performed in Figure 5, the authors used a FLEx-TVA expressed via a retrograde Cre, and followed this by injection of their rabies virus library. The issue here is that there will be many (potentially thousands) of local infection events near the injection site that TVA-mediated but are Cre-dependent (=off-target expression of TVA in the absence of Cre). This is a major confound in interpreting the labeling of these cells. They may express very low levels of TVA, but still have infection be mediated by TVA. The authors did not clearly explore how expression of TVA related to rabies virus infection of cells near the rabies injection site. A modified version of TVA, such as 66T, should have been used to mitigate this issue. Otherwise, it is impossible to determine connectivity locally. The authors do not go to great lengths to interpret the findings of these observations, so I am not sure this is a critical issue, but it should be pointed out by the authors as a caveat to their dataset.

We agree with the reviewer that this type of infection could potentially be a major contributor to no-source networks, which were abundant in our experiment. Because small no-source networks were excluded from our analyses, and large no-source networks were only included for barcodes with low frequency (i.e., it would be nearly impossible statistically to generate such large no-source networks from independent infections), we believe that the effect of independent infections on our analyses were minimized. We have added a control experiment in Fig S5 and Supplementary Note 2, which further supported the hypothesis that there were many independent infections. We also included additional discussion about how this can be assessed and optimized in future studies in the Discussion.

Sixth, the authors are making estimates of rabies spread by comparison to a set of experiments that was performed quite differently. In the two studies cited (Liu et al., done the standard way, and Wertz et al., tracing from a single cell), the authors were likely infecting with a rabies virus using a high multiplicity of infection, which likely yields higher rates of viral expression in these starter cells and higher levels of input labeling. However, in these experiments, the authors need to infect with a low MOI, and explicitly exclude cells with >1 barcode. Having only a single virion trigger infection of starter cells will likely reduce the #s of inputs relative to starter neurons. Thus, the stringent criteria for excluding small networks may not be entirely warranted. If the authors wish to only explore larger networks, this caveat should be explicitly noted.

In the trans-synaptic labeling experiment, we actually used high rabies titer (200 nL, 7.6e10 iu/mL) that was comparable to conventional rabies tracing experiments. We did not exclude cells with multiple barcodes (as opposed to barcodes in multiple source cells), because we could resolve multiple barcodes in the same cell and indeed found many cells with multiple barcodes. We have clarified this in the text.

Overall, if the caveats above are noted and more nuance is added to some of the interpretation and discussion of results, this would greatly help the manuscript, as readers will be looking to the authors as the authority on how to use this technology.

In addition to addressing the specific concerns of the reviewer as described above, we modified the Results and Discussion sections on the trans-synaptic tracing experiment to improve clarity to general readers and expanded the discussion on future optimizations.

Reviewer #1 (Recommendations For The Authors):

The scientific problem is clearly stated and well laid out, the data are clearly presented, and the experiments well justified and nicely discussed. It was overall a very enjoyable read. The figures are generally nice and clear, however, I find the legends excessively concise. A bit too often, they just sort of introduce the title of the panel rather than a proper explanation of what it is depicted. A clear case is for example visible in Fig 2, where the description of the panels is minimal, but this is a general trend of the manuscript. This makes the figures a bit hard to follow as self-contained entities, without having to continuously go back to the main text. I think this could be improved with longer and more helpful descriptions.

We have revised all figure legends to make them more descriptive.

Other minor things:

In the cDNA synthesis step for in-situ sequencing, I believe the authors might have forgotten one detail: the addition of aminoallyl dUTP to the RT reaction. If I recall correctly this is done in BARseq. The fact that the authors crosslink with BS-PEG on day 2, makes me suspect they spike in these nucleotides during the RT but this is not specified in the relevant step. Perhaps this is a mistake that needs correction.

The RT primers we used have an amine group at 5’, which directly allows crosslinking. Thus, we did not need to spike in aminoallyl dUTP in the RT reaction. We have clarified this in the Methods.

Reviewer #2 (Recommendations For The Authors):

Throughout the manuscript, there are frequent references to the "Methods" section for important details. However, it can be challenging to determine which specific section of the Methods the authors are referring to, and in some cases, a thorough examination of the entire Methods section fails to locate the exact information needed to support the authors' claims. Below are a few specific examples of this issue. The authors are encouraged to be more precise in their references to the Methods section.

In the revised manuscript, we numbered each subsection of Methods and updated pointers and associated hyperlinks in the main text to the subsection numbers.

• On page 7, line 14, it is unclear how the authors compared the cell marker gene expression with the marker gene expression in the reference cell type.

We have clarified in the revised manuscript.

• On page 7, line 33, the authors note that some barcodes may have been missed during the sequencing of the rabies virus libraries, but the Methods section lacked a convincing explanation on this issue (see my point 2 above).

We included a separate subsection on the sequencing of rabies libraries and the analysis of the sequencing depth in the Methods. In this new subsection, we further clarified our reasoning for identifying the lack of sequencing depth as a reason for missing barcodes, especially in comparison to sequencing depth required for establishing exact molecule counts used in established MAPseq and BARseq techniques with Sindbis libraries.

• On page 9, line 44, the authors state that they considered a barcode to be associated with a cell if they found at least six molecules of that barcode in a cell, as detailed in the Methods section. However, the rationale behind this level of stringency is not provided in the Methods.

We initially chose this threshold based on visual inspection of the sequencing images of the barcoded cells. Because the labeled cell types were consistent with our expectations (Fig. 4E-G), we did not further optimize the threshold for detecting retrogradely labeled barcoded cells.

• I have noticed that some important explanations of figure panels are missing in the legends, making it challenging to understand the figures. Below are typical examples of this issue.

In addition to the examples that the reviewer mentioned below, we also revised many other figure panels to make them clear to the readers.

• In Fig. 2, "RV into SC" in panel C does not make sense, as RV was injected into the thalamus. There is no explanation of the images in this panel C.

We have corrected the typo in the revision.

• In Fig. 3, information on the endogenous gene panel for cell type classification (Table S3) could be mentioned in the legend or corresponding text.

We now cite Table S3 both in Fig 3 legend and in the main text. We also included a list of the 104 cell type marker genes we used in Table S3.

• In panel J, it is unclear why the total number of BC cells is 2,752, and not 4,130 as mentioned in the text.

This is a typo. We have corrected this in the revision. The correct number (3,746) refers to the number of cells that did not belong to either of the two categories at the bottom of the panel, and not the total number of neurons. To make this clear, we now also include the total number of barcoded cells at the top of the panel.

• In Fig. 4, the definitions of "+" and "−" symbols in panels K and L are unclear. Also, it seems that the second left column of panel K should read "T −."

We corrected the typo in K, further clarified the “Area” labels, and changed the “S” label in 4K to “−”. This change does not change the original meaning of the figure: when considering the variance explained in L4/5 IT neurons, considering the subclass compositional profile is equivalent to not using the compositional profiles of cell types, because L4/5 IT neurons all belong to the same subclass (L4/5 IT subclass). Although operationally we simply considered subclass-level compositional profiles when calculating the variance explained, we think that changing this to “−” is clearer for the readers.

• In Fig. 5, panel E is uninterpretable.

We revised the main text and the figure to clarify how we manually proofread cells to determine the QC thresholds for barcoded cells. These plots showed a summary of the proofreading. We also revised the figures to indicate that they showed the fraction of barcoded cells that were considered real after proofreading. In the revised version, we moved these plots to Fig. S5.

• In Fig. S1, I do not understand the identity of the six samples on the X-axis of panel A (given that only two animals were described in the main text) and what panel B shows, including the definition of map_cluster_conf and map_cluster_corr.

In the revised Fig. S1, we made it more explicit that the six animals include both animals used for retrograde tracing (2 animals) and those used for trans-synaptic tracing (4 animals). We updated the y axis labels to be more readable and cited the relevant Methods section for definitions.

• In Fig. S2, please provide the definitions of blue and red dots and values in panel A, as well as the color codes and size of the circles in panel B. My overall impression from panel B is that there is no significant difference between RV-infected and non-infected cells. The authors should provide more quantitative and statistical support for the claim that "RV-infected cells had higher expression of immune response-related genes."

We toned down the statement to “Consistent with previous studies […], some immune response related genes were up-regulated in virus-infected cells compared to non-infected cells.” Because the main point of the single-cell RNAseq analysis was that rabies did not affect the ability to distinguish transcriptomic types, the change in immune response-related genes was not essential to the main conclusions. We clarified the red and blue dots in panel A and changed panel B to show the top up-regulated immune response-related genes in the revised manuscript.

• In Fig. S3, the definitions of the color code and circle size are missing.

We have added the legends in Fig. S3.