Author Response

Reviewer 1

Employing in vitro and Drosophila model, the authors interrogate which domain of Hsp27 binds to which region on Tau, and how these interactions facilitate the proteinaceous aggregation. They utilized various biochemical, biophysical, cellular, and genetic tools to dissect the association, and identified the structural basis for the specific recognition of Hsp27 to pathogenic p-Tau. Conceivably, Hsp27 may play some role in preventing Tau abnormal aggregation and p-Tau pathology in AD. Overall, the data support the main claim, especially, the biophysical data are very impressive. Nevertheless, the manuscript could be strengthened by complementary cellular or biochemical methods for validation. For example, the authors can use a stably transfected Tau cell line to interrogate Hsp27's role in its cellular aggregation or proteinaceous inclusions by immunoblotting. Immunofluorescent and immunohistochemical staining and IB with different antibodies may be conducted to validate the observations.

REPLY: We sincerely thank the reviewer for the positive assessment of our work, and for providing very insightful suggestions. We appreciate the reviewer for considering our biophysical data to be impressive. We totally agree with the reviewer that the work could be strengthened by complementary cellular methods for validation. In our work, we used the Drosophila tauopathy model, where expression of human TauR406W in the Drosophila nervous system leads to age-dependent neurodegeneration recapitulating some of the salient features of tauopathy in FTDP-171,2, to interrogate the role of Hsp27 in aggregation and proteinaceous inclusions of pTau.

In our Drosophila Tau model study, three different antibodies including a total Tau antibody 5A63, a pTauSer262 specific antibody4, and a hyper-phosphorylated Tau antibody AT8 that recognizes hyper-phosphorylation of Tau at Ser202 and Thr205 sites5 were used in western blot analysis to explore the role of Hsp27. As shown in Figure R1-1A and 1B, overexpression of Hsp27 significantly reduced the level of both pTauSer262 and hyper-phosphorylated Tau at both 2 and 10 days after eclosion (DAE). In addition, we further examined the morphology of the fly brain as well as the accumulation of hyper-phosphorylated Tau by immunofluorescence staining. Consistent with previous findings, brains with neuronal expression of TauR406W exhibited an accumulation of filamentous pTau and a reduction of brain neuropil size indicative of neurodegeneration (Figure R11C-F). Importantly, overexpression of Hsp27 restored the size of brain neuropil and suppressed the accumulation of filamentous pTau (Figure R1-1C-F), suggesting that Hsp27 protects against mutant TauR406W - induced neurodegeneration. Taken together, our Drosophila results show that Hsp27 protects against synaptic dysfunction in a Drosophila tauopathy model by reducing pTau aggregation, which well supports our biophysical data.

Figure R1-1 Hsp27 reduces pTau level and protects against pTau-induced synaptopathy in Drosophila. (This figure represents Fig. 2A-F in the revised manuscript) (A) Brain lysates of 2 and 10 days after eclosion (DAE) wild-type (WT) flies (lanes 1 and 6), flies expressing human Tau with GFP (lanes 4 and 9), or human Tau with Hsp27 (lanes 5 and 10) in the nervous system were probed with antibodies for disease-associated phospho-tau epitopes S262, Ser202/Thr205 (AT8), and total Tau (5A6). Actin was probed as a loading control. Brain lysates of flies carrying only UAS elements were loaded for control (lanes 2, 3, 7, and 8). (B) Quantification of protein fold changes in (A). The levels of Tau species were normalized to actin. Fold changes were normalized to the Tau+GFP group at 2 DAE. n = 3. (C) Brains of WT flies or flies expressing Tau+GFP or Tau+Hsp27 in the nervous system at 2 DAE were probed for AT8 (heatmap) and Hsp27 (green), and stained with DAPI (blue). Scale bar, 30 μm. (D-F) Quantification of the Hsp27 intensity (D, data normalized to WT), brain optic lobe size (E), and AT8 intensity (F, data normalized to the Tau+GFP group). n = 4.

Reviewer 2

Abnormal accumulation and aggregation of amyloid-β protein are one of the main pathological hallmarks of Alzheimer's disease. It is well known that molecular chaperones play central roles in regulating tau function and amyloid assembly in disease. In this manuscript, Zhang, Zhu, Lu, Liu, et al., have investigated that Hsp27, a member of the small heat shock protein, specifically binds to phosphorylated Tau, which prevents pTau fibrillation in vitro and in a Drosophila tauopathy model. Using NMR spectroscopy and cross-linking mass spectrometry, the authors found that the N-terminal domain of Hsp27 directly binds to phosphorylation sites of pTau. Overall, the study is important and provides the demonstration of interactions between Hsp27 and pTau.

REPLY: We sincerely thank the reviewer for the positive remarks of this work, and appreciate that the reviewer summarizes the major conclusions of our manuscript, and evaluates our work is important in the area of fundamental biology of the interaction between chaperones and clients, and its implications in AD pathology.

Author Response

Reviewer #2 (Public Review):

Activation of TEAD-dependent transcription by YAP/TAZ has been implicated in the development and progression of a significant number of malignancies. For example, loss of function mutations in NF2 or LATS1/2 (known upstream regulators that promote YAP phosphorylation and its retention and degradation in the cytoplasm) promote YAP nuclear entry and association with TEAD to drive oncogenic gene transcription and occurs in >70% of mesothelioma patients. High levels of nuclear YAP have also been reported for a number of other cancer cell types. As such, the YAP-TEAD complex represents a promising target for drug discovery and therapeutic intervention. Based on the recently reported essential functional role for TEAD palmitoylation at a conserved cysteine site, several groups have successfully targeted this site using both reversible binding non-covalent TEAD inhibitors (i.e., flufenamic acid (FA), MGH-CP1, compound 2 and VT101~107), as well as covalent TEAD inhibitors (i.e., TED-347, DC-TEADin02, and K-975), which have been demonstrated to inhibit YAP-TEAD function and display antitumor activity in cells and in vivo.

Here, Fan et al. disclose the development of covalent TEAD inhibitors and report on the therapeutic potential of this class of agents in the treatment of TEAD-YAP-driven cancers (e.g., malignant pleural mesothelioma (MPM)). Optimized derivatives of a previously reported flufenamic acid-based acrylamide electrophilic warhead-containing TEAD inhibitor (MYF-01-37, Kurppa et al. 2020 Cancer Cell), which display improved biochemical- and cell-based potency or mouse pharmacokinetic profiles (MYF-03-69 and MYP-03-176) are described and characterized.

Strengths:

All of the authors' claims and conclusions are very well supported and justified by the data that is provided. Clear improvements in biochemical- and cell-based potencies have been made within the compound series. Cell-based selective activities in the HIPPO pathway defective versus normal/control cell types are established. Transcriptional effects and the regulation of BMF proapoptotic mRNA levels are characterized. A 1.68 A X-Ray co-crystal structure of MYF-03-69 covalently bound to TEAD1 via Cys359 is provided. In vivo efficacy in a relevant xenograft is demonstrated, using a 30 mg/kg, BID PO dose.

We thank the reviewer for appreciating and highlighting the strengths of our study.

Weaknesses:

Beyond the impact on BMF gene regulation, new biological insights reported here for this compound series are moderate. Progress and differentiation with respect to activity and/or ADME PK profiles relative to the very closely related and previously described (Keneda et al. 2020 Am J Cancer Res 10:4399. PMID 33415007) acrylamide-based covalent TEAD inhibitor K-975 (identical 11 nM cell-based potencies when compared head-to-head and identical reported in vivo efficacy doses of 30 mg/kg) is not entirely clear. Demonstration of on-target in vivo activity is lacking (e.g., impact on BMF gene expression at the evaluated exposure levels).

We thank the reviewer’s question. We have compared mouse liver microsome stability and hepatocyte stability of K-975 and MYF-03-176 and found that K-975 is metabolically less stable.

Consistently, when NCI-H226 cells derived xenograft mice were dosed with 30 mg/kg K-975 twice daily, the tumors kept growing and reach more than 1.5-fold volume on 14th day. While with the same dosage, MYF-03-176 showed a significant tumor regression. K-975 did not reach such efficacy even with 100 or 300 mg/kg twice daily, either in NCI-H226 or MSTO-211H CDX mouse model according to the paper (Keneda et al. 2020 Am J Cancer Res 10:4399).

To demonstrate the on-target in vivo activity, we tested expression of the TEAD downstream genes and BMF in tumor sample after 3-day BID treatment (PD study) and we observed reduction of CTGF, CYR61, ANKRD1 and an increase of BMF, which indicates an on-target activity in vivo.

Author Response

Reviewer #2 (Public Review):

This paper by Angueyra, et al., adds to the field’s current understanding of photoreceptor specification and factors regulating opsin expression in vertebrates. Current models of specification of vertebrate photoreceptors are largely based on studies of mammals. However, a great number of animals including teleosts express a wider array of photoreceptor subtypes. Zebrafish for example have 4 distinct cone subtypes and rods. The approach is sound and the data are quite convincing. The only minor weaknesses are that the statistical analyses need to be revisited and the discussion should be a bit more focused.

To identify differentially expressed transcription factors, the authors performed bulk RNA-seq of pooled, hand-sorted photoreceptors. The selection criterion was tightly controlled to limit unhealthy cells and cellular debris from other photoreceptors subtypes. The pooling of cells provided a considerable depth of sequencing, orders of magnitude better than scSeq. The authors identified known transcription factors and several that appear to be novel or their role has not been determined. The data are made available on the PIs website as is a program to access and compare the gene expression data.

The authors then used CRISPR/Cas9 gene targeting of two known and several novel factors identified in their analysis for effects on cell fate decisions and opsin expression. Phenotyping performed on the injected larvae is possible, and the target genes were applied and sequenced to demonstrate the efficiency of the gene targeting. Targeting of 2 genes with know functions in photoreceptor specification in zebrafish, Tbx2b and Foxq2 resulted in the anticipated changes in cell fate, albeit, the strength of the alterations in cell fate in the F0 larvae appears to be less than the published phenotypes for the inherited alleles. Interestingly, the authors also identified the expression of an RH2 opsin in the SWS2 another cone type. The changes are subtle but important.

The authors then targeted tbx2a, the function of which was not known. The result is quite interesting as it matches the increase of rods and decrease of UV cones observed in tbx2b mutants. However, the injected animals also showed RH2 opsin expression but are now in the LWS cone subtype. These data suggest that Tbx2 transcription factors repress misexpression of opsins in the wrong cell type.

The authors also show that targeting additional differentially expressed factors does not affect photoreceptor fate or survival in the time frame investigated. These are important data to present. For these or any of the other targeted genes above, did the authors test for changes in photoreceptor number or survival?

We have attempted to address this point, but the answer is not clear cut. We used activated caspase-3 inmmunolabeling as a marker of apoptosis (Lusk and Kwan 2022). At 5 dpf, the age we chose to make quantifications, we don’t see an increase in activated caspase-3 positive cells when we compare control and tbx2a F0 mutants (Reviewer Figure 1A-B). Labeled cells are very rare and located near the ciliary marginal zone irrespective of genotype. This suggests that there is no detectable active death at this late stage of development in tbx2 F0 mutants. Earlier in development, at 3 dpf, when photoreceptor subtypes first appear, there is also a normal wave of apoptosis in the retina (Blume et al. 2020; Biehlmaier, Neuhauss, and Kohler 2001), resulting in many cells positive for activated caspase-3; our preliminary quantifications don’t show a marked increase in the number of labeled cells in tbx2a F0 mutants, but we consider that it’s likely that subtle effects might be obscured by the physiological wave of apoptosis (Reviewer Figure 1C-D).

Reviewer Figure 1 - Assessment of apoptosis in tbx2a F0 mutants. (A-B) Confocal images of 5 dpf larval eyes of control (A and A’) and tbx2a F0 mutants (B and B’) counterstained with DAPI (grey) and immunolabeled against activated Caspase 3 (yellow) show sparse and dim labeling, restricted to cells located in the ciliary marginal zone, without clear differences between groups. (C-D) Confocal images of 3 dpf larval eyes of control (C and C’) and tbx2a F0 mutants (D and D’) immunolabeled against activated Caspase 3 show many positive cells, located in all retinal layers, as expected from physiological apoptosis at this stage of development and without clear differences between groups.

Furthermore, the additional single-cell RNA-seq datasets we have reanalyzed suggest that tbx2a and tbx2b are expressed by other retinal neurons and progenitors and not just photoreceptors (Reviewer Figure 2), further confounding attempts at the quantification of apoptosis specifically in photoreceptor progenitors.

Reviewer Figure 2 – Expression of tbx2 paralogues across retinal cell types. The transcription factors tbx2a and tbx2b are expressed by many retinal cells. Plots show average counts across clusters in RNA-seq data obtained by Hoang et al. (2020).

At this stage, we consider that fully resolving this issue is important and will require considerably more work, which we will pursue in the future using full germline mutants and live-imaging experiments.

Reviewer #3 (Public Review):

Angueyra et al. tried to establish the method to identify key factors regulating fate decisions in the retinal visual photoreceptor cells by combining transcriptomic and fast genome editing approaches. First, they isolated and pooled five subtypes of photoreceptor cells from the transgenic lines in each of which a specific subtype of photoreceptor cells are labeled by fluorescence protein, and then subjected them to RNA-seq analyses. Second, by comparing the transcriptome data, they extracted the list of the transcription factor genes enriched in the pooled samples. Third, they applied CRISPR-based F0 knockout to functionally identify transcription factor genes involved in cell fate decisions of photoreceptor subtypes. To benchmark this approach, they initially targeted foxq2 and nr2e3 genes, which have been previously shown to regulate S-opsin expression and S-cone cell fate (foxq2) and to regulate rhodopsin expression and rod fate (nr2e3). They then targeted other transcription factor genes in the candidate list and found that tbx2a and tbx2b are independently required for UV-cone specification. They also found that tbx2a expressed in the L-cone subtype and tbx2b expressed in L-cones inhibit M-opsin gene expression in the respective cone subtypes. From these data, the authors concluded that the transcription factors Tbx2a and Tbx2b play a central role in controlling the identity of all photoreceptor subtypes within the retina.

Overall, the contents of this manuscript are well organized and technically sound. The authors presented convincing data, and carefully analyzed and interpreted them. It includes an evaluation of the presented data on cell-type specific transcriptome by comparing it with previously published ones. I think the current transcriptomic data will be a valuable platform to identify the genes regulating cell-type specific functions, especially in combination with the fast CRISPR-based in vivo screening methods provided here. I hope that the following points would be helpful for the authors to improve the manuscript appropriately.

1) The manuscript uses the word “FØ” quite often without any proper definition. I wonder how “Ø” should be pronounced - zero or phi? This word is not common and has not been used in previous publications. I feel the phrase “F0 knockout,” which was used in the paper cited by the authors (Kroll et al 2021), is more straightforward. If it is to be used in the manuscript, please define “FØ” and “CRISPR-FØ screening” appropriately, especially in the abstract.

We have made changes to replace “FØ” to “F0.” In our other citation (Hoshijima et al., 2019), “F0 embryo” was used throughout the paper. Following our references and Dr Kojima’s suggestion, we adopted “F0 mutant larva” as the most straightforward and less confusing term. We have also made changes in the abstract to define our approach more clearly and made appropriate changes throughout the manuscript.

2) Figure 1-supplement 1 shows that opn1mw4 has quite high (normalized) FPKM in one of the S-cone samples in contrast to the least (or no) expression in the M-cone samples, in which opn1mw4 is expected to be detected. The authors should address a possible origin of this inconsistent result for opn1mw4 expression as well as a technical limitation of using the Tg(opn1mw2:egfp) line for detection of opn1mw4 expression in the GFP-positive cells.

In Figure 1 - Supplement 1, we had attempted to provide a summarized figure of all phototransduction genes, but the big differences in expression levels — in particular, the high expression of opsins genes — forced us to use gene-by-gene normalization for display. Without normalization, the expression of opn1mw4 is very low across all samples, and its detection in that sole S-cone sample can likely be attributed to some degree of inherent noise in our methods. We have revised Figure 1 - Supplement 1: we find that we can avoid gene-by-gene normalization and still provide a good summary of the expression of phototransduction genes if the heatmap is broken down by gene families, which have more similar expression levels. In addition, we have added caveats to the use of the Tg(opn1mw2:egfp) line as our sole M-cone marker in the results section describing our RNA-seq approach, including our inability to provide data on Opn1mw4-expressing M cones.

3) The manuscript lacks a description of the sampling time point. It is well known that many genes are expressed with daily (or circadian) fluctuation (cf. Doherty & Kay, 2010 Annu. Rev. Genet.). For example, the cone-specific gene list in Fig.2C includes a circadian clock gene, per3, whose expression was reported to fluctuate in a circadian manner in many tissues of zebrafish including the retina (Kaneko et al. 2006 PNAS). It appears to be cone-specific at this time point of sample collection as shown in Fig.2, but might be expressed in a different pattern at other time points (eg, rod expression). The authors should add, at least, a clear description of the sampling time points so as to make their data more informative.

We have included this information in the materials and methods. We collected all our samples during the most active peak of the zebrafish circadian rhythm between 11am and 2pm (3h to 6h after light onset) to avoid the influence of circadian fluctuations in our analysis.

Author Response

Reviewer #1 (Public Review):

The authors use a newly developed object-space memory task comprising of a "Stable" version and "Overlapping" version where two objects are presented in two locations per trial in a square open field. Each version consists of 5 training trials of 5-min presentations of an object-space configuration, with both object locations staying constant across training trials in the Stable condition, and only one object location staying fixed in the Overlapping condition. Memory is tested in a test trial 24 hours later where the opposite configuration is presented - overlapping configuration presented for the Stable condition and stable configuration presented for the Overlapping condition - with the thesis that memory in this test trial for the Overlapping condition will depend on the accumulated memory of spatial patterns over the training trials, whereas memory for the test trial in the Stable condition can be due to episodic memory of last trial or accumulated memory. Memory is quantified using a Discrimination Index (DI), comparing the amount of time animals spend exploring the two object locations.

Here, animals in other groups are also presented with an interference trial equivalent to the test trial, to test if the memory of the Overlapping condition can be disrupted. The behavioral data show that for RGS14 over-expressing animals, memory in the Overlapping condition is diminished compared to controls with no interference or controls where over-expression is inhibited, whereas memory in the Stable condition is enhanced. This is interpreted as interference in semantic-like memory formation, whereas one-shot episodic memory is improved. The authors speculate that increased cortical plasticity should lead to increased and larger delta waves according to the sleep homeostasis hypothesis, and observe that instead increased cortical plasticity leads to less non-REM sleep and smaller delta waves, with more prefrontal neurons with slower firing rates (presumably more plastic neurons). They further report increased hippocampal-cortical theta coherence during task and REM sleep, increased NonREM oscillatory coupling, and changes in hippocampal ripples in RGS14 over-expressing animals.

While these results are interesting, there are several issues that need to be addressed, and the link between physiology and behavioral results is unclear.

1) The behavioral results rely on the interpretation that the Overlapping condition corresponds to semantic-like memory and the Stable condition corresponds to episodic-like memory. While the dissociation in memory performance due to interference seen in these two conditions is intriguing, the Stable condition can correspond not just to the memory of the previous trial but also accumulated memory of a stable spatial pattern over the 5 testing trials, similar to accumulated memory of a changing spatial pattern in the Overlapping pattern.

Yes! We completely agree on this. We do not claim the stable condition corresponds to episodic-like memory, instead we refer to it as simple memory, since it can be solved either way (one trial memory or cumulative memory). We now expanded this in the discussion to make it clearer.

Here, it is puzzling that in the behavioral control with no interference (Figure 1D), memory in the Stable and Overlapping condition is unchanged in the test trial, with the DI statistically at 0 in the test trial. In the original description of the Object Space task by the authors in the referenced paper, the measure of memory was a Discrimination Index significantly higher than 0 in both the Stable and Overlapping conditions. This discrepancy needs to be reconciled. Is the DI for the interference trial shown in Fig. S1 significantly different than 0? No statistics or description is provided in the figure legend here.

As mentioned above, we apologize that we oversimplified the description. The 24h interference trial would be what corresponds to the original test trial. We added a clarifying figure for comparison in S1 (bar graph in addition to the violin plot) and stats. Performance was for all groups and conditions above chance, replicating our previous results.

2) The physiology experiments compare Home cage (HC) conditions to the Object Space task (OS) throughout the manuscript. While some differences are seen in the control and RGS14 over-expressing animals, there is no comparison of the Stable vs. Overlapping condition in the physiology experiments. This precludes making explicit links between physiological observations and behavioral effects.

As also mentioned above, we have now added analysis exploring the detailed OS conditions. We would like to thank the reviewers for giving us the opportunity of doing so.

3) The authors speculate that learning will result in larger and more delta waves as per the synaptic homeostasis hypothesis. It should be noted here that an alternative hypothesis is that there should also be a selective increase in synaptic plasticity for learning and consolidation. The authors do observe that control animals show more frequent and higher-amplitude delta waves, but rather than enhancing this process, RGS14 animals with increased plasticity show the opposite effect. How can this be reconciled and linked with the behavioral data in the Stable and Overlapping condition?

In the context of the Object Space Task, we would expect all behavioural conditions (Stable and Overlapping) to induce synaptic changes since learning does occur also in the Stable condition (see also performance on 24h trial). Thus, especially homeostatic responses such as increase in delta amplitude, we would expect for all experiences independent if subtle statistical rules are presented or not. In contrast, detailed processing, extracting underlying regularities is rather proposed by the Sleep for Active Systems Consolidation Hypothesis to occur during hippocampal-cortical interactions in form of delta/ripple/spindle interactions (with different theories emphasising different types of interactions). As mentioned above, we now add a more specific analysis in this regards, where we can show that the two OS conditions that involve moving objects (where thus potentially statistical regularities can be extracted) show a higher percentage of ripples occurring after large slow oscillations in comparison to home cage or the simple learning condition Stable. In contrast, RGS14 already has higher participation in both control conditions, emphasising that in these animals all experiences are treated by the brain as significant learning condition, explaining the behavioural effect (increased interference due to better memory for the interference). Further, we expanded in the discussion how in RGS we sometimes see an enhancement of learning effects but sometimes see a more complex interaction of what we would expect from physiological learning.

Similarly, there is an increase in slower-firing neurons in RGS14 over-expressing animals. Slower-firing neurons have been proposed to be more plastic in the hippocampus based on their participation in learned hippocampal sequences, but appropriate references or data are needed to support the assertion that slower-firing neurons in the prefrontal cortex are more plastic.

As described above, we have expanded the discussion including other citations that also consider the cortex. We can show that our changes would be expected if one turns the cortex as plastic as the hippocampus.

4) It is noted that changing cortical plasticity influences hippocampal-cortical coupling and hippocampal ripples, suggesting a cortical influence on hippocampal physiological patterns. It has been previously shown that disrupting prefrontal cortical activity does alter hippocampal ripples and hippocampal theta sequences (Schmidt et al., 2019; Schmidt and Redish, 2021). The current results should be discussed in this context.

We would like to thank the reviewer for these suggestions, they are now incorporated in the manuscript.

Reviewer #2 (Public Review):

In this paper, the authors provide evidence to support the longstanding proposition that a dual-learning system/systems-level consolidation (hippocampus attains memories at a fast pace which are eventually transmitted to the slow-learning neocortex) allows rapid acquisition of new memories while protecting pre-existing memories. The authors leverage many techniques (behavior, pharmacology, electrophysiology, modelling) and report a host of behavioral and electrophysiological changes on induction of increased medial prefrontal cortex (mPFC) plasticity which are interesting and will be of significant interest to the broad readership.

The experimental design and analyses are convincing (barring some instances which are discussed below). The following recommendations will bolster the strength/quality of the manuscript:

1) Certain concerns regarding the interpretation and analysis of the behavioral data remain. The authors need to clarify if increased mPFC plasticity leads to only an increase in one-shot memory or 'also' interference of previous information. It seems that the behavioral results could also be explained by the more parsimonious explanation that one-shot memory is improved. Do the current controls tease apart these two scenarios?

We agree we cannot disentangle if one memory is just stronger than the other or if its an overwriting effect. We added this now to the discussion. Of note, we do not think it actually would be possible to distinguish these two effects behaviourally in rodents, or at least we cannot think of a fitting study design that would enable the contrast.

Additionally, the authors need to clarify why the 'no trial' and 'anisomycin' controls for the stable task perform at chance levels on exposure to a new object-place association on test day (Fig 1D).

Violin plots are sometimes hard to see. Here simple bar plots where you can see that the animals are not at chance at the 72h test in the control conditions.

Finally, further description of how the discrimination index (exploration time of novel-exploration time of familiar/sum of both) is recommended i.e., in the stable condition, which 'object' is chosen as 'novel' (as both are in the same locations) for computing the index (Fig 1). Do negative DI values imply a neophobia to novel objects (and thus are a form of memory; this is also crucial because the modelling results (Fig 1E) use both neophilia and neophobia while negative discrimination indexes are considered similar to 0 for interpreting the behavioral results, as stated on page 3, lines 84-86?

We added this now to the methods (For Overlapping it is moved location – stable location, for Stable it is location-to-be-moved-at-test – stable location and for random which is assigned as moved and stable is random, and then for each divided by total time). We agree that neophilia/neophobia (especially changes in the distribution) can be an issue and have discussed it in detail in Schut et al NLM 2020 where we see difference in absolute beta values (thus controlling for philia/phobia differences). We also discuss there why it is difficult to control for this in the DI in more detail. In short, one could use absolute values but then it is difficult to determine what a group chance-level would look like. However, luckily here there is not issue since we did not observe difference in neophilic or phobic tendencies while running the experiments. Critically the interference trial (that can also function as simple test trial) confirms that as a group animals show positive DI and neophilia.

2) The authors report lower firing rates in RGS14414 animals during the task in Fig 2F. It is indeed remarkable how large the reported differences are. The authors need to rule out any differences in the behavioral state of the animals in the two groups during the task, i.e., rest vs. active exploration/movement dynamics. Are only epochs during the task while the animals interact with the objects used for computing the firing rates (same epochs as Fig 1)? If not, doing so will provide a useful comparison with Fig 1. Additionally, although the authors make the case for slow firing rate neurons being important for plasticity (based on Grosmark and Buzsaki, 2016), it is crucial to note that the firing rate dynamic (slow vs. fast) in that study for the hippocampus is defined based on the whole recorded session (predominated by sleep), indeed the firing rates of the two groups (slow vs. fast/plastic vs. rigid) during the task/maze-running do not differ in that study. Therefore, the results here seem incongruent with the Grosmark and Buzsaki paper. Since this finding is central to the main claim of the authors, it either warrants further investigation or a re-interpretation of their results.

As mentioned in the main points, we now added the firing rate analysis (including new groups splits) for wake in the sleep box, NREM and REM separately. Each time the same results are obtained. Currently, we do not yet have the tracking and video synchronization set-up, therefore we cannot split the task for specific behaviours.

However, we now also cite Buzsaki’s original log-normal brain review, where he first proposed the idea. There he also shows same effects as we do, in that the general firing rate distribution is the same for task and different sleep stages, just overall shifted. The analysis from Grosmark included more strigent subselection of neurons to be able to also argue that incorporation into run/replay-sequences could not have been biased by firing rate per se (instead of plasticity). However, the original proposition from Buzsaki does fit to our results. He further presents hippocampus vs cortex firing rates, which also confirm the idea (hippocampus more plastic and has slower firing rates). We included this figure above in the general comments. Further, we now expanded the discussion in this point.

3) A concern remains as to how many of the electrophysiological changes they observe (firing rate differences, LFP differences including coupling, sleep state differences, Figs. 2-4) support their main hypothesis or are a by-product of injection of RGS14414 (for instance, one might argue that an increased 'capability' to learn new information/more plasticity might lead to more NREM sleep for consolidation, etc.). The authors need to carefully interpret all their data in light of their main hypothesis, which will substantially improve the quality/strength of the manuscript.

We now expanded the discussion, included more structure and also include that we cannot disentangle if the cellular changes or sleep oscillation changes or an interaction of both is the cause of the result. Furthermore, we added that we cannot distinguish if the interference memory is stronger or actually overwrites the original training memory.

Reviewer #3 (Public Review):

The authors set out to test the idea that memories involve a fast process (for the acquisition of new information) and a slow process (where these memories are progressively transferred/integrated into more-long term storage). The former process involves the hippocampus and the latter the cerebral cortex. This 'dual-learning' system theoretically allows for new learning without causing interference in the consolidation of older memories. They test this idea by artificially increasing plasticity in the pre-limbic cortex and measuring changes in different learning/memory tasks. They also examined electrophysiological changes in sleep, as sleep is linked to memory formation and synaptic plasticity.

The strengths of the study include a) meticulous analyses of a variety of electrophysiological measurements b) a combination of neurobiological and computational tools c) a largely comprehensive analysis of sleep-based changes. Some weaknesses include questions about the technique for increasing cortical plasticity (is this physiological?) and the absence of some additional experiments that would strengthen the conclusions. However, overall, the findings appear to support the general idea under examination.

This study is likely to be very impactful as it provides some really new information about these important neural processes, as well as data that challenges popular ideas about sleep and synaptic plasticity.

We would like to thank the reviewer for these positive comments. Answers to the weaknesses are presented below in the recommendations for the authors.

Author Response

Reviewer 1 (Public Review):

To me, the strengths of the paper are predominantly in the experimental work, there's a huge amount of data generated through mutagenesis, screening, and DMS. This is likely to constitute a valuable dataset for future work.

We are grateful to the reviewer for their generous comment.

Scientifically, I think what is perhaps missing, and I don't want this to be misconstrued as a request for additional work, is a deeper analysis of the structural and dynamic molecular basis for the observations. In some ways, the ML is used to replace this and I think it doesn't do as good a job. It is clear for example that there are common mechanisms underpinning the allostery between these proteins, but they are left hanging to some degree. It should be possible to work out what these are with further biophysical analysis…. Actually testing that hypothesis experimentally/computationally would be nice (rather than relying on inference from ML).

We agree with the reviewer that this study should motivate a deeper biophysical analysis of molecular mechanisms. However, in our view, the ML portion of our work was not intended as a replacement for mechanistic analysis, nor could it serve as one. We treated ML as a hypothesis-generating tool. We hypothesized that distant homologs are likely to have similar allosteric mechanisms which may not be evident from visual analysis of DMS maps. We used ML to (a) extract underlying similarities between homologs (b) make cross predictions across homologs. In fact, the chief conclusion of our work is that while common patterns exist across homologs, the molecular details differ. ML provides tantalizing evidence to this effect. The conclusive evidence will require, as the reviewer rightly suggests, detailed experimental or molecular dynamics characterization. Along this line, we note that we have recently reported our atomistic MD analysis of allostery hotspots in TetR (JACS, 2022, 144, 10870). See ref. 41.

Changes to manuscript:<br /> “Detailed biophysical or molecular dynamics characterization will be required to further validate our conclusions(38).”

Reviewer 3 (Public Review):

However - at least in the manuscript's present form - the paper suffers from key conceptual difficulties and a lack of rigor in data analysis that substantially limits one's confidence in the authors' interpretations.

We hope the responses below address and allay the reviewer’s concerns.

A key conceptual challenge shaping the interpretation of this work lies in the definition of allostery, and allosteric hotspot. The authors define allosteric mutations as those that abrogate the response of a given aTF to a small molecule effector (inducer). Thus, the results focus on mutations that are "allosterically dead". However, this assay would seem to miss other types of allosteric mutations: for example, mutations that enhance the allosteric response to ligand would not be captured, and neither would mutations that more subtly tune the dynamic range between uninduced ("off) and induced ("on") states (without wholesale breaking the observed allostery). Prior work has even indicated the presence of TetR mutations that reverse the activity of the effector, causing it to act as a co-repressor rather than an inducer (Scholz et al (2004) PMID: 15255892). Because the work focuses only on allosterically dead mutations, it is unclear how the outcome of the experiments would change if a broader (and in our view more complete) definition of allostery were considered.

We agree with the reviewer that mutations that impact allostery manifest in many different ways. Furthermore, the effect size of these mutations runs the full gamut from subtle changes in dynamic range to drastic reversal of function. To unpack allostery further, allostery of aTF can be described, not just by the dynamic range, but by the actual basal and induced expression levels of the reporter, EC50 and Hill coefficient. Given the systemic nature of allostery, a substantial fraction of aTF mutations may have some subtle impact on one or more of these metrics. To take the reviewer’s argument one step further, one would have to accurately quantify the effect size of every single amino acid mutation on all the above properties to have a comprehensive sequence-function landscape of allostery. Needless to say, this is extremely hard! Resolution of small effect sizes is very difficult, even at high sequencing depth. To the best of our knowledge, a heroic effort approaching such comprehensive analysis has been accomplished so far only once (PMID: 3491352).

Our focus, therefore, was to screen for the strongest phenotypic impact on allostery i.e., loss of function. Mutations leading to loss of function can be relatively easily identified by cell-sorting. Because our goal was to compare hotspots across homologs, we surmised that loss of function mutations, given their strong phenotypic impact, are likely to provide the clearest evidence of whether allosteric hotspots are conserved across remote homologs.

The reviewer raised the point of activity-reversing mutations. Yes, there are activity reversing mutations in TetR. However, they represent an insignificant fraction. In the paper cited by the reviewer, there are 15 activity-reversing mutations among 4000 screened. Furthermore, the paper shows that activity-reversing in TetR requires two-tofour mutations, while our library is exclusively single amino acid substitutions. For these reasons, we did not screen for activity-reversing mutations. Nonetheless, we agree with the reviewer that screening for activity-reversing mutations across homologs would be very interesting.

The separation in fluorescence between the uninduced and induced states (the assay dynamic range, or fold induction) varies substantially amongst the four aTF homologs. Most concerningly, the fluorescence distributions for the uninduced and induced populations of the RolR single mutant library overlap almost completely (Figure 1, supplement 1), making it unclear if the authors can truly detect meaningful variation in regulation for this homolog.

Yes, the reviewer is correct that the fold induction ratio varies among the four aTF homologs. However, we note that such differences are common among natural aTFs. Depending on the native downstream gene regulated by the aTF, some aTFs show higher ligand-induced activation, and others are lower. While this is not a hard and fast rule, aTFs that regulate efflux pumps tend to have higher fold induction than those that regulate metabolic enzymes. In summary, the variation in fold induction among the four aTFs is not a flaw in experimental design nor indicates experimental inconsistency but is instead just an inherent property of protein-DNA interaction strength and the allosteric response of each aTF.

Among the four aTFs, wildtype RolR has the weakest fold induction (15-fold) which makes sorting the RolR library particularly challenging. To minimize false positives as much as possible, we require that dead mutant be present in (a) non-fluorescent cells after ligandinduction (b) non-fluorescent cells before ligand-induction (c) at least two out of the three replicates for both sorts. Additionally, for RolR specifically, we adjusted the nonfluorescent gate to be far more stringent than the other three aTFs (Fig. 1 – figure supplement 1). Furthermore, we assign residues as allosteric hotspots, not individual dead mutations. This buffers against false strong signals from stray individual dead mutations. Finally, the top interquartile range winnows them to residues showing strong consistent dead phenotype. As a result of these “safeguards” we have built in, the number of allosteric hotspots of RolR (57) is comparable to the other three aTFs (51, 53 and 48). This suggests that we are not overestimating the number of hotspots despite the weaker fold induction of RolR. We highlight in a new supplementary figure (Figure 1 – figure supplement 4) that changing the read count threshold from 5X to 10X produces near identical patterns of mutations suggesting that our results are also robust to changes in ready depth stringency.

Changes to manuscript: In response to the reviewer's comment, we have added the following sentence.

“We note that the lower fold induction (dynamic range) of RolR makes it particularly challenging to separate the dead variants from the rest.”

The methods state that "variants with at least 5 reads in both the presence and absence of ligand in at least two replicates were identified as dead". However, the use of a single threshold (5 reads) to define allosterically dead mutations across all mutations in all four homologs overlooks several important factors:

Depending on the starting number of reads for a given mutation in the population (which may differ in orders of magnitude), the observation of 5 reads in the gated nonfluorescent region might be highly significant, or not significant at all. Often this is handled by considering a relative enrichment (say in the induced vs uninduced population) rather than a flat threshold across all variants.

We regret the lack of clarity in our presentation. We wish to better explain the rationale behind our approach. First, we understand the reviewer’s point on considering relative enrichment to define a threshold. This approach works well in DMS experiments involving genetic selections, which is commonly the case, because activity scales well with selection stringency. One can then pick enrichment/depletion relative to the middle of the read count distribution as a measure of gain or loss of function.

Second, this strategy does not, in practice, work well for cell-sorting screens. While it may be tempting to think of cell sorting as comparably activity-scaled as genetic selections, in reality, the fidelity of fluorescent-activated cell sorters is much lower. Making quantitative claims of activity based on cell sorting enrichment can be risky. It is wiser to treat cell sorting results as yes/no binary i.e., does the mutation disrupt allostery or not. More importantly, the yes/no binary classification suffices for our need to identify if a certain mutation adversely impacts allosteric activity or not.

Third, the above argument does not imply that all mutations have the same effect size on allostery. They don’t. We capture the effect size on individual residues, not individual mutations, by counting the number of dead mutations at a residue position. This is an important consideration because it safeguards us from minor inconsistencies that inevitably arise from cell sorting.

Fourth, a variant to be classified as allosterically dead, it must be present both in uninduced and induced DNA-bound populations in at least two out of three replicates (four conditions total). This is a stringent criterion for selecting dead variants resulting in highly consistent regions of importance in the protein even upon varying read count thresholds. To the extent possible, we have minimized the possibility of false positive bleed-through.

Finally, two separate normalizations were performed on the total sequence reads to be able to draw a common read count threshold 1) between experimental conditions & replicates and 2) across proteins. First, total sequencing reads were normalized to 200k total across all sample conditions (presorted, -inducer, and +inducer) and replicates for each homolog, allowing comparisons within a single protein. Next, reads were normalized again to account for differences in the theoretical size of each protein’s single-mutant library, allowing for comparisons across proteins by drawing a commont readcount cutoff. For example, total sequencing reads of RolR (4,332 possible mutants) increased by 1.18x relative to MphR (3,667 possible mutants) for a total of 236k reads.

Changes to manuscript: We have provided substantial additional details in the Fluorescence-activated cell sorting and NGS preparation and analysis sections.

We also added the following in the main text.

“In other words, we use cell sorting as a binary classifier i.e., does the mutation disrupt allostery or not. We capture the effect size on individual residues, not individual mutations, by counting the number of dead mutations at a residue position. This is an important consideration because it safeguards us from minor inconsistencies that inevitably arise from cell sorting.”

Depending on the noise in the data (as captured in the nucleotide-specific q-scores) and the number of nucleotides changed relative to the WT (anywhere between 1-3 for a given amino acid mutation) one might have more or less chance of observing five reads for a given mutation simply due to sequencing noise.

All the reads considered in our analyses pass the Illumina quality threshold of Q-score ≥ 30 which as per Illumina represent “perfect reads with no errors or ambiguities”. This translates into a probability of 1 in 1000 incorrect base call or 99.9% base call accuracy.

We use chip-based oligonucleotides to build our DMS library, which allows us to prespecify the exact codon that encodes a point mutation. This means the nucleotide count and protein count are the same. The scenario referred to by the reviewer i.e., “anywhere between 1-3 for a given amino acid mutation” only applies to codon randomized or errorprone PCR library generation. We regret if the chip-based library assembly part was unclear.

Depending on the shape and separation of the induced (fluorescent) and uninduced (non-fluorescent) population distributions, one might have more or less chance of observing five reads by chance in the gated non-fluorescent region. The current single threshold does not account for variation in the dynamic range of the assay across homologs.

We have addressed the concern raised by the reviewer on fluorescent population distributions in answers to questions 10 and 11.

The reviewer makes an important point about the choice of sequencing threshold. We use the sequencing threshold to simply make a binary choice for whether a certain variant exists in the sorted population or not. We do not use the sequencing reads as to scale the activity of the variant. To address the reviewer's comment, we have included a new supplementary figure (Fig 1 – figure supplement 4) where we compare the data by adjust the threshold two levels – 5 and 10 reads. As is evident in the new figure, the fundamental pattern of allosteric hotspots and the overall data interpretation does not change.

TetR: 5x – 53 hotspots, 10x – 51 hotspots

TtgR: 5x – 51 hotspots, 10x – 51 hotspots

MphR: 5x – 48 hotspots, 10x – 48 hotspots

RolR: 5x – 57 hotspots, 10x – 60 hotspots

In other words, changing the threshold to be more or less strict may have a modest impact on the overall number of hotspots in the dataset. Still, the regions of functional importance are consistent across different thresholds. We have expanded the discussion in the manuscript to address this point.

Changes to manuscript: We have now included a new supplementary comparing hotspot data at two thresholds: Figure 1 – figure supplement 4.

We also added the following in the main text.

“To assess the robustness of our classification of hotspots, we determined the number of hotspots at two different sequencing thresholds – 5x and 10x. At 5x and 10x, the number of hotspots are – TetR: 53, 51; TtgR: 51, 51; MphR: 48, 48 and RolR: 57,60, respectively. Changing the threshold has a modest impact on the overall number of hotspots and the regions of functional importance are consistent at both thresholds”

The authors provide a brief written description of the "weighted score" used to define allosteric hotspots (see y-axis for figure 1B), but without an equation, it is not clear what was calculated. Nonetheless, understanding this weighted score seems central to their definition of allosteric hotspots.

We regret the lack of clarity in our presentation. The weighted score was used to quantify the “deadness” of every residue position in the protein. At each position in the protein, the number of mutations that inhibited activity was summed up and the ‘deadness’ of each mutation was weighted based on how many replicates is appeared to inactivate the protein. Weighted score at each residue position is given by

Where at position x in the protein, D1 is the number of mutations dead in one replicate only, D2 is the number of mutations dead in 2 replicates, D3 is the number of mutations dead in 3 replicates, and Total is the total number of variants present in the data set (based on sequencing data). Any dead mutation that is seen in only one replicate is discarded and does not contribute to the “deadness” of the residue. Mutations seen in two and three replicates contribute to the score. We have included a new supplementary figure (Fig. 1 – figure supplement 2) to give the reader a detailed heatmap of all mutations and their impact for each protein.

Changes to manuscript: The weighted scoring scheme is now described in greater detail under Materials and Methods in the “NGS preparation and analysis” section.

The authors do not provide some of the standard "controls" often used to assess deep mutational scanning data. For example, one might expect that synonymous mutations are not categorized as allosterically dead using their methods (because they should still respond to ligand) and that most nonsense mutations are also not allosterically dead (because they should no longer repress GFP under either condition). In general, it is not clear how the authors validated the assay/confirmed that it is giving the expected results.

As we state in response to question 12, we use chip-based oligonucleotides to build our DMS library, which allows us to pre-specify the exact codon that encodes a point mutation. We have no synonymous or nonsense mutations in our DMS library. Each protein mutation is encoded by a single unique codon. The only stop codon is at 3’end of the gene.

The authors performed three replicates of the experiment, but reproducibility across replicates and noise in the assay is not presented/discussed.

Changes to manuscript: A new supplementary table (Table 1) is now provided with the pairwise correlation coefficients between all replicates for each protein.

In the analysis of long-range interactions, the authors assert that "hotspot interactions are more likely to be long-range than those of non-hotspots", but this was not accompanied by a statistical test (Figure 2 - figure supplement 1).

In response to the reviewer's comment, we now include a paired t-test comparing nonhotspots and hotspots with long-range interactions in the main text.

Changes to manuscript: In all four aTFs, hotspots constituted a higher fraction of LRIs than non-hotspots (Figure 2 – figure supplement 1; P = 0.07).

Author Response

Reviewer #1 (Public Review):

In this study, the authors describe an elegant genetic screen for mutants that suppress defects of MCT1 deletions which are deficient in mitochondrial fatty acid synthesis. This screen identified many genes, including that for Sit4. In addition, genes for retrograde signaling factors (Rtg1, Rtg2 and Rtg3), proteins influencing proteasomal degradation (Rpn4, Ubc4) or ribosomal proteins (Rps17A, Rps29A) were found. From this mix of components, the authors selected Sit4 for further analysis. In the first part of the study, they analyzed the effect of Sit4 in context of MCT1 mutant suppression. This more specific part is very detailed and thorough, the experiments are well controlled and convincing. The second, more general part of the study focused on the effect of Sit4 on the level of the mitochondrial membrane potential. This part is of high general interest, but less well developed. Nevertheless, this study is very interesting as it shows for the first time that phosphate export from mitochondrial is of general relevance for the membrane potential even in wild type cells (as long as they live from fermentation), that the Sit4 phosphatase is critical for this process and that the modulation of Sit4 activity influences processes relying on the membrane potential, such as the import of proteins into mitochondria. However, some aspects should be further clarified.

1) It is not clear whether Sit4 is only relevant under fermentative conditions. Does Sit4 also influence the membrane potential in respiring cells? Fig. S2D shows the membrane potential in glucose and raffinose. Both carbon sources lead to fermentative growths. The authors should also test whether Sit4 levels influence the membrane potential when cells are grown under respirative conditions, such in ethanol, lactate or glycerol. Even if deletions of Sit4 affect respiration, mutants with altered activity can be easily analyzed.

sit4Δ cells fail to grow on nonfermentable media as shown by us (Figure 2—figure supplement 1C) and others (Arndt et al., 1989; Dimmer et al., 2002; Jablonka et al., 2006). In our opinion, the exact reason is unclear, but there is an interesting observation that addition of aspartate can partially restore growth on ethanol (Jablonka et al., 2006). Despite the lack of thorough investigation on this sit4Δ defect, an early study speculated that this defect could be related to the cAMP-PKA pathway (Sutton et al., 1991). This study pointed out genetic interactions of SIT4 with multiple genes in cAMP-PKA (Sutton et al., 1991). In addition, sit4Δ cells have similar phenotypes as those cAMP-PKA null mutants, such as glycogen accumulation, caffeine resistant, and failure to grow on nonfermentable media (Sutton et al., 1991). We have not found sit4Δ mutants that could grow on nonfermentable media based on literature search.

2) The authors should give a name to the pathway shown in Fig. 4D. This would make it easier to follow the text in the results and the discussion. This pathway was proposed and characterized in the 90s by George Clark-Walker and others, but never carefully studied on a mechanistic level. Even if the flux through this pathway cannot be measured in this study, the regulatory role of Sit4 for this process is the most important aspect of this manuscript.

We now refer this mechanism as the mitochondrial ATP hydrolysis pathway.

3) To further support their hypothesis, the authors should show that deletion of Pic1 or Atp1 wipes out the effect of a Sit4 deletion. In these petite-negative mutants, the phosphate export cycle cannot be carried out and thus, Sit4, should have no effect.

The mitochondrial phosphate transport activity is electroneutral as it also pumps a proton together with inorganic phosphate. The F1 subunit of the ATP synthase (Atp1 and Atp2) is suggested among many literatures to be responsible for the ATP hydrolysis. We performed tetrad dissection to generate atp1Δ or atp2Δ in pho85Δ background. After streaking the single colony to a fresh plate, we noticed that atp1Δ mct1Δ and atp2Δ mct1Δ cells are lethal, and knocking out PHO85 rescued this synthetic lethality. It is not surprising that atp1Δ mct1Δ or atp2Δ mct1 Δ cells are lethal since the F1 subunit is important to generate a minimum of MMP in mct1 Δ cells when the ETC is absent (i.e., rho0 cells). However, knocking out PHO85 can generate MMP independent of F1 subunit of ATP synthase, which is suggested by the viable atp1Δ mct1Δ pho85Δ and atp2Δ mct1Δ pho85Δ cells. There are many ATPases in the mitochondrial matrix that could hydrolyze ATP for ADP/ATP carrier to generate MMP theoretically. However, we do not currently know exactly which ATPase(s) is activated by phosphate starvation. This data is now included as Figure 5—figure supplement 1F-G.

4) What is the relevance of Sit4 for the Hap complex which regulates OXPHOS gene expression in yeast? The supplemental table suggests that Hap4 is strongly influenced by Sit4. Is this downstream of the proposed role in phosphate metabolism or a parallel Sit4 activity? This is a crucial point that should be addressed experimentally.

To investigate the role of the Hap complex in MMP generation in sit4Δ cells, we overexpressed and knocked out HAP4, the catalytic subunit of the Hap complex, separately in wild-type and sit4Δ cells. We confirmed the HAP4 overexpression by the enriched abundance of ETC complexes as shown in the BN-PAGE (Figure 2—figure supplement 1E). However, we did not observe any rescue of ETC or ATP synthase in mct1Δ cells when HAP4 was overexpressed. The enriched level of ETC complexes by HAP4 overexpress is not sufficient to rescue the MMP (Figure 2—figure supplement 1F).

Next, we knocked out HAP4 in sit4Δ cells. Knocking out SIT4 could still increase MMP in hap4Δ cells with a much-reduced magnitude, which phenocopied ETC subunit and RPO41 deletion in sit4Δ cells (Figure 2—figure supplement 1G).

In conclusion, the Hap complex is involved in the MMP increase when SIT4 is absent. However, it is not sufficient to increase MMP by overexpressing HAP4. The Hap complex discussion is now included in the manuscript, and the data is presented as Figure 2—figure supplement 1E-G.

5) The authors use the accumulation of Ilv2 precursors as proxy for mitochondrial protein import efficiency. Ilv2 was reported before as a protein which, if import into mitochondria is slow, is deviated into the nucleus in order to be degraded (Shakya,..., Hughes. 2021, Elife). Is it possible that the accumulation of the precursor is the result of a reduced degradation of pre-Ilv2 in the nucleus rather than an impaired mitochondrial import? Since a number of components of the ubiquitin-proteasome system were identified with Sit4 in the same screen, a role of Sit4 in proteasomal degradation seems possible. This should be tested.

We thank the reviewer for pointing out this potential caveat with our Ilv2-FLAG reporter. With limited search and tests, we could not find another reporter that behaves like Ilv2FLAG. The reason Ilv2-FLAG is a perfect reporter for this study is because in wild-type cells, Ilv2-FLAG is not 100% imported. Therefore, we could demonstrate that mitochondria with higher MMP import more efficiently. Unfortunately, all of the mitochondrial proteins that we tested could efficiently import in wild-type cells. To identify other suitable mitochondrial proteins that behave like Ilv2-FLAG, we would need to conduct a more comprehensive screen.

To address the concern of the involvement of protein degradation in obscuring the interpretation of Ilv2-FLAG import, we performed two experiments. First, we measured the proteasomal activity in wild-type and our mutants using a commercial kit (Cayman). We did not observe a statistically significant difference in 20S proteasomal activity between wild-type and sit4Δ cells.

In the second experiment, we reduced the MMP of sit4 cells using CCCP treatment and measured the Ilv2-FLAG import. We first treated sit4Δ cells with different dosage of CCCP for six hours and measured their MMP. sit4Δ cells treated with 75 µM CCCP had comparable MMP to wild-type cells. When we treated sit4Δ cells with higher concentrations of CCCP, most of the cells did not survive after six hours. Next, we performed the Ilv2-FLAG import assay. We observed similar level of unimported Ilv2FLAG (marked with *) in sit4Δ cells treated with 75 µM CCCP. This result confirms that sit4Δ cells have similar Ilv2-FLAG turnover mechanism and activity as the wild-type cells, because when we lower the MMP in sit4Δ background we observe a similar level of unimported Ilv2-FLAG. We thus feel confident in concluding that the Ilv2-FLAG import results are indeed an accurate proxy for MMP level. These data are now included as Figure 1—figure supplement 1H-J in the manuscript.

Author response image 1.

Reviewer #2 (Public Review):

This study reports interesting findings on the influence of a conserved phosphatase on mitochondrial biogenesis and function. In the absence of it, many nucleus-encoded mitochondrial proteins among which those involved in ATP generation are expressed much better than in normal cells. In addition to a better understanding of th mechanisms that regulate mitochondrial function, this work may help developing therapeutic strategies to diseases caused by mitochondrial dysfunction. However there are a number of issues that need clarification.

1) The rationale of the screening assay to identify genes required for the gene expression modifications observed in mct1 mutant is not clear. Indeed, after crossing with the gene deletion libray, the cells become heterozygote for the mct1 deletion and should no longer be deficient in mtFAS. Thank you for clarifying this and if needed adjust the figure S1D to indicate that the mated cells are heterozygous for the mct1 and xxx mutations.

We updated the methods section and the graphic for the genetic screen to clarify these points within the SGA workflow overview. After we created the heterozygote by mating mct1Δ cells with the individual KO cells in the collection, these diploids underwent sporulation and selection for the desired double KO haploid. As a result, the luciferase assay was performed in haploid cells with MCT1 and one additional non-essential gene deleted.

2) The tests shown in Fig. S1E should be repeated on individual subclones (at least 100) obtained after plating for single colonies a glucose culture of mct1 mutant, to determine the proportion of cells with functional (rho+) mtDNA in the mct1 glucose and raffinose cultures. With for instance a 50% proportion of rho- cells, this could substantially influence the results of the analyses made with these cells (including those aiming to evaluate the MMP).

We agree that this would provide a more confident estimate for population-level characterization of these colonies. It is important to note that we randomly chose 10 individual subclones, and 100% of these colonies were verified to be rho+. This suggests the population has functional mtDNA, and thus felt confident in the identity of our populations.

3) The mitochondria area in mct1 cells (Fig.S1G) does not seem to be consistent with the tests in Fig. 1C. that indicate a diminished mitochondrial content in mct1 cells vs wild-type yeast. A better estimate (by WB for instance) of the mitochondrial content in the analyzed strains would enable to better evaluate MMP changes monitored with Mitotracker since the amount of mitochondria in cells correlate with the intensity of the fluorescence signal.

As this reviewer pointed out, we quantified mitochondrial area based on Tom70-GFP signal. This measurement is quantified by mitochondrial area over cell size. Cell size is an important parameter when measuring organelle size as most of the organelles scale up and down with the cell size. mct1Δ cells generally have smaller cell size than WT cells. Therefore, the mitochondrial area of mct1Δ cells was not significantly different from WT cells when scaled to cell size. We believe this is the best method to compare mitochondrial area. As for quantifying MMP from these microscopy images, we measured the average MitoTracker Red fluorescence intensity of each mitochondria defined by Tom70-GFP. This method inherently normalizes to subtract the influence of mitochondria area when quantifying MMP.

4) Page 12: "These data demonstrate that loss of SIT4 results in a mitochondrial phenotype suggestive of an enhanced energetic state: higher membrane potential, hyper-tubulated morphology and more effective protein import." Furthermore, the sit4 mutant shows higher levels of OXPHOS complexes compared to WT yeast.

Despite these beneficial effects on mitochondria, the sit4 deletion strain fails to grow on respiratory substrates. It would be good to know whether the authors have some explanation for this apparent contradiction.

We agree that this was initially puzzling. We provide a more complete explanation above (see comments to reviewer #1 - major concern #1). Briefly, the growth deficiency in non-fermentable media with sit4Δ cells was reported and studied by multiple groups (Arndt et al., 1989; Dimmer et al., 2002; Jablonka et al., 2006). These seems to indicate that sit4Δ cells contain more ETC complexes and more OCR but cannot respire on nonfermentable carbon source. However, we do not think there is yet a clear explanation for this phenotype. One interesting observation reported is the addition of aspartate partly restoring cells’ growth on ethanol (Jablonka et al., 2006). One early study speculates that this defect could be related to the cAMP-PKA pathway. Sutton et al. pointed out genetic interactions with sit4 and multiple genes in cAMP-PKA (Sutton et al., 1991). In addition, sit4Δ cells have similar phenotypes as those cAMP-PKA null mutants, such as glycogen accumulation, caffeine resistance, and failure to grow on non-fermentable media. However, to keep this manuscript succinct, we opted to stay focused on MMP.

Reviewer #3 (Public Review):

In this study, the authors investigate the genetic and environmental causes of elevated Mitochondrial Membrane Potential (MMP) in yeast, and also some physiological effects correlated with increased MMP.

The study begins with a reanalysis of transcriptional data from a yeast mutant lacking the gene MCT1 whose deletion has been shown to cause defects in mitochondrial fatty acid synthesis. The authors note that in raffinose mct1del cells, unlike WT cells, fail to induce expression of many genes that code for subunits of the Electron Transport Chain (ETC) and ATP synthase. The deletion of MCT1 also causes induction of genes involved in acetyl-CoA production after exposure to raffinose. The authors therefore conduct a screen to identify mutants that suppress the induction of one of these acetylCoA genes, Cit2. They then validate the hits from this screen to see which of their suppressor mutants also reduce expression in four other genes induced in a mct1del strain. This yielded 17 genes that abolished induction of all 5 genes tested in an mct1del background during growth on raffinose.

The authors chose to focus on one of these hits, the gene coding for the phosphatase SIT4 (related to human PP6) which also caused an increase in expression of two respiratory chain genes. The authors then investigated MMP and mitochondrial morphology in strains containing SIT4 and MCT1 deletions and surprisingly saw that sit4del cells had highly elevated MMP, more reticular mitochondria, and were able to fully import the acetolactate synthase protein Ilv2p and form ETC and ATP synthase complexes, even in cells with an mct1del background, rescuing the low MMP, fragmented mitochondria, low import of Ilv2 and an inability to form ETC and ATP synthase complexes phenotypes of the mct1del strain. Surprisingly, the authors find that even though MMP is high and ETC subunits are present in the sit4del mct1del double deletion strain, that strain has low oxygen consumption and cannot grow under respiratory conditions, indicating that the elevated MMP cannot come from fully functional ETC subunits. The authors also observe that deleting key subunits of ETC complex III (QCR2) and IV (COX5) strongly reduced the MMP of the sit4del mutant, which would suggest that the majority of the increase in MMP of the sit4del mutant was dependant on a partially functional ETC. The authors note that there was still an increase in MMP in the qcr2del sit4del and cox4del sit4del strains relative to qcr2del and cox4del strains indicating that some part of the increase in MMP was not dependent on the ETC.

The authors dismiss the possibility that the increase in MMP could have been through the reversal of ATP synthase because they observe that inhibition of ATP synthase with oligomycin led to an increase of MMP in sit4del cells. Indicating that ATP synthase is operating in a forward direction in sit4del cells.

Noting that genes for phosphate starvation are induced in sit4del cells, the authors investigate the effects of phosphate starvation on MMP. They found that phosphate starvation caused an increase in MMP and increased Ilv2p import even in the absence of a mitochondrial genome. They find that inhibition of the ADP/ATP carrier (AAC) with bongkrekic acid (BKA) abolishes the increase of MMP in response to phosphate starvation. They speculate that phosphate starvation causes an increase in MMP through the import and conversion of ATP to ADP and subsequent pumping of ADP and inorganic phosphate out of the mitochondria.

They further show that MMP is also increased when the cyclin dependent kinase PHO85 which plays a role in phosphate signaling is deleted and argue that this indicates that it is not a decrease in phosphate which causes the increase in MMP under phosphate starvation, but rather the perception of a decrease in phosphate as signalled through PHO85. Unlike in the case of SIT4 deletion, the increase in MMP caused by the deletion of pho85 is abolished when MCT1 is deleted.

Finally they show an increase in MMP in immortalized human cell lines following phosphate starvation and treatment with the phosphate transporter inhibitor phosphonoformic acid (PFA). They also show an increase in MMP in primary hepatocytes and in midgut cells of flies treated with PFA.

The link between phosphate starvation and elevated MMP is an important and novel finding and the evidence is clear and compelling. Based on their experiments in various mammalian contexts, this link appears likely to be generalizable, and they propose and begin to test an interesting hypothesis for how MMP might occur in response to phosphate starvation in the absence of the Electron Transport Chain.

The link between phosphate starvation and deletion of the conserved phosphatase SIT4 is also interesting and important, and while the authors' experiments and analysis suggest some connection between the two observations, that connection is still unclear.

Major points

Mitotracker is great fluorescent dye, but it measures membrane potential only indirectly. There is a danger when cells change growth rates, ion concentrations, or when the pH changes, all MMP indicating dyes change in fluorescence: their signal is confounded Change in phosphate levels can possibly do both, alter pH and ion concentrations. Because all conclusions of the manuscript are based on a change in MMP, it would be a great precaution to use a dye-independent measure of membrane potential, and confirm at least some key results.

Mitochondrial MMP does strongly influence amino acid metabolism, and indeed the SIT4 knockout has a quite striking amino acid profile, with histidine, lysine, arginine, tyrosine being increased in concentration. http://ralser.charite.de/metabogenecards/Chr_04/YDL047W.html Could this amino acid profile support the conclusions of the authors? At least lysine and arginine are down in petites due to a lack of membrane potential and iron sulfur cluster export.- and here they are up. Along these lines, according to the same data resource, the knock-outs CSR2, ASF1, SSN8, YLR0358 and MRPL25 share the same metabolic profile. Due to limited time I did not re-analyse the data provided by the authors- but it would be worth checking if any of these genes did come up in the screens of the authors.

We tested the mutants within the same cluster as SIT4 shown in this paper from the deletion collection and measured their MMP. yrl358cΔ cells have similar high MMP as observed in sit4Δ cells. However, this gene has a yet undefined function. Beyond YRL358C, we did not observe similar MMP increases in other gene deletions from this panel, which does not support the notion that amino acids such as histidine, lysine, arginine, or tyrosine play a determining effect in driving MMP.

The media condition and strain used in the suggested paper is very different from what we used in our study. Instead of growing prototrophic cells in minimal media without any amino acids, we used auxotrophic yeast strains and grew them in media containing complete amino acids. So far, none of the other defects or signaling associated with SIT4 deletion could influence MMP as much as the phosphate signaling. We interpret these data to support the hypothesis that the MMP observation in sit4Δ cells is connected with the phosphate signaling as illustrated by the second half of the story in our manuscript.

Author reponse image 2.

One important claim in the manuscript attempts to explain a mechanism for the MMP increase in response to phosphate starvation which is independent of the ETC and ATP synthase.

It seems to me the only direct evidence to support this claim is that inhibition of the AAC with BKA stops the increase of mitotracker fluorescence in response to phosphate starvation in both WT and rho0 cells (Figs 4B and 4C). It would strengthen the paper if the authors could provide some orthogonal evidence.

This is a similar comment as raised by reviewer #1 - major concern #3. We refer the reviewer to our discussion and the new data above. Briefly, we do not think F1 subunit is responsible for the ATP hydrolysis activity to generate MMP in phosphate depleted situation. We believe there are additional ATPase(s) in the mitochondrial matrix that can be utilized to couple to ADP/ATP carrier for MMP generation during phosphate starvation. However, we have not identified the relevant ATPase(s) at this point, and it is likely that multiple ATPases could contribute to this activity.

Introduction/Discussion The author might want to make the reader of the article aware that the 'reversal' of the ATP synthase directionality -i.e. ATP hydrolysis by the ATP synthase as a mechanism to create a membrane potential (in petites), has always been a provocative idea - but one that thus far could never be fully substantiated. Indeed some people that are very familiar with the topic, are skeptical this indeed happens. For instance, Vowinckel et al 2021 (PMID: 34799698) measured precise carbon balances for peptide cells, and found no evidence for a futile cycle - peptides grow slower, but accumulate the same biomass from glucose as peptides that re-evolve at a fast growth rate . Perhaps the manuscript could be updated accordingly.

We thank the reviewer for pointing out this additional relevant study. We have rephased the referenced sentence in the introduction. The MMP generation in phosphate starvation is independent of the F1 portion of ATP synthase. Therefore, our data neither supports or refutes either of these arguments.

In the introduction and conclusion there is discussion of MMP set points. In particular the authors state:

"Critically, we find that cells often prioritize this MMP setpoint over other bioenergetic priorities, even in challenging environments, suggesting an important evolutionary benefit."

This does not seem to be consistent with the central finding of the manuscript that MMP changes under phosphate starvation. MMP doesn't seem so much to have a 'set point' but rather be an important physiological variable that reacts to stimuli such as phosphate starvation.

The reviewer raises a rational alternative hypothesis to the one that we have proposed. In reality, both of these are complete speculations to explain the data and we can’t think of any way to test the evolutionary basis for the mechanisms that we describe. We recognize that untested/untestable speculative arguments have limitations and there are viable alternative hypotheses. We have softened our language to ensure that it is clear that this is only a speculation.

The authors suggest that deletion of Pho85 causes an increase in MMP because of cellular signaling. However, they also state in the conclusion:

"Unlike phosphate starvation, the pho85D mutant has elevated intracellular phosphate concentrations. This suggests that the phosphate effect on MMP is likely to be elicited by cellular signaling downstream of phosphate sensing rather than some direct effect of environmental depletion of phosphate on mitochondrial energetics."

The authors should cite the study that shows deletion of PHO85 causes increased intracellular phosphate concentrations. It also seems possible that the 'cellular signaling' that causes the increase in MMP could be a result of this increase in intracellular phosphate concentrations, which could constitute a direct effect of an environmental overload of phosphate on mitochondrial energetics.

We now cited the literature that shows higher intracellular phosphate in pho85Δ cells (Gupta et al., 2019; Liu et al., 2017). Depleting phosphate in the media drastically reduced intracellular phosphate concentration, which is the opposing situation as pho85Δ cells. Nevertheless, we observed higher MMP in either situation. We concluded from these two observations that the increase in MMP is a response to the signaling activated by phosphate depletion rather than the intracellular phosphate abundance.

Related to this point, in the conclusion, the authors state:

"We now show that intracellular signaling can lead to an increased MMP even beyond the wild-type level in the absence of mitochondrial genome."

In sum, the data shows that signaling is important here- but signaling alone is only the message - not the biophysical process that creates a membrane potential. The authors then could revise this slightly.

We have rephrased this sentence as suggested, which now reads “We now show that intracellular signaling triggers a process that can lead to an increased MMP even beyond the wild-type level in the absence of mitochondrial genome”.

The authors state in the conclusion that

"We first made the observation that deletion of the SIT4 gene, which encodes the yeast homologue of the mammalian PP6 protein phosphatase, normalized many of the defects caused by loss of mtFAS, including gene expression programs, ETC complex assembly, mitochondrial morphology, and especially MMP (Fig. 1)"

The data shown though indicates that a defect in mtFAS in terms of MMP, deletion of SIT4 causes a huge increase (and departure away from normality) whether or not mct1 is present (Fig 1D)

We changed the word “normalized” to “reversed”. In the discussion section, we also emphasized that many of these increases are independent of mitochondrial dysfunction induced by loss of mtFAS.

The language "SIT4 is required for both the positive and negative transcriptional regulation elicited by mitochondrial dysfunction" feels strong. SIT4 seems to influence positive transcriptional regulation in response to mitochondrial dysfunction caused by MCT1 deletion (but may not be the only thing as there appears to be an increase in CIT2 expression in a sit4del background following a further deletion of MCT1). In terms of negative regulation, SIT4 deletion clearly affects the baseline, but MCT1 deletion still causes down regulation of both examples shown in Fig 1B, showing that negative transcriptional regulation can still occur in the absence of SIT4. The authors might consider showing fold change of expression as they do in later figures (Figs 4B and C) to help the reader evaluate the quantitative changes they demonstrate.

We now displayed the fold change as suggested. This sentence now reads “These data suggest that SIT4 positively and negatively influences transcriptional regulation elicited by mitochondrial dysfunction”.

The authors induce phosphate starvation by adding increasing amounts of potassium phosphate monobasic at a pH of 4.1 to phosphate dropout media supplemented with potassium. The authors did well to avoid confounding effects of removing potassium. The final pH of YNB is typically around 5.2. Is it possible that the authors are confounding a change in pH with phosphate starvation? One would expect the media in the phosphate starvation condition to have a higher pH than the phosphate replacement or control media. Is a change in pH possibly a confounding factor when interpreting phosphate starvation? Perhaps the authors could quantify the pH of the media they use for the experiment to understand how much of a factor that could be. One needs to be careful with Miotracker and any other fluorescent dye when pH changes. Albeit having constraints on its own, MitoLoc as a protein rather than small molecule marker of MMP might be a good complement.

We followed the protocol used by many other studies that depleted phosphate in the media. The reason we and others adjusted the media without inorganic phosphate to a pH of 4.1 is because that is the pH of phosphate monobasic. From there, we could add phosphate monobasic to create +Pi media without changing the media pH. Therefore, media containing different concentrations of phosphate all have the exact same pH. We now emphasize that all media containing different levels of inorganic phosphate have the same pH to the manuscript to eliminate such concern (see page 18).

Even though all media have the similar pH, we also provided complementary data using a parallel approach to measure the MMP by assessing mitochondrial protein import as demonstrated previously with Ilv2-FLAG, which shares the same principle as mitoLoc.

Reference

Arndt, K. T., Styles, C. A., & Fink, G. R. (1989). A suppressor of a HIS4 transcriptional defect encodes a protein with homology to the catalytic subunit of protein phosphatases. Cell, 56(4), 527–537. https://doi.org/10.1016/00928674(89)90576-X

Dimmer, K. S., Fritz, S., Fuchs, F., Messerschmitt, M., Weinbach, N., Neupert, W., & Westermann, B. (2002). Genetic basis of mitochondrial function and morphology in Saccharomyces cerevisiae. Molecular Biology of the Cell, 13(3), 847–853. https://doi.org/10.1091/mbc.01-12-0588

Gupta, R., Walvekar, A. S., Liang, S., Rashida, Z., Shah, P., & Laxman, S. (2019). A tRNA modification balances carbon and nitrogen metabolism by regulating phosphate homeostasis. ELife, 8, e44795. https://doi.org/10.7554/eLife.44795

Jablonka, W., Guzmán, S., Ramírez, J., & Montero-Lomelí, M. (2006). Deviation of carbohydrate metabolism by the SIT4 phosphatase in Saccharomyces cerevisiae. Biochimica et Biophysica Acta (BBA) - General Subjects, 1760(8), 1281–1291. https://doi.org/10.1016/j.bbagen.2006.02.014

Liu, N.-N., Flanagan, P. R., Zeng, J., Jani, N. M., Cardenas, M. E., Moran, G. P., & Köhler, J. R. (2017). Phosphate is the third nutrient monitored by TOR in Candida albicans and provides a target for fungal-specific indirect TOR inhibition. Proceedings of the National Academy of Sciences, 114(24), 6346–6351. https://doi.org/10.1073/pnas.1617799114

Sutton, A., Immanuel, D., & Arndt, K. T. (1991). The SIT4 protein phosphatase functions in late G1 for progression into S phase. Molecular and Cellular Biology, 11(4), 2133–2148.

Author Response

Reviewer #1 (Public Review):

This study provides further detailed analysis of recently published Fly Atlas datasets supplemented with newly generated single cell RNA-seq data obtained from 6,000 testis cells. Using these data, the authors define 43 germline cell clusters and 22 somatic cell clusters. This work confirms and extends previous observations regarding changing gene expression programs through the course of germ cell and somatic cell differentiation.

This study makes several interesting observations that will be of interest to the field. For example, the authors find that spermatocytes exhibit sex chromosome specific changes in gene expression. In addition, comparisons between the single nucleus and single cell data reveal differences in active transcription versus global mRNA levels. For example, previous results showed that (1) several mRNAs remain high in spermatids long after they are actively transcribed in spermatocytes and (2) defined a set of post-meiotic transcripts. The analysis presented here shows that these patterns of mRNA expression are shared by hundreds of genes in the developing germline. Moreover, variable patterns between the sn- and sc-RNAseq datasets reveals considerable complexity in the post-transcriptional regulation of gene expression.

Overall, this paper represents a significant contribution to the field. These findings will be of broad interest to developmental biologists and will establish an important foundation for future studies. However, several points should be addressed.

In figure 1, I am struck by the widespread expression of vasa outside of the germ cell lineage. Do the authors have a technical or biological explanation for this observation? This point should be addressed in the paper with new experiments or further explanation in the text.

Thank you for pointing this out. We found that our single cell dataset shows a similar (low) level of vasa expression outside the germline, suggesting that this is not due to single nucleus versus single cell RNA-seq (cluster 1, red in the lefthand umap).

Analyzing the single nucleus RNA-seq in more detail revealed that, compared to the germline, both the fraction of cells in a cluster expressing vasa and the level at which they express it are very low. This analysis is included in a new Figure 1 – figure supplement 1. It is likely that much of this is due to a technical artifact, such as ambient RNA. Finally, we note in the resubmission that vasa is in fact expressed in embryonic somatic cells, and thus some of the vasa expression we observe may be real (Renault. Biol Open 2012; https://doi.org/10.1242/bio.20121909).

Plots in the original submission drew undue attention to the few somatic cells that exhibited vasa signal, due to the fact that expressing cell points were forced to the front of the plot. Given our new analysis reporting the low levels and fraction of cells exhibiting vasa expression (Figure 1 – figure supplement 1), we have modified the panels of Figure 1, changing point size to more faithfully reflect the small proportion of somatic cells with some vasa expression.

The proposed bifurcation of the cyst cells into head and tail populations is interesting and worth further exploration/validation. While the presented in situ hybridization for Nep4, geko, and shg hint at differences between these populations, double fluorescent in situs or the use of additional markers would help make this point clearer. Higher magnification images would also help in this regard.

We thank the reviewer for their suggestions on clarifying the differences between HCC and TCC populations. As suggested, we have repeated the FISH experiments of Nep4 and geko with higher resolution, and included the additional marker Coracle that demarcates the junction between HCC and TCC (Figure 6O,Q,S,T). These panels replaced previous Nep4 and geko FISH images (see previous Figure 6Q,U,U’). FISH for Nep4 validated the split, and the enrichment of geko strongly suggests that this arm represents one cell type (HCCs). We have not yet identified a gene reciprocally enriched to the other arm. Therefore, in the revised submission, we call the assignment of TCC identity, and to a lesser extent, HCC identity ‘tentative’, but point out that genes predicted to be enriched to one or the other arm represent fertile candidates for the field to test.

Reviewer #2 (Public Review):

In this manuscript the authors explain in greater detail a recent testis snRNAseq dataset that many of these authors published earlier this year as part of the Fly Cell Atlas (FCA) Li et al. Science 2022. As part of the current effort additional collaborators were recruited and about 6,000 whole cell scRNAseq cells were added to the previous 42,000 nuclei dataset. The authors now describe 65 snRNseq clusters, each representing potential cell types or cell states, including 43 germline clusters and 22 somatic clusters. The authors state that this analysis confirms and extends previously knowledge of the testis in several important areas.

“However, in areas where testis biology is well studied, such as the development of germ cells from GSC to the onset of spermatocyte differentiation, the resolution seems less than current knowledge by considerable margins. No clusters correspond to GSCs, or specific mitotic spermatogonia, and even the major stages of meiotic prophase are not resolved. Instead, the transitions between one state and the next are broad and almost continuous, which could be an intrinsic characteristic of the testis compared to other tissues, of snRNAseq compared to scRNAseq, or of the particular experimental and software analysis choices that were used in this study.”

Note that the referee raises the same issue later in their review also. To respond succinctly, we placed the relevant sentence from a later portion of this referee’s comment here

“Support for the view that the problems are mostly technical, rather than a reflection of testis biology, comes from studies of scRNAseq in the mouse, where it has been possible to resolve a stem cell cluster, and germ cell pathways that follow known germ cell differentiation trajectories with much more discrete steps than were reported here (for example, Cao et al. 2021 cited by the authors).”

Respectfully, we have a different interpretation of other work as cited by this referee. Our data, as well as that from others, supports the notion that transitions are generally broad and continuous and are indeed a feature of testis biology. As we report here, data from both single cell and single nucleus RNAseq exhibit transitions from one cluster to the next. Thus, this feature cannot be due to the choice of method (single cell versus single nucleus).

In fact, prior scRNA-seq results on systems containing a continuously renewing cell population, such as is the case in the testis, do indeed exhibit a contiguous trajectory rather than discrete, well-separated cell states in gene expression space (that is, in a UMAP presentation). For example, this is the case from single-cell or single-nucleus sequencing from spermatogenesis in mouse (Cao et al 2021), human (Sohni et al 2019), and zebrafish (Qian et al 2022).

Along differentiation trajectories in these tissues, successive clusters are defined by their aggregate, transcript repertoire. Indeed, differentially-expressed genes can be identified for clusters, with expression enriched in a given cluster. However, expression is rarely restricted to a cluster. For instance, Cao et al. subcluster spermatogonia into four subgroups, termed SPG1-4. They state clearly that these SPG1-4 “follow a continuous differentiation trajectory,” as can be inferred by marker expression across cells in this lineage. Similar to our findings, while the spermatogonia can fall into discrete clusters, gene expression patterns are contiguous. For example, the “undifferentiated” marker used in Cao et al, Crabp1, clearly shows expression in SPG1-3, annotated as spermatogonial stem cells, undifferentiated spermatogonia, and early differentiated spermatogonia, respectively. Likewise, markers for the “SPG3” state spermatogonia have detectable expression in SPG2 and SPG4, and likewise for markers of the “SPG4” state (with expression found also in SPG3). <br /> Analogous study of human spermatogenesis arrives at a similar conclusion. In that work, although clusters are named as “spermatogonial stem cell (SSC)”, the authors are careful to specifically point out that, “…while we refer to the SSC-1 and SSC-2 cell clusters as ‘‘SSCs,’’ scRNA-seq is not a functional assay and thus we do not know the percentage of cells in these clusters with SSC activity. These subsets almost certainly contain other A-SPG cells [A type spermatogonia], including SPG progenitors that have committed to differentiate.” (Sohi et al 2019)

Thus, the work in several disparate systems, all involving renewing lineages, finds that discrete clusters, such as a “stem cell cluster” are not identified. In the Drosophila testis, germline differentiation flows in a continuous-like manner similar to spermatogenesis in several other organisms studied by scRNA-seq, and our finding is not a function of the methodology, but rather a facet of the biology of the organ.

Operating in parallel with continuous differentiation, we did find evidence of, and extensively discussed in concert with Figure 4, huge and dramatic shifts in transcriptional state in spermatocytes compared to spermatogonia, in early spermatids compared to spermatocytes, and in late spermatid elongation. Lastly, as we describe further below, new data in this resubmission identify four distinct genes with stage-selective expression as predicted by our analysis (new Figure 2 - figure supplement 1), illustrating the utility of our study for the field to find new markers and new genes to test for function.

A goal of the study was to identify new rare cell types, and the hub, a small apical somatic cell region, was mentioned as a target region, since it regulates both stem cell populations, GSCs and CySCs, is capable of regeneration, and other fascinating properties. However the analysis of the hub cluster revealed more problems of specificity. 41 or 120 cells in the cluster were discordant with the remaining 79 which did express markers consistent with previous studies. Why these cells co-clustered was not explained and one can only presume that similar problems may be found in other clusters.

Our writing seems not to have been clear enough on this point and we thank the reviewer. We have revised the section. In addition, we have added new data (Figure 7 - figure supplement 2). We had already stated that only 79 of these 120 nuclei were near to each other in 2D UMAP space, while other members of original cluster 90 were dispersed. Thus the 79 hub nuclei in fact clustered together on the UMAP. Other nuclei that mapped at dispersed positions were initially ‘called’ as part of this cluster in the original Fly Cell Atlas (FCA) paper (Li et al., 2022), making it obvious that a correction to that assignment was necessary, which we carried out. To our eye, no other called cluster was represented by such dispersed groupings. For the hub, we definitively established the 79 nuclei to represent hub cells by marker gene analysis, including the identification of a new maker, tup, that was included in the 79 annotated hub nuclei but excluded from the 41 other nuclei (Figure 7). In this resubmission, to independently verify the relationship of the 79 nuclei to each other, we subjected the 120 nuclei from the original cluster 90 defined by the FCA study to hierarchical clustering using only genes that are highly expressed and variable in these nuclei (Figure 7 - figure supplement 2). This computationally distinct approach strongly supported our identification of the 79 definitive hub nuclei.

Indeed, many other indications of specificity issues were described, including contamination of fat body with spermatocytes, the expression of germline genes such as Vasa in many somatic cell clusters like muscle, hemocytes, and male gonad epithelium, and the promiscuous expression of many genes, including 25% of somatic-specific transcription factors, in mid to late spermatocytes. The expression of only one such genes, Hml, was documented in tissue, and the authors for reasons not explained did not attempt to decisively address whether this phenomenon is biologically meaningful.

We discussed the question of vasa expression in somatic clusters in some detail above, in response to referee #1, and included new analysis in the resubmission.

With respect to the observation of ‘somatic gene’ expression in spermatocytes, we are also intrigued. We do not believe this is due to “contamination,” but rather a spermatocyte expression program that includes expression of somatic genes. First, these somatic markers were not observed in other germline clusters, which would be expected if this was due to general transcript contamination. Second, we observed expression of somatic markers in spermatocytes independently in the single-cell and single-nucleus data, making it unlikely to be an artifact of preparation of isolated nuclei. Finally, in the resubmission, in addition to Hml, we validated ‘somatic’ marker expression in spermatocytes by FISH of a somatic, tail cyst cell marker, Vsx1. Vsx1 is predicted to be expressed at low levels in spermatocytes in our dataset and is clearly visible in germline cells by FISH (Figure 3 – figure supplement 2G,H). We also refer the referee to Figure 6K, where the mRNA for the somatic cyst cell marker eya was observed by FISH at low levels in spermatocytes.

A truly interesting question mentioned by the authors is why the testis consistently ranks near the top of all tissues in the complexity of its gene expression. In the Li et al. (2022) paper it was suggested that this is due an inherently greater biological complexity of spermiogenesis than other tissues. It seems difficult to independently and rationally determine "biological complexity," but if a conserved characteristic of testis was to promiscuously express a wide range of (random?) genes, something not out of the question, this would be highly relevant and important.

We agree that the massive transcriptional program found in spermatocytes is, indeed, truly interesting. There are many speculations as to why spermatocytes are so highly transcriptional, including the possibility of “transcriptional scanning” (e.g., Xia et al. 2020) regulating the evolution of new genes. Testing such models is beyond the scope of this paper. However, one must also keep in mind that spermatogenesis involves one of the most dramatic cellular transformations in biology, where cellular components spanning from nuclei to chromatin to Golgi, cell cycle, extensive membrane addition, changes in cell shape, and building of a complex swimming organelle all must occur and be temporally coordinated. Small wonder that many genes must be expressed to accomplish these tasks.

Unfortunately, the most likely problems are simply technical. Drosophila cells are small and difficult to separate as intact cells. The use of nuclei was meant to overcome this inherent problem, but the effectiveness of this new approach is not yet well-documented. Support for the view that the problems are mostly technical, rather than a reflection of testis biology, comes from studies of scRNAseq in the mouse, where it has been possible to resolve a stem cell cluster, and germ cell pathways that follow known germ cell differentiation trajectories with much more discrete steps than were reported here (for example, Cao et al. 2021 cited by the authors).

We respectfully disagree with the referee about this collection of statements. First, the use of snRNASeq has been extensively characterized and compared to scRNA-seq in brain tissue by McLaughlin et al., 2021 (cited in the original submission) and was shown to be effective (McLaughlin, et al. eLife 2021;10:e63856. DOI: https://doi.org/10.7554/eLife.63856). snRNA-seq has a distinct advantage when dealing with long, thin cells, such as neurons or cyst cells (as featured in this work), where cytoplasm can easily be sheared off during cell isolation. Second, in a previous portion of our response to this referee, we discussed how our interpretation of Cao et al., 2021 differs from that expressed by this referee. Lastly, as requested in ‘Essential revision’ 2, we adjusted clustering methods and selected four genes, two predicted to be markers for early stage germline cells, and two for mid-spermatocyte stage development. FISH analysis demonstrates that expression for each of these maps to the appropriate stages (new Figure 2 - figure supplement 1). This confirms that the datasets we present in this manuscript can be mined to identify unique, diagnostic markers for various stages.

The conclusions that were made by the authors seem to either be facts that are already well known, such as the problem that transcriptional changes in spermatocytes will be obscured by the large stored mRNA pool, or promises of future utility. For example, "mining the snRNA-seq data for changes in gene expression as one cluster advances to the next should identify new sub-stage-specific markers." If worthwhile new markers could be identified from these data, surely this could have been accomplished and presented in a supplemental Table. As it currently stands, the manuscript presents the dataset including a fair description of its current limitations, but very little else of novel biological interest is to be found.

“In sum, this project represents an extremely worthwhile undertaking that will eventually pay off. However, some currently unappreciated technical issues, in cell/nuclear isolation, and certainly in the bioinformatic programs and procedures used that mis-clustered many different cells, has created the current difficulties.

Most scRNAseq software is written to meet the needs of mammalian researchers working with cultured cells, cellular giants compared to Drosophila and of generally similar size. Such software may not be ideal for much smaller cells, but which also include the much wider variation in cell size, properties and biological mechanisms that exist in the world of tissues.”

We appreciate the referee’s acknowledgement that this ‘undertaking will eventually pay off’. It was not our intention to address ‘function’ for this study, but rather to make the system accessible to the broadest community possible. We are uncertain if there is any remaining reservation held by this referee. A brief summary of what we covered in the manuscript may help allay any residual concern. Obviously, study of the Drosophila testis and spermatogenesis benefits from the knowledge of a large number of established cell-type and stage-selective markers. Thus, we extensively used the community’s accepted markers to assign identity to clusters in both the sn- and sc-RNA-seq UMAPs. We believe that effort well establishes the validity and reliability of the dataset . Furthermore, we identified upwards of a dozen new markers out of the cluster analysis, and verified their expression by FISH or reporter line in various figures throughout (tup, amph, piwi, geko, Nep4, CG3902, Akr1B, loqs, Vsx1, Drep2, Pxt, CG43317, Vha16-5, l(2)41Ab). To our mind, these contributions, coupled with annotation of the datasets, suggest strongly that they will serve the community well. This is especially true as we provide users with objects that they can feed into commonly used software algorithms such as Seurat and Monocle to explore the datasets to their purposes. Rather than simply relying on default settings within some of the applications, we also adjusted parameters for various clusterings as called for; some of which were in response to astute comments from referees, and included in the resubmission. Of course, it is possible that rare issues may arise in the datasets as these are further studied, but that is the case with all scRNA-seq data, and is not specific to work on this model organism.

Reviewer #3 (Public Review):

In this study, the authors use recently published single nucleus RNA sequencing data and a newly generated single cell RNA sequencing dataset to determine the transcriptional profiles of the different cell types in the Drosophila ovary. Their analysis of the data and experimental validation of key findings provide new insight into testis biology and create a resource for the community. The manuscript is clearly written, the data provide strong support for the conclusions, and the analysis is rigorous. Indeed, this manuscript serves as a case study demonstrating best practices in the analysis of this type of genomics data and the many types of predictions that can be made from a deep dive into the data. Researchers who are studying the testis will find many starting points for new projects suggested by this work, and the insightful comparison of methods, such as between slingshot and Monocle3 and single cell vs single nucleus sequencing will be of interest beyond the study of the Drosophila testis.

We greatly appreciate the reviewer’s comments.

Reviewer #4 (Public Review):

This is an extraordinary study that will serve as key resource for all researchers in the field of Drosophila testis development. The lineages that derive from the germline stem cells and somatic stem cells are described in a detail that has not been previously achieved. The RNAseq approaches have permitted the description of cell states that have not been inferred from morphological analyses, although it is the combination of RNAseq and morphological studies that makes this study exceptional. The field will now have a good understanding of interactions between specific cell states in the somatic lineage with specific states in the germ cell lineage. This resource will permit future studies on precise mechanisms of communication between these lineages during the differentiation process, and will serve as a model for studies of co-differentiation in other stem cell systems. The combination of snRNAseq and scRNAseq has conclusively shown differences in transcriptional activation and RNA storage at specific stages of germ cell differentiation and is a unique study that will inform other studies of cell differentiation.

Could the authors please describe whether genes on the Y chromosome are expressed outside of the male germline. For example, what is represented by the spots of expression within the seminal vesicle observed in Figure 3D?

Prior work demonstrated that proteins encoded by Y-linked genes are not expressed outside of the germline (Zhang et al. Genetics 2020. https://doi.org/10.1534/genetics.120.303324). In our snRNAseq dataset, we find that genes on the Y chromosome are not highly expressed outside of the male germline (on the order of ~100-fold lower in other tissues). In fact, we observe Y chromosome transcripts at this level in many nuclei across tissues collected for the Fly Cell Atlas project, including the ovary. Since we have not followed up on the Fly Cell Atlas observations directly using FISH to examine Y chromosome transcript expression outside the germline, we cannot rule out the possibility that such low level expression is real. However, the detection across several tissues argues that this is likely technical artifact. With regard to ‘spots of expression within the seminal vesicle’ (Figure 3D), a spot is colored red if the average expression level of genes on the Y chromosome is greater in that cell than in an average cell on our plot. These red spots are likely due to ambient RNA being carried over.

I would appreciate some discussion of the "somatic factors" that are observed to be upregulated in spermatocytes (e.g. Mhc, Hml, grh, Syt1). Is there any indication of functional significance of any of these factors in spermatocytes?

This is an excellent question. Although we validated expression for several (Hml, Vsx1 and eya), we did not test for their function here and this issue remains to be studied. This is now directly stated in the main text.

In the discussion of cyst cell lineage differentiation following cluster 74 the authors state that neither the HCC or TCC lineages were enriched for eya (Figure 6V). It seems in this panel that cluster 57 shows some enrichment for eya - is this regarded as too low expression to be considered enriched?

We thank the reviewer for their insightful comment and we agree with their conclusions. We have modified the text to reflect the low, but present, expression of eya in the HCC and TCC lineages. The text now reads as follows at line (insert line # here): “Enrichment of eya was dramatically reduced in the clusters along either late cyst cell branch compared to those of earlier lineage nuclei (Figure 6J,U).”

Author Response

Reviewer #1 (Public Review):

This paper presents analysis of an impressive dataset acquired from sibling pairs, where one child had a specific gene mutation (22q11.2DS), whereas other child served as a blood-related, healthy control. The authors gathered rich, multi-faced data, including genetic profile, behavioral testing, neuropsychiatric questionnaires, and sleep PSG.

The analyses explore group differences (gene mutation vs. healthy controls) in terms of sleep architecture, sleep-specific brain oscillations and performance on a memory task.

The authors utilized a solid mix-model statistical approach, which not only controlled for the multi-comparison problem, but also accounted for between-subject and within-family variance. This was supplemented by mediation analysis, exploring the exact interaction between the variables. Remarkably, the two subject groups were gender balanced, and were matched in terms of age and sex.

Thank you for this endorsement of our approach.

There are some aspects requiring clarification. In the discussion section, some claims come across as too general, or too speculative, and lack proper evidence in the current analysis of in the references.

We have extensively revised our discussion, including introducing more referencing and adding subheadings which we hope makes our conclusions both more structured and better evidenced (Discussion, pages 27 – 31)

Furthermore, the authors seem to treat their (child) participants with the gene mutation as forerunners of (adult) schizophrenic patients, to whom their repeatedly compare the findings. However, less than half of these children with 22q11.2DS are expected to develop psychotic disorders. In fact, they are at risk of many other neuropsychiatric conditions (incl. intellectual disability, ASD, ADHD, epilepsy) (cf. introduction section).

We have revised our introduction (page 4 -5) and discussion to clarify the significant comorbidity in 22q11.2DS. We discuss the limitations and future directions section of our work in the discussion (page 30)

Furthermore, the liberal criteria for detecting slow-waves, along with odd topography of the detections, limit the credibility of the slow-wave-related results.

As there is no single common method for SW detection, as noted on page 37, we prioritised rate of detection in order to provide a robust dataset for spindle-SW coupling analysis. We considered the use of an absolute detection threshold (e.g. – 75 microVolts) – however, because our participants were of a wide range of ages (6 to 20 years), and it is established that the absolute amplitude of the EEG decreases across childhood (e.g. Hahn et al 2020), our view is that the use of an absolute detection threshold would potentially bias the detection of slow waves by age. We have added comments on this matter to the methods section (page 37)

Lastly, we cannot be sure whether the presented memory effects reflect between-group difference in general cognitive capacities, or, as claimed, in overnight memory consolidation.

We have added statistical analysis of the overnight change in performance (results, page 6) to explore this point. We clarify that although 22q11.2DS is associated with slower learning and worse accuracy in the test session, there is not a difference in overnight change in 22q11.2DS.

Generally, the current study introduces dataset connecting various aspects of 22q11.2DS. It has a great potential for complementing the current state of knowledge not only in the clinical, but also in sleep-science field.

Thank you

Reviewer #2 (Public Review):

This study examines 22q11.2 microdeletion syndrome in 28 individuals and their unaffected siblings. Though the sample size is small, it is on par with many neuroimaging studies of the syndrome. Part of the interest in this disorder arises from the risk this syndrome confers for neuropsychiatric disorders in general and psychosis specifically. The authors examine sleep neurophysiology in 22q11.2DS and their siblings. Principal findings include increase slow wave and spindle amplitudes in deletion carriers as compared to controls.

Strengths of this manuscript include:

• The inclusion of siblings as a control group, which minimizes environmental and (other) genetic confounds

• The data analyses of the sleep EEG are appropriate and in-depth

• High-density sleep EEG allows for topographic mapping

We thank the reviewer for this positive endorsement of our work

Weaknesses of this manuscript include:

• The manuscript is framed as an investigation of the psychosis and schizophrenia; however, psychotic experiences did not differ between 22q11.2DS and healthy controls. Therefore, the emphasis on schizophrenia and psychosis does not pertain to this sample and the manuscript introduction and discussion should be carefully reframed. The final sentence of the abstract is also not supported by the data: "... out findings may therefore reflect delayed or compromised neurodevelopmental processes which precede, and may be biomarkers for, psychotic disorders".

We have expanded our abstract, introduction and discussion to reflect the complex neurodevelopment phenotype observed in 22q11.2DS, and discuss the links between our findings, and elements of this phenotype

• What is the rationale for using a mediation model to test for the association between genotype and psychiatric symptoms? Given the modest sample size would a regression to test the association between genotype and psychiatric symptoms be more appropriate?

Our rationale for mediation analysis was to expand on making simple group comparisons for various measures by asking if genotype effects on particular psychiatric/behavioural measures were potentially mediated by EEG measures. This is of considerable interest because, as noted above, the behavioural and psychiatric phenotype in 22q11.2DS is complex, and therefore dissection of links between particular EEG features and phenotypes, and asking if EEG measures can be biomarkers for these phenotypes, may give insight into this complexity.

• From Table 1, which presents means, standard deviations and statistics, it is hard to tell if there is a range of symptoms or if there are some participants with 22q11.2DS who met diagnostic criteria for a the listed disorder while others who have no or sub-threshold symptoms. This is important and informs the statistical analysis. Given the broad range of psychiatric symptoms, I also wonder if a composite score of psychopathology may be more appropriate. What about other psychiatric symptoms such as depression?

We have added a supplementary figure to figure 1 to provide individual participants scores on psychiatric measures and FSIQ to fully inform the reader about individual data.

We have taken the approach of using symptom scores, rather than using binary cut offs for diagnosis, to maximise the use of our dataset, and given many/all psychiatric phenotypes exist on a spectrum, to reflect the difference between clinical and research diagnoses.

Regarding depression, it has been previously demonstrated in 22q11.2DS that mood disorders are rare at young ages (Chawner et al 2019), therefore given the low frequency, we have not included depression in this dataset

We have considered the utility of a composite psychopathology score; however, it is already established that 22q11.2DS can be associated with a broad range of psychiatric/behavioural difficulties; in this study we were primarily interested in exploring the links (if any) between specific groups of symptoms, and specific features of the sleep phenotype. Therefore, we feel a composite psychopathology score would not add to the overall clarity of the manuscript

• The age range is very broad spanning 6 to 20 years. As there are marked changes in the sleep EEG with age, it is important to understand the influence of age. The small sample size precludes investigating age by group interactions meaningfully, but the presentation of the ages of 22q11.2DS and controls, rather than means, standard deviations and ranges, would be helpful for the reader to understand the sample.

We have added scatter plots of EEG measures and age to each figure supplement to allow the reader to see changes with age

Also, a figure showing individual data (e.g., spindle power) as a function of age and group would be informative. The authors should also discuss the possibility that the difference between the groups may vary as a function of age as has been shown for cortical grey matter volume (Bagaiutdinova et al., Molecular Psychiatry, 2021).

We have provided plots of individual data with age for our main figures, in the figure supplements. We also note we have included age as a covariate in all main statistical models (methods, page 39). We thank the reviewer for the additional reference, this has been added to the discussion (page 29)

• There is a large group difference with regards to full scale IQ. IQ is associated with sleep spindles (e.g., Gruber et al., Int J of Psychphsy, 2013; Geiger et al., SLEEP, 2011). For this reason, the authors should control for IQ in all analyses.

We note that the relationship between spindle characteristics and IQ has been questioned (e.g. Reynolds et al 2018 performed a meta-analysis which suggests no correlation with FSIQ, which would suggest against the suggested approach). We also note that genotype effects on FSIQ were not mediated by spindle properties. Furthermore, the phenotype in 22q11.2DS is complex, while lower IQ is a well evidenced part, it is only one component. We are unclear if it would be justified to regress out only one component of a phenotype.

• The authors find greater power in the delta and sigma bands in 22q11.2DS compared to their siblings. Looking at the Figure 2, it appears power is elevated across frequencies. If this were the case, this would likely change the interpretation of the findings, and suggest that the sleep EEG likely reflects changes in cortical thickness between controls and 22q11.2DS participants.

We thank the review for this interesting comment. We have now altered the approach taken to our analysis of spectral data in order to probe overall differences in overall power, using the IRASA approach described by Hahn et al 2020. We present these results on page 13, and use measures derived from this analysis in the mediation and behavioural analyses, and discuss these findings in the discussion (page 29)

• Along the same lines as the above comment, it would be interesting to examine REM sleep and test how specific to sleep spindles and slow waves these findings are.

We have now added analysis of REM-derived spectral measures, which we believe complement our finding of altered proportions of REM sleep in 22q11.2DS compared to controls (page 13)

Reviewer #3 (Public Review):

In this study, Donnelly and colleagues quantified sleep oscillations and their coordination in in young people with 22q11.2 Deletion Syndrome and their siblings. They demonstrate that 22q11.2DS was associated with enhanced power the in slow wave and sleep spindle range, elevated slow-wave and spindle amplitudes and altered coupling between spindles and slow-waves. In addition, spindle and slow-wave amplitudes in 22q11.2DS correlated negatively with the outcomes of a memory test. Overall, the topic and the results of the present study are interesting and timely. The authors employed many thoughtful analyses, making sense out of complicated data. However, some features of the manuscript need further clarification.

1.) Several interesting results of the manuscript are related to altered sleep spindle characteristics in 22q11.2DS (increased power, increased amplitudes and altered coupling with slow waves). On top of that the authors report, that the spindle frequency was correlated with age. I was wondering whether the authors might want to take these individual (age-related) differences into account in their analyses. The authors could detect the peak spindle frequency per participant and inform their spindle detection procedure accordingly. This procedure might lead to an even more clear cut picture concerning altered spindle activity in 22q11.2DS.

We thank the review for this informative suggestion. We have now implemented this method, detecting spindles for each individual at a frequency defined through IRASA analysis of the EEG (results, page 13; methods, page 35), and then using the properties of spindles detected through this method in further analysis.

We have included age as a covariate in all main models (methods, page 39), and present individual data scattered with age in our figure supplements.

2.) The authors state in the methods section that EEG data was re-referenced to a common average during pre-processing. Did the authors take into account that this reference scheme will lead to a polarity inversion of the signal, potentially over parietal/occipital areas? This inversion will not affect spindle related analyses, but might misguide the detection of slow waves and hence confound related analyses and results.

We have reviewed our data preprocessing pipeline, and updated it based on the latest methods suggested from the EEGlab authors (methods, page 33). As a supplementary analysis we applied a heuristic signal polarity measure described by the authors of the luna software package https://zzz.bwh.harvard.edu/luna/vignettes/nsrr-polarity/ and did not observe any inversion of polarity in our sample.

In the included figure (below) we calculated the Hjorth measure of signal polarity as described in luna, at every electrode and plotted a topoplot of the measure. In the figure numbers > 0 represent signals with a positive polarity, values < 0 a negative polarity. As demonstrated in the figure, there were no electrodes with a positive polarity, although we note that the most peripheral electrodes had an approximately neutral polarity, whereas more central electrodes had a slight negative bias.

We also note that we only detected negative half waves with our slow wave detection algorithm, using a threshold set for each channel based on its own characteristics, so would not necessarily expect alterations in slow waves detection. Further, other authors have suggested that average referencing does not impact SW detection (e.g. Wennberg 2010)

3.) I have some issues understanding the reported slow wave - spindle coupling results. Figure 5A indicates that ~100 degrees correspond to the down-state of the slow wave. Figure 5E shows that spindles preferentially clustered at fronto-central electrodes between 0 and 90 degrees, hence they seem to peak towards the slow wave downstate. This finding is rather puzzling given the prototypical grouping of sleep spindles by slow wave up-states (Staresina, 2015; Helfrich, 2018; Hahn, 2020). Could it be that the majority of detected spindles represent slow spindles (9-12 Hz; Mölle, 2011)?

We observed peaks of spindle activity in the range of 9 – 24 degrees (so on the descending slope from the positive peak of the slow wave), but an average spindle frequencies in the 12 – 13 Hz range. Given we allowed each individual to have an individual spindle detection frequency, as above, and did not observe bimodal distributions of power in the sigma frequency band (Figure 2 Supplement 1), we do not believe our spindles specifically represent slow spindles

Slow spindles are known to peak rather at the up- to down-state transition (which would fit the reported results) and show a frontal distribution (which again would fit to the spindle amplitude topographies in Fig 3E). If that was the case, it would make sense to specifically look at fast spindles (12-16 Hz) as well, given their presumed role in memory consolidation (Klinzing, 2019).

We agree with the reviewer’s assessment of the distribution of the putative spindles we have detected. However, as we and other authors (Hahn et al 2020) have noted, we did not observe discrete fast and slow spindle frequency peaks in our analysis of the PSD (as has been observed by other authors e.g. Cox et al 2017). For this reason, and to reduce the complexity of the manuscript, we believe the best approach with our dataset is to focus on spindles at large, rather than detecting spindles in arbitrary frequency bands.

In addition, is it possible that the rather strong phase shift from fronto-central to occipital sites is driven by a polarity inversion due to using a common reference (see comment 2)?

As noted above, we do not observe significant polarity inversion in our signals using the luna heuristic measure. We were not able to identify published literature to inform our investigation of this suggestion, but would be happy to consider any specific suggestions from the reviewer

Apart from that I would suggest to statistically evaluate non-uniformity using e.g. the Rayleigh test (both within and across participants).

We have added an analysis of non-uniformity to the results section (results, page 20).

4.) Somewhat related to the point raised above. The authors state that in the methods that slow wave spindle events were defined as time-windows were the peaks of spindles overlapped with slow waves. How was the duration of slow waves defined in this scenario? If it was up- to up-state the authors might miss spindles which lock briefly after the post down-state upstate, thereby overrepresenting spindles that lock to early phases of slow waves. Why not just defining a clear slow wave related time-window, such as slow wave down-state {plus minus} 1.5 seconds?

We have implemented this suggestion (methods, page 38)

5.) The authors correlated the NREM sleep features with the outcomes of a post-sleep memory test (both encoding and an initial memory test took place pre-sleep). If the authors want to show a clear association between sleep-related oscillations and the behavioural expressions of memory consolidation, taking just the post sleep memory task is probably not the best choice. The post-sleep test will, as the pre-sleep test, in isolation rather reflect general memory related abilities. To uncover the distinct behavioural effects of consolidation the authors should assess the relative difference between the pre- and post-sleep memory performance and correlate this metric with their EEG outcomes.

We have added evening-morning performance difference as a measure to the results (page 6); however as there was no difference between groups in overnight change in performance, we focus on morning performance in relating behaviour to EEG outcomes (explored in results, page 6)

Author Response:

Reviewer #1 (Public Review):

Cell surface proteins are of vital interest in the functions and interactions of cells and their neighbors. In addition, cells manufacture and secrete small membrane vesicles that appear to represent a subset of the cell surface protein composition.

Various techniques have been developed to allow the molecular definition of many cell surface proteins but most rely on the special chemistry of amino acid residues in exposed on the parts of membrane proteins exposed to the cell exterior.

In this report Kirkemo et al. have devised a method that more comprehensively samples the cell surface protein composition by relying on the membrane insertion or protein glycan adhesion of an enzyme that attaches a biotin group to a nearest neighbor cellular protein. The result is a more complex set of proteins and distinctive differences between normal and a myc oncogene tumor cells and their secreted extracellular vesicle counterparts. These results may be applied to the identification of unique cell surface determinants in tumor cells that could be targets for immune or drug therapy. The results may be strengthened by a more though evaluation of the different EV membrane species represented in the broad collection of EVs used in this investigation.

We thank the reviewer for recognizing the importance of the work outlined in the manuscript. We have addressed the necessary improvements in the essential revisions section above.

Reviewer #2 (Public Review):

This paper describes two methods for labeling cell-surface proteins. Both methods involve tethering an enzyme to the membrane surface to probe the proteins present on cells and exosomes. Two different enzyme constructs are used: a single strand lipidated DNA inserted into the membrane that enables binding of an enzyme conjugated to a complementary DNA strand (DNA-APEX2) or a glycan-targeting binding group conjugated to horseradish peroxidase (WGA-HRP). Both tethered enzymes label proteins on the cell surface using a biotin substrate via a radical mechanism. The method provides significantly enhanced labeling efficiency and is much faster than traditional chemical labeling methods and methods that employ soluble enzymes. The authors comprehensively analyze the labeled proteins using mass spectrometry and find multiple proteins that were previously undetectable with chemical methods and soluble enzymes. Furthermore, they compare the labeling of both cells and the exosomes that are formed from the cells and characterize both up- and down-regulated proteins related to cancer development that may provide a mechanistic underpinning.

Overall, the method is novel and should enable the discovery of many low-abundance cell-surface proteins through more efficient labeling. The DNA-APEX2 method will only be accessible to more sophisticated laboratories that can carry out the protocols but the WGA-HRP method employs a readily available commercial product and give equivalent, perhaps even better, results. In addition, the method cannot discriminate between proteins that are genuinely expressed on the cell from those that are non-specifically bound to the cell surface.

The authors describe the approach and identify two unique proteins on the surface of prostate cell lines.

Strengths:

Good introduction with appropriate citations of relevant literature Much higher labeling efficiency and faster than chemical methods and soluble enzyme methods. Ability to detect low-abundance proteins, not accessible from previous labeling methods.

Weaknesses: The DNA-APEX2 method requires specialized reagents and protocols that are much more challenging for a typical laboratory to carry out than conventional chemical labeling methods.

The claims and findings are sound. The finding of novel proteins and the quantitative measurement of protein up- and down-regulation are important. The concern about non-specifically bound proteins could be addressed by looking at whether the detected proteins have a transmembrane region that would enable them to localize in the cell membrane.

We thank the reviewer for recognizing the strengths and importance of this work. We also thank the reviewer for mentioning the issue of non-specifically bound proteins. As addressed above in the essential revisions sections, we believe that any low affinity, non-specific binding proteins are likely removed in the multiple wash/centrifugation steps on cells or the multiple centrifugation steps and sucrose gradient purification on EVs. Given the likelihood for removal of non-specific binders, we believe that the secreted proteins identified are likely high affinity interactions and their differential expression on either cells or EVs play an important part in the downstream biology of both sample types. However, the previous data presentation did not clarify which proteins pertained to the transmembrane plasma membrane proteome versus secreted protein forms. For further clarity in the data presentation (Figure 3D, 4D, 5D), we have bolded proteins that are also found in the SURFY database that only includes surface annotated proteins with a predicted transmembrane domain (Bausch-Fluck et al., The in silico human surfaceome. PNAS. 2018). We have also italicized proteins that are annotated to be secreted from the cell to the extracellular space (Uniprot classification). We have updated the text and caption as shown below:

New Figure 3:

Figure 3. WGA-HRP identifies a number of enriched markers on Myc-driven prostate cancer cells. (A) Overall scheme for biotin labeling, and label-free quantification (LFQ) by LC-MS/MS for RWPE-1 Control and Myc over-expression cells. (B) Microscopy image depicting morphological differences between RWPE-1 Control and RWPE-1 Myc cells after 3 days in culture. (C) Volcano plot depicting the LFQ comparison of RWPE-1 Control and Myc labeled cells. Red labels indicate upregulation in the RWPE-1 Control cells over Myc cells and green labels indicate upregulation in the RWPE-1 Myc cells over Control cells. All colored proteins are 2-fold enriched in either dataset between four replicates (two technical, two biological, p<0.05). (D) Heatmap of the 15 most upregulated transmembrane (bold) or secreted (italics) proteins in RWPE-1 Control and Myc cells. Scale indicates intensity, defined as (LFQ Area - Mean LFQ Area)/standard deviation. Extracellular proteins with annotated transmembrane domains are bolded and annotated secreted proteins are italicized. (E) Table indicating fold-change of most differentially regulated proteins by LC-MS/MS for RWPE-1 Control and Myc cells. (F) Upregulated proteins in RWPE-1 Myc cells (Myc, ANPEP, Vimentin, and FN1) are confirmed by western blot. (G) Upregulated surface proteins in RWPE-1 Myc cells (Vimentin, ANPEP, FN1) are detected by immunofluorescence microscopy. The downregulated protein HLA-B by Myc over-expression was also detected by immunofluorescence microscopy. All western blot images and microscopy images are representative of two biological replicates. Mass spectrometry data is based on two biological and two technical replicates (N = 4).

New Figure 4:

Figure 4. WGA-HRP identifies a number of enriched markers on Myc-driven prostate cancer EVs. (A) Workflow for small EV isolation from cultured cells. (B) Labeled proteins indicating canonical exosome markers (ExoCarta Top 100 List) detected after performing label-free quantification (LFQ) from whole EV lysate. The proteins are graphed from least abundant to most abundant. (C) Workflow of exosome labeling and preparation for mass spectrometry. (D) Heatmap of the 15 most upregulated proteins in RWPE-1 Control or Myc EVs. Scale indicates intensity, defined as (LFQ Area - Mean LFQ Area)/SD. Extracellular proteins with annotated transmembrane domains are bolded and annotated secreted proteins are italicized. (E) Table indicating fold-change of most differentially regulated proteins by LC-MS/MS for RWPE-1 Control and Myc cells. (F) Upregulated proteins in RWPE-1 Myc EVs (ANPEP and FN1) are confirmed by western blot. Mass spectrometry data is based on two biological and two technical replicates (N = 4). Due to limited sample yield, one replicate was performed for the EV western blot.

New Figure 5:

Figure 5. WGA-HRP identifies a number of EV-specific markers that are present regardless of oncogene status. (A) Matrix depicting samples analyzed during LFQ comparison--Control and Myc cells, as well as Control and Myc EVs. (B) Principle component analysis (PCA) of all four groups analyzed by LFQ. Component 1 (50.4%) and component 2 (15.8%) are depicted. (C) Functional annotation clustering was performed using DAVID Bioinformatics Resource 6.8 to classify the major constituents of component 1 in PCA analysis. (D) Heatmap of the 25 most upregulated proteins in RWPE-1 cells or EVs. Proteins are listed in decreasing order of expression with the most highly expressed proteins in EVs on the far left and the most highly expressed proteins in cells on the far right. Scale indicates intensity, defined as (LFQ Area - Mean LFQ Area)/SD. Extracellular proteins with annotated transmembrane domains are bolded and annotated secreted proteins are italicized. (E) Table indicating fold-change of most differentially regulated proteins by LC-MS/MS for RWPE-1 EVs compared to parent cells. (F) Western blot showing the EV specific marker ITIH4, IGSF8, and MFGE8.Mass spectrometry data is based on two biological and two technical replicates (N = 4). Due to limited sample yield, one replicate was performed for the EV western blot.

Authors mention time-sensitive changes but it is unclear how this method would enable one to obtain this kind of data. How would this be accomplished? The statement "Due to the rapid nature of peroxidase enzymes (1-2 min), our approaches enable kinetic experiments to capture rapid changes, such as binding, internalization, and shuttling events." Yes, it is faster, but not sure I can think of an experiment that would enable one to capture such events.

We thank the reviewer for this comment and giving us an opportunity to elaborate on the types of experiments enabled by this new method. A previous study (Y, Li et al. Rapid Enzyme-Mediated Biotinylation for Cell Surface Proteome Profiling. Anal. Chem. 2021) showed that labeling the cell surface with soluble HRP allowed the researchers to detect immediate surface protein changes in response to insulin treatment. They demonstrated differential surfaceome profiling changes at 5 minutes vs 2 hours following treatment with insulin. Only methods utilizing these rapid labeling enzymes could allow for this type of resolution. A few other biological settings that experience rapid cell surface changes are: response to drug treatment, T-cell activation and synapse formation (S, Valitutti, et al. The space and time frames of T cell activation at the immunological synapse. FEBS Letters. 2010) and GPCR activation (T, Gupte et al. Minute-scale persistence of a GPCR conformation state triggered by non-cognate G protein interactions primes signaling. Nat. Commun. 2019). We also believe the method would be useful for post-translational processes where proteins are rapidly shuttling to the cell surface. We have updated the discussion to elaborate on these types of experiments.

"Due to the fast kinetics of peroxidase enzymes (1-2 min), our approaches could enable kinetic experiments to capture rapid post-translational trafficking of surfaces proteins, such as response to insulin, certain drug treatments, T-cell activation and synapse formation, and GPCR activation."

The authors do not have any way to differentiate between proteins expressed by cells and presented on their membranes from proteins that non-specifically bind to the membrane surface. Non-specific binding (NSB) is not addressed. Proteins can non-specifically bind to the cell or EV surface. The results are obtained by comparisons (cells vs exosomes, controls vs cancer cells), which is fine because it means that what is being measured is differentially expressed, so even NSB proteins may be up- and down-regulated. But the proteins identified need to be confirmed. For example, are all the proteins being detected transmembrane proteins that are known to be associated with the membrane?

As mentioned above, we utilized the most rigorous informatics analysis available (Uniprot and SURFY) to annotate the proteins we find as having a signal sequence and/or TM domain. Data shown in heatmaps are based off of significance (p < 0.05) across all four replicates, which supports that any secreted proteins present are likely due to actual biological differences between oncogenic status and/or sample origin (i.e. EV vs cell). We have addressed this point in a previous comment above.

The term "extracellular vesicles" (EVs) might be more appropriate than "exosomes" to describe the studied preparation.

As we describe above in response to earlier comments, we have systematically changed from using exosomes to small extracellular vesicles and better defined the isolation procedure that we used in the methods section.

Reviewer #3 (Public Review):

The article by Kirkemo et al explores approaches to analyse the surface proteome of cells or cell-derived extracellular vesicles (EVs, called here exosomes, but the more generic term "extracellular vesicles" would be more appropriate because the used procedure leads to co-isolation of vesicles of different origin), using tools to tether proximity-biotinylation enzymes to membranes. The authors determine the best conditions for surface labeling of cells, and demonstrate that tethering the enzymes (APEX or HRP) increases the number of proteins detected by mass-spectrometry. They further use one of the two approaches (where HRP binds to glycans), to analyse the biotinylated proteome of two variants of a prostate cancer cell line, and the corresponding EVs. The approaches are interesting, but their benefit for analysis of cells or EVs is not very strongly supported by the data.

First, the authors honestly show (fig2-suppl figures) that only 35% of the proteins identified after biotinylation with their preferred tool actually correspond to annotated surface proteins. This is only slightly better than results obtained with a non-tethered sulfo-NHS-approach (30%).

We thank the reviewer for this comment. The reason we utilize membrane protein enrichment methods is that membrane protein abundance is low compared to cytosolic proteins and their identification can be overwhelmed by cytosolic contaminants. Nonetheless, despite our best efforts to limit labeling to the membrane proteins, cytosolic proteins can carry over. Thus, we utilize informatics methods to identify the proteins that are annotated to be membrane associated. The Uniprot GOCC (Gene Ontology Cellular Component) Plasma Membrane database is the most inclusive of membrane proteins only requiring they contain either a signal sequence, transmembrane domain, GPI anchor or other membrane associated motifs yielding a total of 5,746 proteins. This will include organelle membrane proteins. It is known that proteins can traffic from the internal organelles to the cell surface so these can be bonified cell surface proteins too. To increase the informatics stringency for membrane proteins we have now applied a new database aggregated from work by the Wollscheid lab, called SURFY (Bausch-Fluck et al., The in silico human surfaceome. PNAS. 2018). This is a machine learning method trained on 735 high confidence membrane proteins from the Cell Surface Protein Atlas (CSPA). SURFY predicts a total of 2,886 cell surface proteins. When we filter our data using SURFY for proteins, peptides and label free quantitation (LFQ) area for three methods, we find that the difference between NHS-Biotin and WGA-HRP expands considerably (see new Figure 3-Supplemental Figure 1 below). We observe these differences when the datasets are searched with either the GOCC Plasma Membrane database or the entire human Uniprot database. The difference is especially large for LFQ analysis, which quantitatively scores peptide intensity as opposed to simply count the number hits as for protein and peptide analysis. Cytosolic carry over is the major disadvantage of NHS-Biotin, which suppresses signal strength and is reflected in the lower LFQ values (24% for NHS-biotin compared to 40% for WGA-HRP). We have updated the main text and supplemental figure below:

"Both WGA-HRP and biocytin hydrazide had similar levels of cell surface enrichment on the peptide and protein level when cross-referenced with the SURFY curated database for extracellular surface proteins with a predicted transmembrane domain (Figure 3 - Figure supplement 1A). Sulfo-NHS-LC-LC-biotin and whole cell lysis returned the lowest percentage of cell surface enrichment, suggesting a larger portion of the total sulfo-NHS-LC-LC-biotin protein identifications were of intracellular origin, despite the use of the cell-impermeable format. These same enrichment levels were seen when the datasets were searched with the curated GOCC-PM database, as well as the Uniprot entire human proteome database (Figure 3 - Figure supplement 1B). Importantly, of the proteins quantified across all four conditions, biocytin hydrazide and WGA-HRP returned higher overall intensity values for SURFY-specified proteins than either sulfo-NHS-LC-LC-biotin or whole cell lysis. Importantly, although biocytin hydrazide shows slightly higher cell surface enrichment compared to WGA-HRP, we were unable to perform the comparative analysis at 500,000 cells--instead requiring 1.5 million--as the protocol yielded too few cells for analysis."

Figure 3-Figure Supplement 1. Comparison of surface enrichment between replicates for different mass spectrometry methods. (A) The top three methods (NHS-Biotin, Biocytin Hydrazide, and WGA-HRP) were compared for their ability to enrich cell surface proteins on 1.5 M RWPE-1 Control cells by LC-MS/MS after being searched with the Uniprot GOCC Plasma Membrane database. Shown are enrichment levels on the protein, peptide, and average MS1 intensity of top three peptides (LFQ area) levels. (B) The top three methods (NHS-Biotin, Biocytin Hydrazide, and WGA-HRP) were compared for their ability to enrich cell surface proteins on 1.5 M RWPE-1 Control cells by LC-MS/MS after being searched with the entire human Uniprot database. Shown are enrichment levels on the protein, peptide, and average MS1 intensity of top three peptides (LFQ area) levels. Proteins or peptides detected from cell surface annotated proteins (determined by the SURFY database) were divided by the total number of proteins or peptides detected. LFQ areas corresponding to cell surface annotated proteins (SURFY) were divided by the total area sum intensity for each sample. The corresponding percentages for two biological replicates were plotted.

There are additional advantages to WGA-HRP over NHS-biotin. These include: (i) labeling time is 2 min versus 30 min, which would afford higher kinetic resolution as needed, and (ii) the NHS-biotin labels lysines, which hinders tryptic cleavage and downstream peptide analysis, whereas the WGA-HRP labels tyrosines, eliminating impacts on tryptic patterns. WGA-HRP is slightly below biocytin hydrazide in peptide and protein ID and somewhat more by LFQ. However, there are significant advantages over biocytin hydrazide: (i) sample size for WGA-HRP can be reduced a factor of 3-5 because of cell loss during the multiple washing steps after periodate oxidation and hydrazide labeling, (ii) the time of labeling is dramatically reduced from 3 hr for hydrazide to 2 min for WGA-HRP, and (iii) the HRP enzyme has a large labeling diameter (20-40 nm, but also reported up to 200 nm) and can label non-glycosylated membrane proteins as opposed to biocytin hydrazide that only labels glycosylated proteins. The hydrazide method is the current standard for membrane protein enrichment, and we feel that the WGA-HRP will compete especially when cell sample size is limited or requires special handling. In the case of EVs, we were not able to perform hydrazide labeling due to the two-step process and small sample size.

Indeed the list of identified proteins in figures 4 and 5 include several proteins whose expected subcellular location is internal, not surface exposed, and whose location in EVs should also be inside (non-exhaustively: SDCBP = syntenin, PDCD6IP = Alix, ARRDC1, VPS37B, NUP35 = nucleopore protein)…

We thank the reviewer for this comment. We have elaborated on this point in a number of response paragraphs above. The proteins that the reviewer points out are annotated as “plasma membrane” in the very inclusive GOCC plasma membrane database. However, this means that they may also spend time in other locations in the cell or reside on organelle membranes. We have done further analysis to remove any intracellular membrane residing proteins that are included in the GOCC plasma membrane database, including the five proteins mentioned above. We also have further highlighted proteins that appear in the SURFY database, as discussed above and in our response to Reviewer 2’s comment. To increase stringency, we have bolded proteins that are found in the more selective SURFY database and italicized secreted proteins. Due to our new analysis and data presentation, it is more clear which markers are bona fide extracellular resident membrane proteins. We have updated the Figures and Figure legends as mentioned above, as well as added this statement in the Data Processing and Analysis methods:

"Additionally, to not miss any key surface markers such as secreted proteins or anchored proteins without a transmembrane domain, we chose to initially avoid searching with a more stringent protein list, such as the curated SURFY database. However, following the analysis, we bolded proteins found in the SURFY database and italicized proteins known to be secreted (Uniprot)."

The membrane proteins identified as different between the control and Myc-overexpressing cells or their EVs, would have been identified as well by a regular proteomic analysis.

To directly compare surfaceomes of EVs to cells, we are compelled to use the same proteomic method. For parental cell surfaceomic analysis, a membrane enrichment method is required due to the high levels of cytosolic proteins that swamp out signal from membrane proteins. Although EVs have a higher proportion of membrane to cytosol, whole EV proteomics would still have significant cytosolic contamination.

Second, the title highlights the benefit of the technique for small-scale samples: this is demonstrated for cells (figures 1-2), but not for EVs: no clear quantitative indication of amount of material used is provided for EV samples. Furthermore, no comparison with other biotinylation technics such as sulfo-NHS is provided for EVs/exosomes. Therefore, it is difficult to infer the benefit of this technic applied to the analysis of EVs/exosomes.

We appreciate the reviewer for this comment. We have updated the methods as mentioned above in our response to the Essential Revisions. In brief, the yield of EVs post-sucrose gradient isolation was 3-5 µg of protein from 16x15 cm2 plates of cells, totaling 240 mL of media. Since we had previously demonstrated that our method was superior to sulfo-NHS for enriching surface proteins on cells, we proceeded to use the WGA-HRP for the EV labeling experiments.

In addition, the WGA-based tethering approach, which is the only one used for the comparative analysis of figures 4 and 5, possibly induces a bias towards identification of proteins with a particular glycan signature: a novelty would possibly have come from a comparison of this approach with the other initially evaluated, the DNA-APEX one, where tethering is induced by lipid moieties, thus should not depend on glycans. The authors may have then identified by LC-MS/MS specific glycan-associated versus non-glycan-associated proteins in the cells or EVs membranes. Also ideally, the authors should have compared the 4 combinations of the 2 enzymes (APEX and HRP) and 2 tethers (lipid-bound DNA and WGA) to identify the bias introduced by each one.

We thank the reviewer for this comment. We performed analysis to determine whether there was a bias towards Uniprot annotated “Glyco” vs “Non-Glyco” surface proteins within the SURFY database identified across the WGA-HRP, APEX2-DNA, APEX2, and HRP labeling methods. We performed this analysis by measuring the total LFQ area detected for each category (glycoprotein vs non-glycoprotein) and dividing that by the total LFQ area found across all proteins detected in the sample. We found similar normalized areas of non-glyco surface proteins between WGA-HRP and APEX2-DNA suggesting there is not a bias against non-glycosylated proteins in the WGA-HRP sample. There were slightly elevated levels of Glycoproteins in the WGA-HRP sample over APEX2-DNA. It is not surprising to us that there is little bias because the free-radicals generated by biotin-tyramide can label over tens of nanometers and thus can label not just the protein they are attached to, but neighbors also, regardless of glycosylation status. We have added this as Figure 2-Supplement 3, and amended the text in the manuscript below in purple.

Figure 2 – Figure Supplement 3: Comparison of enrichment of Glyco- vs Non-Glyco-proteins. (A) TIC area of Uniprot annotated Glycoproteins compared to Non-Glycoproteins in the SURFY database for each labeling method compared to total TIC area. There was not a significant difference in detection of Non-Glycoproteins detected between WGA-HRP and APEX2-DNA and only a slightly higher detection of Glycoproteins in the WGA-HRP sample over APEX2-DNA.

"As the mode of tethering WGA-HRP involves GlcNAc and sialic acid glycans, we wanted to determine whether there was a bias towards Uniprot annotated 'Glycoprotein' vs 'Non-Glycoprotein' surface proteins identified across the WGA-HRP, APEX2-DNA, APEX2, and HRP labeling methods. We looked specifically looked at surface proteins founds in the SURFY database, which is the most restrictive surface database and requires that proteins have a predicted transmembrane domain (Bausch-Fluck et al., The in silico human surfaceome. PNAS. 2018). We performed this analysis by measuring the average MS1 intensity across the top three peptides (area) for SURFY glycoproteins and non-glycoproteins for each sample and dividing that by the total LFQ area found across all GOCC annotated membrane proteins detected in each sample. We found similar normalized areas of non-glyco surface proteins across all samples (Figure 2 - Figure supplement 4). If a bias existed towards glycosylated proteins in WGA-HRP compared to the glycan agnostic APEX2-DNA sample, then we would have seen a larger percentage of non-glycosylated surface proteins identified in APEX2-DNA over WGA-HRP. Due to the large labeling radius of the HRP enzyme, we find it unsurprising that the WGA-HRP method is able to capture non-glycosylated proteins on the surface to the same degree (Rees et al. Selective Proteomic Proximity Labeling Assay SPPLAT. Current Protocols in Protein Science. 2015). There is a slight increase in the area percentage of glycoproteins detected in the WGA-HRP compared to the APEX2-DNA sample but this is likely due to the fact that a greater number of surface proteins in general are detected with WGA-HRP."

As presented the article is thus an interesting technical description, which does not convince the reader of its benefit to use for further proteomic analyses of EVs or cells. Such info is of course interesting to share with other scientists as a sort of "negative" or "neutral" result. Maybe a novelty of the presented work is the differential proteome analysis of surface enriched EV/cell proteins in control versus myc-expressing cells. Such analyses of EVs from different derivatives of a tumor cell line have been performed before, for instance comparing cells with different K-Ras mutations (Demory-Beckler, Mol Cell proteomics 2013 # 23161513). However, here the authors compare also cells and EVs, and find possibly interesting discrepancies in the upregulated proteins. These results could probably be exploited more extensively. For instance, authors could give clearer info (lists) on the proteins differentially regulated in the different comparisons: in EVs from both cells, in EVs vs cells, in both cells.

We appreciate the reviewer for this critique and have updated the manuscript accordingly. We have changed the title to “Cell surface tethered promiscuous biotinylators enable small-scale comparative surface proteomic analysis of human extracellular vesicles and cells” to more accurately depict the focus of our manuscript which, as the reviewer highlighted, is that this technology allows for comparative analysis between the surfaceomes of cells vs EVs. We appreciate the fine work from the Coffey lab on whole EV analysis of KRAS transformed cells. They identified a mix of surface and cytosolic proteins that change in EVs from the transformed cells, whereas our data focuses specifically on the surfaceome differences in Myc transformed and non-transformed cells and corresponding small EVs. We believe this makes important contributions to the field as well.

To further address the reviewer’s suggestions, we additionally have significantly reorganized the figures to better display the differentially regulated proteins. We have removed the volcano plots and instead included heatmaps with the top 30 (Figure 3 and Figure 4) and top 50 (Figure 5) differentially regulated proteins across cells and EVs. We have also updated the lists of proteins in the supplemental source tables section. See responses to Reviewer 2 above for the updates to Figures 3-5. We have additionally included supplemental figures with lists of differentially upregulated proteins in the EV and Cell samples, which are shown below:

Figure 3 – Supplement 3: List of proteins comparing enriched targets (>2-fold) in Myc cells versus Control cells. Targets that were found enriched (Myc/Control) in the Control cells (left) and Myc cells (right). The fold-change between Myc cells and Control cells is listed in the column to the right of the gene name.

Figure 4 – Supplement 1: List of proteins comparing enriched targets (>1.5-fold) in Myc EVs versus Control EVs. Targets that were found enriched (Myc/Control) in the Control EVs (left) and Myc EVs (right). The fold-change between Myc EVs and Control EVs is listed in the column to the right of the gene name.

Figure 4 – Figure Supplement 2: Venn diagram comparing enriched targets (>2-fold) in Cells and EVs. (A) Targets that were found enriched in the Control EVs (purple) and Control cells (blue) when each is separately compared to Myc EVs and Myc cells, respectively. The 5 overlapping enriched targets in common between Control cells and Control EVs are listed in the center. (B) Targets that were found enriched in the Myc EVs (purple) and Myc cells (blue) when each is separately compared to Control EVs and Control cells, respectively. The 12 overlapping enriched targets in common between Myc cells and Myc EVs are listed in the center.

Figure 5 - Supplement 1: List of proteins comparing enriched targets (>2-fold) in Control EVs versus Control cells and Myc EVs versus Myc cells. (A)Targets that were found enriched (EV/cell) in the Control samples are listed. The fold-change values between Control EVs and Control cells are listed in the column to the right of the gene name. (B)Targets that were found enriched (EV/cell) in the Myc samples are listed. The fold-change values between Myc EVs and Myc cells are listed in the column to the right of the gene name.

Author Response

Reviewer #2 (Public Review):

The work proposes a new computational rule for classifying synaptic plasticity outcome based on the geometry of synaptic enzyme dynamics. Specifically, the authors implement a multi-timescale model of hippocampal synaptic plasticity induction that takes into account the dynamics of the membrane potential, calcium concentration as well as CaMKII and calcineurin signalling pathways. They show that the proposed rule could be applied to reproduce the outcomes from nine published experimental studies involving different spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. The model has been also used to generate predictions regarding the effect of spike-timing irregularity on plasticity outcomes. The proposed approach constitutes an interesting and original idea that contributes to the ongoing effort in discovering the rules of synaptic plasticity.

The conclusions of this paper are mostly well supported by data, but some model assumptions and interpretation of modelling results need to be clarified and extended.

1) The proposed model captures well the stochastic nature of the dendritic spine ion channels and receptors except for the calcium-sensitive potassium (SK) channel that has been modelled deterministically. Given that the same justification in terms of small number of channels present in the small dendritic spine compartment applies to the SK channels as well as to the voltage gated calcium channels and the AMPA and NMDA receptors, it is not clear why the authors have chosen a deterministic representation in the case of SK. The implications of this assumption needs to be investigated and discussed.

There are several stochastic models of AMPA and NMDA receptors based on single-channel recordings. Additionally, we had enough experimental data on single channel recordings to build a custom Markov chain model of VGCCs. For the SK channel, we could not find enough experimental data (age-dependence activity, temperature sensitivity, etc.) to custom-build a stochastic model. We thus decided to implement a deterministic model. Yet, we understand the reviewers’ comment that in theory, a stochastic model of SK channels could impact our results. We thus now provide a simulation with a stochastic model of SK, comparing it to the deterministic model implemented in the study.

We describe a minimal version of a stochastic model of SK compatible with the deterministic version. The deterministic model of SK channel fit at ~35C is described in the methods section.

Because of the factor ρ 𝑓𝑆𝐾 in the equation, which multiplies r(Ca) by ~2, the equation cannot be related to a 2-state Markov chain (MC). This could probably be possible with a 3-state MC but we used a different strategy. Noting that ρ 𝑆𝐾 ∼ 2 , we introduce a new equation

As 0 < r(Ca) < 1, it is straightforward to introduce a 2-state MC for which the above equation describes the probability of the open state. We then simulate two such independent (for a given Ca concentration) channels and approximate 𝑚 𝑆𝐾 as the sum (which belongs to [0,2Nsk]) of the open states for the 2 channels.

As the reviewer can see in the figure below, we do not find a major difference in the simulations of 3 protocols. Thus, we argue that adding a stochastic version of the SK channels in our current study would not fundamentally alter our main conclusions.

Figure Legend: a comparison using Tigaret et al. 2016 1Pre2Post10 and 1Pre2Post50 protocols, and 900 at 50 Hz protocol from Dudek and Bear 1992 (100 repetitions) between the model with the deterministic SK channel (original model - blue), and the modified model including the stochastic SK channel (stochastic SK - red). Deterministic vs stochastic SK channel does not significantly modify the model’s behaviour.

To explain our rationale of using a deterministic version of SK channel, we provide this sentence in the Methods when describing SK channel model: “"Due to a lack of single-channel recordings of SK channels, and a lack of published stochastic models of SK channels, we modelled SK channels deterministically. In tests we found that this assumption had only a negligible impact on the outcomes of plasticity protocols (data not shown)" (page 40).

2) Many of the model parameters have been set to values previously estimated from synaptic physiology and biochemistry experiments, However, a significant number of important parameter values have been tuned to reproduce the plasticity experiments targeted in this study. As such, it needs to be explained which of the plasticity outcomes have been reproduced because the parameters are chosen to do so. A clarification would have helped to substantiate the authors' conclusions.

Most parameters were set with values previously defined by experimental work. We referred to these publications where necessary throughout the Methods and Tables in our original manuscript. For the few free parameters that were adjusted, we now provide additional information wherever necessary for the Tables concerned.

● In the legend of Table 4 (neuron electrical properties), we explain which parameters are different from values obtained from the literature to fit experimental data (Golding et al. 2001; Buchanan et al. 2007).

● Parameters for the sodium and potassium conductance (Table 5) are labelled as generic since they are intentionally set to produce the BaP dynamics we have shown in the paper.

● Table 6 has no free parameters.

● Table 7 caption now includes a description saying ’Note that the buffer concentration, calcium diffusion coefficient, calcium diffusion time constant and calcium permeability were considered free parameters to adjust the calcium dynamics’.

● In Table 8 we had originally pointed out how we adapted the GluN2B rates from a published GluN2A model (Popescu et al. 2004; and Iacobucci and Popesco 2018). We now describe this adaptation in the Table 8 legend. In this Table, we now also better explain how we adjusted the NMDAr model to reflect the ratio between GluN2B and GluN2A, fitted from Sinclair et al. 2016; and the NMDAr conductance depending on calcium fitted from Maki and Popescu 2014.

● In Table 9 caption we now explain how the GABAr number and conductance were modified to fit GABAr currents as in Figures 15 b and e. The relevant parameters are indicated in the table.

● In Table 10 caption we now state the number of VGCCs per subtype that we used as a free parameter to reproduce the calcium dynamics (Figure 12).

3) Adding experimental testing of model predictions, for example, that firing variability can alter the rules of plasticity, in the sense that it is possible to add noise to cause LTP for protocols that did not otherwise induce plasticity would be needed to increase confidence in the presented modelling results.

We agree that it would be interesting in the future to test the many model predictions suggested in this work with biological experiments. This would however require a lot of work and will be the subject of further studies.

Reviewer #3 (Public Review):

This manuscript presents and analyzes a novel calcium-dependent model of synaptic plasticity combining both presynaptic and postsynaptic mechanisms, with the goal of reproducing a very broad set of available experimental studies of the induction of long-term potentiation (LTP) vs. long-term depression (LTD) in a single excitatory mammalian synapse in the hippocampus. The stated objective is to develop a model that is more comprehensive than the often-used simplified phenomenological models, but at the same time to avoid biochemical modeling of the complex molecular pathways involved in LTP and LTD, retaining only its most critical elements. The key part of this approach is the proposed "geometric readout" principle, which allows to predict the induction of LTP vs. LTD by examining the concentration time course of the two enzymes known to be critical for this process, namely (1) the Ca2+/calmodulin-bound calcineurin phosphatase (CaN), and (2) the Ca2+/calmodulin-bound protein kinase (CaMKII). This "geometric readout" approach bypasses the modeling of downstream pathways, implicitly assuming that no further biochemical information is required to determine whether LTP or LTD (or no synaptic change) will arise from a given stimulation protocol. Therefore, it is assumed that the modeling of downstream biochemical targets of CaN and CaMKII can be avoided without sacrificing the predictive power of the model. Finally, the authors propose a simplified phenomenological Markov chain model to show that such "geometric readout" can be implemented mechanistically and dynamically, at least in principle.

Importantly, the presented model has fully stochastic elements, including stochastic gating of all channels, stochastic neurotransmitter release and stochastic implementation of all biochemical reactions, which allows to address the important question of the effect of intrinsic and external noise on the induction of LTP and LTD, which is studied in detail in this manuscript.

Mathematically, this modeling approach resembles a continuous stochastic version of the "liquid computing" / "reservoir computing" approach: in this case the "hidden layer", or the reservoir, consists of the CaMKII and CaM concentration variables. In this approach, the parameters determining the dynamics of these intermediate ("hidden") variables are kept fixed (here, they are constrained by known biophysical studies), while the "readout" parameters are being trained to predict a target set of experimental observations.

Strengths:

1) This modeling effort is very ambitious in trying to match an extremely broad array of experimental studies of LTP/LTD induction, including the effect of several different pre- and post-synaptic spike sequence protocols, the effect of stimulation frequency, the sensitivity to extracellular Ca2+ and Mg2+ concentrations and temperature, the dependence of LTP/LTD induction on developmental state and age, and its noise dependence. The model is shown to match this large set of data quite well, in most cases.

2) The choice for stochastic implementation of all parts of the model allows to fully explore the effects of intrinsic and extrinsic noise on the induction of LTP/LTD. This is very important and commendable, since regular noise-less spike firing induction protocols are not very realistic, and not every relevant physiologically.

3) The modeling of the main players in the biochemical pathways involved in LTP/LTD, namely CaMKII and CaN, aims at sufficient biological realism, and as noted above, is fully stochastic, while other elements in the process are modeled phenomenologically to simplify the model and reveal more clearly the main mechanism underlying the LTP/LTD decision switch.

4) There are several experimentally verifiable predictions that are proposed based on an in-depth analysis of the model behavior.

We thank the reviewer for pointing out these strengths.

Weaknesses:

1) The stated explicit goal of this work is the construction of a model with an intermediate level of detail, as compared to simplified "one-dimensional" calcium-based phenomenological models on the one hand, and comprehensive biochemical pathway models on the other hand. However, the presented model comes across as extremely detailed nonetheless. Moreover, some of these details appear to be avoidable and not critical to this work. For instance, the treatment of presynaptic neurotransmitter release is both overly detailed and not sufficiently realistic: namely, the extracellular Ca2+ concentration directly affects vesicle release probability but has no effect on the presynaptic calcium concentration. I believe that the number of parameters and the complexity in the presynaptic model could be reduced without affecting the key features and findings of this work.

This point is largely answered in Essential Revisions point 4 where we argue the choices we made for the presynaptic model. We acknowledge, however, that in this current version, we did not incorporate all biophysical components, such as the modulation of presynaptic calcium concentration with external calcium variations and multivesicular release. The calcium-dependence of presynaptic release, as modeled currently, is however fitted in Figure 8e against data from Hardingham et al. 2006 and Tigaret et al. 2016. These current limitations could be addressed in a next version of our presynaptic model where we also plan to incorporate age and temperature influence.

2) The main hypotheses and assumptions underlying this work need to be stated more explicitly, to clarify the main conclusions and goals of this modeling work. For instance, following much prior work, the presented model assumes that a compartment-based (not spatially-resolved) model of calcium-triggered processes is sufficient to reproduce all known properties of LTP and LTD induction and that neither spatially-resolved elements nor calcium-independent processes are required to predict the observed synaptic change. This could be stated more explicitly. It could also be clarified that the principal assumption underlying the proposed "geometric readout" mechanisms is that all information determining the induction of LTP vs. LTP is contained in the time-dependent spine-averaged Ca2+/calmodulin-bound CaN and CaMKII concentrations, and that no extra elements are required. Further, since both CaN and CaMKII concentrations are uniquely determined by the time course of postsynaptic Ca2+ concentration, the model implicitly assumes that the LTP/LTD induction depends solely on spine-averaged Ca2+ concentration time course, as in many prior simplified models. This should be stated explicitly to clarify the nature of the presented model.

We thank the reviewer for the suggestions on how to clarify the main hypotheses and assumptions of our work. We slightly modified the sentences provided by the reviewer and added them in the main text (page 2, lines 82 and page 19, lines 593).

3) In the Discussion, the authors appear to be very careful in framing their work as a conceptual new approach in modeling STD/STP, rather than a final definitive model: for instance, they explicitly discuss the possibility of extending the "geometric readout" approach to more than two time-dependent variables, and comment on the potential non-uniqueness of key model parameters. However, this makes it hard to judge whether the presented concrete predictions on LTP/LTD induction are simply intended as illustrations of the presented approach, or whether the authors strongly expect these predictions to hold. The level of confidence in the concrete model predictions should be clarified in the Discussion. If this confidence level is low, that would call into question the very goal of such a modeling approach.

These are very good questions. Let us first comment on the parameter uniqueness. We believe, like in E. Marder’s work on ion channels expression in neurons, that the synapse has the possibility to adapt its internal parameters (proteins number, transition rates, etc) to provide a given functioning behaviour. As a by-product, there is non uniqueness of parameters associated with behavior. Additionally, since our model is able to reproduce 9 published experimental outcomes with a single set of parameters, it is a functioning synapse with adjusted parameters which output the expected behaviours. Thus by extrapolation, our confidence in the further predictions is high. We modified sentences in the discussion section to argue this point (page 21, line 707).

Let us comment now on increasing the complexity. To our best, we strived to design a plasticity readout as simple as possible yet providing a functioning synapse. Given our success to reproduce 9 published experimental outcomes with a single set of parameters, adding more complexity would be akin to overfitting.

4) The authors presented a simplified mechanistic dynamical Markov chain process to prove that the "geometric readout" step is implementable as a dynamical process, at least in principle. However, a more realistic biochemical implementation of the proposed "region indicator" variables may be complex and not guaranteed to be robust to noise. While the authors acknowledge and touch upon some of these issues in their discussion, it is important that the authors will prove in future work that the "geometric readout" is implementable as a biochemical reaction network. Barring such implementation, one must be extra careful when claiming advantages of this approach as compared to modeling work that attempts to reconstruct the entire biochemical pathways of LTP/LTD induction.

We acknowledge this issue and agree this would be an interesting subject for future work.

Author Response

Reviewer #1 (Public Review):

1) Comment: To determine the effect of diseased monocytes on retinal health, light-injured mouse retinas were injected with monocytes isolated from AMD patients (Figure 1 - figure supplement 1). This resulted in a reduction in photoreceptor number and ERG b-wave amplitude. However, the light-injured control eye was injected with PBS only, so no cells were present. The reasoning for using this control was not provided. The appropriate injection control would include monocytes isolated from non-AMD patients. This control should be performed side-by-side with cells from AMD patients.

We thank the reviewer for this important comment. The purpose of the current study was to identify the macrophage subtype that may be associated with cell death in aAMD. We have previously reported that macrophages from AMD patient demonstrate a different phenotype compared with healthy patient in the rodent model for laser induced CNV (Hagbi-Levi S et al, 2016). Per the reviewer comment, we have performed additional experiments to assess the effect of monocytes from healthy controls in the photic retinal injury model. Results showed that monocytes from AMD and healthy patients exert different impact on the retina in this rodent model for aAMD. Interestingly, we found that monocytes from healthy patients were more neurotoxic to photoreceptors compared with monocytes from AMD patients. These results are included in the revised ms. as Figure 1- figure supplement 1H. A possible explanation for these findings is discussed in lines 179-190 of the revised manuscript. This finding reinforces the idea that the use of monocytes from AMD patients in the experiments is required to obtain a comprehensive understanding of their involvement in the progression of the disease.

2) Comment: The authors hypothesize, from the experiments presented in Figure 1 - figure supplement 1, that the injected monocytes generated macrophages in the retina, which were responsible for the observed neurotoxicity (Lines 143-145). However, no direct evidence was presented. This idea should be tested in vivo. This could be done by injecting tracer-labeled human AMD-derived monocytes into light-injured mouse retinas. If the authors' hypothesis is true, collected retinas should contain tracer-labeled cells that express macrophage markers. Tracer-labeled M2a macrophage cells should be present since subsequent experiments identify this subclass as being associated with retinal cell death.

Thank you for this important comment. To address the reviewers comment, retinal section from mice exposed to photic-retinal injury and injected with Dio-tracer labelled monocytes were stained with two M2a macrophages markers, CD206 (mannose receptor) and VEGF (Kadomoto, S et al, 2022; Jayasingam SD et al, 2019). Interestingly, we found co-localization of Dio-tracer staining (representing the injected human macrophages) with CD206 and VEGF markers in monocytes localized in different retinal layers, but not in monocytes remaining in the vitreous cavity. These data indicate that M2a markers are expressed during the polarization of monocytes into M2a phenotype which is maintained only upon entry into the retina tissue. These results were included in Figure 1- figure supplement 1K-S and discussed in the revised manuscript in lines 179-182.

3) Comment: Photoreceptor number and b-wave amplitudes were measured in light-injured retinas injected with one of four macrophage cell types generated from human AMD-derived monocytes. The authors conclude that only injection of M2a cells reduced photoreceptor number and b-wave amplitudes (Figure 1C, E). This may be true, but it is difficult for the reader to make a conclusion (especially in Fig. 1E) due to the large error bars and five different traces overlapping each other. To make these results easier to interpret, graph control cells with only one experimental sample (cell type) at a time.

Thank you for this comment. Per the reviewer comment, the graphs were modified in the revised ms. (Figure 1, panel H-K).

4) Comment: Most injected macrophages were located in the vitreous. In the case of M2a cells, the authors note that "several of the cells migrated across the retinal layers reaching the subretinal space" (Lines 167,168). One possible explanation for why M0, M1, and M2c macrophages did not induce retinal degeneration is that they did not migrate to the subretinal space and around the optic nerve head. Supplementary figures should be added to demonstrate that this is not the case.

Thank you for this comment. To address the reviewer comment we compared the migration patterns of the different macrophage phenotypes following intravitreal injection in mice exposed to photic-injury. Our results indicated that M0, M1 and M2c macrophages, similarly to M2a macrophages, migrated to the subretinal space and around the optic nerve. Thus, the neurotoxic effect of M2a is not explained by their capacity to infiltrate the retinal tissues. These results was included in Figure 1- figure supplement 2 E-H of the revised manuscript. These results are supported by our ex-vivo experiments, showing that co-culture of M2a macrophages with a retinal explants was associated with increased photoreceptor cells death compared to M1 macrophages. The results are presented and discussed in the revised manuscript in lines 200-203.

5) Comment: Figure 1 - figure supplement 2: Panel A, B cells were stained with CD206 to demonstrate the presence of M2a macrophages (panel B). The authors conclude that panel A contains M1 and panel B contains M2a cells. The lack of CD206 expression illustrates that panel A cells are not M2a macrophages but do not demonstrate they are M1 macrophages. A control using an M1 cell marker is necessary to show that panel A cells are M1 and M1 cells are not detected in M2a cultures.

Thank you for this comment. We have validated the phenotype of each macrophages subtype by qPCR (Figure 1 panel A). To further address the reviewer comment, we have performed additional immunocytochemistry for M1 macrophages using anti-CD80 antibody which is utilized as M1 macrophages marker (Bertani FR et al.2017). Results of the staining confirmed the identity of the M1 macrophages. These new results were included in Figure 1- figure supplement 2A, and are discussed in lines 168-170.

6) Comment: Ex vivo, apoptotic photoreceptor and RPE cells are observed when cultured with M2a macrophages (Figure 2). Do injected M2a cells also induce apoptosis of RPE cells in vivo? This is important to establish that retinal explants are a good model for in vivo experiments.

Thank you for this comment. To address the reviewer comment, we assessed RPE apoptosis (using TUNEL, Caspase 3 staining and RPE65 marker) after M2A cells delivery, in the in-vivo photic injury model. We could not detect apoptotic signal in the RPE layers 7 days after photic injury and therefore could not evaluate the effect of M2a macrophages on the RPE cells in-vivo (see Author response image 1). One possible explanation is that RPE cells that have undergone apoptosis are rapidly removed from the damaged tissue and are no longer detectable unlike photoreceptors. Furthermore, a study that investigated the impact of bright light on RPE cells in-vivo, showed that although RPE cells undergone structural and chemical modifications after photic-injury, TUNEL signal was not detected because RPE cell die by necrosis mechanism and not apoptosis (Jaadane I et al, 2017). Other studies validated that blue light induces RPE necrosis (Song W et al, 2022; Mohamed A et al, 2022). Taken together, it seems that ex-vivo retinal explant and in-vivo photic injury both simulate the mechanism of retinal cell death. However, the use of ex-vivo model allows for establishing the direct impact of M2a macrophages on retina in non-inflammatory context.

Author responnse image 1.

7) Comment: Reactive oxygen species (ROS) production was measured to determine if M2a cell-mediated neurotoxicity was due to oxidative stress. It is concluded that a ROS increase is partly responsible (Line 218). The data do not support this conclusion. ROS was detected in cultured M2a macrophages. More importantly, however, there was no increase in oxidative damage in vivo. The in vivo and cell culture results contradict each other so no conclusion can be made. The lack of in vivo confirmation weakens the argument that ROS drives M2a neurotoxicity. Text suggesting a role for ROS in neurotoxicity should be appropriately edited (Lines including 218, 244, 401,406,481).

Thank you for this comment. The manuscript was revised according to the reviewer suggestion (Lines 250-256).

8) Comment: The authors ask if the photoreceptor cell death is cytokine-mediated. Multiple cytokines were enriched in M2a-conditioned media. Of particular interest were CCR1 ligands MPIF1 and MCP4. The implication is that these two ligands mediate the M2a macrophages to photoreceptor cell death through CCR1. However, there is no attempt to show that either MPIF1 or MCP4 are present in vivo, or are sufficient to induce the retinal response observed. This could be demonstrated by injection of MPIF1 or MCP4. Evidence that either ligand phenocopies M2a macrophage injection would be direct evidence that CCR1 ligands activate the retinal response. Furthermore, co-injection with BX174 should block the effect of these ligands if they work through CCR1.

Thank you for this comment. The identification of CCR1 ligands expression from M2a polarized macrophages directed our decision to study CCR1 in the context of atrophic AMD. We do not claim that these specific CCR1 ligands are sufficient to activate CCR1 and exert retinal injury. The mechanism is likely more complex. Yet, to address the reviewer comment, we have performed the experiments suggested by the reviewer. Mice were exposed to photic injury and immediately injected in one eye with MPIF1, MCP-4, or a combination of both and in second eye with PBS as vehicle. Intravitreal cytokines delivery was repeated two days later (following the half-life time of these cytokines) and ERG were recorded two days after the last injection. Injection of cytokines at a concentration of 300 ng per eye did not exacerbated photoreceptor death. Then, the same experiment was repeated with two higher concentrations of cytokine, 1.2 ug/eye and 2 ug/eye, but no changes are observed between the cytokines treated-eyes and the vehicle treated-eyes. Based on previous studies reporting the physiological concentration of different cytokines in eyes of un/healthy individuals and on experiments in which different cytokines are injected in rodent eye (Estevao C et al, 2021. Zeng Y et al, 2019; Roybal CN et al, 2018; Mugisho OO et al, 2018), the cytokine concentrations used in our experiment are in the range in which effect on the retina is expected.

It is likely that a synergistic effect of M2a-secreted proteins in a particular microenvironment is necessary to increase the level of retinal damage (Bartee E et al, 2013). It is also likely that in the photic retinal injury model there is upregulation of cytokines that may mask additional delivery of exogenous cytokines. Comprehensive understanding of the complex interactions of these cytokines during retinal degeneration is beyond the scope of the current manuscript which is not focus on identifying ligand-induced CCR1 activation and its consequences. Additionally, we suggest that due to cytokine redundancy (Nicola NA; 1994), demonstrating that MPIF-4 or MCP-3 can increase photoreceptor death is not required for proving CCR1 receptor involvement.

Author Response:

Reviewer #1:

Charpentier et al. use facial recognition technology to show that mothers in a group of mandrills lead their offspring to associate with phenotypically similar offspring. Mandrills are a species of primate that live in large, matrilineal troops, with a single, dominant male that fathers the majority of the offspring. Male breeder turnover and extra-pair mating by females can lead to variation in relatedness between group members and the potential for kin-selected benefits from preferentially cooperating with closer relatives within the group. The authors argue that the strategy of influencing the social network of their offspring could be favoured by "second-order kin selection", a mechanism by which inclusive fitness benefits are accrued to female actors through kin-selected benefits to their offspring. This interpretation is supported by a theoretical model.

The paper highlights a previously unappreciated mechanism for favouring association between non-kin in social groups and also contributes a nice insight into the complexity of social interactions in a relatively understudied wild primate species. The conclusions are strengthened by data showing associations between mothers were not influenced by the facial similarity of their offspring -- this suggests that mothers are making decisions based on the appearance of offspring and not their mothers.

Some remaining questions regarding the strength of the authors' interpretation exist: Given the challenges of studying mandrills in the field, the fact that the study reports data from a single group is understandable but potential issues remain with the independence of data points. There may be an additional issue arising from the fact that this troop is semi-captive.

The study group is not semi-captive. Instead, it originated from two release events of a few captive individuals into the wild (in 2002 and 2006). The population is now composed of more than 250 individuals and all of them, except for 7 founder females (<3%), were born in the wild. In addition, the study group is not fed and occasionally wanders into a fenced protected area. Fences of the park do not represent a boundary for mandrills and most of the time (c.a. 80% of days), the study group ranges outside the park. We have clarified this misunderstanding.

Regarding the independence of data points, we would be grateful if this reviewer could clarify her/his thoughts. As a tentative response, we indeed have access to a single (although large) study group, but that’s unfortunately often the case when studying primates or other large mammals. Regarding our study questions, we have clearly demonstrated increased nepotism among paternally related mandrills in two different social groups (Charpentier et al. 2007: semi-captive mandrills; Charpentier et al. 2020: wild mandrills). More generally, we do not see any parsimonious explanations for why the studied mandrills would behave or experienced selective pressures that may have differently shaped their genetic structure and social organization compared to other wild mandrill groups.

The number of genotyped offspring is relatively small (n = 15) and paternity is inferred from the identity of the dominant male. However, the authors also refer to the fact that it's normal for female mandrills to mate with several males during ovulation.

Indeed, both sexes mate promiscuously during the mating season. We have very recently (June 2022) obtained new genetic profiles for a subset of the study infants (it took two years to obtain these data). We have now increased our sample size of infants with a known father, from 15 to 32. With these new data, we were able to distinguish between four categories of infant-infant dyads: those sharing the same father (PHS), those not sharing the same father (not PHS), those conceived during the same alpha male tenure, and those that were not (both infants with unknown dads). The graph below shows the average facial distance among individuals for each of these four categories. It shows that infants conceived during the same alpha male tenure are significantly more similar to each other than infants sired by different fathers or during the tenure of different alpha males, but they are also significantly less similar to each other than infants born to the same father (the four categories are all significantly different from each other, except when comparing infants born to different fathers with those conceived during different alpha male tenures). As suggested by this reviewer, the fact that females mate predominantly with the alpha male, but to some extent also with other males, likely explains the difference between “same father” and “same alpha male tenure”. Importantly, however, considering all infants conceived during the same alpha male tenure as “PHS” is highly conservative. It is thus likely that knowing the paternity of every infant would produce even clearer effects (and indeed, increasing the data set from 15 to 32 strengthened this result). We have now updated this result (first model) based on this new sample.

What evidence is there to support a beneficial effect of nepotism in this species?

In mandrills, females who affiliate more (groom more/associate more) with their groupmates (kin or non-kin) during juvenility also reproduce 1 year earlier than those females that are poorly socially integrated (Charpentier et al. 2012). These results are similar to what is known in many mammalian species (see for review Snyder-Mackler et al. 2020). However, the positive effects of a rich social life are generally triggered by all group members, not only close kin. However, if beneficial social relationships impact the direct fitness of individuals, as reported in mandrills and other species, then kin selection theory predicts that these effects should further translate into indirect fitness benefits.

We have now added this relevant reference (Charpentier et al. 2012) in the revised version of our manuscript and present the results of this early study on mandrills.

What form could nepotism take and does it necessarily have to involve full sibs?

We are unsure why this reviewer is mentioning full-sibs here. For this reviewer information, on the 2556 study dyads (model 1 on the impact of maternal and paternal origins on facial distance), only one dyad was a full-sib pair. Full-sibs are therefore very rare in the study population due to male migration patterns and generally short alpha male tenures.

If a female did not associate with offspring as shown here, would nepotistic interactions simply arise between her offspring and offspring that were less facially similar?

We guess that facial similarity would not be a predictor of spatial association anymore. Indeed, we think that young mandrills do not use self-referent phenotype matching, precluding the self-evaluation of those infants that look like them. However, as stated below, we cannot fully exclude the possibility that other social partners, such as fathers, may also influence infant-infant relationships, although we think that this alternative mechanism is less parsimonious than the one we propose and test.

Reviewer #2:

This paper uses data on patterns of spatial association and facial similarity in mandrills to develop a new hypothesis for the evolution of kin recognition based on facial cues. Previous work on this system has shown that, among females, paternal half-sibs resemble each other visually more than maternal half-sisters do. The authors hypothesise that this paternally inherited facial similarity provides opportunities for kin selection, but it is unclear how offspring themselves could recognise kin using phenotype matching since they are unable to see their own face. One answer to this puzzle is that third parties -- mothers -- may promote social interactions between their own offspring and other offspring that resemble them since these other offspring are likely to share the same father. In support of this hypothesis, the authors find that mothers and offspring show spatial proximity to infants that are facially more similar than average. They also use an analytical evolutionary model to confirm the logic of this hypothesis. The model shows that mothers can gain inclusive fitness benefits by encouraging reciprocal social interaction among their offspring and other paternally-related offspring. They term this idea 'second-order' kin selection and identify a range of other circumstances in which it might play an important role in shaping the evolution of social behaviour.

The main strengths of the paper are the interesting mandrill data and the cutting-edge methods used to analyse facial similarity, which have stimulated the development of a theoretically interesting hypothesis about the evolution of facially based kin recognition. The theoretical model enhances the generality and rigour of the work. The paper will be of wide interest and the concept of second-order kin selection may be applicable to other social circumstances, such as interactions among in-laws in close-knit family groups. Thus, I can see that this paper will be a stimulus for future work.

We are grateful for these positive comments.

The data are, I think, rather overinterpreted in terms of the degree to which they support the hypothesis. The spatial proximity data are interesting, but on their own, they are not definitive support for the hypothesis or model. A more critical approach to the hypothesis, clearly setting out the limitations of the data, and what tests in future could be used to falsify the hypothesis or model, would make for a stronger paper.

We agree with this general comment and have addressed it by 1. Adding a model on grooming relationships between females and infants, 2. Toning down our interpretation throughout the manuscript and 3. Propose future directions of research.

Overall the authors have presented data that support a fascinating new mechanism by which natural selection can influence social interactions among the members of family groups, in potentially surprising ways. I also find it remarkable that 60 years after the development of the kin selection theory new implications of this theory are still being uncovered. The concept of second-order kin selection may prove important in understanding the evolution of social organisation and behaviour in species that live in groups containing a mixture of kin and non-kin, such as many primates and of course humans.

We are grateful to this reviewer for this very positive comment. We fully agree with the fact that 60 years after the kin selection theory has emerged, we are still discovering further implications!

Reviewer #3:

This is a very interesting and impressive manuscript. It is complex in its multiple components, and in some ways that makes it a difficult manuscript to evaluate. There is a lot in it, including empirical analyses of a face dataset and of behavioral association data, combined with a theoretical model.

We are very grateful for this positive comment and are glad that you liked our manuscript.

The three main findings are: 1) Paternal siblings look alike (similar to, and building on, a recent manuscript the authors published elsewhere); 2) Infants that are more facially similar tend to associate; and 3) mothers tend to be found in association with other unrelated infants that look more like their own infants. Such results are interesting, and indeed one potential interpretation, perhaps even the most likely, is that mothers are behaving in such a way that promotes association between their own infants and the paternal kin of their infants.

Nonetheless, the evidence provided is logically only consistent with the authors' hypothesis, rather than being strong direct evidence for it. As such, the current framing and indeed the title, "Primate mothers promote proximity between their offspring and infants who look like them", are both problematic. (In addition, the title should be about mandrills, not "primates", since this manuscript does not provide evidence from any other species.) The evidence provided is consistent with the hypothesis, but also consistent with other potential hypotheses. The evidence given to dismiss other potential hypotheses is not strong, and rests on the fact that many males are not around all year to influence things, and that "males that were present during a given reproductive cycle are not responsible for maintaining proximity with either infants or their mothers (MJEC and BRT, pers. obs.)".

We agree with this comment. Although, after examining several alternative mechanisms, in the light of the natural history of mandrills we are confident that the proposed mechanism is at work in that species, although we cannot firmly exclude some of these alternative mechanisms. To address this comment, we have changed the title of our manuscript that now reads “Mandrill mothers associate with infants who look like their own offspring using phenotype matching”. We have also included an additional model on grooming relationships (see response to R1) and have toned down the interpretation of our results throughout our revised manuscript. Finally, we have further discussed alternative scenario, in particular the one involving fathers (see details above).

My opinion is that these are really interesting analyses and data, which are being somewhat undermined by the insistence that only one hypothesis can explain the observed association patterns. It could easily be presented differently, as a demonstration that paternal siblings look alike and that they associate. The authors could then go on to explore different possible explanations for this using their association data, make the case that maternal behavior is the most plausible (but not the only) explanation, and present their model of how such behavior could bring fitness benefits.

In my view, such a presentation would be both more cautious and more appropriate, without in any way reducing the impact or importance of the data. In the current iteration, I think there are issues because the data do not provide sufficient support for the surety of the title and conclusion, as presented.

We think that the current organization of our manuscript was not that different from the one proposed here and follows a reasoning already proposed in a former manuscript (Charpentier et al. 2020). Indeed, we first start by reminding the reader what we already know from that previous studies: paternal siblings look alike and they associate. We then go on exploring different mechanisms. That being said, and as suggested, we have been more cautious in interpreting our results, that are indeed only correlative.

Author Response

Reviewer #1 (Public Review):

In this work George et al. describe RatInABox, a software system for generating surrogate locomotion trajectories and neural data to simulate the effects of a rodent moving about an arena. This work is aimed at researchers that study rodent navigation and its neural machinery.

Strengths:

• The software contains several helpful features. It has the ability to import existing movement traces and interpolate data with lower sampling rates. It allows varying the degree to which rodents stay near the walls of the arena. It appears to be able to simulate place cells, grid cells, and some other features.

• The architecture seems fine and the code is in a language that will be accessible to many labs.

• There is convincing validation of velocity statistics. There are examples shown of position data, which seem to generally match between data and simulation.

Weaknesses:

• There is little analysis of position statistics. I am not sure this is needed, but the software might end up more powerful and the paper higher impact if some position analysis was done. Based on the traces shown, it seems possible that some additional parameters might be needed to simulate position/occupancy traces whose statistics match the data.

Thank you for this suggestion. We have added a new panel to figure 2 showing a histogram of the time the agent spends at positions of increasing distance from the nearest wall. As you can see, RatInABox is a good fit to the real locomotion data: positions very near the wall are under-explored (in the real data this is probably because whiskers and physical body size block positions very close to the wall) and positions just away from but close to the wall are slightly over explored (an effect known as thigmotaxis, already discussed in the manuscript).

As you correctly suspected, fitting this warranted a new parameter which controls the strength of the wall repulsion, we call this “wall_repel_strength”. The motion model hasn’t mathematically changed, all we did was take a parameter which was originally a fixed constant 1, unavailable to the user, and made it a variable which can be changed (see methods section 6.1.3 for maths). The curves fit best when wall_repel_strength ~= 2. Methods and parameters table have been updated accordingly. See Fig. 2e.

• The overall impact of this work is somewhat limited. It is not completely clear how many labs might use this, or have a need for it. The introduction could have provided more specificity about examples of past work that would have been better done with this tool.

At the point of publication we, like yourself, also didn’t know to what extent there would be a market for this toolkit however we were pleased to find that there was. In its initial 11 months RatInABox has accumulated a growing, global user base, over 120 stars on Github and north of 17,000 downloads through PyPI. We have accumulated a list of testimonials[5] from users of the package vouching for its utility and ease of use, four of which are abridged below. These testimonials come from a diverse group of 9 researchers spanning 6 countries across 4 continents and varying career stages from pre-doctoral researchers with little computational exposure to tenured PIs. Finally, not only does the community use RatInABox they are also building it: at the time of writing RatInABx has received logged 20 GitHub “Issues” and 28 “pull requests” from external users (i.e. those who aren’t authors on this manuscript) ranging from small discussions and bug-fixes to significant new features, demos and wrappers.

Abridged testimonials:

● “As a medical graduate from Pakistan with little computational background…I found RatInABox to be a great learning and teaching tool, particularly for those who are underprivileged and new to computational neuroscience.” - Muhammad Kaleem, King Edward Medical University, Pakistan

● “RatInABox has been critical to the progress of my postdoctoral work. I believe it has the strong potential to become a cornerstone tool for realistic behavioural and neuronal modelling” - Dr. Colleen Gillon, Imperial College London, UK

● “As a student studying mathematics at the University of Ghana, I would recommend RatInABox to anyone looking to learn or teach concepts in computational neuroscience.” - Kojo Nketia, University of Ghana, Ghana

● “RatInABox has established a new foundation and common space for advances in cognitive mapping research.” - Dr. Quinn Lee, McGill, Canada

The introduction continues to include the following sentence highlighting examples of past work which relied of generating artificial movement and/or neural dat and which, by implication could have been done better (or at least accelerated and standardised) using our toolbox.

“Indeed, many past[13, 14, 15] and recent[16, 17, 18, 19, 6, 20, 21] models have relied on artificially generated movement trajectories and neural data.”

• Presentation: Some discussion of case studies in Introduction might address the above point on impact. It would be useful to have more discussion of how general the software is, and why the current feature set was chosen. For example, how well does RatInABox deal with environments of arbitrary shape? T-mazes? It might help illustrate the tool's generality to move some of the examples in supplementary figure to main text - or just summarize them in a main text figure/panel.

Thank you for this question. Since the initial submission of this manuscript RatInABox has been upgraded and environments have become substantially more “general”. Environments can now be of arbitrary shape (including T-mazes), boundaries can be curved, they can contain holes and can also contain objects (0-dimensional points which act as visual cues). A few examples are showcased in the updated figure 1 panel e.

To further illustrate the tools generality beyond the structure of the environment we continue to summarise the reinforcement learning example (Fig. 3e) and neural decoding example in section 3.1. In addition to this we have added three new panels into figure 3 highlighting new features which, we hope you will agree, make RatInABox significantly more powerful and general and satisfy your suggestion of clarifying utility and generality in the manuscript directly.

On the topic of generality, we wrote the manuscript in such a way as to demonstrate how the rich variety of ways RatInABox can be used without providing an exhaustive list of potential applications. For example, RatInABox can be used to study neural decoding and it can be used to study reinforcement learning but not because it was purpose built with these use-cases in mind. Rather because it contains a set of core tools designed to support spatial navigation and neural representations in general. For this reason we would rather keep the demonstrative examples as supplements and implement your suggestion of further raising attention to the large array of tutorials and demos provided on the GitHub repository by modifying the final paragraph of section 3.1 to read:

“Additional tutorials, not described here but available online, demonstrate how RatInABox can be used to model splitter cells, conjunctive grid cells, biologically plausible path integration, successor features, deep actor-critic RL, whisker cells and more. Despite including these examples we stress that they are not exhaustive. RatInABox provides the framework and primitive classes/functions from which highly advanced simulations such as these can be built.”

Reviewer #3 (Public Review):

George et al. present a convincing new Python toolbox that allows researchers to generate synthetic behavior and neural data specifically focusing on hippocampal functional cell types (place cells, grid cells, boundary vector cells, head direction cells). This is highly useful for theory-driven research where synthetic benchmarks should be used. Beyond just navigation, it can be highly useful for novel tool development that requires jointly modeling behavior and neural data. The code is well organized and written and it was easy for us to test.

We have a few constructive points that they might want to consider.

• Right now the code only supports X,Y movements, but Z is also critical and opens new questions in 3D coding of space (such as grid cells in bats, etc). Many animals effectively navigate in 2D, as a whole, but they certainly make a large number of 3D head movements, and modeling this will become increasingly important and the authors should consider how to support this.

Agents now have a dedicated head direction variable (before head direction was just assumed to be the normalised velocity vector). By default this just smoothes and normalises the velocity but, in theory, could be accessed and used to model more complex head direction dynamics. This is described in the updated methods section.

In general, we try to tread a careful line. For example we embrace certain aspects of physical and biological realism (e.g. modelling environments as continuous, or fitting motion to real behaviour) and avoid others (such as the biophysics/biochemisty of individual neurons, or the mechanical complexities of joint/muscle modelling). It is hard to decide where to draw but we have a few guiding principles:

1. RatInABox is most well suited for normative modelling and neuroAI-style probing questions at the level of behaviour and representations. We consciously avoid unnecessary complexities that do not directly contribute to these domains.

2. Compute: To best accelerate research we think the package should remain fast and lightweight. Certain features are ignored if computational cost outweighs their benefit.

3. Users: If, and as, users require complexities e.g. 3D head movements, we will consider adding them to the code base.

For now we believe proper 3D motion is out of scope for RatInABox. Calculating motion near walls is already surprisingly complex and to do this in 3D would be challenging. Furthermore all cell classes would need to be rewritten too. This would be a large undertaking probably requiring rewriting the package from scratch, or making a new package RatInABox3D (BatInABox?) altogether, something which we don’t intend to undertake right now. One option, if users really needed 3D trajectory data they could quite straightforwardly simulate a 2D Environment (X,Y) and a 1D Environment (Z) independently. With this method (X,Y) and (Z) motion would be entirely independent which is of unrealistic but, depending on the use case, may well be sufficient.

Alternatively, as you said that many agents effectively navigate in 2D but show complex 3D head and other body movements, RatInABox could interface with and feed data downstream to other softwares (for example Mujoco[11]) which specialise in joint/muscle modelling. This would be a very legitimate use-case for RatInABox.

We’ve flagged all of these assumptions and limitations in a new body of text added to the discussion:

“Our package is not the first to model neural data[37, 38, 39] or spatial behaviour[40, 41], yet it distinguishes itself by integrating these two aspects within a unified, lightweight framework. The modelling approach employed by RatInABox involves certain assumptions:

1. It does not engage in the detailed exploration of biophysical[37, 39] or biochemical[38] aspects of neural modelling, nor does it delve into the mechanical intricacies of joint and muscle modelling[40, 41]. While these elements are crucial in specific scenarios, they demand substantial computational resources and become less pertinent in studies focused on higher-level questions about behaviour and neural representations.

2. A focus of our package is modelling experimental paradigms commonly used to study spatially modulated neural activity and behaviour in rodents. Consequently, environments are currently restricted to being two-dimensional and planar, precluding the exploration of three-dimensional settings. However, in principle, these limitations can be relaxed in the future.

3. RatInABox avoids the oversimplifications commonly found in discrete modelling, predominant in reinforcement learning[22, 23], which we believe impede its relevance to neuroscience.

4. Currently, inputs from different sensory modalities, such as vision or olfaction, are not explicitly considered. Instead, sensory input is represented implicitly through efficient allocentric or egocentric representations. If necessary, one could use the RatInABox API in conjunction with a third-party computer graphics engine to circumvent this limitation.

5. Finally, focus has been given to generating synthetic data from steady-state systems. Hence, by default, agents and neurons do not explicitly include learning, plasticity or adaptation. Nevertheless we have shown that a minimal set of features such as parameterised function-approximator neurons and policy control enable a variety of experience-driven changes in behaviour the cell responses[42, 43] to be modelled within the framework.

• What about other environments that are not "Boxes" as in the name - can the environment only be a Box, what about a circular environment? Or Bat flight? This also has implications for the velocity of the agent, etc. What are the parameters for the motion model to simulate a bat, which likely has a higher velocity than a rat?

Thank you for this question. Since the initial submission of this manuscript RatInABox has been upgraded and environments have become substantially more “general”. Environments can now be of arbitrary shape (including circular), boundaries can be curved, they can contain holes and can also contain objects (0-dimensional points which act as visual cues). A few examples are showcased in the updated figure 1 panel e.

Whilst we don’t know the exact parameters for bat flight users could fairly straightforwardly figure these out themselves and set them using the motion parameters as shown in the table below. We would guess that bats have a higher average speed (speed_mean) and a longer decoherence time due to increased inertia (speed_coherence_time), so the following code might roughly simulate a bat flying around in a 10 x 10 m environment. Author response image 1 shows all Agent parameters which can be set to vary the random motion model.

Author response image 1.

• Semi-related, the name suggests limitations: why Rat? Why not Agent? (But its a personal choice)

We came up with the name “RatInABox” when we developed this software to study hippocampal representations of an artificial rat moving around a closed 2D world (a box). We also fitted the random motion model to open-field exploration data from rats. You’re right that it is not limited to rodents but for better or for worse it’s probably too late for a rebrand!

• A future extension (or now) could be the ability to interface with common trajectory estimation tools; for example, taking in the (X, Y, (Z), time) outputs of animal pose estimation tools (like DeepLabCut or such) would also allow experimentalists to generate neural synthetic data from other sources of real-behavior.

This is actually already possible via our “Agent.import_trajectory()” method. Users can pass an array of time stamps and an array of positions into the Agent class which will be loaded and smoothly interpolated along as shown here in Fig. 3a or demonstrated in these two new papers[9,10] who used RatInABox by loading in behavioural trajectories.

• What if a place cell is not encoding place but is influenced by reward or encodes a more abstract concept? Should a PlaceCell class inherit from an AbstractPlaceCell class, which could be used for encoding more conceptual spaces? How could their tool support this?

In fact PlaceCells already inherit from a more abstract class (Neurons) which contains basic infrastructure for initialisation, saving data, and plotting data etc. We prefer the solution that users can write their own cell classes which inherit from Neurons (or PlaceCells if they wish). Then, users need only write a new get_state() method which can be as simple or as complicated as they like. Here are two examples we’ve already made which can be found on the GitHub:

Author response image 2.

Phase precession: PhasePrecessingPlaceCells(PlaceCells)[12] inherit from PlaceCells and modulate their firing rate by multiplying it by a phase dependent factor causing them to “phase precess”.

Splitter cells: Perhaps users wish to model PlaceCells that are modulated by recent history of the Agent, for example which arm of a figure-8 maze it just came down. This is observed in hippocampal “splitter cell”. In this demo[1] SplitterCells(PlaceCells) inherit from PlaceCells and modulate their firing rate according to which arm was last travelled along.

• This a bit odd in the Discussion: "If there is a small contribution you would like to make, please open a pull request. If there is a larger contribution you are considering, please contact the corresponding author3" This should be left to the repo contribution guide, which ideally shows people how to contribute and your expectations (code formatting guide, how to use git, etc). Also this can be very off-putting to new contributors: what is small? What is big? we suggest use more inclusive language.

We’ve removed this line and left it to the GitHub repository to describe how contributions can be made.

• Could you expand on the run time for BoundaryVectorCells, namely, for how long of an exploration period? We found it was on the order of 1 min to simulate 30 min of exploration (which is of course fast, but mentioning relative times would be useful).

Absolutely. How long it takes to simulate BoundaryVectorCells will depend on the discretisation timestep and how many neurons you simulate. Assuming you used the default values (dt = 0.1, n = 10) then the motion model should dominate compute time. This is evident from our analysis in Figure 3f which shows that the update time for n = 100 BVCs is on par with the update time for the random motion model, therefore for only n = 10 BVCs, the motion model should dominate compute time.

So how long should this take? Fig. 3f shows the motion model takes ~10-3 s per update. One hour of simulation equals this will be 3600/dt = 36,000 updates, which would therefore take about 72,000*10-3 s = 36 seconds. So your estimate of 1 minute seems to be in the right ballpark and consistent with the data we show in the paper.

Interestingly this corroborates the results in a new inset panel where we calculated the total time for cell and motion model updates for a PlaceCell population of increasing size (from n = 10 to 1,000,000 cells). It shows that the motion model dominates compute time up to approximately n = 1000 PlaceCells (for BoundaryVectorCells it’s probably closer to n = 100) beyond which cell updates dominate and the time scales linearly.

These are useful and non-trivial insights as they tell us that the RatInABox neuron models are quite efficient relative to the RatInABox random motion model (something we hope to optimise further down the line). We’ve added the following sentence to the results:

“Our testing (Fig. 3f, inset) reveals that the combined time for updating the motion model and a population of PlaceCells scales sublinearly O(1) for small populations n > 1000 where updating the random motion model dominates compute time, and linearly for large populations n > 1000. PlaceCells, BoundaryVectorCells and the Agent motion model update times will be additionally affected by the number of walls/barriers in the Environment. 1D simulations are significantly quicker than 2D simulations due to the reduced computational load of the 1D geometry.”

And this sentence to section 2:

“RatInABox is fundamentally continuous in space and time. Position and velocity are never discretised but are instead stored as continuous values and used to determine cell activity online, as exploration occurs. This differs from other models which are either discrete (e.g. “gridworld” or Markov decision processes) or approximate continuous rate maps using a cached list of rates precalculated on a discretised grid of locations. Modelling time and space continuously more accurately reflects real-world physics, making simulations smooth and amenable to fast or dynamic neural processes which are not well accommodated by discretised motion simulators. Despite this, RatInABox is still fast; to simulate 100 PlaceCell for 10 minutes of random 2D motion (dt = 0.1 s) it takes about 2 seconds on a consumer grade CPU laptop (or 7 seconds for BoundaryVectorCells).”

• Regarding the Geometry and Boundary conditions, would supporting hyperbolic distance might be useful, given the interest in alternative geometry of representations (ie, https://www.nature.com/articles/s41593-022-01212-4)?

Whilst this would be very interesting it would likely represent quite a significant edit, requiring rewriting of almost all the geometry-handling code. We’re happy to consider changes like these according to (i) how simple they will be to implement, (ii) how disruptive they will be to the existing API, (iii) how many users would benefit from the change. If many users of the package request this we will consider ways to support it.

• In general, the set of default parameters might want to be included in the main text (vs in the supplement).

We also considered this but decided to leave them in the methods for now. The exact value of these parameters are subject to change in future versions of the software. Also, we’d prefer for the main text to provide a low-detail high-level description of the software and the methods to provide a place for keen readers to dive into the mathematical and coding specifics.

• It still says you can only simulate 4 velocity or head directions, which might be limiting.

Thanks for catching this. This constraint has been relaxed. Users can now simulate an arbitrary number of head direction cells with arbitrary tuning directions and tuning widths. The methods have been adjusted to reflect this (see section 6.3.4).

• The code license should be mentioned in the Methods.

We have added the following section to the methods:

6.6 License RatInABox is currently distributed under an MIT License, meaning users are permitted to use, copy, modify, merge publish, distribute, sublicense and sell copies of the software.

Author Response

Reviewer #2 (Public Review):

“The authors wish to relate beat-to-beat coordination of cardiac function (in this case as measured left ventricular pressure) to the activity of sympathetic neuron spiking within the stellate ganglion. A strength includes the challenging measurements from multiple stellate neuron activity over long durations in situ in the anesthetized pig.”

We thank the reviewer for their feedback.

“A major and overriding weakness is the founding assumption of the analysis that the underlying sympathetic neurons are all cardiac functioning in nature - an assumption that is overwhelmingly unlikely given the evidence in other species including humans that stellate postganglionic neurons are functionally mixed and have functional noncardiac targets. The use of broad and poorly explained/defined terms such as "event entropy" is difficult to follow and find meaning from. The manuscript is filled with difficult-to-follow text like "The neural specificity metric (Sudarshan et al., 2021). Fig. 5", is used to evaluate the degree to which neural activity is biased toward control target states taken here as LVP" and "The neural specificity is reduced from a multivariate signal to a univariate signal by computing the Shannon entropy at each timestamp of the mapped neural specificity metric". The figures are difficult to understand with axes that often bear no units or are quite compressed obscuring the intuitive meaning of the data trends. Fundamentally, cardiac pressure cycles with each heartbeat - roughly once per second - yet fluctuations in the depicted mean spike rate data with changes perhaps ten times in 25 minutes. Such plots are disorienting and difficult to associate with cardiac or neuron "functioning". Only 17 of the 38 references are not self-citations and thus the cited literature represents a narrow view of sympathetic regulation and sympathetic/stellate ganglion knowledge. Much of the foundations are self-professed in earlier publications by the present group and assumed to be accepted.”

“Fundamentally, cardiac pressure cycles with each heartbeat - roughly once per second - yet fluctuations in the depicted mean spike rate data with changes perhaps ten times in 25 minutes. Such plots are disorienting and difficult to associate with cardiac or neuron "functioning”

We would like to clarify this point with the understanding that the reviewer is referring to the time axis in Figure 3C in the manuscript.

The coactivity matrix constructed in Figure 3C computes the cross correlation in sliding mean/std spike activities for different pairs of channels. The mean spiking activities across channels, as the reviewer correctly pointed out, do indeed have a weak autocorrelation with the period of the heart rate. The weak correlation for the heart rate period, possibly due to slow firing rates, was seen across all channels of both control and HF animals. But, the cause of a large proportion of channel-pairs exhibiting high coactivity, termed as cofluctuation (Shown as red tracings in Fig 3D), is not known and cannot be directly associated with cardiac functioning.

The cofluctuation was also found to be aperiodic in nature approximating a lognormal distribution (Fig R1) with the HF animals containing heavy tails outside their confidence intervals (Fig R1B). The event rate computed from the cofluctuation time series (shown as blue steps in Fig 3E) for an animal is a measure of spatial coherence among SG neural populations and was developed as a novel metric to be used in future studies.

Figure R1: Cofluctuation histograms (calculated from mean or standard deviation of sliding spike rate, referred as Cofluctuation_MEAN and Cofluctuation_STD, respectively) and log-normal fits for each animal group. μF IT and σF IT are the respective mean and standard deviation (STD) of fitted distribution, used for 68% confidence interval bounds. A-B: Control animals have narrower bounds and represent a better fit to log-normal distribution. C-D: Heart failure (HF) animals display more heavily skewed distributions that indicate heavy tails.

“Only 17 of the 38 references are not self-citations and thus the cited literature represents a narrow view of sympathetic regulation and sympathetic/stellate ganglion knowledge. Much of the foundations are self-professed in earlier publications by the present group and assumed to be accepted.”

We thank the reviewer for pointing this out. We have added four additional citations that include methods such as neural population bias and spatiotemporal dynamics linkages to control targets in the neuroscience literature. We have added these citations to page 15 in the “Conclusion” section of the manuscript. In addition, it is our group’s specialty to carry these cardiac nervous system experiments, we are not aware of another group collecting multi-electrode array data from the cardiac nervous system and studying population dynamics of cardiac neurons. Hence we build on based on our previous learnings. The most relevant literature (not necessarily related to cardiac nervous system) can be found in the neuroscience references we cited that contain applications of neural population recordings for different brain areas, mainly in neuropsychiatry domain to understand disease dynamics.

“For the expert or even the uninformed reader, this report is broadly confused and confusing. The premises (beat to beat or whether LVP conveys cardiac function) are poorly supported. The conclusions are quite vague.”

Thank you for your feedback. To simplify the understanding, we moved all mathematical details to supplementary material, re-wrote the abstract and the conclusion from scratch, and splitted the methods figures that may be confusion. We believe that our novel metrics event rate and entropy capture non-trivial linkages between heart failure status, cardiac neural activity (spike activity), and peripheral activity (LVP). We have supported our metrics with 17 animals with state-of-the-art surgical techniques and technology, and reported our results with detailed statistical analyses. Our manuscript essentially highlights that event rate and entropy metrics are significantly different between control animals and animals with heart failure. These metrics can be used to design future studies with these animal models to provide a more quantitative approach to heart disease, rather than binary (yes or no) descriptions.

“Discussion: The abstract does not convey conclusions from the findings and contains broad statements such as "signatures based on linking neuronal population cofluctuation and examine differences in "neural specificity" of SG network" that have little substantive value or conclusion for the reader. Fundamentally what does the title "signatures based on linking neuronal population" cofluctuation mean to the reader? What changed in HF?”

Thank you for this comment. We completely revised the abstract and conclusion as detailed in our response to Essential Revision #1. Event rate is a metric related to neural activity recordings and entropy is related to the association of neural activity to left ventricular blood pressure. Our findings suggest that both the neural population activity itself (event rate) and its ability to pay attention to cycles of left ventricular pressure (neural specificity) are significantly higher in animals with HF compared to controls.

Author Response

Reviewer #1 (Public Review):

(1.1) The work by Porciello and colleagues provides scientific evidence that the acidic content of the stomach covaries with the experienced level of disgust and fear evoked by disgusting videos. The working of the inside of the gut during cognitive or emotional processes have remained elusive due to the invasiveness of the methods to study it. The major strength of the paper is the use of the non-invasive smart pill technology, which senses changes in Ph, pressure and temperature as it travels through the gut, allowing authors to investigate how different emotions induced with validated video clips modulate the state of the gut. The experimental paradigm used to evoke distinct emotions was also successful, as participants reported the expected emotions after each emotion block. While the reported evidence is correlational in nature, I believe these results open up new avenues for studying brain-body interactions during emotions in cognitive neuroscience, and future causal manipulations will shed more insight on this phenomena. Indeed, this is the first study to provide evidence for a link between gastric acidity and emotional experience beyond single patient studies, and it has major implications for the advancement of our understanding of disorders with psycho-somatic influences, such as stress and it's influence of gastritis.

1.1 First of all, we want to thank Reviewer#1 for his cogent comments and for highlighting that our findings may inspire future research on brain-body interactions. We took into the highest consideration all the remarks and changed the manuscripts accordingly.

(1.2) As for the limitations, little insight is provided on the mechanisms, time scales, and inter-individual variability of the link between gastric Ph and emotional induction. Since this is a novel phenomena, it would be important to further validate and characterize this finding. On this line, one of the most well known influences of disgust on the gut is tachygastria, the acceleration of the gastric rhythm. It would be important to understand how acid secretion by disgusting film is related to tachygastria, but authors only examine the influence of disgusting film on the normogastric frequency range.

1.2 We are aware that at the moment our data are mainly descriptive and do not provide a clear picture of the causal mechanisms. However, to deal with this outstanding issue we added a new series of analysis.

Most of the data on gastric activity come from analysis of the normogastric band. However, information about the EGG tachygastric rhythm in humans is of potential great importance. To deal with the reviewer’s comment and considering the previously published literature, we re-examined the EGG data focusing on the tachygastric rhythm. The methodology remained consistent with the process described for normogastric peak extraction but this time, we extracted the peak in the tachygastric band, specifically 0.067 to 0.167 Hz (i.e., 4–10 cpm). The ANOVA performed over the tachygastric cycle revealed a significant main effect of the type of video clip (F(4, 112) = 2.907, p = 0.025, Eta2 (partial) = 0.09). However, the Bonferroni corrected post hoc tests did not show any significant difference between the different type of emotional video clips and the neutral condition. The sole significant comparison was observed between participants viewing happy and fearful video clips, indicating that participants’ tachygastric cycles were faster when exposed to happy rather than fearful video clips (p = 0.038). For a visual representation of the outcomes, please see Fig S6.

We revised the main text (Page 17, lines: 472-482) to include this analysis. The revised text now reads as follows:

“Finally, we explored whether normogastric and/or tachygastric cycle changed in response to specific emotional experience. After checking that normogastric and tachygastric peak frequencies were normally distributed (all ps > 0.05), we ran two separate ANOVAs on the individual peak frequencies in the normogastric and tachygastric range. Each analysis had the type of video clip as within-subjects factor. The ANOVA performed on the normogastric rhythm was not significant (F(4, 44) = 1.037, p = 0.399) suggesting that the gastric rhythm did not change while participants observed the different emotional video clips. In contrast, the ANOVA performed on the tachygastric rhythm did show a significant main effect (F(4, 112) = 2.907, p = 0.025, Eta2 (partial) = 0.09). However, the only comparison that survived the Bonferroni correction was the one between happy and fearful video clips, namely participants’ tachygastric cycle was faster when they observed happy vs fearful video clips (p = 0.038) see Fig. S6 for a graphical representation of the results.”

To deal with the Reviewer’s comment, we also correlated the average pH value with the corresponding frequency of the tachygastric cycle recorded in the disgusting, happy and the fearful video clips, namely the emotions associated to changes in pH. The only significant correlation was the one found during the disgusting video clips (r= 0.435; p= 0.023, all the other rs ≤ 0.351, all the other ps ≥ 0.073). Differently from what we expected, we found a positive correlation suggesting that when participants were exposed to disgusting video clips the less acidic was the pH the higher was the frequency of the tachygastric cycle. Instead, we know from our pill data that disgusting video clips are associated to more acid values, and from literature (not replicated by us) to a faster gastric rhythm. Since we did not find strong support in the EGG analysis suggesting a relationship between the gastric rhythm and the emotional experience, we believe that additional evidence will help to clarify the relationship between pH and gastric rhythm.

(1.3) Additionally, only one channel of the electrogastrogram (EGG) was used to measure the gastric rhythm, and no information is provided on the quality of the recordings. With only one channel of EGG, it is often impossible to identify the gastric rhythm as the position of the stomach varies from person to person, yielding inaccurate estimates of the frequency of the gastric rhythm.

1.3 We agree with Reviewer 1 on this point. We acknowledge the potential limitation associated with one-channel EGG recording in our study. To deal with this remark, in a separate (ongoing) study (N# participants= 25) we recorded the electrogastrogram following the methodology outlined by Wolpert et al., 2020 published on Psychophysiology. Thus, in order to study the EGG in association to the emotional experience, we used a bipolar 4-channels montage while participants observed the same emotional video clips used in our current study (see picture below for the montage set-up).

Author response image 1 shows the 4-channels EGG bipolar recording montage reproducing the one proposed by Wolpert et al., 2020.

Author response image 1.

Then, we extracted the gastric cycle in both the normogastric and the tachygastric bands.

After checking that data were normally distributed (Kolmogorov-Smirnov ds > 0.10; ps> .20), in the case of the gastric cycle extracted in the normogastric band, we ran a repeated measures ANOVA with the type of video clip as the only within-subjects factor measured on the 5 levels (i.e. the five types of video clip: Disgusting, Fearful, Happy, Neutral, and Sad). The ANOVA shows that the gastric cycle recorded during the different video clips did not differ (F (4,96) = 0.39; p= 0.81), see the plot on Author response image 2.

Author response image 2.

Gastric cycle (normogastric band) recorded via multiple-channels electrogastrogram (EGG) during the emotional experience. The plot shows the gastric cycle extracted in the normogastric band while participants were observing the five categories of the video clips (i.e. those inducing disgust, fear, happiness, sadness and, as control, a neutral state).

We also extracted the gastric cycle in the tachygastric band, the distribution of the data was not normal in one condition (Kolmogorov-Smirnov ds > 0.27; p < 0.05), therefore we ran a Friedman ANOVA to compare the gastric cycle during the different emotional experiences. The Friedman ANOVA was not statistically significant (χ2 (4) = 2.88; p = 0.58), suggesting that, similarly to the gastric cycle extracted in the normogastric band, also the one extracted in the tachygastric band was not clearly associated to the investigated emotional states, see Author response image 3.

Author response image 3.

Gastric cycle (tachygastric band) recorded via multiple-channels electrogastrogram (EGG) during the emotional experience. The plot above shows the gastric cycle extracted in the tachygastric band while participants were observing the five categories of the video clips (i.e. those inducing disgust, fear, happiness, sadness and as control a neutral state).

Results from this control study seem to suggest that the non-significant effect of the gastric cycle was probably not due to the fact that we use a one-channel egg montage, at least for what concerns the gastric cycle extracted from the normogastric band.

For what concerns the tachygastric frequency associated to the emotional experience these results from a multi-channel EGG recording seem to go in the same direction of the normogastric one, namely no frequency of the gastric cycles recorded during the emotional video clips was different from the control condition.

The only significant difference that we found in our 1-channel EGG study was the one between the happy and the fearful video clips (see Fig. S6 contained in the supplementary materials and above). Specifically, we found that happy video clips were associated to higher gastric frequency compared to the fearful ones. However, we did not replicate these findings in our multi-channels EGG study.

Although suggestive, this evidence is not conclusive. Indeed, we are aware that a final word on the results of our multi-channel study can be said only when a larger sample is obtained.

(1.4) Finally, I believe that the results do not show evidence in favor of the discrete nature of emotions theory as they claim in the discussion. Authors chose to use stimuli inducing discrete emotions, and only asked subjective reports of these same discrete emotions, so these results shed no light on whether emotions are represented discretely vs continuously in the brain.

We revised the discussion in order to better describe our results and toned down the interpretation that the present findings directly support the discrete nature of emotions, as suggested by this Reviewer.

Now page 21&22 lines 622-631 reads as follow:

“Overall, and in line with theoretical and empirical evidence (Damasio, 1999; Harrison et al., 2010; James, 1994, Lettieri et al., 2019; Stephens et al., 2010), our findings may suggest that specific patterns of subjective, behavioural, and physiological measures are linked to unique emotional states...We acknowledge that our results, although novel, are restricted to a sample of male participants, and more importantly they need to be replicated. We also acknowledge that future studies should better investigate the mechanisms underlying the role of the pH in the emergence of specific emotion. For instance, pharmacologically manipulating stomach pH during emotional induction, not only for basic emotions but also for exploring complex emotions such as moral disgust (Rozin et al., 2009), would enable researchers to generalize these findings and examine the directionality of this relationship.”

Reviewer #2 (Public Review):

To measure the role of gastric state in emotion, the authors used an ingestible smart pill to measure pH, pressure, and temperature in the gastrointestinal tract (stomach, small bowel, large bowel) while participants watched videos that induced disgust, fear, happiness, sadness, or a control (neutral). The study has a number of strengths, including the novelty of the measurement (very few studies have ever measured these gut properties during emotion processing) and the apparent robustness of their main finding (that during disgusting video clips, participants who experienced more feelings of disgust (and to a lesser degree which might not survive more stringent multiple comparison correction, fear) had more acidic stomach measurements, while participants who experienced more happiness during the disgusting video clips had a less acidic (more basic) stomach pH. Although the study is correlational (which all discussion should carefully reflect) and is restricted to a moderately-sized, homogenous sample, the results support their general conclusion that stomach pH is related to emotion experience during disgust induction. There may be additional analyses to conduct in order for the authors to claim this effect is specific to the stomach. Nevertheless, this work is likely to have a large impact on the field, which currently tends to rely on noninvasive measures of gastric activity such as electrogastrography (which the authors also collect for comparison); the authors' minimally-invasive approach yields new and useful measurements of gastric state. These new measures could have relevance beyond emotion processing in understanding the role of gut pH (and perhaps temperature and pressure) in cognitive processes (e.g. interoception) as well as mental and physical health.

We are very grateful to Reviewer#2 for skilfully managing the paper and highlighting its strengths, particularly the innovative measurement approach and the potential implications these findings might offer for future research into the impact of gastric signals on emotional experiences and potentially on many other higher-order cognitive functions. Additionally, we would like to thank her for the highly valuable feedback. We have incorporated all the comments into the revised manuscript, aiming to enhance its quality.

Reviewer #3 (Public Review):

This study used novel ingestible pills to measure pH and other gastric signals, and related these measures to self-report ratings of emotions induced by video clips. The main finding was that when participants viewed videos of disgust, there was an association between gastric pH and feelings of disgust and fear, and (in the opposite direction) happiness. These findings may be the first to relate objective measures of gastric physiology to emotional experience. The methods open up many new questions that can be addressed by future studies and are thus likely to have an impact on the field.

We thank very much also Reviewer#3 for the accurate reading of our manuscript; for highlighting the strengths of our study; and for providing valuable feedback. Below, a point-by-point response to all the comments raised by this Reviewer. We have incorporated their comments, and we hope they are satisfied by the new version of the manuscript.

(3.1) My main concern is with the reliability of the results. The study associates many measures (pH, temperature, pressure, EGG) in stomach, small bowel, and large bowel with multiple emotion ratings. This amounts to many statistical tests. Only one of these measures (pH in the stomach) shows a significant effect. Furthermore, the key findings, as displayed in Figure 4 do not look particularly convincing. Perhaps this is a display issue, but the relations between stomach pH and Vas ratings of disgust, fear, and happiness were not apparent from the scatter plot and may be influenced by outliers (e.g., happiness).

3.1 We thank Reviewer#3 for raising this issue which was also raised by Reviewer#1 and #2, se replies above. As reported above we worked on the data analysis in order to provide more evidence supporting our claim, i.e. that pH plays a role in the emotional experience of disgust, happiness and fear. We modified Figure 4 (now 5) as also requested by Reviewer 1 and 2, and we now hope that it is clearer. We included a new analysis, in which we used all the datapoints recorded from the ingestible device and we performed a mixed models analysis with pH as dependent variable, type of video clips and number of datapoints (‘Time’) as fixed factors, and the by-subject intercepts as random effects. This analysis not only supported the results of the original one but provided evidence for a causal role of the emotional induction on the pH of the stomach. Results of this analysis are described in point 1.7 in the response to Reviewer#1 and results of the new analysis and the revised version of the main figure can be found in track change in the manuscript (Page 15&16, lines: 408-439) in the main text and copied and pasted below.

“To explore how the emotional induction could modulate the pH of the stomach and how the length of the exposure to that specific emotional induction could also play a role in modulating pH variations, we ran an additional model, Model 2. This model included all the pH datapoints registered using the Smartpill as dependent variable, the type of video clip and the number of the datapoints (“Time”) as fixed effects, and the by-subject intercepts as random effects (see Supplementary information for a detailed description of the model). Model 2 had a marginal R2 = 0.014 and a conditional R2 = 0.79. Visual inspection of the plots did reveal some small deviations from homoscedasticity, visual inspection of the residuals did not show important deviations from normality. As for collinearity (tested by means of vif function of car package), all independent variables had a GVIF^(1/(2*Df)))^2 < 10.

Type III analysis of variance of Model 2 showed a statistically significant main effect of the Time (F = 20.237, p < 0.001, Eta2 < 0.01) suggesting that independently from the type of video clip observed, the stomach pH significantly decreased as a function of the time of exposure to the induction. A significant main effect of the type of video clip was also found (F = 22.242, p < 0.001, Eta2 = 0.01) suggesting that pH of the stomach changes when participants experienced different types of emotions. In particular, post hoc analysis revealed that pH was more acidic when participants observed disgusting compared to fearful (t= -11.417; p < 0.001), happy (t= -15.510; p < 0.001) and neutral (t= -3.598; p = 0.003) video clips.

Also, pH was more acidic when participants observed fearful compared to happy (t= -4.064; p < 0.001), and less acidic compared to neutral (t= 7.835; p < 0.001) and sad scenarios (t= 9.743; p < 0.001). Finally, pH was less acidic when participants observed happy compared to neutral (t= 11.923; p < 0.001). and sad videoclips (t= 13.806; p < 0.001), see Fig.6, left panel. Interestingly, also the double interaction Time X Type of video clip was significant (F = 3.250, p = 0.0113, Eta2 < 0.01) suggesting that the time of the exposure to the induction differentially influenced the pH of the stomach depending on to the type of the observed video clip. Simple slope analysis showed that while pH did not change over time when observing disgusting (t= -1.2691; p = 0.2045) and happy (t= 0.4466; p = 0.6552) clips, it did significantly decrease over time when observing fearful (t= -4.4212; p < 0.001), sad (t= -2.0487; p = 0.0405) and neutral video clips (t= -2.7956; p = 0.0052), see Fig.6, right panel."

We believe that the new evidence reported provides support of our claims and we hope that the reviewer agrees with us. However, as we also mentioned in the paper, we are aware that replications are needed and we are already working on this.

Author Response:

Reviewer #1:

The largest concern with the manuscript is its use of resting-state recordings in Parkinson's Disease patients on and off levodopa, which the authors interpret as indicative of changes in dopamine levels in the brain but not indicative of altered movement and other neural functions. For example, when patients are off medication, their UPDRS scores are elevated, indicating they likely have spontaneous movements or motor abnormalities that will likely produce changed activations in MEG and LFP during "rest". Authors must address whether it is possible to study a true "resting state" in unmedicated patients with severe PD. At minimum this concern must be discussed in the manuscript.

We agree that Parkinson’s disease can lead to unwanted movements such as tremor as well as hyperkinesias. This would of course be a deviation from a resting state in healthy subjects. However, such movements are part of the disease and occur unwillingly. The main tremor in Parkinson’s disease is a rest tremor and - as the name already suggests – it occurs while not doing anything. Therefore, such movements can arguably be considered part of the resting state of Parkinson’s disease. Resting state activity with and without medication is therefore still representative for changes in brain activity in Parkinson’s patients and indicative of alterations due to medication.

To further investigate the effect of movement in our patients, we subdivided the UPDRS part 3 score into tremor and non-tremor subscores. For the tremor subscore we took the mean of item 15 and 17 of the UPDRS, whereas for the non-tremor subscore items 1, 2, 3, 9, 10, 12, 13, and 14 were averaged. Following Spiegel et al., 2007, we classified patients as akinetic-rigid (non-tremor score at least twice the tremor score), tremor-dominant (tremor score at least twice as large as the non-tremor score), and mixed type (for the remaining scores). Of the 17 patients, 1 was tremor dominant and 1 was classified as mixed type (his/her non-tremor score was greater than tremor score). None of our patients exhibited hyperkinesias during the recording. To exclude that our results are driven by tremor-related movement, we re-ran the HMM without the tremor-dominant and the mixed-type patient (see Figure R1 response letter).

ON medication results for all HMM states remained the same. OFF medication results for the Ctx-Ctx and STN-STN state remained the same as well. The Ctx-STN state OFF medication was split into two states: Sensorimotor-STN connectivity was captured in one state and all other types of Ctx-STN connections were captured in another state (see Figure 1 response letter. The important point is that the biological conclusions stand across these solutions. Regardless, both with and without the two subjects a stable covariance matrix entailing sensorimotor-STN connectivity was determined, which is the main finding for the Ctx-STN state OFF medication.

We therefore discuss this issue now within the limitation section (page 20):

“Both motor impairment and motor improvement can cause movement during the resting state in PD. While such movement is a deviation from a resting state in healthy subjects, such movements are part of the disease and occur unwillingly. Therefore, such movements can arguably be considered part of the resting state of Parkinson’s disease. None of the patients in our cohort experienced hyperkinesia during the recording. All patients except for two were of the akinetic-rigid subtype. We verified that tremor movement is not driving our results. Recalculating the HMM states without these 2 subjects, even though it slightly changed some particular aspects of the HMM solution did not materially affect the conclusions.”

Figure R1: States obtained after removing one tremor dominant and one mixed type patient from analysis. Panel C shows the split OFF medication cortico-STN state. Most of the cortico-STN connectivity is captured by the state shown in the top row (Figure 1 C OFF). Only the motor-STN connectivity in the alpha and beta band (along with a medial frontal-STN connection in the alpha band) is captured separately by the states labeled “OFF Split” (Figure 1 C OFF SPLIT).

This reviewer was unclear on why increased "communication" in the medial OFC in delta and theta was interpreted as a pathological state indicating deteriorated frontal executive function. Given that the authors provide no evidence of poor executive function in the patients studied, the authors must at least provide evidence from other studies linking this feature with impaired executive function.

If we understand the comment correctly it refers to the statement in the abstract “Dopaminergic medication led to communication within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with deteriorated frontal executive functioning as a side effect of dopamine treatment in Parkinson’s disease”

This statement is based on the dopamine overdose hypothesis reported in the Parkinson’s disease (PD) literature (Cools 2001; Kelly et al. 2009; MacDonald and Monchi 2011; Vaillancourt et al. 2013). We have elaborated upon the dopamine overdose hypothesis in the discussion on page 16. In short, dopaminergic neurons are primarily lost from the substantia nigra in PD, which causes a higher dopamine depletion in the dorsal striatal circuitry than within the ventral striatal circuits (Kelly et al. 2009; MacDonald and Monchi 2011). Thus, dopaminergic medication to treat the PD motor symptoms leads to increased dopamine levels in the ventral striatal circuits including frontal cortical activity, which can potentially explain the cognitive deficits observed in PD (Shohamy et al. 2005; George et al. 2013). We adjusted the abstract to read:

“Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in Parkinson’s disease.”

In this article, authors repeatedly state their method allows them to delineate between pathological and physiological connectivity, but they don't explain how dynamical systems and discrete-state stochasticity support that goal.

To recapitulate, the HMM divides a continuous time series into discrete states. Each state is a time-delay embedded covariance matrix reflecting the underlying connectivity between brain regions as well as the specific temporal dynamics in the data when such state is active. See Packard et al., (1980) for details about how a time-delay embedding characterises a linear dynamical system.

Please note that the HMM was used as a data-driven, descriptive approach without explicitly assuming any a-priori relationship with pathological or physiological states. The relation between biology and the HMM states, thus, purely emerged from the data; i.e. is empirical. What we claim in this work is simply that the features captured by the HMM hold some relation with the physiology even though the estimation of the HMM was completely unsupervised (i.e. blind to the studied conditions). We have added this point also to the limitations of the study on page 19 and the following to the introduction to guide the reader more intuitively (page 4):

“To allow the system to dynamically evolve, we use time delay embedding. Theoretically, delay embedding can reveal the state space of the underlying dynamical system (Packard et al., 1980). Thus, by delay-embedding PD time series OFF and ON medication we uncover the differential effects of a neurotransmitter such as dopamine on underlying whole brain connectivity.”

Reviewer #2:

Sharma et al. investigated the effect of dopaminergic medication on brain networks in patients with Parkinson's disease combining local field potential recordings from the subthalamic nucleus and magnetencephalography during rest. They aim to characterize both physiological and pathological spectral connectivity.

They identified three networks, or brain states, that are differentially affected by medication. Under medication, the first state (termed hyperdopaminergic state) is characterized by increased connectivity of frontal areas, supposedly responsible for deteriorated frontal executive function as a side effect of medical treatment. In the second state (communication state), dopaminergic treatment largely disrupts cortico-STN connectivity, leaving only selected pathways communicating. This is in line with current models that propose that alleviation of motor symptoms relates to the disruption of pathological pathways. The local state, characterized by STN-STN oscillatory activities, is less affected by dopaminergic treatment.

The authors utilize sophisticated methods with the potential to uncover the dynamics of activities within different brain network, which opens the avenue to investigate how the brain switches between different states, and how these states are characterized in terms of spectral, local, and temporal properties. The conclusions of this paper are mostly well supported by data, but some aspects, mainly about the presentation of the results, remain:

We would like to thank the reviewer for his succinct and clear understanding of our work.

1) The presentation of the results is suboptimal and needs improvement to increase readers' comprehension. At some points this section seems rather unstructured, some results are presented multiple times, and some passages already include points rather suitable for the discussion, which adds too much information for the results section.

We have removed repetitions in the results sections and removed the rather lengthy introductory parts of each subsection. Moreover, we have now moved all parts, which were already an interpretation of our findings to the discussion.

2) It is intriguing that the hyperdopaminergic state is not only identified under medication but also in the off-state. This is intriguing, especially with the results on the temporal properties of states showing that the time of the hyperdopaminergic state is unaffected by medication. When such a state can be identified even in the absence of levodopa, is it really optimal to call it "hyperdopaminergic"? Do the results not rather suggest that the identified network is active both off and on medication, while during the latter state its' activities are modulated in a way that could relate to side effects?

The reviewer’s interpretations of the results pertaining to the hyper-dopaminergic state are correct. The states had been named post-hoc as explained in the results section. The hyper-dopaminergic state’s name derived from it showing the overdosing effects of dopamine. Of course, these results are only visible on medication. But off medication, this state also exists without exhibiting the effects of excess dopamine. To avoid confusion or misinterpretation of the findings and also following the relevant comment by reviewer 1, we renamed all states to be more descriptive:

Hyperdopaminergic > Cortico-cortical state

Communication > Cortico-STN state

Local > STN-STN state.

3) Some conclusions need to be improved/more elaborated. For example, the coherence of bilateral STN-STN did not change between medication off and on the state. Yet it is argued that a) "Since synchrony limits information transfer (Cruz et al. 2009; Cagnan, Duff, and Brown 2015; Holt et al. 2019) , local oscillations are a potential mechanism to prevent excessive communication with the cortex" (line 436) and b) "Another possibility is that a loss of cortical afferents causes local basal ganglia oscillations to become more pronounced" (line 438). Can these conclusions really be drawn if the local oscillations did not change in the first place?

We apologize for the unclear description. Our conclusion was based on the following results:

a) We state that STN-STN connectivity as measured by the magnitude of STN-STN coherence does not change OFF vs ON medication in the Cortico-STN state. This result is obtained using inter-medication analysis.

b) But ON medication, STN-STN coherence in the Cortico-STN state was significantly different from mean coherence within the ON condition. These results are obtained using intra-medication analysis.

Based on this, we conclude that in the Cortico-STN state, although OFF vs ON medication the magnitude of STN-STN coherence was unchanged, the STN-STN coherence was significantly different from mean coherence in the ON medication condition. The emergence of synchronous STN-STN activity may limit information exchange between STN and cortex ON medication.

An alternative explanation for these findings might be a mechanism preventing connectivity between cortex and the STN ON medication. This missing interaction between STN and cortex might cause STN-STN oscillations to increase compared to the mean coherence within the ON state. Unfortunately, we cannot test such causal influences with our analysis.

We have added the following discussion to the manuscript on page 17 in order to improve the exposition:

“Bilateral STN–STN coherence in the alpha and beta band did not change in the cortico-STN state ON versus OFF medication (InterMed analysis). However, STN-STN coherence was significantly higher than the mean level ON medication (IntraMed analysis). Since synchrony limits information transfer (Cruz et al. 2009; Cagnan, Duff, and Brown 2015; Holt et al. 2019), the high coherence within the STN ON medication could prevent communication with the cortex. A different explanation would be that a loss of cortical afferents leads to increased local STN coherence. The causal nature of the cortico-basal ganglia interaction is an endeavour for future research.”

Reviewer #3:

In PD, pathological neuronal activity along the cortico-basal ganglia network notably consists in the emergence of abnormal synchronized oscillatory activity. Nevertheless, synchronous oscillatory activity is not necessarily pathological and also serve crucial cognitive functions in the brain. Moreover, the effect of dopaminergic medication on oscillatory network connectivity occurring in PD are still poorly understood. To clarify these issues, Sharma and colleagues simultaneously-recorded MEG-STN LFP signals in PD patients and characterized the effect of dopamine (ON and OFF dopaminergic medication) on oscillatory whole-brain networks (including the STN) in a time-resolved manner. Here, they identified three physiologically interpretable spectral connectivity patterns and found that cortico-cortical, cortico-STN, and STN-STN networks were differentially modulated by dopaminergic medication.

Strengths:

1) Both the methodological and experimental approaches used are thoughtful and rigorous.

a) The use of an innovative data-driven machine learning approach (by employing a hidden Markov model), rather than hand-crafted analyses, to identify physiologically interpretable spectral connectivity patterns (i.e., distinct networks/states) is undeniably an added value. In doing so, the results are not biased by the human expertise and subjectivity, which make them even more solid.

b) So far, the recurrent oscillatory patterns of transient network connectivity within and between the cortex and the STN reported in PD was evaluated/assessed to specific cortico-STN spectral connectivity. Conversely, whole-brain MEG studies in PD patients did not account for cortico-STN and STN-STN connectivity. Here, the authors studied, for the first time, the whole-brain connectivity including the STN (whole brain-STN approach) and therefore provide new evidence of the brain connectivity reported in PD, as well as new information regarding the effect of dopaminergic medication on the recurrent oscillatory patterns of transient network connectivity within and between the cortex and the STN reported in PD.

2) Studying the temporal properties of the recurrent oscillatory patterns of transient network connectivity both ON and OFF medication is extremely important and provide interesting and crucial information in order to delineated pathological versus physiologically-relevant spectral brain connectivity in PD.

We would like to thank the reviewer for their valuable feedback and correct interpretation of our manuscript.

Weaknesses:

1) In this study, the authors implied that the ON dopaminergic medication state correspond to a physiological state. However, as correctly mentioned in the limitations of the study, they did not have (for obvious reasons) a control/healthy group. Moreover, no one can exclude the emergence of compensatory and/or plasticity mechanisms in the brain of the PD patients related to the duration of the disease and/or the history of the chronic dopamine-replacement therapy (DRT). Duration of the disease and DRT history should be therefore considered when characterizing the recurrent oscillatory patterns of transient network connectivity within and between the cortex and the STN reported in PD, as well as when examining the effect of the dopaminergic medication on the functioning of these specific networks.

We would like to thank the reviewer for pointing this out. We regressed duration of disease (year of measurement – year of onset) on the temporal properties of the HMM states. We found no relationship between any of the temporal properties and disease duration. Similarly, we regressed levodopa equivalent dosage for each subject on the temporal properties and found no relationship. We now discuss this point in the manuscript (page 20):

“A further potential influencing factor might be the disease duration and the amount of dopamine patients are receiving. Both factors were not significantly related to the temporal properties of the states.”

2) Here, the authors recorded LFPs in the STN activity. LFP represents sub-threshold (e.g., synaptic input) activity at best (Buzsaki et al., 2012; Logothetis, 2003). Recent studies demonstrated that mono-polar, but also bi-polar, BG LFPs are largely contaminated by volume conductance of cortical electroencephalogram (EEG) activity even when re-referenced (Lalla et al., 2017; Marmor et al., 2017). Therefore, it is likely that STN LFPs do not accurately reflect local cellular activity. In this study, the authors examined and measured coherence between cortical areas and STN. However, they cannot guarantee that STN signals were not contaminated by volume conducted signals from the cortex.

We appreciate this concern and thank the reviewer for bringing it up. Marmor et al. (2017) investigated this on humans and is therefore most closely related to our research. They find that re-referenced STN recordings are not contaminated by cortical signals. Furthermore, the data in Lalla et al. (2017) is based on recordings in rats, making a direct transfer to human STN recordings problematic due to the different brain sizes. Since we re-referenced our LFP signals as recommended in the Marmor paper, we think that contamination due to cortical signals is relatively minor; see Litvak et al. (2011), Hirschmann et al. (2013), and Neumann et al. (2016) for additional references supporting this. That being said, we now discuss this potential issue in the paper on page 20.

“Lastly, we recorded LFPs from within the STN –an established recording procedure during the implantation of DBS electrodes in various neurological and psychiatric diseases. Although for Parkinson patients results on beta and tremor activity within the STN have been reproduced by different groups (Reck et al. 2010, Litvak et al. 2011, Florin et al. 2013, Hirschmann et al. 2013, Neumann et al. 2016), it is still not fully clear whether these LFP signals are contaminated by volume-conducted cortical activity. However, while volume conduction seems to be a larger problem in rodents even after re-referencing the LFP signal (Lalla et al. 2017), the same was not found in humans (Marmor et al. 2017).”

3) The methods and data processing are rigorous but also very sophisticated which make the perception of the results in terms of oscillatory activity and neural synchronization difficult.

To aid intuition on how to interpret the result in light of the methods used, one can compare the analysis pipeline to a windowing approach. In a more standard approach, windows of different time length can be defined for different epochs within the time series and for each window coherence and connectivity can be determined. The difference in our approach is that we used an unsupervised learning algorithm to select windows of varying length based on recurring patterns of whole brain network activity. Within those defined windows we then determine the oscillatory properties via coherence and power – which is the same as one would do in a classical analysis. We have added an explanation of the concept of “oscillatory activity” within our framework to the introduction (page 2 footnote):

“For the purpose of our paper, we refer to oscillatory activity or oscillations as recurrent, but transient frequency–specific patterns of network activity, even though the underlying patterns can be composed of either sustained rhythmic activity, neural bursting, or both (Quinn et al. 2019).”

Moreover, we provide a more intuitive explanation of the analysis within the first section of the results (page 4):

“Using an HMM, we identified recurrent patterns of transient network connectivity between the cortex and the STN, which we henceforth refer to as an ‘HMM state’. In comparison to classic sliding-window analysis, an HMM solution can be thought of as a data-driven estimation of time windows of variable length (within which a particular HMM state was active): once we know the time windows when a particular state is active, we compute coherence between different pairs of regions for each of these recurrent states.”

4) Previous studies have shown that abnormal oscillations within the STN of PD patients are limited to its dorsolateral/motor region, thus dividing the STN into a dorsolateral oscillatory/motor region and ventromedial non-oscillatory/non-motor region (Kuhn et al. 2005; Moran et al. 2008; Zaidel et al. 2009, 2010; Seifreid et al. 2012; Lourens et al. 2013, Deffains et al., 2014). However, the authors do not provide clear information about the location of the LFP recordings within the STN.

We selected the electrode contacts based on intraoperative microelectrode recordings (for details, see page 23). The first directional recording height after the entry into the STN was selected to obtain the three directional LFP recordings from the respective hemisphere. This practice has been proven to improve target location (Kochanski et al., 2019; Krauss et al., 2021). The common target area for DBS surgery is the dorsolateral STN. To confirm that the electrodes were actually located within this part of the STN, we now reconstructed the DBS location with Lead-DBS (Horn et al. 2019). All electrodes – except for one – were located within the dorsolateral STN (see figure 7 of the manuscript). To exclude that our results were driven by outlier, we reanalysed our data without this patient. No change in the overall connectivity pattern was observed (see figure R3 of the response letter).

Figure R2: Lead DBS reconstruction of the location of electrodes in the STN for different subjects. The red electrodes have not been placed properly in the STN. The contacts marked in red represent the directional contacts from which the data was used for analysis.

Figure R3: HMM states obtained after running the analysis without the subject with the electrode outside the STN.

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Author Response

Reviewer #1 (Public Review):

This study provided evidence to interpret and understand the aging and developmental processes in children. The main strength of the study is it measures a set of biological age measures and a set of developmental measures, thus providing multi-faceted evidence to explain the associations between aging and development in children. The main weakness of this study is that how to measure and test the aging hypothesis of "a buildup of biological capital model" and "wear and tear" is not well-explained. Why the observed associations between biological age measures and developmental measures could support the aforementioned aging theories?

Thank you. On reflection we agree that how to test the aging hypotheses of "a buildup of biological capital model" and "wear and tear" is not well-explained in the manuscript. We have addressed this issue in the point-by-point responses below:

1) Abstract - conclusion: The aging hypothesis of "a buildup of biological capital model" and "wear and tear" were mentioned in the conclusion without an explanation of these theories in the previous section. Readers who are not experts in the field may not understand the logic.

We have replaced these phrases in the abstract with the following interpretation, which we hope will be more readily understood:

“Patterns of associations suggested that accelerated immunometabolic age may be beneficial for some aspects of child development while accelerated DNA methylation age and telomere attrition may reflect early detrimental aspects of biological ageing, apparent even in children.”

2) Result - Biological age marker performance: the correlation between transcriptome age and chronological age is very strong (r =0.94). I am afraid that very little age-independent information could be captured by the transcriptome age. Is it possible to down-regulate the age dependency of the transcriptome age in the training process?

Thank you for this important comment: We agree the high accuracy of this clock may in fact reduce its relevance as a biological age marker and note that this is a concern generally in the field. We have explored the possibility of using a less accurate transcriptome age model as follows: Instead of elastic net modelling we tested using the lasso penalisation only, which will result in more parsimonious (sparse) models as less important features are dropped as the strength of the lambda parameter is increased. Plotting the correlation in the test set against number of features in models, as the lambda is sequentially increased, we can see (as shown in Author response image 1 by the blue line) that after the inclusion of around 200 features, the gain in accuracy becomes less steep.

Author response image 1.

We then tested the sensitivity of a model optimised for sparsity at the expense of some prediction accuracy, selected based on visual inspection (blue line, r in test set =0.87, number of features= 187) of the above plot, against developmental measures, compared to the most accurate model as presently included in the manuscript:

Author response image 2.

We find that, across all outcomes tested, the less accurate model, based on only the most important features, does not provide an improvement in sensitivity to developmental outcomes compared to the currently used model.

We therefore prefer to keep the more accurate model in this study. Especially as it is consistent with the methodology used in the Horvath and Immunometabolic age models and generally in the field, and otherwise it is not obvious how the biological clock should be trained (especially for children without mortality data) without altering the whole approach of the study. We have acknowledged and discussed this issue on page 15.

3) The study population comes from several cohorts, which might influence the results. How the cohort effects were controlled for in the analyses?

The possible influence of cohort is a limitation of the study which we have discussed on page 16. We did not include cohort as a predictor in any of the candidate biological clocks since this may reduce detection of some age -related features. Instead, we include a variable for cohort as a fixed effect in all analyses with risk factors and developmental outcomes and examined the performance of candidate biological clocks in predicting chronological age within each cohort. As a further check, we have added an additional sensitivity analysis (Figure 4-figure supplement 6), against developmental outcomes significant in the main analysis, stratified by cohort. We find generally consistent effects across cohorts.

4) Figure 3 only showed the number of p values. Can the author also provide the number of point estimates and 95% confidence intervals, perhaps in the supplemental table?

This information was originally provided in supplemental table 5 (now Supplementary file 7), combined with the sensitivity analyses. To make this information easier to find, we have made this a stand-alone table (table 3). We now direct readers to this information within the caption of Figure 4 (previously figure 2).

Reviewer #2 (Public Review):

The study had an especially relevant aim for aging research and utilized various data types in an especially interesting human population. Multi-omics perspective adds great value to the work. The researchers aimed to evaluate how different indicators of biological age (BA) behave in children during their developmental stage. In the analysis, relationships between indicators of BA, health risk factors, and developmental factors were assessed in cross-sectional data comprising children aged 5-12 years. The manuscript is well-written and easy to follow. The methodology is good. The authors succeeded to reach the aim in most parts.

In the study, previously known and unknown biological age indicators were used. Known indicators included telomere length and Horvath's epigenetic age. Unknown (novel) indicators, transcriptomic and immunometabolic clocks, were developed in the present study and they showed a strong correlation with calendar age in this population, also in the validation data set. Although the transcriptomic and immunometabolic clocks have the potential of being true indicators of biological age, they are still lacking scientific evidence of being such indicators in adults. That is, their associations with age-related diseases and mortality are yet to be shown. Thus, the major remark of the study relates to the phrasing: these novel transcriptomic and immunometabolic clocks should be presented as BA indicator candidates waiting for the needed evidence.

Thank you for this important observation. However, we still find that “biological age indicator” is a useful umbrella term in this manuscript and there is not an obvious alternative. We therefore have added the following sentence on page 8, and highlighted the difference between the markers at key points in the abstract, introduction, results and discussion.

“We note that since a common definition of markers of biological age is that they should be associated with age-related disease and mortality [69] these new clocks may only currently be considered “candidate” biological age markers. However, we have referred to both the established and candidate markers as biological age markers throughout to simplify presentation.”

Author Response

Reviewer #1 (Public Review)

[...] One potential issue is that the high myelination signal is associated with the compartment in V2 (pale stripes) which was not functionally defined itself but by the absence of specific functional activations. No difference was reported between those stripes that were defined functionally. Other explanations for the differential pattern of a qMRI signals, e.g. ROI distribution for presumed pale stripes is not evenly distributed (more foveal), ROIs with low activations due to some other factor show higher myelin-related signals, cannot be excluded based on the analysis presented.

Indeed, it would have been advantageous to directly functionally delineate pale stripes in V2. Since we were not able to achieve this by fMRI, we needed an indirect method to infer pale stripe contributions in the analysis. We also added a statement in the discussion section to emphasize this more (p. 9, lines 286–288).

Furthermore, different myelination between thin and thick stripes was not tested, since we did not have a concrete hypothesis on this. Despite the conflicting findings of stronger myelination in dark or pale CO stripes in the literature, no histological study stated myelination differences between dark CO thin and thick stripes. Therefore, our primary interest and hypothesis was lying in comparing the different myelination of thin/thick and pale stripes using MRI.

Thank you very much for this comment about potential other sources of differential qMRI parameter patterns. Indeed, based on the original analysis we could not exclude that the absence of functional activation around the foveal representation may have biased our analysis. We therefore added a supporting analysis, in which we excluded the region around the foveal representation from the analysis. The excluded cortical region was kept consistent between participants by excluding the same eccentricity range in all maps. We added more details in the results section of the revised manuscript (p. 8, lines 189–202). In Figure 5-Supplement 1 and Figure 5-Supplement 3, results from this supporting analysis are shown which reproduced the primary findings from the main analysis, particularly the relatively higher myelination of pale stripes.

ROI definitions solely based on fMRI activation amplitude have additional limitations. However, we find it unlikely that a small fMRI effect size and low contrast-to-noise ratio (i.e. stochastic cause of low statistical parameter values/”activation”) has impacted the results, since Figure 3 shows that we could achieve a high degree of reproducibility for each participant.

We would note that the fact that we found consistent differences across MPM and MP2RAGE sessions makes some potential artifacts driving the differences unlikely. We also find it unlikely that systematic cerebral blood volume differences between stripes would have driven the results. A higher local blood volume would lead to increased BOLD responses but also to a higher R1 value due to the deoxy-hemoglobin induced relaxation, which is opposite to the observation of higher activity in the thick/thin stripes but lower R1 values.

Further studies using other functional metrics (e.g. VASO, ASL etc.) may help us to even more clearly demonstrate specificity but were out of the scope of this already rather extensive study. Although we have added extensive further analyses in the revised manuscript such as controlling for foveal effects or registration performance, we did not see a possibility to fully exclude a systematic bias that might potentially be caused by unknown factors.

Another theoretical and practical issue is the question of "ground truth" for the non-invasive qMRI measures, as the authors - as their starting point - roundly dismiss direct histological tissue studies as conflicting, rather than take a critical look at the merit of the conflicting study results and provide a best hypothesis. If so, they need to explain better how they calibrate their non-invasive MR measurements of myelin.

We agree and have now further elaborated on the limits of specificity of the R1 and R2* signal as cortical myelin marker (p. 2, lines 68–88; p. 6, line 163; p. 8, line 216; p. 9, lines. 257–260). However, we still think that it is important for the reader to appreciate the conflicting results in histological studies using staining methods for myelin, which adds to the study’s background.

We did not intend to give the impression that MRI provides the missing ground-truth to adjudicate histological controversies, but that it provides an alternative and additional view on the open questions. We changed the introduction to better reflect the aspect that the study offers a unique view by providing myelination proxies and functional measures in the same individual, which allows for direct comparison and investigation of structure-function relationships (see p. 2, lines 68–70; p. 3, lines 93–95), which is not accessible to any other approach. Nevertheless, we would like to note that R1 has been well established as a myelin marker under particular conditions (Kirilina et al., 2020; Mancini et al., 2020; Lazari and Lipp, 2021). It has also been widely used for cortical myelin mapping across a variety of populations, systems and field strengths. We added this statement to the introduction (see p. 2, lines 82-85). We note that we excluded volunteers with pathologies or neurological disorders from the study and their mean age was about 28 years. Thus, we had conditions comparable to previous (validation) studies.

Because of the contradictory findings of histological studies, we could not further finesse the hypothesis beyond our previous a priori hypothesis that we expected differences in the myelin sensitive MRI metrics between the thin/thick versus pale stripes. To improve the contextual understanding, we added a paragraph in the discussion section covering in more depth how the MRI results relate to known histological findings (see pp. 8–9, lines 216–240).

While this paper makes an important contribution to the question of the association of specific myelination patterns defining the columnar architecture in V2, it is not entirely clear whether the authors can fully resolve it with the data presented.

Indeed, we agree that non invasive aggregate measures, such as the R1 metrics, offer limited specificity which precludes a fully conclusive inference about cortical myelination. We have further emphasized this on several occasions in the text (see p. 2, lines 68–88; p. 6, line 163; p. 8, line 216; p. 9, lines. 257–260). Since the correspondence of cortical myelin levels and R1 (and other metrics) is an active area of research, we expect that the understanding, sensitivity and specificity of R1 to cortical myelination will further improve. We note that the use of qMRI is a substantial advance over weighted MRI typically used, which suffers from lack of specificity due to instrumental idiosyncrasies and varying measurement conditions.

Reviewer #2 (Public Review)

[...] Unfortunately, this particular study seems to fall into an unhappy middle ground in terms of the conclusions that can be drawn: the relaxometry measures lack the specificity to be considered "ground truth", while the authors claim that the literature lacks consensus regarding the structures that are being studied. The authors propose that their results resolve whether or not stripes differ in their patterns of myelination, but R1 lacks the specificity to do this. While myelin is a primary driver of relaxation times in cortex, relaxometry cannot be considered to be specific to myelin. It is possible that the small observed changes in R1 are driven by myelin, but they could also reflect other tissue constituents, particularly given the small observed effect sizes. If the literature was clear on the pattern of myelination across stripes, this study could confirm that R1 measurements are sensitive to and consistent with this pattern. But the authors present the work as resolving the question of how myelination differs between stripes, which over-reaches what is possible with this method. As it stands, the measured differences in R1 between functionally-defined cortical regions are interesting, but require further validation (e.g., using invasive myelin staining).

We agree that we have inadvertently overstated the specificity of R1 at several occasions in the text. We therefore toned down the statements concerning the correspondence between R1 and myelin throughout the manuscript (e.g. see p. 2, lines 68–88; p. 6, line 163; p. 8, line 216; p. 9, lines. 257–260).

We also removed the phrase that gave the impression that MRI can conclusively resolve the conflicting results found in histological studies. In the Introduction, we changed the corresponding paragraph by emphasizing the alternative view, which can be obtained from MRI by the possibility to investigate structure-function relationships in the living human brain, which would not be possible by invasive myelin staining (see p. 2, lines 68–70; p. 3, lines 93–95).

We acknowledge that – perhaps aside from electron microscopy – all common markers have shortcomings, which limit their specificity. For example, classic histology is not quantitative and resulted in conflicting results. It even includes the very fundamental issue, that the composition of myelin varies across the brain and within brain areas significantly (e.g., its lipid composition (González de San Román et al., 2018)). Thus, we regard the different invasive/non-invasive measures as complementary. R1 adds to this arsenal of measures and can be acquired non invasively. It has been shown to be a reliable myelin marker under certain circumstances. It follows the known myeloarchitecture patterns of the human brain, which was also checked for the data of the present study (see Figure 4 and Appendix 2). It is responsive to traumatic changes (Freund et al., 2019), development (Whitaker et al., 2016; Carey et al., 2018; Natu et al., 2019) and plasticity (Lazari et al., 2022). Since we studied healthy volunteers with no known pathologies that were sampled randomly from the population, we believe that the previous results generally apply and suggest sufficient specificity of the R1 marker. Of course, we cannot fully exclude bias due to unknown factors that have not been investigated/discovered by validation studies yet. However, in this case we expect that the systematic differences between stripe types would remain an important result most likely pointing to another interesting biological difference between stripes.

While more research is needed to clarify the precise role of R1 for cortical myelin, we think that the meaningful determination of quantitative MR parameter within one cortical area is still interesting for the neuroscientific community.

Moreover, the results make clear that R1 differences are not sufficiently strong to provide an independent measure of this structure (e.g., for segmentation of stripe). As such, one would still require fMRI to localise stripes, making it unclear what role R1 measures would play in future studies.

Indeed, the observed small effect sizes in the present study still requires a functional localization with fMRI. We expected small effect sizes using R1 and R2* due to the known small inter-areal or intra-cortical differences of MRI myelin markers. Therefore, this study aimed at a proof-of-concept investigating whether intra-areal R1 differences at the spatial scale of columnar structures can be detected using non-invasive MRI. Our study shows that these differences can be seen but currently not at the single voxel level. We anticipate that with further improvements in sequence development and scanner hardware, high-resolution R1 estimates with sufficient SNR can be acquired making fMRI redundant (for this kind of investigations). Please see the reply to the next comment concerning the impact of using R1 in future studies.

The Introduction concludes with the statement that "Whereas recent studies have explored cortical myelination ... using non-quantitative, weighted MR images... we showed for the first time myelination differences using MRI on a quantitative basis". As written, this sentence implies that others have demonstrated that simpler non-quantitative imaging can achieve the same aims as qMRI. Simply showing that a given method is able to achieve an aim would not be sufficient: the authors should demonstrate that this constitutes an important advance.

Thank you for this comment. It goes to the heart of the concerns raised about specificity and sensitivity of MRI based myelin metrics. We elaborate here on the main advantage of using qMRI in our current study and why it is more specific than weighted MR imaging. However, we emphasize that a thorough comparison between qMRI and weighted MRI is highly complex and refer to our recent review paper on qMRI for further details (Weiskopf et al., 2021), which are beyond the scope of our paper. The signal in weighted MRI, even when optimally optimized to the tissue of interest, additionally depends on both inhomogeneities in the RF transmit and receive (bias) fields. Other methods like using a ratio image (T1w/T2w) can cancel out the receive field bias entirely (in the case of no subject movements between scans) but not the transmit field bias. This hampers the direct analysis and interpretation of signal differences between distant regions of the brain. For high resolution imaging applications, the usage of high magnetic fields such as 7 T is beneficial or even mandatory due to signal-to-noise (SNR) penalties. With increasing field strength, these inhomogeneities also apply to small regions as V2. For these cases, qMRI is advantageous since it provides metrics which are free from these technical biases, significantly improving the specificity. As high-field MRI has the potential to non invasively study the structure and function of the human brain at the spatial scale of cortical layers and cortical columns, we believe that the results of our current study, which successfully demonstrate the applicability of qMRI to robustly detect small differences at the level of columnar systems, is relevant for future studies in the field of neuroscience.

We emphasized these considerations in the revised manuscript (see. p. 9, lines 273–285).

The study includes a very small number of participants (n=4). The advantage of non-invasive in-vivo measurements, despite the fact that they are indirect measures, should be that one can study a reasonable number of subjects. So this low n seems to undermine that point. I rarely suggest additional data collection, but I do feel that a few more subjects would shore up the study's impact.

The present study was conducted in line with a deep phenotyping study approach. That is, we focused on acquiring highly reliable datasets on individuals. We did not intend to capture the population variance, which is often the goal of other group studies, since low level and basic features such as stripes in V2 are expected to be present in all healthy individuals. Thus we traded off and prioritized test-retest measurements for fMRI sessions and using an alternative MP2RAGE acquisition over a larger number of individuals. This resulted in 6–7 scanning sessions on different days for each individual, summing up to 26 long scanning session in total. We also note that the used sample size is not smaller than in other studies with a similar research question. For example, another fMRI study investigating V2 stripes in humans used the same sample size of n=4 (Dumoulin et al., 2017).

The paper overstates what can be concluded in a number of places. For example, the paper suggests that R1 and R2 are highly-specific to myelin in a number of places. For example, on p7 the text reads" "We tested whether different stripe types are differentially myelinated by comparing R1 and R2..." Relaxation times lack the specificity to definitively attribute these changes purely to myelin. Similarly, on p11: "Our study showed that pale stripes which exhibit lower oxidative metabolic activity according to staining with CO are stronger myelinated than surrounding gray matter in V2." This implies that the study directly links CO staining to myelination. In addition to using non-specific estimates of myelination, the study does not actually measure CO.

We agree that we did not clearly point out the limitations of R1 myelin mapping. Therefore, we toned down the statements about the connection between cortical myelin and R1. The mentioned statements in the reviewer’s comment were changed accordingly (see p. 6, line 163; p. 11, lines 353–354). We also included a small paragraph to clarify the used terminology (color-selective thin stripes, disparity-selective thick stripes) in the manuscript (see p. 4, lines 110–114) to avoid the inadvertent conflation of CO staining and actually measured brain activity.

I'm confused by the analysis in Figure 5. I can appreciate why the authors are keen to present a "tripartite" analysis (thick, thin, and pale stripes). But I find the gray curves confusing. As I understand it, the gray curves as generated include both the stripe of interest (red or blue plots) and the pale stripes. Why not just generate a three-way classification? Generating these plots in effect has already required hard classification of thin and thick stripes, so it is odd to create the gray plots, which mix two types of stripes. Alternatively, could you explicitly model the partial volume for a given cortical location (e.g., under the assumption that partial volume of thick and thin strips is indicated by the z-score) for the corresponding functional contrast? One could then estimate the relaxation times as a simple weighted sum of stripe-wise R1 or R2.

Figure on weighted average of stripe-wise R1 and R2. (a) shows the weighted sum of R1 (de-meaned and de-curved) over all V2 voxels. z-scores from color-selective thin stripe experiments and disparity-selective thick stripes were used as weights in the left and middle group of bars, respectively. An intermediate threshold of zmax=1.96 was used, i.e., final weights were defined as weights=(z-1.96). Weights with z<0 were set to 0. For pale stripes (right group of bars), we used the maximum z-score value from thin and thick stripe measurements. We then set all weights with z≥1.96 to 0 and used the inverse as final weights. i.e., weights = -1 * (max(z)-1.96). (b) shows the same analysis for R2. Error bars indicate 1 standard error of the mean.

(1) Yes, indeed. We agree that modeling the partial volume of each compartment (thin, thick and pale stripes) in each V2 voxel would be the most elegant approach. However, we note that z-scores between thin and thick stripe experiments may not reflect the voxel-wise partial volume effect, since they are a purely statistical measure and not a partial volume model. Having said this, we think that this general approach can give some additional insights and we provide results for a similar analysis here. We calculated the weighted sum of R1 and R2 values over all V2 voxels for each stripe compartment (thin, thick and pale stripes) independently (see above figure). For R1, we see the same pattern of R1 between stripe types as in the manuscript (Figure 5). Additionally, we show the differences here for each subject, which further demonstrates the reproducibility across subjects in our study. For R2, no clear pattern across subjects emerged, confirming the results in our manuscript. Since, this analysis did not add relavant new information to the manuscript, we refrained from adding this figure to the manuscript, in order not to overload it.

(2) In our current study, we were not primarily interested in investigating differences between thin/thick stripes and pale stripes. While histological analysis found differences (though not consistent) between CO dark stripes (more myelinated, (Tootell et al., 1983)) and CO pale stripes (more myelinated, Krubitzer and Kaas, 1989)), no study stated myelin differences between CO dark stripes. This does not fully exclude the possibility of myelination differences but suggests that if myelination differences between CO dark stripes existed, they would presumably be smaller than differences between CO dark and CO pale stripes. Thus, it would be even more difficult to demonstrate than the hypothesis of this manuscript.

Therefore, we decided to directly test two compartments against each other instead of modeling all three compartments within a single model. In our analysis, we thereby loosely followed the analysis methods described in Li et al. (2019), which compared myelin differences between thin/thick and pale stripes in macaques. We note that this demonstrates further consistency, since it is not trivial that both thick and thin stripes show lower R1 values than the pale stripes. For example, there may be no or opposite differences.

(3) Just for clarification, the plots in Figure 5 show the comparison of R1 (or R2*) between two compartments in V2. The red (blue) curve includes the thin (thick) stripe of interest. The gray curve includes everything in V2 minus contributions from thick (thin) stripes of interest. If we take the thin stripe comparison as example (Figure 5a), then red contains the thin stripes of interest while gray contains everything minus the thick stripes. Therefore, assuming a tripartite stripe arrangement, the gray curve contains both thin and pale stripe contributions.

References

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Author Response

Reviewer #1 (Public Review):

Determination of the biomechanical forces and downstream pathways that direct heart valve morphogenesis is an important area of research. In the current study, potential functions of localized Yap signaling in cardiac valve morphogenesis were examined. Extensive immunostainings were performed for Yap expression, but Yap activation status as indicated by nuclear versus cytoplasmic localization, Yap dephosphorylation, or expression of downstream target genes was not examined.

We thank the reviewer for appreciating the significance of this work, and we also thank the reviewer for the constructive suggestions. Following these suggestions, we have improved analysis of YAP activation status and used nuclear versus cytoplasmic localization to quantify YAP activation. To address the reviewer’s concerns, we have conducted extra qPCR analysis of YAP downstream target genes and YAP upstream genes in Hippo pathway. Please find the detailed revisions in our responses to the Recommendations for authors.

The goal of the work was to determine Yap activation status relative to different mechanical environments, but no biomechanical data on developing heart valves were provided in the study.

We appreciate the reviewer for raising this concern. We have previously published the biomechanical data of developing chick embryonic heart valves in the following study:

Buskohl PR, Gould RA, Butcher JT. Quantification of embryonic atrioventricular valve biomechanics during morphogenesis. Journal of Biomechanics. 2012;45(5):895-902.

In that study, we used micropipette aspiration to measure the nonlinear biomechanics (strain energy) of chick embryonic heart valves at different developmental stages. Here in this study, we used the same method to measure the strain energy of YAP activated/inhibited cushion explants and compared it to the data from our previous study. Our findings were summarized in the Results: “YAP inhibition elevated valve stiffness”, and the detailed measurements, including images and data, are presented in Figure S4.

There are several major weaknesses that diminish enthusiasm for the study.

1) The Hippo/Yap pathway activation leads to dephosphorylation of Yap, nuclear localization, and induced expression of downstream target genes. However, there are no data included in the study on Yap nuclear/cytoplasmic ratios, phosphorylation status, or activation of other Hippo pathway mediators. Analysis of Yap expression alone is insufficient to determine activation status since it is widely expressed in multiple cells throughout the valves. The specificity for activated Yap signaling is not apparent from the immunostainings.

We thank the reviewer for pointing out this weakness. We have now implemented nuclear versus cytoplasmic localization as recommended to quantify YAP activation. We have also conducted additional experiments to analyze via qPCR YAP downstream target genes and YAP upstream genes in Hippo pathway. Please see the detailed revisions in our responses to the Recommendations for authors.

2) The specific regionalized biomechanical forces acting on different regions of the valves were not measured directly or clearly compared with Yap activation status. In some cases, it seems that Yap is not present in the nuclei of endothelial cells surrounding the valve leaflets that are subject to different flow forces (Fig 1B) and the main expression is in valve interstitial subpopulations. Thus the data presented do not support differential Yap activation in endothelial cells subject to different fluid forces. There is extensive discussion of different forces acting on the valve leaflets, but the relationship to Yap signaling is not entirely clear.

We thank the reviewer for these important questions. The region-specific biomechanics have been well mapped and studied, thanks to the help from Computational Fluid Dynamics supported by ultrasound velocity and pressure measurements. For example:

Yalcin, H.C., Shekhar, A., McQuinn, T.C. and Butcher, J.T. (2011), Hemodynamic patterning of the avian atrioventricular valve. Dev. Dyn., 240: 23-35.

Bharadwaj KN, Spitz C, Shekhar A, Yalcin HC, Butcher JT. Computational fluid dynamics of developing avian outflow tract heart valves. Ann Biomed Eng. 2012 Oct;40(10):2212-27. doi: 10.1007/s10439-012-0574-8.

Ayoub S, Ferrari G, Gorman RC, Gorman JH, Schoen FJ, Sacks MS. Heart Valve Biomechanics and Underlying Mechanobiology. Compr Physiol. 2016 Sep 15;6(4):1743-1780.

Salman HE, Alser M, Shekhar A, Gould RA, Benslimane FM, Butcher JT, et al. Effect of left atrial ligation-driven altered inflow hemodynamics on embryonic heart development: clues for prenatal progression of hypoplastic left heart syndrome. Biomechanics and Modeling in Mechanobiology. 2021;20(2):733-50.

Ho S, Chan WX, Yap CH. Fluid mechanics of the left atrial ligation chick embryonic model of hypoplastic left heart syndrome. Biomechanics and Modeling in Mechanobiology. 2021;20(4):1337-51.

Those studies have shown that USS develops on the inflow surface of valves while OSS develops on the outflow surface of valves, CS develops in the tip region of valves while TS develops in the regions of elongation and compaction. Here in this study, we mimic those forces in our in-vitro and ex-vivo models. This allows us to study the direct effect of specific force on the YAP activity in different cell lineages. The results showed that OSS promoted YAP activation in VECs while USS inhibited it, CS promoted YAP activation in VICs while TS inhibited it. This result well explained the spatiotemporal distribution of YAP activation in Figure 1. For example, nuclear YAP was mostly found in VECs on the fibrosa side, where OSS develops, and YAP was not expressed in the nuclei in VECs of the atrialis/ventricularis side, where USS develops. It is also worth noting that formation of OSS on the outflow side is slower, and thus the side specific YAP activation in VECs was not in effect at the early stage, from E11.5 to E14.5.

3) The requirement for Yap signaling in heart valve remodeling as described in the title was not demonstrated through manipulation of Yap activity.

With respect, it is unclear what the reviewer is asking for given no experiments are suggested nor an elaboration of alternative interpretations of our results that emphasize against YAP requirement. It has been previously shown that YAP signaling is required for early EMT stages of valvulogenesis using conditional YAP deletion in mice:

Zhang H, von Gise A, Liu Q, Hu T, Tian X, He L, et al. Yap1 Is Required for Endothelial to Mesenchymal Transition of the Atrioventricular Cushion. Journal of Biological Chemistry. 2014;289(27):18681-92.

Signaling roles for early regulators at these later fetal stages are different, sometimes opposite early EndMT stages, thus contraindicating reliance on these early data to explain later events:

Bassen D, Wang M, Pham D, Sun S, Rao R, Singh R, et al. Hydrostatic mechanical stress regulates growth and maturation of the atrioventricular valve. Development. 2021;148(13).

However, embryos with YAP deletion failed to form endocardial cushions and could not survive long enough for the study of its roles in later cushion growth and remodeling into valve leaflets. In this work,

We first showed the localization of YAP activity and its direct link with local shear or pressure domains. Then we explicitly applied controlled gain and loss of function of YAP via specific molecules. We also applied critical mechanical gain or loss of function studies to demonstrate YAP mechanoactivation necessity and sufficiency to achieve growth and remodeling.

Reviewer #2 (Public Review)

This study by Wang et al. examines changes in YAP expression in embryonic avian cultured explants in response to high and low shear stress, as well as tensile and compressive stress. The authors show that YAP expression is increased in response to low, oscillatory shear stress, as well as high compressive stress conditions. Inhibition of YAP signaling prevents compressive stress-induced increases in circularity, decreased pHH3 expression, and increases VE-cadherin expression. On the other hand, YAP gain of function prevents tensile stress-induced decreases in pHH3 expression and VE-cadherin expansion. It also decreases the strain energy density of embryonic avian cushion explants. Finally, using an avian model of left atrial ligation, the authors demonstrate that unloaded regions within the primitive valve structures are associated with increased YAP expression, compared to regions of restricted flow where YAP expression is low. Overall, this study sheds light on the biomechanical regulation of YAP expression in developing valves.

We thank the reviewer for the accurate summary and their enthusiasm for this work.

Strengths of the manuscript include:

• Novel insights into the dynamic expression pattern of YAP in valve cell populations during post-EMT stages of embryonic valvulogenesis.

• Identify the positive regulation of YAP expression in response to low, oscillatory shear stress, as well as high compressive stress conditions.

• Identify a link between YAP signaling in regulating stress-induced cell proliferation and valve morphogenesis.

• The inclusion of the atrial left atrial ligation model is innovative, and the data showing distinguishable YAP expression levels between restricted, and non-restricted flow regions is insightful.

We thank the reviewer for appreciating the strengths of this work.

This is a descriptive study that focuses on changes in YAP expression following exposure to diverse stress conditions in embryonic avian cushion explants. Overall, the study currently lacks mechanistic insights, and conclusions based on data are highly over-interpreted, particularly given that the majority of experimental protocols rely on one method of readout.

We thank the reviewer for constructive suggestions.

Reviewer #3 (Public Review)

In this manuscript, Wang et al. assess the role of wall shear stress and hydrostatic pressure during valve morphogenesis at stages where the valve elongates and takes shape. The authors elegantly demonstrate that shear and pressure have different effects on cell proliferation by modulating YAP signaling. The authors use a combination of in vitro and in vivo approaches to show that YAP signaling is activated by hydrostatic pressure changes and inhibited by wall shear stress.

We thank the reviewer for their enthusiasm for the impact of our work.

There are a few elements that would require clarification:

1) The impact of YAP on valve stiffness was unclear to me. How is YAP signaling affecting stiffness? is it through cell proliferation changes? I was unclear about the model put forward:

• Is it cell proliferation (cell proliferation fluidity tissue while non-proliferating tissue is stiffer?)

• Is it through differential gene expression?

This needs clarification.

We thank the reviewer for raising this important question. Cell proliferation can affect valve stiffness but is a minor factor compared with ECM deposition and cell contractility Our micropipette aspiration data showed that the higher cell proliferation rate induced by YAP activation did lead to stiffer valves when compared to the controls. This may be because at the early stages, cells are more elastic than the viscous ECM. However, the stiffness of YAP activated valves were only about half of that of YAP inhibited valves, showing that the transcriptional level factor plays a more important role. This also suggests that YAP inhibited valves exhibited a more mature phenotype. An analogous role of YAP has also been found in cardiomyocytes. Many theories propose that in cardiomyocytes when YAP is activated the proliferation programs are turned on, while when YAP is inhibited the proliferation programs are turned off and maturation programs are released. Similarly, here we hypothesize that YAP works like a mechanobiological switch, converting mechanical signaling into the decision between growth and maturation. We have revised the Discussion to include this hypothesis.

2) The model proposes an early asymmetric growth of the cushion leading to different shear forces (oscillatory vs unidirectional shear stress). What triggers the initial asymmetry of the cushion shape? is YAP involved?

Although the initial geometry of the cushion model is symmetric, the force acting on it is asymmetric. The detailed numerical simulation of how the initial forces trigger the asymmetric morphogenesis can be found in our previous publication:

Buskohl PR, Jenkins JT, Butcher JT. Computational simulation of hemodynamic-driven growth and remodeling of embryonic atrioventricular valves. Biomechanics and Modeling in Mechanobiology. 2012;11(8):1205-17.

The color maps represent the dilatation rates when a) only pressure is applied, b) only shear stress is applied, and c) both pressure and shear stress are applied. It is such load that initiates an asymmetric morphological change, as shown in d). In addition, we believe YAP is involved during the initiation because it is directly nuclear activated by CS and OSS or cytoplasmically activated by TS and LSS.

3) The differential expression of YAP and its correlation to cell proliferation is a little hard to see in the data presented. Drawings highlighting the main areas would help the reader to visualise the results better.

We thank the reviewer for this helpful suggestion, we have improved the visualization of Figure 3C and Figure 4C with insets of higher magnification.

4) The origin of osmotic/hydrostatic pressure in vivo. While shear is clearly dependent upon blood flow, it is less clear that hydrostatic pressure is solely dependent upon blood flow. For example, it has been proposed that ECM accumulation such as hyaluronic acid could modify osmotic pressure (see for example Vignes et al.PMID: 35245444). Could the authors clarify the following questions:

• How blood flow affects osmotic pressure in vivo?

• Is ECM a factor that could affect osmotic pressure in this system?

We thank the reviewer for sharing this interesting study. The osmotic pressure plays a critical role in mechanotransduction and the development of many tissues including cardiovascular tissues and cartilage. As proposed in the reference, osmotic pressure is an interstitial force generated by cardiac contractility. Here in our study, the hydrostatic pressure is different, which is an external force applied by flowing blood. According to Bernoulli's law, when an incompressible fluid flows around a solid, the static pressure it applies on the solid is equal to its total pressure minus its dynamic pressure.

Despite the difference, the osmotic pressure can mimic the effect of hydrostatic pressure in-vitro. The in-vitro osmotic pressure model has been widely used in cartilage research, for example:

P. J. Basser, R. Schneiderman, R. A. Bank, E. Wachtel, and A. Maroudas, “Mechanical properties of the collagen network in human articular cartilage as measured by osmotic stress technique.,” Arch. Biochem. Biophys., vol. 351, no. 2, pp. 207–19, 1998.

D. a. Narmoneva, J. Y. Wang, and L. a. Setton, “Nonuniform swelling-induced residual strains in articular cartilage,” J. Biomech., vol. 32, no. 4, pp. 401–408, 1999.

C. L. Jablonski, S. Ferguson, A. Pozzi, and A. L. Clark, “Integrin α1β1 participates in chondrocyte transduction of osmotic stress,” Biochem. Biophys. Res. Commun., vol. 445, no. 1, pp. 184–190, 2014.

Z. I. Johnson, I. M. Shapiro, and M. V. Risbud, “Extracellular osmolarity regulates matrix homeostasis in the intervertebral disc and articular cartilage: Evolving role of TonEBP,” Matrix Biol., vol. 40, pp. 10–16, 2014.

When maturing cushions shift from GAGs dominated ECM to collagen dominated ECM, the water and ion retention capacity of the tissue would be greatly changed, and thus reducing the osmotic pressure. This could in turn accelerate the maturation of cushions. By contrast, the ECM of growing cushions remain GAGs dominated, which would delay maturation and prolong the growth.

The revised second section of Results is as follows:

Shear and hydrostatic stress regulate YAP activity

In addition to the co-effector of the Hippo pathway, YAP is also a key mediator in mechanotransduction. Indeed, the spatiotemporal activation of YAP correlated with the changes in the mechanical environment. During valve remodeling, unidirectional shear stress (USS) develops on the inflow surface of valves, where YAP is rarely expressed in the nuclei of VECs (Figure 2A). On the other side, OSS develops on the outflow surface, where VECs with nuclear YAP localized. The YAP activation in VICs also correlated with hydrostatic pressure. The pressure generated compressive stress (CS) in the tips of valves, where VICs with nuclear YAP localized (Figure 2B). Whereas tensile stress (TS) was created in the elongated regions, where YAP was absent in VIC nuclei.

To study the effect of shear stress on the YAP activity in VECs, we applied USS and OSS directly onto a monolayer of freshly isolated VECs. The VEC was obtained from AV cushions of chick embryonic hearts at HH25. The cushions were placed on collagen gels with endocardium adherent to the collagen and incubated to enable the VECs to migrate onto the gel. We then removed the cushions and immediately applied the shear flow to the monolayer for 24 hours. The low stress OSS (2 dyn/cm2) promoted YAP nuclear translocation in VEC (Figure 2C, E), while high stress USS (20 dyn/cm2) restrained YAP in cytoplasm.

To study the effect of hydrostatic stress on the YAP activation in VICs, we used media with different osmolarities to mimic the CS and TS. CS was induced by hypertonic condition while TS was created by hypotonic condition, and the Unloaded (U) condition refers to the osmotically balanced media. Notably, in-vivo hydrostatic pressure is generated by flowing blood, while in-vivo osmotic pressure is generated by cardiac contractility and plays a critical role in the mechanotransduction during valve development (30). Despite the different in-vivo origination, the osmotic pressure provides a reliable model to mimic the hydrostatic pressure in-vitro (31). We cultured HH34 AV cushion explants under different loading conditions for 24 hours and found that the trapezoidal cushions adopted a spherical shape (Figure 2D). TS loaded cushions significantly compacted, and the YAP activation in VICs of TS loaded cushions was significantly lower than that in CS loaded VICs (Figure 2F).

Author Response

Reviewer #1 (Public Review):

Huang et al. sought to study the cellular origin of Tuft cells and the molecular mechanisms that govern their specification in severe lung injury. First the authors show ectopic emergence of Tuft cells in airways and distal parenchyma following different injuries. The authors also used lineage tracing models and uncovered that p63-expressing cells and to some extent Scgb1a1-lineaged labeled cells contribute to tuft cells after injury. Further, the authors modulated multiple pathways and claim that Notch inhibition blocks tuft cells whereas Wnt inhibition enhances Tuft cell development in basal cell cultures. Finally, the authors used Trpm5 and Pou2f3 knock-out models to claim that tuft cells are indispensable for alveolar regeneration.

In summary, the findings described in this manuscript are somewhat preliminary. The claim that the cellular origin of Tuft cells in influenza infection was not determined is incorrect. Current data from pathway modulation is preliminary and this requires genetic modulation to support their claims.

We thank the reviewer for the comments and we have performed extensive experiments to address the reviewer’s comments. In the revised manuscript we provide additional data including genetic modulation findings to support our model.

Major comments:

1) The abstract sounds incomplete and does not cover all key aspects of this manuscript. Currently, it is mainly focusing on the cellular origin of Tuft cells and the role of Wnt and notch signaling. However, it completely omits the findings from Trpm5 and Pou2f3 knock-out mice. In fact, the title of the manuscript highlights the indispensable nature of tuft cells in alveolar regeneration.

We have modified the abstract and title accordingly.

2) In lines 93-94, the authors state that "It is also unknown what cells generate these tuft cells.....". This statement is incorrect. Rane et al., 2019 used the same p63-creER mouse line and demonstrated that all tuft cells that ectopically emerge following H1N1 infection originate from p63+ lineage labeled basal cells. Therefore, this claim is not new.

We thank the reviewer’s comment. Although Rane et al. reported the p63-expressing lineage-negative epithelial stem/progenitor cells (LNEPs) could contribute to the ectopic tuft cells after PR8 virus infection, it is still not clear whether the p63+ cells immediately give rise to tuft cells or though EBCs. Thus, we performed TMX injection after PR8 infection, different from Rane et al (Rane et al., 2019). who performed Tmx injection before viral infection to indicate the ectopic tuft cells are derived from EBCs, as shown in revised Figure 2.

3) Lines 152-153 state that "21.0% +/- 2.0 % tuft cells within EBCs are labeled with tdT when examined at 30 dpi...". It is not clear what the authors meant here ("within EBC's")? And also, the same sentence states that "......suggesting that club cell-derived EBCs generate a portion of tuft cells....". In this experiment, the authors used club cell lineage tracing mouse lines. So, how do the authors know that the club cell lineage-derived tuft cells came through intermediate EBC population? Current data do not show evidence for this claim. Is it possible that club cells can directly generate tuft cells?

We apologize for the confusion and revised the text accordingly. Here, “within EBCs” means within the “pods” area where p63+ basal cells are ectopically present. The sentence is revised as “21.0% +/- 2.0 % tuft cells that are ectopically present in the parenchyma are labeled by tdT. Notably, these lineage labeled tuft cells were co-localized with EBCs.” We don’t know whether the club cell lineage-derived tuft cells transit through intermediate EBCs and that is why we use “suggest”. It is also possible that club cells can directly generate tuft cells. To avoid the confusion, we delete the sentence.

4) Based on the data from Fig-3A, the authors claim that treatment with C59 significantly enhances tuft cell development in ALI cultures. Porcupine is known to facilitate Wnt secretion. So, which cells are producing Wnt in these cultures? It is important to determine which cells are producing Wnt and also which Wnt? Further, based on DBZ treatments, it appears that active Notch signaling is necessary to induce Tuft cell fate in basal cells. Where are Notch ligands expressed in these tissues? Is Notch active only in a small subset of basal cells (and hence generate rate tuft cells)? This is one of the key findings in this manuscript. Therefore, it is important to determine the expression pattern of Wnt and Notch pathway components.

We thank the reviewer’s interesting questions and agree the importance of identifying the specific ligands and receptors for relevant Wnt and Notch signaling during tuft cell derivation. That being said, we think the topic is beyond the scope of this study which is focused on the role of tuft cells in alveolar regeneration. The point is well taken and we will investigate the topic in our future study.

5) How do the authors explain different phenotypes observed in Trpm5 knockout and Pou2f3 mutants? Is it possible that Trpm5 knockout mice have a subset of tuft cells and that they might be something to do with the phenotypic discrepancy between two mutant models?

Again we thank the reviewer for the interesting question. As discussed in the discussion section, Trpm5 is also reported to be expressed in B lymphocytes (Sakaguchi et al., 2020). It is possible that loss of Trpm5 modulates the inflammatory responses following viral infection, which may contribute to improved alveolar regeneration. However, it is also possible that Trpm5-/- mice keep a subset of tuft cells that facilitate lung regeneration as suggested by the reviewer.

6) One of the key findings in this manuscript is that Wnt and Notch signaling play a role in Tuft cell specification. All current experiments are based on pharmacological modulation. These need to be substantiated using genetic gain loss of function models.

We have performed the genetic studies.

Reviewer #2 (Public Review):

In this manuscript, the authors describe the ectopic differentiation of tuft cells that were derived from lineage-tagged p63+ cells post influenza virus infection. These tuft cells do not appear to proliferate or give rise to other lineages. They then claim that Wnt inhibitors increase the number of tuft cells while inhibiting Notch signaling decreases the number of tuft cells within Krt5+ pods after infection in vitro and in vivo. The authors further show that genetic deletion of Trpm5 in p63+ cells post-infection results in an increase in AT2 and AT1 cells in p63 lineage-tagged cells compared to control. Lastly, they demonstrate that depletion of tuft cells caused by genetic deletion of Pou2f3 in p63+ cells has no effect on the expansion or resolution of Krt5+ pods after infection, implying that tuft cells play no functional role in this process.

Overall, in vivo and in vitro phenotypes of tuft cells and alveolar cells are clear, but the lack of detailed cellular characterization and molecular mechanisms underlying the cellular events limits the value of this study.

We thank the reviewer for the comments and acknowledging that our findings are clear. In the revised manuscript we provide more detailed characterization and genetic evidence to elucidate the role of tuft cells in lung regeneration.

1) Origin of tuft cells: Although the authors showed the emergence of ectopic tuft cells derived from labelled p63+ cells after infection, it cannot be ruled out that pre-existing p63+Krt5- intrapulmonary progenitors, as previously reported, can also contribute to tuft cell expansion (Rane et al. 2019; by labelling p63+ cells prior to infection, they showed that the majority of ectopic tuft cells are derived from p63+ cells after viral infection). It would be more informative if the authors show the differentiation of tuft cells derived from p63+Krt5+ cells by tracing Krt5+ cells after infection, which will tell us whether ectopic tuft cells are differentiated from ectopic basal cells within Krt5+ pods induced by virus infection.

We thank the reviewer for the helpful suggestion. We have performed the experiment accordingly.

2) Mechanisms of tuft cell differentiation: The authors tried to determine which signaling pathways regulate the differentiation of tuft cells from p63+ cells following infection. Although Wnt/Notch inhibitors affected the number of tuft cells derived from p63+ labelled cells, it remains unclear whether these signals directly modulate differentiation fate. The authors claimed that Wnt inhibition promotes tuft cell differentiation from ectopic basal cells. However, in Fig 3B, Wnt inhibition appears to trigger the expansion of p63+Krt5+ pod cells, resulting in increased tuft cell differentiation rather than directly enhancing tuft cell differentiation. Further, in Fig 3D, Notch inhibition appears to reduce p63+Krt5+ pod cells, resulting in decreased tuft cell differentiation. Importantly, a previous study has reported that Notch signalling is critical for Krt5+ pod expansion following influenza infection (Vaughan et al. 2015; Xi et al. 2017). Notch inhibition reduced Krt5+ pod expansion and induced their differentiation into Sftpc+ AT2 cells. In order to address the direct effect of Wnt/Notch signaling in the differentiation process of tuft cells from EBCs, the authors should provide a more detailed characterization of cellular composition (Krt5+ basal cells, club cells, ciliated cells, AT2 and AT1 cells, etc.) and activity (proliferation) within the pods with/without inhibitors/activators.

Again we thank the reviewer for the insightful suggestions. We agree that it will be interesting to further address the direct effect of Wnt/Notch signaling in the differentiation process of tuft cells from EBCs. In this revised manuscript we added new findings of EBC differentiation into tuft cells in mice with genetic deletion of Rbpjk.

3) Impact of Trpm5 deletion in p63+ cells: It is interesting that Trpm5 deletion promotes the expansion of AT2 and AT1 cells derived from labelled p63+ cells following infection. It would be informative to check whether Trpm5 regulates Hif1a and/or Notch activity which has been reported to induce AT2 differentiation from ectopic basal cells (Xi et al. 2017). Although the authors stated that there was no discernible reduction in the size of Krt5+ pods in mutant mice, it would be interesting to investigate the relationship between AT2/AT1 cell retaining pods and the severity of injury (e.g. large Krt5+ pods retain more/less AT2/AT1 cells compared to small pods. What about other cell types, such as club and goblet cells, in Trpm5 mutant pods? Again, it cannot be ruled out that pre-existing p63+Krt5- intrapulmonary progenitor cells can directly convert into AT2/AT1 cells upon Trpm5 deletion rather than p63+Krt5+ cells induced by infection.

We thank the reviewer for the comments and suggestions. Our new data using KRT5-CreER mouse line confirmed that pod cells (Krt5+) do not contribute to AT2/AT1 cells, consistent with previous studies (Kanegai et al., 2016; Vaughan et al., 2015). Our data also show that p63-CreER lineage labeled AT2/AT1 cells are separated from pod cell area, suggesting pod cells and these AT2/AT1 cells are generated from different cell of origin. We also checked the Notch activity in pod cells in Trpm5-/- mice, and some pod cell-derived cells are Hes1 positive, whereas some are Hes1 negative (RLFigure 1). As indicated in discussion we think that AT2/AT1 cells are possibly derived from pre-existing AT2 cells that transiently express p63 after PR8 infection. It will be interesting to test whether Trpm5 regulates Hif1a in this population (p63+,Krt5-), and this will be our next plan.

RLFigure 1. Representative area staining in Trpm5-/- mice at 30 dpi. Area 1: Notch signaling is active (Hes1+, arrows) in pod cells following viral infection. Area 2: pod cells exhibit reduced Notch activities. Note few Hes1+ cells in pods (arrows). Scale bar: 50 µm.

4) Ectopic tuft cells in COVID-19 lungs: The previous study by the authors' group revealed the presence of ectopic tuft cells in COVID-19 patient samples (Melms et al. 2021). There appears to be no additional information in this manuscript.

In Melms et al., Nature, 2021 (Melms et al., 2021), we showed tuft cell expansion in COVID-19 lungs but not the potential origin of tuft cells. In this manuscript we show some cells co-expressing POU2F3 and KRT5, suggesting a pod-to-tuft cell differentiation.

5) Quantification information and method: Overall, the quantification method should be clarified throughout the manuscript. Further, in the method section, the authors stated that the production of various airway epithelial cell types was counted and quantified on at least 5 "random" fields of view. However, virus infection causes spatially heterogeneous injury, resulting in a difficult to measure "blind test". The authors should address how they dealt with this issue.

We clarified that quantification method as suggested. For the in vitro cell culture assays on the signaling pathways, we took pictures from at least five random fields of view for quantification. For lung sections, we tile-scanned the lung sections including at least three lung lobes and performed quantification.

Reviewer #3 (Public Review):

In this manuscript Huang et al. study how the lung regenerates after severe injury due to viral infection. They focus on how tuft cells may affect regeneration of the lung by ectopic basal cells and come to the conclusion that they are not required. The manuscript is intriguing but also very puzzling. The authors claim they are specifically targeting ectopic basal progenitor cells and show that they can regenerate the alveolar epithelium in the lung following severe injury. However, it is not clear that the p63-CreERT2 line the authors are using only labels ectopic basal cells. The question is what is a basal cell? Is an ectopic basal progenitor cell only defined by Trp63 expression?

The accompanying manuscript by Barr et al. uses a Krt5-CreERT2 line to target ectopic basal cells and using that tool the authors do not see a signification contribution of ectopic basal cells towards alveolar epithelial regeneration. As such the claim that ectopic basal cell progenitors drive alveolar epithelial regeneration is not well-founded.

We appreciate the reviewer for the positive comments and agreeing that our findings are interesting.

The title itself is also not very informative and is a bit misleading. That being said I think the manuscript is still very interesting and can likely easily be improved through a better validation of which cells the p63-CreERT2 tool is targeting.

We have revised the title accordingly and performed extensive experiments to address the reviewer’s concerns.

I, therefore, suggest the following experiments.

1) Please analyze which cells p63-CreERT2 labels immediately after PR8 and tamoxifen treatment. Are all the tdTomato labeled cells also Krt5 and p63 positive or are some alveolar epithelial cells or other airway cell types also labeled?

We thank the reviewer for the question. To answer the reviewer’s question, we performed PR8 infection (250 pfu) on three Trp63-CreERT2;R26tdT mice and TMX treatment at days 5 and 7 post viral infection. We didn't perform TMX injection immediately as the mice were sick at a few days post infection. The lung samples were collected at 14 dpi. We observed that tdT+ cells are present in the airways (rebuttal letter RLFigure 2A, B), and it appears that the lineage labeled cells (tdT+) include club cells (CC10+) that are underlined by tdT+Krt5+ basal cells (RLFigure 2C). We think that these labeled basal cells give rise to club cells. However, we also noticed that rare club cells and ciliated cells (FoxJ1+) are labeled by tdT in the areas absent of surrounding tdT+ basal cells (RLFigure 2D). Moreover, a minor population of tdT+ SPC+ cells are present in the terminal airways that were disrupted by viral infection (RLFigure 2E and D). We did not see any pods formed in this experiment and we did not observe any tdT+ cells in the intact alveoli (uninjured area).

RLFigure 2. Trp63-CreERT2 lineage labeled cells in the airways but not alveoli when Tamoxifen was induced at day 5 and 7 after PR8 H1N1 viral infection. Trp63-CreERT2;R26-tdT mice were infected with PR8 at 250 pfu and Tmx were delivered at a dose of 0.25 mg/g bodyweight by oral gavage. Lung samples were collected and analyzed at 14 dpi. Stained antibodies are as indicated. Scale bar: 100 µm.

2) Please also show if p63-CreERT2 labels any cells in the adult lung parenchyma in the absence of injury after tamoxifen treatment.

Dr. Wellington Cardoso’s group demonstrated that Trp63-CreERT2 only labels very few cells in the airways but not the lung parenchyma in the absence of injury after tamoxifen treatment (Yang et al., 2018). Dr. Ying Yang has revisited the data and she did not observe any labeling in the lung parenchyma (n = 2).

3) Please analyze if p63-CreERT2 labels any cells with tdTomato in the absence of injury or after PR8 infection but without tamoxifen treatment.

We performed the experiment and didn't observe any labeled cells in the lung parenchyma without Tamoxifen treatment (n = 4).

4) Please analyze when after PR8 infection do the first p63-CreERT2 labeled tdTomato positive alveolar epithelial cells appear.

We administered tamoxifen at day 5 and 7 after PR8 infection and harvested lung tissues at day 14. As shown in Figure 1, we observed a few tdT+ SPC+ cells in the terminal airways that are disrupted by viral infection. Notably, we did not observe any lineage labeled cells in the intact alveoli (uninjured) in this experiment..

5) A clonal analysis of p63-CreERT2 labeled cells using a confetti reporter might also help interpret the origin of p63-CreERT2 labeled cells.

We thank the reviewer for the suggestion. Our new data demonstrate that a rare population of SPC+tdT+ cells are present in the disrupted terminal airways of Trp63-CreERT2;R26tdT mice. Our data in the original manuscript and the new data suggest that the initial SPC+;tdT+ cells are rare because we have to administrate multiple doses of Tamoxifen to label them. Given the less labeling efficiency of confetti than R26tdT mice, it is possible we will not be able to label these SPC+ cells. Moreover, our original manuscript clearly shows individual clones of SPC+tdT+ cells in the regenerated lung, and they do not seem to compose of multiple clones. Therefore we think that use of confetti mice may not add new information..

6) Lastly could the authors compare the single-cell RNAseq transcription profile of p63-CREERT2 labeled cells immediately after PR8 and tamoxifen treatment and also at 60dpi. A pseudotime analysis and trajectory interference analysis could help elucidate the identity of p63-CreERT2 labeled cells that are actually not ectopic basal progenitor cells.

We appreciated the reviewer’s suggestion and agree that single cell RNA sequencing with pseudotime analysis can provide further information regarding the origin of the lineage labeled alveolar cells of Trp63-CreERT2;R26tdT mice. That said, our new data clearly show that KRT5-CreER lineage labeled cells do not give rise to AT1/2 cells as previously described (Kanegai et al., 2016; Vaughan et al., 2015), suggesting that the ectopic basal progenitor cells do not generate alveolar cells. By contrast, Trp63-CreERT2 lineage labeled cells do give rise to AECs, suggesting that this p63+ cell population capable of generating AECs are different from Krt5+ ectopic basal progenitor cells. Our single cell core has an extremely long waiting list due to the pandemic and we hope that our new findings are enough to address the reviewer’s concern without the need of single cell analysis..

Author Response

Reviewer #1 (Public Review):

This manuscript applies the framework of information theory to study a subset of cellular receptors (called lectins) that bind to glycan molecules, with a specific focus on the kinds of glycans that are typical of fungal pathogens. The authors use the concentration of various types of ligands as the input to the signaling channel, and measure the "response" of individual cells using a GFP reporter whose expression is driven by a promoter that responds to NFκB. While this work is overall technically solid, I would suggest that readers keep several issues in mind while evaluating these results.

1) One of the largest potential limitations of the study is the reliance of the authors on exogenous expression of the relevant receptors in U937 cells. Using a cell-line system like this has several advantages, most notably the fact that the authors can engineer different reporters and different combinations of receptors easily into the same cells. This would be much more difficult with, say, primary cells extracted from a mouse or a human. While the ability to introduce different proteins into the cells is a benefit, the problem is that it is not clear how physiologically relevant the results are. To their credit, the authors perform several controls that suggest that differences in transfection efficiency are not the source of the differences in channel capacity between, say, dectin-1 and dectin-2. As the authors themselves clearly demonstrate, however, the differences in the properties of these signaling system are not based on receptor expression levels, but rather on some other property of the receptor. Now, it could be that the dectin-2 receptor is somehow just more "noisy" in terms of its activity compared to, say, dectin-1. This seems a somewhat less likely explanation, however, and so it is likely that downstream details of the signaling systems differ in some way between dectin-2 and the more "information efficient" receptors studied by the authors.

The channel capacity of a cell signaling network depends critically on the distributions of the downstream signaling molecules in question: see the original paper by Cheong et al. (2011, Science 334 (6054), 354-8) and subsequent papers (notably Selimkhanov et al. (2014) Science 346 (6215), 1370-3 and Suderman et al. (2018) Interface Focus 8 (6), 20180039). The U937 cells considered here clearly don't serve the physiological function of detecting the glycans considered by the authors; despite the fact that this is an artificial cell line, the fact the authors have to exogenously express the relevant receptors indicates that these cells are not necessarily a good model for the types of cells in the body that actually have evolved to sense these glycan molecules.

Signaling molecules readily exhibit cell-type-specific expression levels that influence cellular responses to external stimuli (Rowland et al.(2017) Nat Commun 8, 16009). So it is unclear that the distributions of downstream signaling molecules in U937 cells mirror those that would be observed in the immune cell types relevant to this response. As such, the physiological relevance of the differences between dectin-2 channel capacities and those exhibited by the other receptors are currently unclear.

We appreciate Reviewer #1’s in-depth comments related to physiological relevance of the U937 cell. A big benefit of using information theory to investigate a biological communication channel is the realization of quantitative measurement of information that the channel transmits without having detailed measurement of spatiotemporal dynamics of receptors and downstream signaling cascades. In addition, the quantity of measured information itself in turn gives us a decent prediction about detailed signaling mechanisms by comparing the information quantity difference. For example, we investigated how transmission of glycan information from dectin-2 is synergistically modulated in the presence of either dectin-1, DC-SIGN or mincle. Our approach allows to investigate how individual lectins on immune cells contribute to glycan information transmission and be integrated in the presence other type of lectins. Therefore, the findings describe how physiologically relevant lectins are integrating the extracellular signal in a more defined way. Furthermore, we found that our model cell line has one order of magnitude higher expression of dectin-2 compared with primary human monocytes and exhibits a similar zymosan binding pattern (will be described in Recommendations for the authors and Figure R8).

We fully agree that acquiring more information on the information transmission capability of primary immune cells would increase physiological relevance. In the revised manuscript we addressed this concern by comparing the receptor expression levels of our model cell lines with primary monocytes, for which we find an agreement of cellular heterogeneity. However, we would also like to point out that the very basic nature of our question, of how information stored in glycans is processed by lectins, is not tightly bound to these difference of primary cells and cell lines.

Line 382: Finally, it is important to take into consideration that our conclusions came from model cell lines, which were used as a surrogate for cell-type-specific lectin expression patterns of primary immune cells. Human monocytes and dectin-2 positive U937 cells have comparable receptor densities and respond similar to stimulation with zymosan particles (SI Fig. 6A and B).

2) Another issue that readers might want to keep in mind is that the details of the channel capacity calculation are a bit unclear as the manuscript is currently written. The authors indicate that their channel capacity calculations follow the approach of Cheong et al. (2011) Science 334 (6054), 354-8. However, the extent to which they follow that previous approach is not obvious. For instance, the calculations presented in the 2011 work use a combined bootstrapping/linear extrapolation approach to estimate the mutual information at infinite population size in order to deal with known inaccuracies in the calculation that arise from finite-size effects. The Cheong approach also deals with the question of how many bins to use in order to estimate the joint probability distribution across signal and response.

They do this by comparing the mutual information they calculate for the real data with that calculated for random data to ensure that they are not calculating spuriously high mutual information based on having too many bins. While the Cheong et al. paper does a great job explaining why these steps need to be undertaken, a subsequent paper by Suderman et al. (2017, PNAS 114 (22), 5755-60) explains the approach in even greater detail in the supporting information. Those authors also implemented several improvements to the general approach, including a bootstrap method for more accurately estimating the error in the mutual information and channel capacity estimates.

The problem here is that, while the authors claim to follow the approach of Cheong et al., it seems that they have re-implemented the calculation, and they do not provide sufficient detail to evaluate the extent to which they are performing the same exact calculation. Since estimates of mutual information are technically challenging, specific details of the steps in their approach would be helpful in order to understand how closely their results can be compared with the results of previous authors. For instance, Cheong et al. estimate the "channel capacity" by trying a set of likely unimodal and bimodal distributions for the input to the channel, and choosing the maximal value as the channel capacity. This is clearly a very approximate approach, since the channel capacity is defined as the supremum over an (uncountably infinite) set of input probability distributions. In any case, the authors of the current manuscript use a different approach to this maximization problem. Although it is a bit unclear how their approach works, it seems that they treat the probability of each input bin as an independent parameter (under the constraint that the probabilities sum to one) and then use an optimization algorithm implemented in Python to maximize the mutual information. In principle, this could be a better approach, since the set of input distributions considered is potentially much larger. The details of the optimization algorithm matter, however, and those are currently unclear as the paper is written.

We thank Reviewer #1’s recommendation for increasing the legitimacy of the calculation. In the revised manuscript we tried to explain channel capacity calculation procedures in more detail with statistical approaches that adopted from Cheong et al. (2011) and Suderman et al. (2018) (SI section 1 and 2). Furthermore, we decide the number of binning from not only random dataset but also the number of total samples as shown below:

Figure R1. A) Extrapolated channel capacity values of random dataset at infinitely subsampled distribution under various total number of samples and output binning. The white line in the heatmap represents the channel capacity value at 0.01 bit. B) Extrapolated channel capacity values at infinite subsample size of U937 cells’ input (TNF-a doses) and output (GFP reporter) response.

Figure R1 describes channel capacity values from random (A) and experimental dataset (B, TNFAR + TNF-a). The channel capacity values from random data indicates the dependence of channel capacity on the number of the output binning and total number sample. According to this heatmap, we decided the allowed bias as 0.01 bits as shown in contour line shown in Figure R1A. Since our minimum dataset that used for channel capacity calculation in the absence of labelled input is near 90,000, the expected bias in channel capacity calculation is therefore less than 0.01 bits in binning range from 10 to 1000 as shown in Figure R1A.

Furthermore, we demonstrated mutual information maximization procedure using predefined unibimodal input distribution and compared with the systematic method that we used in the work. We found that there is no noticeable difference in channel capacity value between two approaches (SI Figure 3M).

3) Another issue to be careful about when interpreting these findings is the fact that the authors use logarithmic bins when calculating the channel capacity estimates. This is equivalent to saying that the "output" of the cell signaling channel is not the amount of protein produced under the control of the NFκB promoter, but rather the log of the protein level. Essentially, the authors are considering a case where the relevant output of the system is not the amount of protein itself, but the fold change in the amount of protein. That might be a reasonable assumption, especially if the protein being produced is a transcription factor whose own promoters have evolved to detect fold changes. For many proteins, however, the cell is likely responsive to linear changes in protein concentration, not fold changes. And so choosing the log of the protein level as the output may not make sense in terms of understanding how much information is actually contained in this particular output variable. Regardless, choosing logarithmic bins is not purely a matter of convenience or arbitrary choice, but rather corresponds to a very strong statement about what the relevant output of the channel is.

We understand Reviewer #1’s concern regarding the choice of log binning. We found that if the number of binning is higher than 200, no matter the binning methods, including linear, logarithmic or equal frequency, the estimated channel capacities in each binning number are converged into the same value. The only difference is how quickly the values approach the converged channel capacity as increasing the binning number (shown in Figure R2). In the revised manuscript, we used linear binning to represent more relevant protein signaling as the Reviewer mentioned. Note that the channel capacity values calculated from linear binning do not show noticeable different from our previously calculated channel capacity values.

On the other hand, linear binning generates significant bias, if we consider labelled input (i.e., continuous input) into channel capacity calculation, due to the increase of binning in input region.

Figure R2. Output binning number and binning method dependence of channel capacity value for experimental dataset. The inset plots show the relative difference of channel capacity value to the maximum channel capacity value in the entire binning range (i.e., from 10 to 1000) of the corresponding binning method.

According to Reviewer #1’s comment we have changed the binning method from logarithmic binning to linear binning in the whole experimental dataset except in the presence of labelled input (i.e., dectin-2 antibody). If we consider channel capacity between labelled input and NF-kB reporter, equal frequency binning is used for every layer of the channel capacity (i.e., labelled input-binding, binding-GFP, labelled input-GFP)

Reviewer #2 (Public Review):

My expertise is more on the theoretical than the experimental aspects of this paper, so those will be the focus of these comments.

Signal transduction is an important area of study for mathematical biologists and biophysicists. This setting is a natural one for information-theoretic methods, and such methods are attracting increasing research interest. Experimental results that attempt to directly quantify the Shannon capacity of signal transduction are particularly interesting. This paper represents an important contribution to this emerging field.

My main comments are about the rigorousness and correctness of the theoretical results. More details about these results would improve the paper and help the reader understand the results.

We understand reviewer #2’s comment related with rigorousness and correctness of the theoretical results of this work. In the revised manuscript, we added following contents to help the reader to better understand the channel capacity calculation procedures.

• General illustrative introduction regarding how we measured input and output dataset and how we handle those data to prepare joint probability distribution shown in SI section 1.1 and 1.2.

• Exemplified mutual information maximization procedure using experimental and arbitrary dataset shown in SI section 1.3.

The calculation of channel capacity, given in the methods, is quite a standard calculation and appears to be correct. However, I was confused by the use of the "weighting value" w_i, which is not specified in the manuscript. The input distribution appears to be a product of the weight w_i and the input probability value p_i, and these appear always to occur together as a product w_i p_i. (In joint probabilities w_i p(i,j), the input probability can be extracted using Bayes' rule, leaving w_i p_i p(j|i).) This leads met wonder two things. First, what role does w_i play (is it even necessary)? Second, of particular interest here is the capacity-achieving input distribution p_i, but w_i obscures it; is the physical input distribution p_i equal to the capacity-achieving distribution? If not, what is the meaning of capacity?

We thank Reviewer #2’s comment regarding the arbitrariness of the weightings. We realize there was a lack of explanation on the weighting values in the original manuscript. 𝑃x(𝑖) is a marginal probability distribution of input from the original dataset and 𝑃x'(𝑖) is the marginal probability distribution of modified input that maximize the mutual information. In usual case 𝑃x(𝑖) is not equal to 𝑃x'(𝑖) and therefore one needs to find 𝑃x'(𝑖) from 𝑃x(𝑖). Because 𝑃x'(𝑖) is a linear combination of 𝑃x(𝑖), it can be expressed as 𝑤(𝑖)𝑃x(𝑖) , where 𝑤(𝑖) is the weightings, under constraint ∑input/i 𝑤(𝑖)𝑃x (𝑖) = 1 . The changed input distribution, in turn, modifies the joint probability distribution as 𝑃'xy (𝑖, 𝑗) = 𝑤(𝑖)𝑃xy)(𝑖, 𝑗). To help readers understand of this work we expanded the Appendix with illustrative descriptions.

A more minor but important point: the inputs and outputs of the communication channel are never explicitly defined, which makes the meaning of the results unclear. When evaluating the capacity of an information channel, the inputs X and outputs Y should be carefully defined, so that the mutual information I(X;Y) is meaningful; the mutual information is then maximized to obtain capacity. Although it can be inferred that the input X is the ligand concentration, and the output Y is the expression of GFP, it would be helpful if this were stated explicitly.

We agree with Reviewer’s suggestion for better description of input and output in the manuscript. Therefore, we have modified Figure 1 A and B and the main text to describe the source of input and output much clearly, as follows:

Line 92: Accounting for the stochastic behavior of cellular signaling, information theory provides robust and quantitative tools to analyze complex communication channels. A fundamental metric of information theory is entropy, which determines the amount of disorder or uncertainty of variables. In this respect, cellular signaling pathways having high variability of the initiating input signals (e.g. stimulants) and the corresponding highly variable output response (i.e. cellular signaling) can be characterized as a high entropy. Importantly, input and output can have mutual dependence and therefore knowing the input distribution can partly provide the information of output distribution. If noise is present in the communication channel, input and output have reduced mutual dependence. This mutual dependence between input and output is called mutual information. Mutual information is, therefore, a function of input distribution and the upper bound of mutual information is called channel capacity (SI section 1) (Cover and Thomas, 2012). In this report, a communication channel describes signal transduction pathway of C-type lectin receptor, which ultimately lead to NF-κB translocation and finally GFP expression in the reporter model (Fig. 1A). To quantify the signaling information of the communication channels, we used channel capacity. Importantly, the channel capacity isn’t merely describing the resulting maximum intensity of the reporter cells. The channel capacity takes cellular variation and activation across a whole range of incoming stimulus of single cell resolved data into account and quantifies all of that data into a single number.

Author Response

Reviewer #3 (Public Review):

The authors examine the role of secreted BAFF in senescence phenotypes in THP1 AML cells and primary human fibroblasts. In the former, BAFF is found to potentiate the inflammatory phenotype (SASP) and in the latter to potentiate cell cycle arrest. This is an important study because the SASP is still largely considered in generic and monolithic terms, and it is necessary to deconvolute the SASP and examine its many components individually and in different contexts.

Although the results show differences for BAFF in the two cell models, there are many places where key results are missing and the results over-interpreted and/or missing controls.

1) Figure 1. Test whether the upregulation of BAFF is specific to senescence, or also in reversible quiescence arrest.

We appreciate the Reviewer’s requests. We performed the experiments in fibroblasts and THP-1 cells to assess BAFF levels in quiescence. As shown below in the figure for Reviewers, we induced quiescence in fibroblasts by serum starvation (0.1%) for 96 h and confirmed the quiescent state by measuring two markers of quiescence (reduction of CCND1 mRNA and reduction of phopho-S6, when compared to cycling cells, following markers established previously (PMID 25483060) (panel A). In this case, the level of BAFF mRNA was increased upon quiescence (panel B).

In THP-1 cells, we tried to induce quiescence by serum starvation and glutamine depletion for 96 h. Unfortunately, however, inducing quiescence in THP-1 cells was rather challenging, likely because they are cancer cells. Thus, we observed a reduction of cell proliferation in both conditions, but we observed a reduction in phospho-S6 only in the samples without glutamine (panel C). We failed to see increased BAFF mRNA levels in quiescent THP-1 cells after either serum starvation or glutamine depletion (panel D).

In summary, further studies will be necessary to fully understand if the increased expression of BAFF seen in senescent cells is also observed in other conditions of growth suppression (such as quiescence or differentiation), as well as whether this effect is specific to different cell types.

2) Figure 1, Supplement 1G. Show negative control IgG for immunofluorescence.

We thank the Reviewer for this suggestion. Along with other changes during the revision, we decided to remove the immunofluorescence data in order to include more informative data.

3) All results with siRNA should be validated with at least 2 individual siRNAs to eliminate the possibility of off-target effects.

We agree with the Reviewer on the importance of testing individual siRNAs. For BAFF, we originally tested two independent siRNAs (BAFF#1 and BAFF#2) individually, but we also pooled them for additional analysis (and referred to simply as “BAFFsi” along the manuscript). In the revised version of our manuscript, we included the key experiments performed with these two individual BAFF siRNAs. Upon BAFF silencing in THP-1 cells, we observed a reduction of SASP factors and SA-β-Gal activity levels with each individual siRNA (Figure 4-Figure Supplement 1D-F) and with the pooled siRNAs (Figure 4C). For WI-38 cells, we observed a reduction of p53 levels with individual and pooled siRNAs (Figure 7-Figure Supplement 1A), as well as a reduction in IL6 levels and SA-β-Gal activity (Figure 6-Figure Supplement 1D,E). After IRF1 silencing, we observed a reduction in BAFF pre-mRNA with two different pairs of CTRLsi and IRF1si pools (Figure 2I and supplementary Figure 2E). For the data on BAFF receptors, we used SMARTpools from Dharmacon, which are combinations of 4 siRNAs designed by the company to minimize off-target effects. These additions and clarifications are indicated in the revised manuscript.

4) To confirm a role for IRF1 in the activation of BAFF, the authors should confirm the binding of IRF1 to the BAFF promoter by ChIP or ChIP-seq.

We thank the Reviewer for this suggestion. We performed ChIP-qPCR analysis in THP-1 cells that were either proliferating or rendered senescent after exposure to IR (Figure 2H, Materials and methods section), and we confirmed the binding of IRF1 to the proximal promoter region of BAFF. As anticipated, this interaction was stronger after inducing senescence.

5) Key antibodies should be validated by siRNA knockdown of their targets, for example, TACI, BCMA, and BAFF-R in Figure 5. Note that there is an apparent discrepancy between BCMA data in Figure 5B vs 5C.

We fully agree with the Reviewer on this point and we thank him/her for helping us to improve this part of our manuscript. To address the discrepancy regarding BCMA western blot analysis and flow cytometry data, we silenced BCMA in THP-1 cells and tested two different antibodies advertised to recognize BCMA. This experiment allowed us to identify the correct band for BCMA by western blot analysis. We then confirmed that BCMA is upregulated in senescence, as observed by both western blot and flow cytometry analyses. We have modified the manuscript to reflect these changes. Please find these data in Figure 5A,B and Figure 5-Figure Supplement 1A of the revised manuscript.

6) Figure 5E. Negative/specificity controls for this assay should be shown.

We thank the reviewer for this comment and regret that we were unable to provide a negative control. The kit only provides a competitive wild-type oligomer used to test the specificity of the binding. For each sample (CTRLsi, BAFFsi, CTRLsi IR, BAFFsi IR) and each antibody tested (p65, p50, p52, RelB and c-Rel), we evaluated the reductions in signal upon addition of excess competitive oligomer per well (20 pmol/well) compared to wells with an inactive oligomer. However, the negative control was performed only as single replicate, due to the limited quantity of nuclear extracts and the high number of samples and antibodies analyzed. We therefore considered this control as being ‘qualitative’ rather than fully ‘quantitative’.

7) Hybridization arrays such as Figure 5H, Figure 6 - Supplement 1I, and Figure 6H should be shown as quantitated, normalized data with statistics from replicates.

We appreciate this request. We have included the quantification and statistics to the phosphoarrays used for THP-1 and WI-38 cells, which had been performed in triplicate (Figure 7A, Figure 5-Figure Supplement 1D). The original arrays are shown in the respective Source Data Files. In the interest of space, we removed the cytokine array performed on IMR-90 cells and left instead the quantitative ELISA for IL6 (Figure 6-Figure Supplement 1F). The data obtained from the cytokine array analysis in Figure 4F and Figure 4-Supplemental Figure 1C are supported by quantitative multiplex ELISA measurements (Figure 4E and Figure 4C).

8) Figure 6B - Supplement 1. Controls to confirm fractionation (i.e., non-contamination by cytosolic and nuclear proteins) should be shown.

We thank the Reviewer for this suggestion. We tested the efficiency of fractionation and we did in fact observe some degree of contamination from cytosolic proteins using the earlier version of the kit (Pierce, cat. 89881). We therefore purchased an improved version of the kit (Pierce, cat. A44390) and repeated the surface fractionation assay, which this time showed improved fractionation (Figure 7-Figure Supplement 1B). Interestingly, with the improved fractionation strategy, we observed that BAFF receptors in fibroblasts were almost exclusively localized inside the cell and not on the surface, as we found in THP-1 cells. Further validation of BAFF receptor antibodies has been provided in Figure 5-Figure Supplement 1A. As described in the text, the intracellular localization of BAFF receptors was previously reported in other cell types and conditions (PMID 31137630, PMID 19258594, PMID 30333819, PMID 10903733), and thus it is possible that BAFF may act through non-canonical mechanisms in WI-38 cells. Nonetheless, we did detect a small amount of BAFFR on the cell surface, and furthermore, BAFFR silencing reduced the level of p53 in fibroblasts. Therefore, we propose that BAFFR may be the primary receptor involved in p53 regulation in fibroblasts (Figure 7-Figure Supplement 1B,C). Our data on BAFF receptors deserve deeper characterization in a future study of the functions of BAFF receptors in senescence.

9) Figure 6A. Knockdown of BAFF should be shown by western blot.

Yes, definitely. We appreciate this comment and have included BAFF knockdown data in fibroblasts by western blot analysis (Figure 7B).

10) Figure 6G. Although BAFF knockdown decreases the expression of p53, p21 increases. How do the authors explain this?

We thank the Reviewer for the interesting question. We too were surprised to observe that the p53-dependent transcripts regulated by BAFF did not include CDKN1A (p21) mRNA, as confirmed by western blot analysis. The accumulation of p21 in senescence can be also regulated by p53-independent pathways and in p53-/- cells, for example by p90RSK, SP1, and ZNF84 (PMID 24136223, PMID 25051367, PMID 33925586). Eventually, we removed the data relative to p21 and γ-H2AX in favor of other data and to streamline the content of this manuscript for the reader.

Author Response

Reviewer #1 (Public Review):

1-1. I do have some concerns that the differences in network clustering reported in Fig 6 may be due to noise and I think the comparisons against the HCP parcellation could be more robust. Specifically, with regard to the network clustering in Fig 6. The authors use a clustering algorithm (which is not explained) to cluster the parcels into different functional networks. They achieve this by estimating the mean time series for each parcel in each individual, which they then correlate between the n regions, to generate an nxn connectivity matrix. This they then binarise, before averaging across individuals within an age group. It strikes me that binarising before averaging will artificially reduce connections for which only a subset of individuals are set to zero. Therefore averaging should really occur before binarising. Then I think the stability of these clusters should be explored by creating random repeat and generation groups (as done for the original parcells) or just by bootstrapping the process. I would be interested to see whether after all this the observation that the posterior frontoparietal expands to include the parahippocampal gryus from 3-6 months and then disappears at 9 months - remains.

We thank the reviewer for this insightful comment on our clustering process. For the step of “binarizing before averaging”, we followed the method proposed by Yeo et al (1). In this method, all correlation matrices are binarized according to the individual-specific thresholds. Specifically, each individual-specific threshold is determined according to the percentile, and only 10% of connections are kept and set to 1, while all other connections are set to 0. Yeo et al. (1) explained their motivation for doing so as “the binarization of the correlation matrix leads to significantly better clustering results, although the algorithm appears robust to the particular choice of the threshold”. We consider that the possible reason is that the binarization of connectivity in each individual offers a certain level of normalization so that each subject can contribute the same number of connections. If averaging occurs before binarizing, the actual connectivity contributed by different subjects would be different, which leads to bias. Meanwhile, we tested the stability of ‘binarizing first’ and ‘averaging first’, and the result is shown in Fig. R1 below. This figure suggests a similar conclusion as (1), where binarizing first before averaging leads to better clustering stability. We added the motivation of binarizing before averaging in the revised manuscript between line 577 and line 581.

Fig. R1. The comparison of clustering stability of different methods. The red line refers to the clustering stability when binarizing the correlation matrices first and then averaging the matrices across individuals, while the blue line refers to the clustering stability when averaging the correlation matrices across individuals first and then binarizing the average matrix.

For the final clustering results, we performed our clustering method using bootstrapping 100 times, and the final result is a majority voting of each parcel. The comparison of these two results is shown in Fig. R2. Overall, we do observe good repeatability between these two results. However, we also observed that some parcels show different patterns between the two results, especially for those parcels that are spatially located around the boundaries of networks or the medial wall. The pattern of the observation that “the posterior frontoparietal expands to include the parahippocampal gyrus from 3-6 months and then disappears at 9 months – remains” was not repeated in the bootstrapped results. These results might suggest that the clustering method is quite robust, the discovered patterns are relatively stable, and the differences between our original results and bootstrapping results might be caused by noises or inter-subject variabilities.

Fig. R2. Top panel: the network clustering results using all data in the original manuscript. Bottom panel: the network clustering results using majority voting through 100 times of bootstrapping. Black circles and red arrows point to the parahippocampal gyrus, which was included in the posterior frontoparietal network, and is not well repeated in the bootstrapped results. (M: months)

1-2. Then with regard to the comparison against the HCP parcellation, this is only qualitative. The authors should see whether the comparison is quantitatively better relative to the null clusterings that they produce.

Thank you for this great suggestion! As suggested, we added this quantitative comparison using the Hausdorff distance. Similar to the comparison in parcel variance and homogeneity, the 1,000 null parcellations were created by randomly rotating our parcellation with small angles on the spherical surface 1,000 times. We compared our parcellation and the null parcellations by accordingly evaluating their Hausdorff distances to some specific areas of the HCP parcellation on the spherical space, including Brodmann's area 2, 3b, 4+3a, 44+45, V1, and MT+MST. The results are listed in Figure 4. From the results, we can observe that our parcellation generally shows statistically much lower Hausdorff distances to the HCP parcellation, suggesting that our parcellation generates parcel borders that are closer to HCP parcellations compared to the null parcellations.

However, we noticed very few null parcellations that show smaller Hausdorff distances compared to our parcellation. A possible reason comes from our surface registration process with the HCP template purely based on cortical folding, without using functional gradient density maps, which are not available in the HCP template. As a result, this does not ensure high-quality functional alignment between our infant data and the HCP space, thus inevitably increasing the Hausdorff distance between our parcellation and the HCP parcellation.

1-3. … not all individuals appear (from Fig 8) to be acquired exactly at the desired timepoints, so maybe the authors might comment on why they decided not to apply any kernel weighted or smoothing to their averaging? Pg. 8 'and parcel numbers show slight changes that follow a multi-peak fluctuation, with inflection ages of 9 and 18 months' explain - the parcels per age group vary - with age with peaks at 9 and 18 - could this be due to differences in the subject numbers, or the subjects that were scanned at that point?

We do agree with the reviewer that subjects are not scanned at similar time points. This is designed in the data acquisition protocol to seamlessly cover the early postnatal stage so that we will have a quasi-continuous observation of the dynamic early brain development.

We didn’t apply kernel weighted average or smoothing when generating the parcellation, as we would like each scan to contribute equally, and each parcellation map could be representative of the cohort of the covered age, instead of only part of them. Meanwhile, our final ‘age-common parcellation’ could be representative of all subjects from birth to 2 years of age. However, we do agree that the parcellation map that is only designed for the use of a specific age, e.g., 1-year-olds, kernel weighted average, or even a more restricted age range could be a more appropriate solution.

For the parcel number that likely shows fluctuations with subject numbers, we added an experiment, where we randomly selected 100 scans by considering the minimum scan number in each age group using bootstrapping and repeated this process 100 times. The average parcel number of each age is reported in the following Table R1. We didn’t observe strong changes in parcel numbers when reducing scan numbers, which further demonstrates that our parcel numbers do not show a strong relation to subject numbers. However, the parcel number does not increase greatly from 18M to 24M in the bootstrapping results, so we modified the statement in the manuscript about the parcel number to ‘… all parcel numbers fall between 461 to 493 per hemisphere, where the parcel number attains a maximum at around 9 months and then reduces slightly and remains relatively stable afterward. …’, which can be found between line 121 and line 122.

1-4. I also have some residual concerns over the number of parcels reported, specifically as to whether all of this represents fine-grained functional organisation, or whether some of it represents noise. The number of parcels reported is very high. While Glasser et al 2016 reports 360 as a lower bound, it seems unlikely that the number of parcels estimated by that method would greatly exceed 400. This would align with the previous work of Van Essen et al (which the authors cite as 53) which suggests a high bound of 400 regions. While accepting Eickhoff's argument that a more modular view of parcellation might be appropriate, these are infants with underdeveloped brain function.

We thank the reviewer for this insightful comment. We agree that there might be noises for some of the parcels, as noises exist in each step, such as data acquisition, image processing, surface reconstruction, and registration, especially considering functional MRI is noisier than structural MRI. Though our experiments show that our parcellation is fine-grained and is suitable for the study of the infant brain functional development, it is hard to directly quantitatively validate as there is no ground truth available.

Despite these, we are still motivated to create fine-grained parcellations, as with the increase of bigger and higher resolution imaging data and advanced computational methods, parcellations with more fine-grained regions are desired for downstream analyses, especially considering the hierarchical nature of the brain organization (2). And the main reason that our method generates much finer parcellation maps, is that both our registration and parcellation process is based on the functional gradient density, which characterizes a fine-grained feature map based on fMRI. This leads to both better inter-subject alignment in functional boundaries and finer region partitions. This strategy is different from Glasser et al (3), which jointly considers multimodal information for defining parcel boundaries, thus parcels revealed purely by functional MRI might be ignored in the HCP parcellation. We hope our parcellation framework can be a useful reference for this research direction. We added this discussion in the revised manuscript between line 268 and line 271.

For the parcel number, even without performing surface registration based on fine-grained functional features, recent adult fMRI-based parcellations greatly increased parcel numbers, such as up to 1,000 parcels in Schaefer et al. (4), 518 parcels in Peng et al. (5), and 1,600 parcels in Zhao et al. (6). For infants, we do agree that the infant functional connectivity might not be as strong as in adults. However, there are opinions (7-9) that the basic units of functional organization are likely to present in infant brains, and brain functional development gradually shapes the brain networks. Therefore, the functional parcel units in infants could be possibly on a comparable scale to adults. Even so, we do agree that more research needs to be performed on larger datasets for better evaluations. We added this discussion in the revised manuscript between line 275 and line 280.

1-5. Further comparisons across different subjects based on small parcels increases the chances of downstream analyses incorporating image registration noise, since as Glasser et al 2016 noted, there are many examples of topographic variation, which diffeomorphic registration cannot match. Therefore averaging across individuals would likely lose this granularity. I'm not sure how to test this beyond showing that the networks work well for downstream analyses but I think these issues should be discussed.

We agree with the reviewer that averaging across individuals inevitably brings some registration errors to the parcellation, especially for regions with high topographic variation across subjects, which would lead to loss of granularity in these regions. We believe this is an important issue that exists in most methods on group-level parcellations, and the eventual solution might be individualized parcellation, which will be our future work. We added this discussion in the revised manuscript between line 288 and line 292.

We also agree with the reviewer that downstream analyses are important evaluations for parcellations. We provided a beta version of our parcellation with 602 parcels (10) to our colleagues, and they tested our parcellation in the task of infant individual recognition across ages using functional connectivity, to explore infant functional connectome fingerprinting (10). We compared the performance of different parcellations with 602 ROIs (our beta version), 360 ROIs (HCP MMP parcellation (3)), and 68 ROIs (FreeSurfer parcellation (11)). The results (Fig. R3) show that our parcellation with a higher parcellation number yields better accuracy compared to other parcellations. We added a description of this downstream application in the discussion between line 284 and line 287.

Fig. R3. The comparison of different parcellations for infant individual recognition across age based on functional connectivity (figure source: Hu et al. (10)). The parcellation with 602 ROIs is the beta version of our parcellation, 360 ROIs stands for HCP MMP parcellation (3) and 68 ROIs stands for the FreeSurfer parcellation (11). This downstream task shows that a higher parcellation number does lead to better accuracy in the application.

1-6. Finally, I feel the methods lack clarity in some areas and that many key references are missing. In general I don't think that key methods should be described only through references to other papers. And there are many references, particular to FSL papers, that are missing.

We thank the reviewer for this great suggestion. We added related references for FLIRT, FSL, MCFLIRT, and TOPUP For the alignment to the HCP 32k_LR space, we first aligned all subjects to the fsaverage space using spherical demons, and then used part of the HCP pipeline (12) to map the surface from the fsaverage space to HCP 164k_LR space, and downsampled to 32k_LR space. We modified this citation by referencing the HCP pipeline by Glasser et al. (12) instead and detailed this registration process in the revised manuscript between line 434 to line 440 in the revised manuscript and as below:

“… The population-mean surface maps were mapped to the HCP 164k ‘fs_LR’ space using the deformation field that deforms the ‘fsaverage’ space to the ‘fs_LR’ space released by Van Essen et al. (13), which was obtained by landmark-based registration. By concatenating the three deformation fields of steps 1, 3, and 4, we directly warped all cortical surfaces from individual scan spaces to the HCP 164k_LR space and then resampled them to 32k_LR using the HCP pipeline (12), thus establishing vertex-to-vertex correspondences across individuals and ages …”

Reviewer #2 (Public Review):

2-1. Diminishing enthusiasm is the lack of focus in the result section, the frequent use of jargon, and figures that are often difficult to interpret. If those issues are addressed, the proposed atlas could have a high impact in the field especially as it is aligned with the template of the Human Connectome Project.

We’d like to thank Reviewer #2 for the appreciation of our atlas. According to the reviewer’s suggestion, we went through the manuscript again by focusing on correcting the use of jargon, clarity in the result section, as well as figures and figure captions. We hope our corrections can help explain our work to a broader community. Our revisions are accordingly detailed in the following. Meanwhile, our parcellation maps have been aligned with the templates in HCP and FreeSurfer and made available via NITRC at: https://www.nitrc.org/projects/infantsurfatlas/.

References

1. B. Thomas Yeo, F. M. Krienen, J. Sepulcre, M. R. Sabuncu, D. Lashkari, M. Hollinshead, J. L. Roffman, J. W. Smoller, L. Zöllei, J. R. Polimeni, The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of neurophysiology 106, 1125-1165 (2011).

2. S. B. Eickhoff, R. T. Constable, B. T. Yeo, Topographic organization of the cerebral cortex and brain cartography. NeuroImage 170, 332-347 (2018).

3. M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell, E. Yacoub, K. Ugurbil, J. Andersson, C. F. Beckmann, M. Jenkinson, S. M. Smith, D. C. Van Essen, A multi-modal parcellation of human cerebral cortex. Nature 536, 171-178 (2016).

4. A. Schaefer, R. Kong, E. M. Gordon, T. O. Laumann, X.-N. Zuo, A. J. Holmes, S. B. Eickhoff, B. T. J. C. C. Yeo, Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. 28, 3095-3114 (2018).

5. L. Peng, Z. Luo, L.-L. Zeng, C. Hou, H. Shen, Z. Zhou, D. Hu, Parcellating the human brain using resting-state dynamic functional connectivity. Cerebral Cortex, (2022).

6. J. Zhao, C. Tang, J. Nie, Functional parcellation of individual cerebral cortex based on functional mri. Neuroinformatics 18, 295-306 (2020).

7. W. Gao, S. Alcauter, J. K. Smith, J. H. Gilmore, W. Lin, Development of human brain cortical network architecture during infancy. Brain Structure and Function 220, 1173-1186 (2015).

8. W. Gao, H. Zhu, K. S. Giovanello, J. K. Smith, D. Shen, J. H. Gilmore, W. J. P. o. t. N. A. o. S. Lin, Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects. 106, 6790-6795 (2009).

9. K. Keunen, S. J. Counsell, M. J. J. N. Benders, The emergence of functional architecture during early brain development. 160, 2-14 (2017).

10. D. Hu, F. Wang, H. Zhang, Z. Wu, Z. Zhou, G. Li, L. Wang, W. Lin, G. Li, U. U. B. C. P. Consortium, Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months. Journal of Neuroscience 42, 377-389 (2022).

11. R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-980 (2006).

12. M. F. Glasser, S. N. Sotiropoulos, J. A. Wilson, T. S. Coalson, B. Fischl, J. L. Andersson, J. Xu, S. Jbabdi, M. Webster, J. R. Polimeni, The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105-124 (2013).

Author Response:

Reviewer #1 (Public Review):

The authors present a system that allows the measurement of OCR on diverse tissues. Using two optopes, one before the tissue under examination, and one after, allows the OCR to be measured as the difference between the concentration of O2 in the in-flow gas and the concentration of O2 in the out-flow gas. The system maintains the tissue at a set concentration of dissolved O2 so that experiments can be performed over a long period of time. The authors have provided ample data and full methods and their conclusions are most likely reliable.

Currently, we know that O2 is critical for diverse physiological processes, however it is rarely as well controlled for as well as non-gas solutes such as glucose, as we lack methods to control its delivery and infer its consumption. By addressing this need, the authors contribute something valuable to the field, which will hopefully be built on by others. The authors have already begun to show the utility of their system by exploring the complicated biology of H2S. As delivering this gas in a controlled manner is hard, often people use NaHS instead. In line with previous studies (well cited by the authors), differences are observed.

Specific points

1) The gas control system is used with islets, INS-1 832/12 cells, retinas, and liver tissue, demonstrating its broad applicability.

2) The system as a platform can have diverse extra measurement modalities attached to it, for example visible-wavelength absorbance and fluorescence. Metabolite concentrations in the tissue culture outflow could also be measured.

3) The reduction state of cyt c and cyt c oxidase are measured from the second derivative of absorbance at 550 and 605 nm. Ideally, to reliably decompose these signals full spectra around 550-605 nm would be collected. As the authors are only using cytochrome reduction state as a qualitative measure and appear careful to avoid over-interpretation this method should be fine. However, the authors ought to show a representative time course including the fully oxidised and reduced states demonstrating this approach as making these measurements is demanding and will depend on the exact spectroscopic set-up. Without this information it is hard to judge the reliability of the paper.

We appreciate giving us the latitude for a less robust measurement. However, we actually did do what you have suggested should be done. That is, with the Ocean Optics spectrophotometer, we measure the full light spectrum from 400 to 650. Using this spectral data, we calculate the first and second derivatives of the absorption. We have previously published our approach to spectral analysis, as well as the inclusion of the fully oxidized and reduced states (Sweet IR, G Khalil, AR Wallen, M Steedman, KA Schenkman, JA Reems, SE Kahn, JB Callis. Continuous measurement of oxygen consumption by pancreatic islets. Diabetes Tech. Ther. 4: 661-672, 2002; Sweet IR, Cook DL, DeJulio E, Wallen AR, Khalil G, Callis JB, Reems JA: Regulation of ATP/ADP in pancreatic islets. Diabetes 53:401-409. 2004), so we did not include all the details. In order to ensure that our description is clear, we have added a more thorough explanation that we used spectral analysis and not just data obtained as single wavelengths.

Reviewer #2 (Public Review):

The present project is an extension of prior work from this work group in which they describe a technological advancement to their published flow-culture system. Such improvements now incorporate technology that allows for metabolic characterization of mammalian tissues while precisely controlling the concentration of abundant gases (e.g., O2), as well as trace gases (e.g., H2S). The present article demonstrates the utility of this system in the context of hypoxia/re-oxygenation experiments, as well as exposure to H2S. Although the methodology described herein is clearly capable of detecting nuanced metabolic changes in response to variations in O2 or H2S, the lack of a head-to-head comparison with other techniques makes it difficult to discern the potential impact of the technology.

We understand the benefit of comparing compare a new method with the currently utilized methods. However, the novelty of our methodology is that it is able to control the exposure of tissue to levels of both abundant and trace dissolved gas composition, functions that neither of these existing instruments provide. In addition, continuous flow of media allows maintenance and assessment of tissue models that cannot be accommodated by static or spinner systems. Since we are the first to report an entirely novel technology, the direct comparison to benchmarks is not possible. In the past, however, we have tested liver slices and retina in a Seahorse and the tissue died within 120 minutes presumably due to the lack of flow/reoxygenation in the tissue. In addition, islets placed in spinner systems such as the Oxygraph become fragmented and broken very rapidly. So, a head to head comparison on the tissue OCR response to changes in gas composition cannot be meaningfully carried out for the facets of our method that we highlighted. The methodology we present has capabilities that do not exist in any other commercially available system. We have stated this latter point in the last line of the second paragraph of the Introduction. Regarding the general reliability of the O2 consumption measurement: the unprecedented accuracy and stability of the O2 detectors and the ability of our flow system to maintain tissue for days while generating accurate and reproducible measurements of O2 consumption has previously been established (Sweet IR, Gilbert M, Sabek O, Fraga DW, Gaber AO, Reems JA. Glucose Stimulation of Cytochrome c Reduction and Oxygen Consumption as Assessment of Human Islet Quality. Transplantation 80: 1003- 1011, 2005; Neal AS, Rountree AM, Philips CW, Kavanagh TJ, Williams DP, Newham P, Khalil G, Cook DL, Sweet IR. Quantification of low-level drug effects using real-time, in vitro measurement of oxygen consumption rate. Toxicological Sciences 148: 594-602, 2015).

In addition, diffusion gradients both in the bath, as well as the tissue itself likely impact the accuracy of the metabolic measurements. This is likely relevant for the liver slices experiments.

We agree that there are certainly concentration gradients within tissue, and these are increased in the absence of capillary flow. Nonetheless, the gradients will certainly be less than what occurs in static systems. In general, optimal size of tissue pieces are a trade-off between potential for hypoxia if the tissue is too large, and a lack of untraumatized tissue if it is too small. We have added text to address this concern that these effects are to be considered when choosing the size and shape of the liver slices or other tissue models to place into the flow system.

Following resection, liver tissue can be mechanically permeabilized (PMID: 12054447). In the present experiments, no controls were put in place to discern if the tissue was permeabilized. This could be checked by adding in adenylates and additional carbon substrates and assessing the impact on OCR. Similar controls likely need to be implemented for the islet and retina experiments.

As we have used flow systems in the past to maintain islets and liver for 24 hours and more (Neal AS, Rountree AM, Kernan K, Van Yserloo B, Zhang H, Reed BJ, Osborne W, Wang W, Sweet IR. Real time imaging of intracellular hydrogen peroxide in pancreatic islets. Biochem. J. 473:4443-4456, 2016; Neal AS, Rountree AM, Philips CW, Kavanagh TJ, Williams DP, Newham P, Khalil G, Cook DL, Sweet IR. Quantification of low-level drug effects using real-time, in vitro measurement of oxygen consumption rate. Toxicological Sciences 148: p. 594-602, 2015) and based on stable OCR we concluded that the tissue is viable. However, it is possible that the membranes of some of the tissue would become permeabilized which would affect the responses to test compounds. We considered this issue from two perspectives. 1. Whether established models that we used to test the BaroFuse were prone to high cell permeability; and 2. Whether loading and maintenance of the tissue models in the fluidics system resulted in increased permeability. We did do experiments measuring the ADP responses in OCR by islets and retina within the fluidics system. Effects were observable but small. However, these results are not definitive, because it was difficult to know what the response in permeabilized tissue was (and permeabilizing tissue slices was difficult). We then used Propidium Iodide staining to visualize and quantify the level of permeability. In islets, the fluorescence in isolated islets before and after perifusion was negligible compared to that in islets permeabilized by H2O2 treatment (see below).

Fig. 1. Staining of isolated rat islets with the indicator of cell membrane integrity propidium iodide. Islets were stained either before or after a 3-hour perifusion. As a positive control for PI staining, islets were treated with 500 uM H2O2 for 30 minutes and incubated overnight. Each data point was the average +/- SE for an n of 3.

There was some fluorescence in retina and liver however, but it was difficult to interpret this data in terms of a fraction of the tissue that is permeabilized due to the fact that dye close to the surface of the tissue is preferentially imaged. So, we finally assessed the amount of permeabilized tissue in retina and liver by comparing uptake of 3H H2O and an extracellular marker C14 sucrose.

Fig. 2. Fraction of tissue water space that is accessible to the extracellular marker sucrose. Left: Mouse retina. Right: Rat liver slice. Each data point was the average +/- SE for an n of 3.

Extracellular water in liver and retina is well established to be about 25%, close to the volume of distribution of sucrose. Thus, we cannot rule out that there are a small percentage of cells that are permeabilized, but the vast majority are not.

Additional comments are detailed below:

-The experiments with H2S are particularly interesting, as this system does seem well suited to investigate the metabolic effects of H2S.

Thanks! We are excited by the potential for this method to assess the effects of H2S and other trace gases.

-The authors state the transient rise in O2 consumption was surprising; however, accumulation of succinate during ischemia and rapid oxidation upon reperfusion has been previously demonstrated (PMID: 32863205).

This is an interesting paper which describes findings that speak to the role of succinate in supplying fuel that could drive the transient changes in O2 consumption observed following hypoxia. It would be an interesting experiment to perform our hypoxia-reoxygenation experiment in the absence and presence of the permeable malonate to see if the spike in O2 consumption following reoxygenation was absent in the presence of the drug. We have removed the word surprising and cited this paper.

-In the paper, Zaprinast was used to block pyruvate uptake. However, the rationale to use this compound, as opposed to the more specific MPC inhibitor UK5099 is unclear.

We could have used UK5099, but we had used Zaprinast in past studies (Du J, Cleghorn WM, Contreras L, Lindsay K, Rountree AM, Chertov AO, Turner SJ, Sahaboglu A, Linton J, Sadilek M, Satrústegui I, Sweet IR, Paquet-Durand F, Hurley JB. Inhibition of mitochondrial pyruvate transport by Zaprinast causes massive accumulation of aspartate at the expense of glutamate in retinas. J Biol. Chem, 288:36129-40, 2013) and so we knew that in our hands that it blocked pyruvate mitochondrial uptake and would therefore be a good test of the rapid transfer of pyruvate across the plasma membrane.

-Throughout the paper, the authors list 'COVID-19' as a potential application. It is not clear how this technology could be used in the context of COVID-19.

Reference to COVID-19 has been removed.

Author Response

Reviewer #1 (Public Review):

Wang and Dudko derive analytical equations for one special case of a model of Ca-dependent vesicle fusion, in the attempt to find a "general theory" of synaptic transmission. They use a model with 2 kinetically distinct fast and slow pools (equation 1).

Critique

1) Overall, the analytical approach applied here remains limited to the quite arbitrarily chosen 2-pool model. Thus, while the authors are able to re-capitulate the kinetics of transmitter release under a series of defined intracellular Ca-concentration steps, [Ca]i (see Fig. 2B; data from Woelfel et al. 2007 J. Neuroscience), this is nevertheless not surprising because the data by Woelfel et al. was originally also fit with a 2-pool model. More importantly, the 2-pool model is valid for describing release kinetics at high [Ca]i, but it cannot account for other important phenomena of synaptic transmission like e.g. spontaneous and asynchronous release which happen at lower [Ca]i, with different Ca cooperativity (Lou et al., 2005). Along the same lines, the derivations of the equations by Wang and Dudko are not valid in the range of low [Ca]i below about 1 micromolar (see "private recommendations" for details). This, however, limits the applicability of the model to AP-driven transmitter release, and it shows that based on one specific arbitrarily chosen model (here: the 2-pool model), one cannot claim to build a realistic and full "theory" for synaptic transmission.

Our two-pool description is far from being “arbitrarily chosen”. It is based on experimental facts that have been established by multiple independent laboratories: namely, the observed two distinct vesicle fusion kinetics due to the presence of the readily releasable and reserve pools in vivo and due to the presence of two dominant vesicle morphologies in vitro. The two-pool picture has been confirmed and successfully used in numerous experimental papers previously. That being said, our two-pool description refers to a more general notion of separation of timescales and is thus more flexible than a literal interpretation might suggest.

The data from Woelfel et al. 2007 J. Neuroscience, while of excellent quality, are not the only measured kinetics of the action-potential triggered vesicle fusion that our theory has been able to recapitulate (see other experimental data in Fig.2 and Fig.3 of the manuscript). The theory also recapitulates the kinetic measurements from fifteen other independent experimental studies, on ten different types of synapses. The dynamic range (peak release rate) of these synapses vary by 10 orders of magnitude, and the range of Ca2+ concentrations spans more than 3 orders of magnitude. Our work recapitulates these 16 datasets not through 16 different ad-hoc models but through a single, fully analytically solved, theoretical framework. Importantly, beyond recapitulating the existing data, our analytically tractable theory enables one to extract the unique sets of microscopic parameters for particular synapses, such as the activation energies and kinetic rates of their synaptic machinery, the sizes of the vesicle pools and the critical number of SNAREs. We verify that these predictions from our theory have reasonable values for each of the data sets; this is an additional, non-trivial check of our theory. The fact that our theory reproduces observations on such strikingly diverse systems, and has such a degree of predictive power, cannot be dismissed as an artifact or coincidence. We are not aware of any other theory, nor fitting model, of comparable generality and the ability to generate concrete predictions.

Reviewer #1 is mistaken in stating that the derivations of our equations are not valid below 1 micromolar Ca2+ concentrations. It is evident already from Figure R1 below (Fig.2 in the revised manuscript) that the theory performs flawlessly at concentrations as low as 0.1µM. There are indeed non-linear effects at ultra-low Ca2+ concentrations that are not displayed by the experimental data in Fig. R1. Our theory is also applicable in that regime: one simply needs to include a second coordinate (in addition to the number of Ca2+ ions bound, 𝑄‡ ) to account for the multidimensionality of the free energy landscape, analogous to the calculations of the rate constants for multidimensional activated rate processes in chemical physics. This illustrates just one of the many ways in which our theory will enable detailed studies of mechanistic aspects of synaptic transmission.

With further regards to generality, as stated in our Abstract, this paper is concerned with providing a physical theory to describe “rapid and precise neuronal communication” enabled by “a highly synchronous release” of neurotransmitters. Typically, more than 90% of the neurotransmitters are released through synchronous release during the action potential. By applying our theory to each of the multiple Ca!" sensors one will be able to cover the remaining <10% of the neurotransmitters and thus simultaneously describe spontaneous, asynchronous and synchronous release. While detailed studies of these effects are clearly beyond the scope of this work, our theory opens a door for such studies by providing a foundation in the form of a conceptual, analytically tractable framework.

2) In their derivations, Wang and Dudko collapse the intracellular Ca-concentration [Ca]i, a parameter directly quantified in the several original experiments that went into Fig. 2A, into a dimensionless relative [Ca]i "c" (see equation 7). Similarly, the release rates are collapsed into a dimensionless quantity. With these normalizations, Ca-dependent transmitter release measured in several preparations seems to fall onto a single theoretical prediction (Fig. 2A). The deeper meaning behind the equalization of the data was unclear, except a demonstration that the data from these different experiments can in general be described with a two-pool model, which is at the core of the dimensionless equations. One issue might be that many of the original data sets used here derive from the same preparation (the calyx of Held), and therefore the previous data might not scatter strongly between studies. This could be clarified by the authors by also plotting the data from all studies on the non-normalized [Ca]i axis for comparison. Furthermore, it would be useful to include data from other preparations, like the inner hair cells (Beutner et al. 2001 Neuron; their Fig. 3) which likely have a lower Ca-sensitivity, i.e. are right-shifted as compared to the calyx (see discussion in Woelfel & Schneggenburger 2003 J. Neuroscience). Thus, it is unclear why normalization of [Ca]i to "c" should be an advantage, because differences in the intracellular Ca sensitivity of vesicle fusion exist between synapses (see above), and likely represent important physiological differences between secretory systems.

We thank the Reviewer for challenging our work with the hypothesis that the demonstrated universal scaling of the experimental data could in fact be an artefact caused by pre-selecting the data with the same preparation – addressing this hypothesis is indeed a compelling test to probe the true limits of generality of our theory. Below we carry out this test. We implemented the two suggestions of the Reviewer: (i) we added datasets on markedly different synaptic preparations, including the inner hair cells as suggested by the Reviewer, as well as retina bipolar cell, hippocampal mossy fiber, cerebella basket cell, chromaffin cell, insulin-secreting cell, and additional data on Calyx of Held from multiple laboratories, and (ii) we plotted the data on the non-normalized axis of [Ca2+] to reveal the full extent of scatter among the data sets. The resulting plot (Fig. R1 below) speaks for itself: in vivo data for the release rate span 4 orders of magnitude at low [Ca2+] and 6 orders of magnitude at high [Ca2+], and there is a 10 orders of magnitude difference between the release rates from in vivo and in vitro data. The scatter across 4-10 orders of magnitude allows one to appreciate the vastly different sensitivities to [Ca2+] between synaptic preparations (Fig.R1, left). Yet, all these data collapse beautifully on the master curve established by our theory (Fig.R1, right).

Fig. R1. Despite 10 orders of magnitude variation in the release rate of different synaptic preparations and more than 3 orders of magnitude range of calcium concentration (left), the data collapse onto a universal curve predicted by the theory (right). The universal collapse indicates that the established scaling (Eq. 7) is universal across different synapses. The distinct sets of parameters for individual synapses (Appendix 3 Table 2) is a demonstration of the predictive power of the theory as a tool for extracting the unique properties of each synapse from experimental data.

What the Reviewer refers to as “the equalization of the data” is known in statistical physics as universality. The deeper meaning of a universal scaling is its indication that the observed phenomena realized in seemingly unrelated systems are in fact governed by common physical principles. The collapse of the data onto the universal curve in Fig. R1 is a demonstration that the present theory has uncovered, quantitatively, unifying physical principles underneath the striking diversity and bewildering complexity of chemical synapses. The Referee is of course correct that the differences in [Ca2+] sensitivities among synapses likely represent important physiological differences between distinct synapses and distinct secretory systems. The present theory does not negate these differences, but it in fact allows one to quantify these differences through the unique sets of extracted parameters for individual synapses (see Appendix 3 Table 2). We are not aware of any other theory that has demonstrated universality in synaptic transmission through a simple, single scaling relation across 10 orders of magnitude in dynamic range and at the same time allowed the extraction of the microscopic parameters that are unique for the individual synapses and thus reflect the diversity of their synaptic machinery. We included Fig. R1 shown here in the revised manuscript (Figure 2).

3) Finally, the authors use their model to derive the number of SNARE proteins necessary for vesicle fusion, and they arrive at the quite strong conclusion that N = 2 SNAREs are required. Nevertheless, this estimate doesn't fit with the number of n = 4-5 Ca2+ ions which the original studies of Fig. 2A consistently found. The Ca-sensitivity at the calyx of Held, and the steepness of the release rate versus [Ca]i relation is determined by Ca-binding to Synatotagmin-2 (the specific Ca sensor isoform found at the calyx synapse), as has been determined in molecular studies at the calyx synapse (see Sun et al. 2007 Nature; Kochubey & Schneggenburger 2011 Neuron). Furthermore, in other secretory cells, the number of SNARE proteins has been estimated to be {greater than or equal to} 3 (Mohrmann et al., Science 2010).

The Reviewer is incorrect in their claim that there is any discrepancy here. The number of SNAREs N and the number of Ca2+ ions 𝑄‡ , extracted from the fit to our theory, are actually in a good agreement with the findings from the studies mentioned by the Reviewer. To clarify, the parameter 𝑄‡ is the number of 𝐶𝑎!" ions bound to a SNARE at the transition state (not final state) of the free energy landscape of a SNARE complex. Appendix 3 Table 2 shows that, for all synaptic preparations, the extracted values at the transition state are 𝑄‡ < 4 − 5, which is indeed consistent with n = 4 − 5 at the final state. We note that, in addition, our theory enables one to extract the key energetic parameter that governs synaptic vesicle fusion: the activation free energy barrier ∆𝐺‡ of SNARE conformational transition (in the range 8-34 kBT for different synaptic preparations, see Appendix 3 Table 2), which, to our knowledge, has not been possible to extract from these experiments before.

The specific value N=2 was extracted from a particular data set for Calyx of Held (Woelfel et al 2007), for which the temporal curves of cumulative release at different Ca2+ concentrations were available. It is quite possible that the value of N will be different for some other synapses. As we emphasize in the manuscript (see Discussion), the present theory does not declare the same value of N for all types of synapses; the power of the theory lies in providing a fitting tool for extracting this value for a system of interest.

Taken together, the derivation of the analytical equations for the kinetic scheme of a 2-pool model is mathematically interesting, and the scholarly derived equations are trustworthy. Nevertheless, the derived analytical model in fact captures only a specific stage of synaptic transmission focusing on Ca-dependent fusion of vesicles from two pools at [Ca]i >1 microM. Other important processes and mechanistic components (e.g. spontaneous, asynchronous release, Ca-dependent pool replenishment, postsynaptic factors) are either over-simplified or remained out of the scope of the theory. Therefore, the paper is far from providing a general "theory for synaptic transmission", as the title promises.

We appreciate that the Reviewer sees our analytical derivations as being mathematically interesting, scholarly derived, and trustworthy. We believe that we have convincingly refuted the Reviewer’s criticisms regarding perceived limitations. We have shown that our universal scaling and collapse is not limited to high calcium concentrations, and have presented checks using data from vastly different synaptic preparations. As noted above, the generality of a theory is determined not by the amount of details packed in it but by the ability of the theory to reproduce observations and generate predictions regarding the phenomenon of interest (here: rapid and precise neuronal communication) while containing as few details as possible. Our theory accomplishes just that; it delivers precisely what our title promises.

Reviewer #2 (Public Review):

The present MS describes an effort to create a general mathematical model of synaptic neurotransmission. The authors invested great efforts to create a complex model of the presynaptic mechanisms, but their approach of the postsynaptic mechanisms is way oversimplified. The authors claim that their model is consistent with lots of in vivo and in vitro experimental data, but this night be true for a small subselection of experimental papers (they cite 7 experimental papers regularly in the MS!). The authors also indicate that their modeling has a realistic foundation, namely they can relate some parameters in their equations to molecules/molecular mechanisms. One example is the parameter N, which they claim indicate the number of SNARE complexes requires for fusion. The reviewer finds it rather misleading because it alludes that there is a parameter for complexin, Rim1, Rim-BP, Munc13-1 etc... The equations clearly cannot formulate and reflect diversity due to different isoforms of even the above mentioned key presynaptic molecules.

We appreciate that the Reviewer found 7 different experimental papers – covering different synapses and different experimental setups – to be “a small subselection”. We believe that Fig. R1 above (response to Reviewer #1 point 2), which uses 16 different experimental papers, leaves no further doubts that the claims about the consistency between the theory and data are fully justified. Despite up to 10 orders of magnitude variation in the release rate of different synaptic preparations and more than 3 orders of magnitude range of calcium concentrations (Fig. R1, left), all the data collapse onto a universal curve predicted by our theory (Fig. R1, right). These data represent different systems – from the central nervous system to the secretory system – and come from in vivo and in vitro experiments. The data we have used cover the measurements on all synaptic systems that we could find in the literature on the action potential-driven neurotransmitter release. If the Reviewer is aware of any existing data on other synaptic systems that we might have missed, we will gratefully appreciate the opportunity to apply the theory to those data as well.

The diversity of the molecular components in different synapses is captured in our theory through different values of the microscopic parameters Δ𝐺‡, 𝑄‡ and 𝑘( . These parameters describe, respectively, the activation energy barrier, the number of bound Ca2+ ions, and the intrinsic rate of the conformational transition of the SNARE complexes that drive synaptic vesicle fusion in a given synapse. Different isoforms of the individual components of SNARE complexes and scaffold proteins, including the proteins mentioned by the Reviewer, will be reflected in different values of Δ𝐺‡, 𝑄‡ and 𝑘( for specific synaptic preparations, as can be seen in Appendix 3 Table 2 in the manuscript. These parameters capture the energetic and kinetic properties of the synaptic fusion machinery as a complex rather than as a collection of isolated molecules. Because the molecular components within a SNARE complex act collectively (hence the name “complex”) to drive vesicle fusion, it is natural (and indeed fortunate) that the predictive power of the theory can be preserved with only a few key parameters of the molecular machinery as opposed to requiring a long list of parameters for every specific isoform of each of the many individual molecular components.

Author Response

Reviewer #1 (Public Review):

The authors present a strong set of experiments to uncover what type of role non-mutant stromal cells might be playing in the development of VM and AST, two vascular lesions that share some similarities.

Questions about experimental design.

1) For quantification of gene expression in VM and AST specimens in Figure 2, the methods say qPCR data were normalized to housekeeping genes, but it would be helpful to normalize to endothelial content. It might be that increased TGFa is due to increased endothelium.

We thank the Reviewer for this excellent suggestion. We have now added this new data as suggested with normalization of TGFA mRNA to the endothelial marker PECAM-1/CD31 mRNA. A trend towards an increased expression of TGFA mRNA was detected in VM/AST specimens in comparison to the control group. We also show in the manuscript that besides CD31-positive vascular structures, TGFA is expressed in intervascular areas, i.e. between the vessels, in the patients’ lesions (Fig.2) and in lesion-derived CD31negative intervascular stromal cells. These data altogether demonstrate that i) TGFA is expressed also in other cell types than endothelial cells and ii) indicates that the increased expression of TGFA in lesion samples is not only due to increased vasculature/endothelium in the patient samples.

The new RT-qPCR data has now been added to the manuscript as a new Fig. 2 - figure supplement 1.

2) The mutant allelic frequency for the HUVEC-PIK3CA WT versus HUVEC-PIK3CA H1047R should be provided. This is critically needed for the interpretation of the results.

Thank you for this valuable comment. To confirm that PIK3CA H1047R is still present in transduced HUVECs at the end-point of the mouse xenograft experiment, we performed a new ddPCR analysis detecting fractional abundance of PIK3CA p.H1047R from the matrigel plug-in samples. In this new data, mean fractional abundance of PIK3CA p.H1047R in fibroblast containing PIK3CA H1047R EC plugs was shown to be 27.1 % (variation 26.5-27.8 %; n=2 mice in duplicates). This corresponds to ~54 % of PIK3CA p.H1047R mutation positive cells in the plug, assuming a single copy of the mutation in each cell. As a control group, no positivity was detected in samples with fibroblast and in PIK3CAwt EC, as all the cells express the wildtype form of the PIK3CA gene. Please see Author Response Image 1 representative 2D amplification plots of the mutation analysis. Fractional abundances of PIK3CA mutations in the patient tissue samples and patient-derived CD31+ cells can also be seen in Table 1 and were in a range of 5-12 % (whole tissue) and 44-51 % (EC fraction).

3) From Figure 5, it appears that the human primary fibroblasts are not required for the mutant ECs to form perfused vessels (panel H).

We thank the Reviewer for the comment and agree that based on our H&E staining and erythrocyte analysis, perfused vessels are evident in PIK3CA mutant plugs containing ECs with fibroblasts but also in plugs containing ECs alone. This was expected as PIK3CA mutation in ECs alone has shown to be a driver of venous malformation. However, prior to our study the role of fibroblasts in PIK3CA-driven lesions had not been studied. To better understand the role of fibroblasts in lesion formation, we have now added new data to the manuscript containing example images of the PIK3CA H1047R plugs with or without fibroblasts, and added a new quantitation of their erythrocyte amount. Please see Author Response Image 2. Our data demonstrates that there are significantly: i) more CD31-positive vascular structures (Fig. 5E-G), ii) larger lumens (Fig. 5D-F) and iii) more erythrocyte-containing regions, indicative for perfused vessels (new Fig. 5H) in lesions with fibroblasts in comparison to plugs containing ECs alone. This implies that fibroblasts further induce PIK3CA-driven EC lesion formation.

Author Response Image 2. Vascular structures formed with PIK3CA H1047R ECs alone and PIK3CA H1047R ECs + FBs in mouse xenograft plugs. In the figure panel, H&E staining on each individual plug in these groups is presented. Equal size close-up images were taken from the middle of each plug covering > 50% of plug area (scale bar 250µm). More erythrocytes (red) are seen in the plugs with fibroblasts in comparison to ECs alone. Scanned images of the H&E stained whole tissue sections can be seen in the Fig. 5 – source file.

A new quantitative analysis of erythrocyte positive area in relation to whole plug area using SproutAngio quantification tool was additionally performed (). Analysis was done on a blinded manner and showed significantly increased erythrocyte amount in the plugs containing PIK3CA H1047R ECs and fibroblasts (in comparison to EC alone). Describtion of the analysis has now been added in the manuscript (p. 42, rows 839-843) Figures 5G and 5H in the manuscript were updated to show statistics and automated intensitybased quantification of the erythrocyte positive area/ plug instead of erythrocyte scoring (scale 0-3).

Is it possible that TGFa from the ECs is sufficient to drive vascular malformation?

Mutations in genes such as PIK3CA, TEK and KRAS have been shown to drive formation of vascular anomalies. Thus it is unlikely that a single growth factor, such as increased expression of TGFA, would drive this process alone. That being said, our data shows that TGFA is able to regulate proliferation of PIK3CA mutated ECs via secondary mechanism (Fig. 4F), and we show that inhibition of EGFR pathway is able to reduce PIK3CA-driven lesion growth in mice (Fig. 7). As our bulk RNA-sequencing data from patient-derived cells, showed expression of also other growth factors in lesion ECs (Table 3), it is likely that multiple angiogenic growth factors are involved in lesion formation similarly as in tumors and their expression is primarily driven by mutated cells and secondary by cell-cell crosstalk with other lesion cell types. Thus, targeting of multiple signalling pathways could be a beneficial treatment strategy in the future.

Reviewer #2 (Public Review):

In this manuscript, Ilmonen H. et al explored potential crosstalk between endothelial cells and fibroblasts in a context of sporadic vascular malformation (venous malformation and angiomatoses of soft tissue). With a high level of evidence, they found that mutated endothelial cells secrete TGFA that will activate surrounding fibroblasts, leading in turn to VEGFA secretion that will stimulate endothelial cell sprouting and vascular malformation development. Experiments are well-designed and support their hypothesis. Some controls are missing, particularly in Fig. 2. Indeed, it is mandatory to provide data from healthy skin biopsies (that are available in many laboratories): TGFa, CD31, P-EGFR staining.

We thank the Reviewer for the comments. Although it is common that VM presents in skin, in this work we solely focused on intramuscular and subcutaneous AST and VM patient samples and excluded the samples containing skin from this study. We did TGFA immunostainings from healthy skeletal muscle that can be seen Figure 2 – figure supplement 2B. CD31 staining of vessels in healthy skeletal muscle near the resection margin can be seen in Figure 1B. Please see below also tissue locations of all VM and AST samples in this study:

• Intramuscular, 42.1 % of lesions (n=16)

• Intramuscular and subcutaneous, 21.1 % of lesions (n=8)

• Intramuscular, subcutaneous and synovial membrane, 5.3 % of lesions (n=2)

• Intramuscular and synovial membrane, 2.6 % of lesions (n=1)

• Subcutaneous and synovial membrane, 2.6 % of lesions (n=1)

• Subcutaneous only, 26.3 % of lesions (n=10)

• Skin, none of the lesions

Author Response:

Reviewer #1 (Public Review):

The key question that the authors were addressing was how ethnicity differentially affects the microbiota of subjects living in a particular area (in this case East Asians and Caucasians living in San Francisco that have been enrolled in an 'Inflammation, Diabetes, Ethnicity and Obesity cohort - although inflammatory disease was apparently excluded in these subjects).

The existence of differences between different populations allows potential discrimination of the underlying factors - such as host genetics, diet, lifestyle, physiological parameters, body habitus or other environmental influences. In this case body habitus has been selected as a stratification factor between the two ethnicities. Immigration potentially allows distinction of environmental and host genetical influences.

The strength of the study is in the level of robust analysis of the microbiotas by a very experienced group of researchers, distinguishing the microbiota differences, especially in lean subject, with analysis of associations that may be driving the differences. It is interesting that diet is not one of the apparent associations in this study, yet the relationship of microbiota diversity to body habitus is strong in Caucasian subjects. These associations cannot easily be extrapolated to causation or mechanism - a fact well recognized in the paper - but remain important observations that rationalize in vivo modeling with experimental animals or in vitro analyses of microbial interactions between different taxa simulating the context of differences in the intestinal milieu. The paper includes work showing that differences of the microbiota can be recapitulated after transfer to germ-free mice, at least over the short term: this is important to provide tools to model the reasons for differences in consortial composition.

A very large amount of work required to assemble the samples and the clinical phenotypic metadata set making the data an important and definitive contribution for the subjects studied. Of course, this is one sample of extremely variable human conditions and lifestyles that will help build the overall picture of how differences in our genetics and environment shape our intestinal microbiota.

We appreciate the reviewers' positive summary of our manuscript and agree with the reviewer’s assessment of the need for both mechanistic follow-on studies and extensions to larger and more diverse cohorts.

Reviewer #2 (Public Review):

The study's primary aims are to test for the differences in the microbiome between self-identified East Asian and White subjects from the San Francisco area in the new IDEO cohort. The study builds on an growing literature which describes variations among ethnic groups. The major conclusion of "emphasize the utility of studying diverse ethnic groups" is not novel to the literature.

It was not our intention to imply that our study is novel in studying two distinct ethnic groups, but rather to emphasize that differences exist between ethnicities with regard to the gut microbiome and to provide a systematic analysis of this including gnotobiotic mouse models along a key health disparity in Asian Americans. We include references of prior examples of this work in our introduction (including several references in our introductory paragraph). We have modified our abstract to clarify this point further:

“Taken together, our findings add to the growing body of literature describing variation between ethnicities and provide a starting point for defining the mechanisms through which the microbiome may shape disparate health outcomes in East Asians.”

Overall, the strength of the results is that they confirm patterns from different cohorts/studies and demonstrate that ethnic-related differences are common. The results are subject to sample size concerns that may underpin some of the conflicting or lack of significant results. For instance, there is no overlap in highlighted species-level taxonomy differences between 16S and metagenomic analyses, which precludes a clear interpretation of the meaning of those differences and whether taxa should be highlighted in the abstract; there are low AUC values for the random forest modelling; and there is a lack of significance in correlations between BMI and East Asian subjects in F4a where there may be a correlation. While a minor point, it serves to highlight the sample sizes as the range of the variation in East Asian subjects is not as substantial as the White subjects because there are fewer East Asian data points above a 30 BMI (~N=5) relative to those of White subjects (~N=11).

We agree that our study was limited by sample size and that future studies increasing sample size would be valuable to assess the intersection of metabolic health in colocalized EA and W subjects. We include this in our discussion:

“Due to the investment of resources into ensuring a high level of phenotypic information on each cohort member, and due to its restricted geographical catchment area, the IDEO cohort was relatively small at the time of this analysis (n=46 individuals). This study only focused on two of the major ethnicities in the San Francisco Bay Area; as IDEO continues to expand and diversify its membership, we hope to study a sufficient number of participants from other ethnic groups in the future.”

The microbiome transfers from humans to mice also demonstrate that certain features of interpersonal or ethnic-related differences can be established in mice. This is useful for future studies, but it is not unexpected in and of itself given the robustness of transferring microbiome differences in other human-to-mouse studies. If the phenotype data were more compelling, then the utility of these transfers could be valuable.

We respectfully disagree with this point. To our knowledge, this is the first study demonstrating that ethnicity-associated differences in the gut microbiota are stable following transplantation, which is certainly not guaranteed given the marked and currently unpredictable variations between donor and recipient microbiotas shown here and in prior studies by us (Nayak et al., 2021; Turnbaugh et al., 2009b) and others (Walter et al., 2020).

We state this rationale in our results section:

“Taken together, our results support the hypothesis that there are stable ethnicity-associated signatures within the gut microbiota of lean EA vs. W individuals that are independent of diet. To experimentally test this hypothesis, we transplanted the gut microbiotas of two representative lean W and lean EA individuals into germ-free male C57BL/6J mice…Next, we sought to assess the reproducibility of these findings across multiple donors and in the context of a distinctive dietary pressure. We fed 20 germ-free male mice a high-fat, high-sugar (HFHS) diet for 4 weeks prior to colonization with a gut microbiota from one of 5 W and 5 EA donors....”

Furthermore, while the phenotypic data may not be as dramatic as the reviewer had hoped, this is to our knowledge the first demonstration that ethnicity-associated differences in the gut microbiota play a causal role in host phenotypes, as highlighted in our discussion:

“Our results in humans and mouse models support the broad potential for downstream consequences of ethnicity-associated differences in the gut microbiome for metabolic syndrome and potentially other disease areas. However, the causal relationships and how they can be understood in the context of the broader differences in host phenotype between ethnicities require further study.”

However, in the current state, I am concerned with the experimental design since the LFPP experiments used N=1 donor per ethnicity for establishing the mice colonies and are resultantly confounded by mice pseudo-replication with recipient mice derived from one donor of each ethnicity. This concern is relevant to interpreting results back to interpersonal or interethnic variation. Are phenotypic differences due to individual differences or ethnic differences? It's not clear.

We presented our data in summary form integrating the results from 3 independent experiments across two figures. To account for pseudoreplication as the reviewer suggests, we have restricted permutational space to account for one donor for multiple recipient mice using the parameters outlined in the adonis software package. Analyzing our results from 3 separate experiments, our results are statistically significant, which we mention in the revised text:

“In a pooled analysis of all gnotobiotic experiments accounting for one donor for multiple recipient mice, ethnicity and diet were both significantly associated with variations in the gut microbiota (Fig. S9), consistent with the extensive published data demonstrating the rapid and reproducible impact of a HFHS diet on the mouse and human gut microbiota (Bisanz et al., 2019).”

Figure S9. Combined analysis of recipient mice reveals significant associations with donor ethnicity and recipient diet. A PhILR PCoA is plotted based on 16S-Seq data from all gnotobiotic experiments. Individual mice are colored by (A) donor ethnicity or (B) the recipient’s diet. Both ethnicity and diet were statistically significant contributors to variance (ADONIS p-values and estimated variance displayed using blocks restricted by donor identifiers to account for one donor going to multiple recipient mice). We also observed a trend for interaction between diet and ethnicity in this model (p=0.068, R2=0.047, ADONIS).

The HFHS experiment also used N=5 donors that somewhat mitigates these concerns, but mixed sexes were used here and there can be sex-specific human microbiome differences.

Our study was designed to evaluate ethnicity and metabolic health. As we report in our original and updated analysis, we found no significant associations between the gut microbiota and biological sex (Figs. 2E and S4) in the IDEO cohort, perhaps due to the small effect size of sex reported in prior studies by other groups (Arumugam et al., 2011; Ding and Schloss, 2014; Schnorr et al., 2014; Zhang et al., 2021) coupled to the limited size of the current IDEO cohort.

The Turnbaugh and Koliwad labs use mixed sexes as donors for studies in conventionally raised and gnotobiotic mice due to our active funding from the NIH, which has clear guidelines meant to prevent continued discrimination against studies in females. The following link has additional information for your consideration: https://orwh.od.nih.gov/sex-gender/nih-policy-sex-biological-variable.

Importantly, our study was not confounded by sex due to the use of similar numbers of male and female donors (2 male and 2 females in the LFPP experiments and 3 female and 2 males for both ethnicities in the HFHS experiment). All of our recipient mice were male, as specified in our methods section and our revised main text:

“To experimentally test this hypothesis, we transplanted the gut microbiotas of two representative lean W and lean EA individuals into germ-free male C57BL/6J mice…Next, we sought to assess the reproducibility of these findings across multiple donors and in the context of a distinctive dietary pressure. We fed 20 germ-free male mice a high-fat, high-sugar (HFHS) diet for 4 weeks prior to colonization with a gut microbiota from one of 5 W and 5 EA donors....”

To further investigate any potential sex-specific signal we have stratified our analysis for the HFHS experiment by the gender of the donors (Reviewer Figure 2). This reveals that the significance between ethnicity in the microbiota transplantation experiments is preserved in mice that received stool from male donors (Reviewer Fig. 2A) but not female donors (Reviewer Fig. 2B). In Reviewer Fig. 1 above, LFPP1 and LFPP2 were conducted using different donors of different biological sex. Splitting our LFPP experiments up revealed the consistent signal for ethnicity in microbial community composition that we report above. The small sample sizes in this stratified analysis makes it difficult to conclude that there are reproducible sex-specific differences in the microbiome transplant experiments, but we agree with the reviewer that this question should be more thoroughly explored in future work.

We have added a brief note to the discussion to emphasize this important point:

“...differences between the human donor and recipient mouse microbiotas inherent to gnotobiotic transplantation warrant further investigation as do differences in the stability of the gut microbiotas of male versus female donors”

Reviewer Figure 2. (A,B) Principal coordinate analysis of PhILR Euclidean distances of stool from germ-free recipient mice transplanted with stool microbial communities from (A) male (n=2 EA and n=2 W donors) or (B) female (n=3 EA and n=3 W) donors of either ethnicity and fed a HFHS diet. Significance was assessed by ADONIS. Pairs of germ-free mice receiving the same donor sample are connected by a dashed line (n=2 recipient mice per donor). Experimental designs are shown in Fig. S7.

Finally, experimental results are not always consistent and sometimes show opposite trends that may be related to the sampling sizes. For instance, fat and lean mass increased and decreased respectively in LFPP, but there were no statistically-similar differences in HFHS. Moreover, the metabolic fat mass outcomes in mice do not match the expected human donor data. For instance, in LFPP1, White subjects had lower fat mass in humans but recipient mice on average gained more fat. It is difficult to reconcile these differences to a biological or sampling scheme reason.

We wholeheartedly agree with this point and were also surprised that the recipient mouse phenotypes did not match our original hypothesis based upon the observed health disparities between EA and W individuals. These surprising and perhaps counter-intuitive results demand further study and mechanistic dissection. We have tried to capture potential explanations for these findings while highlighting the limitations of our current study in our expanded discussion. With respect to the glucose tolerance data, the lack of a microbiome-driven phenotype might be due to the use of genetically identical mice that are not prone to metabolic illness without significant perturbation. If we had used mice prone to metabolic disease, such as non-obese diabetic (NOD) germ free recipient mice where the microbiome is known to impact the development of diabetes, we may have seen between ethnic differences in glucose tolerance.

Our revised discussion, with key points underlined is copied below for your convenience:

“Our results in humans and mouse models support the broad potential for downstream consequences of ethnicity-associated differences in the gut microbiome for metabolic syndrome and potentially other disease areas. However, the causal relationships and how they can be understood in the context of the broader differences in host phenotype between ethnicities require further study. While these data are consistent with our general hypothesis that ethnicity-associated differences in the gut microbiome are a source of differences in host metabolic disease risk, we were surprised by both the nature of the microbiome shifts and their directionality. Based upon observations in the IDEO (Alba et al., 2018) and other cohorts (Gu et al., 2006; Zheng et al., 2011), we anticipated that the gut microbiomes of lean EA individuals would promote obesity or other features of metabolic syndrome. In humans, we did find multiple signals that have been previously linked to obesity and its associated metabolic diseases in EA individuals, including increased Firmicutes (Basolo et al., 2020; Bisanz et al., 2019), decreased A. muciniphila (Depommier et al., 2019; Plovier et al., 2017), decreased diversity (Turnbaugh et al., 2009a), and increased acetate (Perry et al., 2016; Turnbaugh et al., 2006). Yet EA subjects also had higher levels of Bacteroidota and Bacteroides, which have been linked to improved metabolic health (Johnson et al., 2017). More importantly, our microbiome transplantations demonstrated that the recipients of the lean EA gut microbiome had less body fat despite consuming the same diet. These seemingly contradictory findings may suggest that the recipient mice lost some of the microbial features of ethnicity relevant to host metabolic disease or alternatively that the microbiome acts in a beneficial manner to counteract other ethnicity-associated factors driving disease.

EA subjects also had elevated levels of the short-chain fatty acids propionate and isobutyrate. The consequences of elevated intestinal propionate levels are unclear given the seemingly conflicting evidence in the literature that propionate may either exacerbate (Tirosh et al., 2019) or protect from (Lu et al., 2016) aspects of metabolic syndrome. Clinical data suggests that circulating propionate may be more relevant for disease than fecal levels (Müller et al., 2019), emphasizing the importance of considering both the specific microbial metabolites produced, their intestinal absorption, and their distribution throughout the body. Isobutyrate is even less well-characterized, with prior links to dietary intake (Berding and Donovan, 2018) but no association with obesity (Kim et al., 2019). Unlike SCFAs, we did not identify consistent differences in BCAAs, potentially due to differences in both extraction and standardization techniques inherent to GC-MS and NMR analysis (Cai et al., 2016; Lynch and Adams, 2014; Qin et al., 2012).

There are multiple limitations of this study. Due to the investment of resources into ensuring a high level of phenotypic information on each cohort member coupled to the restricted geographical catchment area, the IDEO cohort was relatively small at the time of this analysis (n=46 individuals). The current study only focused on two of the major ethnicities in the San Francisco Bay Area. As IDEO continues to expand and diversify its membership, we hope to study a sufficient number of participants from other ethnic groups. Stool samples were collected at a single time point and analyzed in a cross-sectional manner. While we used validated tools from the field of nutrition to monitor dietary intake, we cannot fully exclude subtle dietary differences between ethnicities (Johnson et al., 2019), which could be interrogated through controlled feeding studies (Basolo et al., 2020). Our mouse experiments were all performed in wild-type adult males. The use of a microbiome-dependent transgenic mouse model of diabetes (Brown et al., 2016) would be useful to test the effects of inter-ethnic differences in the microbiome on insulin and glucose tolerance. Additional experiments are warranted using the same donor inocula to colonize germ-free mice prior to concomitant feeding of multiple diets, allowing a more explicit test of the hypothesis that diet can disrupt ethnicity-associated microbial signatures. These studies, coupled to controlled experimentation with individual strains or more complex synthetic communities, would help to elucidate the mechanisms responsible for ethnicity-associated changes in host physiology and their relevance to disease.”

Reviewer #3 (Public Review):

The authors aimed to characterise how gut microbiota changes between different ethnic group for bacterial richness and community structure. They also wanted to address how this is associated with ethnic group within a defined geographical location. They have started to their story by comparing the fecal microbiota of relatively small cohort consisting of 46 lean and obese East Asian and White participants living in the San Francisco Bay Area. For that reason they used 16S and shotgun metagenomics. They demonstrated that ethnicity-associated differences in the gut microbiota are stronger in lean individuals and obese did not have a clear difference in the gut microbiota profile between ethnic groups, either suggesting that established obesity or its associated dietary patterns can overwrite long-lasting microbial signatures or alternatively that there is a shared ethnicity-independent microbiome type that predisposes individuals to obesity. The authors did also show the metabolic differences between these ethnic groups and the major differences were in the branched chain amino acid and the short-chain fatty acids. To prove their point, at this stage they have also used different metabolomic methodology. Although some aspects of the work are not very novel, the work does provide additional insights into the effect(s) of ethnicity, current living location and diet on shaping microbiota. Honestly, while reading through the manuscript, I have several questions where I believed that clarification was needed. But somehow, I felt like the authors have been reading my mind every step of the way. At the end of each section whatever I questioned was addressed in the next paragraph There are, however, a few points that I think would like to hear the authors' clarification.

• The authors pursued the story using 16S data. However, they have shotgun Metagenomics data which gives more power and resolution to microbiota profile. Is there any specific reason why the story was not build with shotgun Metagenomic data? However, if this is the case it will be nice to justify in the text or legend which figure was built with what dataset exactly?

As discussed above, 16S rRNA gene and metagenomic sequencing both have strengths and weaknesses. For example, 16S-seq is inexpensive and allows analysis of low abundance species, whereas metagenomics permits analysis of gene and pathway abundances of abundant taxa. As requested, we have now expanded Figure 2 (metagenomics) to better match Figure 1 (16S-seq). The type of technology is defined within each legend and the relevant text within our results.

• Even though the authors mentioned in the discussion that they have not used the same inocula from a donor to different diet, it will be nice if the authors further comments whether they would expect the same results or slightly different results which each different inocula.

As requested, we have modified the text in our discussion to include these comments:

“Additional experiments are warranted using the same donor inocula to colonize germ-free mice prior to concomitant feeding of multiple diets, allowing a more explicit test of the hypothesis that diet can disrupt ethnicity-associated microbial signatures. These studies, coupled to controlled experimentation with individual strains or more complex synthetic communities, would help to elucidate the mechanisms responsible for ethnicity-associated changes in host physiology and their relevance to disease.”

Overall, the study is well executed and claims and conclusions seem relatively well justified by the provided evidence. The findings are interesting for a broad audience of biologists. The findings are interesting for a broad audience of biologists.

Author Response:

Reviewer #1 (Public Review):

Overall, the authors have done a nice job covering the relevant literature, presenting a story out of complicated data, and performing many thoughtful analyses.

However, I believe the paper requires quite major revisions.

We thank the reviewer for their encouraging assessment of our manuscript. We are grateful for their valuable and especially detailed feedback that helped us to substantially improve our manuscript.

Major issues:

I do not believe the current results present a clear, comprehensible story about sleep and motor memory consolidation. As presented, sleep predicts an increase in the subsequent learning curve, but there is a negative relationship between learning curve and task proficiency change (which is, as far as I can tell, similar to "memory retention"). This makes it seem as if sleep predicts more forgetting on initial trials within the subsequent block (or worse memory retention) - is this true? Regardless of whether it is statistically true, there appears another story in these data that is being sacrificed to fit a story about sleep. To my eye, the results may first and foremost tell a circadian (rather than sleep) story. Examining the data in Figure 2A and 2B, it appears that every AM learning period has a higher learning curve (slope) than every PM period. While this could, of course, be due to having just slept, the main story gleaned from such a result is not a sleep effect on retention, which has been the emphasis on motor memory consolidation research in the last couple of decades, but on new learning. The fact that this effect appears present in the first session (juggling blocks 1-3 in adolescents and blocks 1-5 in adults) makes this seem the more likely story here, since it has less to do with "preparing one to re-learn" and more to do with just learning and when that learning is optimal. But even if it does not reach statistical significance in the first session alone, it remains a concern and, in my opinion, should be considered a focus in the manuscript unless the authors can devise a reason to definitively rule it out.

Here is how I recommend the authors proceed on this point: include all sessions from all subjects into a mixed effect model, predicting the slope of the learning curve with time of day and age group as fixed effects and subjects as random effects:

learning curve slope ~ AM/PM [AM (0) or PM (1)] + age [adolescent (0) or adult (1)] + (1|subject)

…or something similar with other regressors of interest. If this is significant for AM/PM status, they should re-try the analysis using only the first session. If this is significant, then a sleep-centric story cannot be defended here at all, in my opinion. If it is not (which could simply result from low power, but the authors could decide this), the authors should decide if they think they can rule out circadian effects and proceed accordingly. I should note that, while to many, a sleep story would be more interesting or compelling, that is not my opinion, and I would not solely opt to reject this paper if it centered a time-of-day story instead.

The authors need to work out precisely what is happening in the behavior here, and let the physiology follow that story. They should allow themselves to consider very major revisions (and drop the physiology) if that is most consistent with the data. As presented, I am very unclear of what to take away from the study.

We thank the reviewer for the opportunity to further elaborate on our behavioral results. We agree that the interpretation of the behavior in the complex gross-motor task is not straight forward, which might be partly due to less controllability compared to for example finger-tapping tasks. The reviewer is correct that, initially sleep seems to predict more forgetting on initial trials within the subsequent block given the dip in task proficiency and a resulting increase in steepness of the learning curve after the sleep retention interval. Notably, this dip in performance after sleep has also been reported for finger-tapping tasks (cf. Eichenlaub et al, 2020). The performance dip is also present in the wake first group (Figure 2) after the first interval. This observation suggests that picking up the task again after a period of time comes at a cost. Interestingly, this performance dip is no longer present after the second retention interval indicating that the better the task proficiency the easier it is to pick up juggling again. In other words, juggling has been better consolidated after additional training. Critically, our results show, that participants with higher SO-spindle coupling strength have a lower dip in performance after the retention interval, thus indicating a learning advantage.

Figure 2

(A) Number of successful three-ball cascades (mean ± standard error of the mean [SEM]) of adolescents (circles) for the sleep-first (blue) and wake-first group (green) per juggling block. Grand average learning curve (black lines) as computed in (C) are superimposed. Dashed lines indicate the timing of the respective retention intervals that separate the three performance tests. Note that adolescents improve their juggling performance across the blocks. (B) Same conventions as in (A) but for adults (diamonds). Similar to adolescents, adults improve their juggling performance across the blocks regardless of group.

We discuss the sleep effect on juggling in the discussion section (page 22 – 23, lines 502 – 514):

"How relevant is sleep for real-life gross-motor memory consolidation? We found that sleep impacts the learning curve but did not affect task proficiency in comparison to a wake retention interval (Figure 2DE). Two accounts might explain the absence of a sleep effect on task proficiency. (1) Sleep rather stabilizes than improves gross-motor memory, which is in line with previous gross-motor adaption studies (Bothe et al, 2019; Bothe et al, 2020). (2) Pre-sleep performance is critical for sleep to improve motor skills (Wilhelm et al, 2012). Participants commonly reach asymptotic pre-sleep performance levels in finger tapping tasks, which is most frequently used to probe sleep effects on motor memory. Here we found that using a complex juggling task, participants do not reach asymptotic ceiling performance levels in such a short time. Indeed, the learning progression for the sleep-first and wake-first groups followed a similar trend (Figure 2AB), suggesting that more training and not in particular sleep drove performance gains."

If indeed the authors keep the sleep aspect of this story, here are some comments regarding the physiology. The authors present several nice analyses in Figure 3. However, given the lack of behavioral difference between adolescents and adults (Fig 2D), they combine the groups when investigating behavior-physiology relationships. In some ways, then, Figure 3 has extraneous details to the point of motor learning and retention, and I believe the paper would benefit from more focus. If the authors keep their sleep story, I believe Figure 3 and 4 should be combined and some current figure panels in Figure 3 should be removed or moved to the supplementary information.

We thank the reviewers for their suggestion and we agree that the figures of our manuscript would benefit from more focus. Therefore, we combined Figure 3 and 4 from the original manuscript into a revised Figure 3 in the updated version of the manuscript. In more detail, subpanels that explain our methodological approach can now be found in Figure 3 – figure supplement 1, while the updated Figure 3 now focuses on developmental changes in oscillatory dynamics and SO-spindle coupling strength as well as their relationship to gross-motor learning.

Updated Figure 3:

(A) Left: topographical distribution of the 1/f corrected SO and spindle amplitude as extracted from the oscillatory residual (Figure 3 – figure supplement 1A, right). Note that adolescents and adults both display the expected topographical distribution of more pronounced frontal SO and centro-parietal spindles. Right: single subject data of the oscillatory residual for all subjects with sleep data color coded by age (darker colors indicate older subjects). SO and spindle frequency ranges are indicated by the dashed boxes. Importantly, subjects displayed high inter-individual variability in the sleep spindle range and a gradual spindle frequency increase by age that is critically underestimated by the group average of the oscillatory residuals (Figure 3 – figure supplement 1A, right). (B) Spindle peak locked epoch (NREM3, co-occurrence corrected) grand averages (mean ± SEM) for adolescents (red) and adults (black). Inset depicts the corresponding SO-filtered (2 Hz lowpass) signal. Grey-shaded areas indicate significant clusters. Note, we found no difference in amplitude after normalization. Significant differences are due to more precise SO-spindle coupling in adults. (C) Top: comparison of SO-spindle coupling strength between adolescents and adults. Adults displayed more precise coupling than adolescents in a centro-parietal cluster. T-scores are transformed to z-scores. Asterisks denote cluster-corrected two-sided p < 0.05. Bottom: Exemplary depiction of coupling strength (mean ± SEM) for adolescents (red) and adults (black) with single subject data points. Exemplary single electrode data (bottom) is shown for C4 instead of Cz to visualize the difference. (D) Cluster-corrected correlations between individual coupling strength and overnight task proficiency change (post – pre retention) for adolescents (red, circle) and adults (black, diamond) of the sleep-first group (left, data at C4). Asterisks indicate cluster-corrected two-sided p < 0.05. Grey-shaded area indicates 95% confidence intervals of the trend line. Participants with a more precise SO-spindle coordination show improved task proficiency after sleep. Note that the change in task proficiency was inversely related to the change in learning curve (cf. Figure 2D), indicating that a stronger improvement in task proficiency related to a flattening of the learning curve. Further note that the significant cluster formed over electrodes close to motor areas. (E) Cluster-corrected correlations between individual coupling strength and overnight learning curve change. Same conventions as in (D). Participants with more precise SO-spindle coupling over C4 showed attenuated learning curves after sleep.

and

Figure 3 - figure supplement 1

(A) Left: Z-normalized EEG power spectra (mean ± SEM) for adolescents (red) and adults (black) during NREM sleep in semi-log space. Data is displayed for the representative electrode Cz unless specified otherwise. Note the overall power difference between adolescents and adults due to a broadband shift on the y-axis. Straight black line denotes cluster-corrected significant differences. Middle: 1/f fractal component that underlies the broadband shift. Right: Oscillatory residual after subtracting the fractal component (A, middle) from the power spectrum (A, left). Both groups show clear delineated peaks in the SO (< 2 Hz) and spindle range (11 – 16 Hz) establishing the presence of the cardinal sleep oscillations in the signal. (B) Top: Spindle frequency peak development based on the oscillatory residuals. Spindle frequency is faster at all but occipital electrodes in adults than in adolescents. T-scores are transformed to z-scores. Asterisks denote cluster-corrected two-sided p < 0.05. Bottom: Exemplary depiction of the spindle frequency (mean ± SEM) for adolescents (red) and adults (black) with single subject data points at Cz. (C) SO-spindle co-occurrence rate (mean ± SEM) for adolescents (red) and adults (black) during NREM2 and NREM3 sleep. Event co-occurrence is higher in NREM3 (F(1, 51) = 1209.09, p < 0.001, partial eta² = 0.96) as well as in adults (F(1, 51) = 11.35, p = 0.001, partial eta² = 0.18). (D) Histogram of co-occurring SO-spindle events in NREM2 (blue) and NREM3 (purple) collapsed across all subjects and electrodes. Note the low co-occurring event count in NREM2 sleep. (E) Single subject (top) and group averages (bottom, mean ± SEM) for adolescents (red) and adults (black) of individually detected, for SO co-occurrence-corrected sleep spindles in NREM3. Spindles were detected based on the information of the oscillatory residual. Note the underlying SO-component (grey) in the spindle detection for single subject data and group averages indicating a spindle amplitude modulation depending on SO-phase. (F) Grand average time frequency plots (-2 to -1.5s baseline-corrected) of SO-trough-locked segments (corrected for spindle co-occurrence) in NREM3 for adolescents (left) and adults (right). Schematic SO is plotted superimposed in grey. Note the alternating power pattern in the spindle frequency range, showing that SO-phase modulates spindle activity in both age groups.

Why did the authors use Spearman rather than Pearson correlations in Figure 4? Was it to reduce the influence of the outlier subject? They should minimally clarify and justify this, since it is less conventional in this line of research. And it would be useful to know if the relationship is significant with Pearson correlations when robust regression is applied. I see the authors are using MATLAB, and the robustfit toolbox (https://www.mathworks.com/help/stats/robustfit.html) is a simple way to address this issue.

We thank the reviewers for their suggestion. We agree that when inspecting the scatter plots it looks like that the correlations could be severely influenced by two outliers in the adult group. Because this is an important matter, we recalculated all previously reported correlations without the two outliers (Figure R4, left column) and followed the reviewer’s suggestion to also compute robust regression (Figure R4, right column) and found no substantial deviation from our original results.

In more detail, increase in task proficiency resulted in flattening of the learning curve when removing outliers (Figure R4A, rhos = -0.70, p < 0.001) and when applying robust regression analysis (Figure R4B, b = -0.30, t(67) = -10.89, rho = -0.80, p < 0.001). Likewise, higher coupling strength still predicted better task proficiency (mean rho = 0.35, p = 0.029, cluster-corrected) and flatter learning curves after sleep (rho = -0.44, p = 0.047, cluster-corrected) when removing the outliers (Figure R4CE) and when calculating robust regression (Figure R4DF, task proficiency: b = 82.32, t(40) = 3.12, rho = 0.45, p = 0.003; learning curve: b = -26.84, t(40) = -2.96, rho = -0.43, p = 0.005). Furthermore, we calculated spearman rank correlations and cluster-corrected spearman rank correlations in our original manuscript, to mitigate the impact of outliers, even though Pearson correlations are more widely used in the field. Therefore, we still report spearman rank correlations for single electrodes instead of robust correlations as it is more consistent with the cluster-correlation analyses.

We now use robust trend lines instead of linear trend lines in our scatter plots. Further, we added the correlations without outliers (Figure R4ACE) to the supplements as Figure 2 – figure supplement 1D and Figure 3 – figure supplement 2 FG. These additional analyses are now reported in the results section of the revised manuscript (page 9, lines 186 – 191):

"[…] we confirmed a strong negative correlation between the change (post retention values – pre retention values) in task proficiency and the change in learning curve after the retention interval (Figure 2F; rhos = -0.71, p < 0.001), which also remained strong after outlier removal (Figure 2 – figure supplement 1D). This result indicates that participants who consolidate their juggling performance after a retention interval show slower gains in performance."

And (page 16, lines 343 – 346):

"[…] Furthermore, our results remained consistent when including coupled spindle events in NREM2 (Figure 3 – figure supplement 2E) and after outlier removal (Figure 3 – figure supplement 2FG)."

Furthermore, we now state that we specifically utilized spearman rank correlations to mitigate the impact of outliers in our analyses in the method section (page 35, lines 808 – 813)::

"For correlational analyses we utilized spearman rank correlations (rhos; Figure 2F & Figure 3DE) to mitigate the impact of possible outliers as well as cluster-corrected spearman rank correlations by transforming the correlation coefficients to t-values (p < 0.05) and clustering in the space domain (Figure 3DE). Linear trend lines were calculated using robust regression."

Figure R4

(A) Spearman rank correlation between task proficiency change and learning curve change collapsed across adolescents (red dot) and adults (black diamonds) after removing two outlier subjects in the adult age group. Grey-shaded area indicates 95% confidence intervals of the robust trend line. (B) Robust regression of task proficiency change and learning curve change of the original sample. (C) Cluster-corrected correlations (right) between individual coupling strength and overnight task proficiency change (post – pre retention) after outlier removal (left, spearman correlation at C4, uncorrected). Asterisks indicate cluster-corrected two-sided p < 0.05. (D) Robust regression of coupling strength at C4 and task proficiency of the original sample. (E) Same conventions as in (C) but for overnight learning curve change. (F) Same conventions as in (D) but for overnight learning curve change.

Additionally, with only a single night of recording data, it is impossible to disentangle possible trait-based sleep characteristics (e.g., Subject 1 has high SO-spindle coupling in general and retains motor memories well, but these are independent of each other) from a specific, state-based account (e.g., Subject 1's high SO-spindle coupling on night 1 specifically led to their improved retention or change in learning, etc., and this is unrelated to their general SO-spindle coupling or motor performance abilities). Clearly, many studies face this limitation, but this should be acknowledged.

We thank the reviewers for their important remark. We agree that it is impossible to make a sound statement about whether our reported correlations represent trait- or state-based aspects of the sleep and learning relationship with the data that we have reported in the manuscript. However, while we are lacking a proper baseline condition without any task engagement, we still recorded polysomnography for all subjects during an adaptation night. Given the expected pronounced differences in sleep architecture between the adaptation nights and learning nights (see Table R3 for an overview collapsed across both age groups), we initially refrained from entering data from the adaptation nights into our original analyses, but we now fully report the data below. Note that the differences are driven by the adaptation night, where subjects first have to adjust to sleeping with attached EEG electrodes in a sleep laboratory.

Table R3. Sleep architecture (mean ± standard deviation) for the adaptation and learning night collapsed across both age groups. Nights were compared using paired t-tests

To further clarify whether subjects with high coupling strength have a motor learning advantage (i.e. trait-effect) or a learning induced enhancement of coupling strength is indicative for improved overnight memory change (i.e. state-effect), we ran additional analyses using the data from the adaptation night. Note that the coupling strength metric was not impacted by differences in event number and our correlations with behavior were not influenced by sleep architecture (please refer to our answer of issue #7 for the results).Therefore, we considered it appropriate to also utilize data from the adaptation night.

First, we correlated SO-spindle coupling strength obtained from the adaptation night with the coupling strength in the learning night. We found that overall, coupling strength is highly correlated between the two measurements (mean rho across all channels = 0.55, Figure R5A), supporting the notion that coupling strength remains rather stable within the individual (i.e. trait), similar to what has been reported about the stable nature of sleep spindles as a “neural finger-print” (De Gennaro & Ferrara, 2003; De Gennaro et al, 2005; Purcell et al, 2017).

To investigate a possible state-effect for coupling strength and motor learning, we calculated the difference in coupling strength between the two nights (learning night – adaptation night) and correlated these values with the overnight change in task proficiency and learning curve. We identified no significant correlations with a learning induced coupling strength change; neither for task proficiency nor learning curve change (Figure R5B). Note that there was a positive correlation of coupling strength change with overnight task proficiency change at Cz (Figure R5B, left), however it did not survive cluster-corrected correlational analysis (rhos = 0.34, p = 0.15). Combined, these results favor the conclusion that our correlations between coupling strength and learning rather reflect a trait-like relationship than a state-like relationship. This is in line with the interpretation of our previous studies that SO-spindle coupling strength reflects the efficiency and integrity of the neuronal pathway between neocortex and hippocampus that is paramount for memory networks and the information transfer during sleep (Hahn et al, 2020; Helfrich et al, 2019; Helfrich et al, 2018; Winer et al, 2019). For a comprehensive review please see Helfrich et al (2021), which argued that SO-spindle coupling predicts the integrity of memory pathways and therefore correlates with various metrics of behavioral performance or structural integrity.

Figure R5

(A) Topographical plot of spearman rank correlations of coupling strength in the adaptation night and learning night across all subjects. Overall coupling strength was highly correlated between the two measurements. (B) Cluster-corrected correlation between learning induced coupling strength changes (learning night – adaptation night) and overnight change in task proficiency (left) as well as learning curve (right). We found no significant clusters, although correlations showed similar trends as our original analyses, with more learning induced changes in coupling strength resulting in better overnight task proficiency and flattened learning curves.

We have now added the additional state-trait analyses (Figure R5) to the updated manuscript as Figure 3 – figure supplement 2HI and report them in the results section (page 17, lines 361 – 375):

"Finally, we investigated whether subjects with high coupling strength have a gross-motor learning advantage (i.e. trait-effect) or a learning induced enhancement of coupling strength is indicative for improved overnight memory change (i.e. state-effect). First, we correlated SO-spindle coupling strength obtained from the adaptation night with the coupling strength in the learning night. We found that overall, coupling strength is highly correlated between the two measurements (mean rho across all channels = 0.55, Figure 3 – figure supplement 2H), supporting the notion that coupling strength remains rather stable within the individual (i.e. trait). Second, we calculated the difference in coupling strength between the learning night and the adaptation night to investigate a possible state-effect. We found no significant cluster-corrected correlations between coupling strength change and task proficiency- as well as learning curve change (Figure 3 – figure supplement 2I).

Collectively, these results indicate the regionally specific SO-spindle coupling over central EEG sensors encompassing sensorimotor areas precisely indexes learning of a challenging motor task."

We further refer to these new results in the discussion section (page 23, lines 521 – 528):

"Moreover, we found that SO-spindle coupling strength remains remarkably stable between two nights, which also explains why a learning-induced change in coupling strength did not relate to behavior (Figure 3 – figure supplement 2I). Thus, our results primarily suggest that strength of SO-spindle coupling correlates with the ability to learn (trait), but does not solely convey the recently learned information. This set of findings is in line with recent ideas that strong coupling indexes individuals with highly efficient subcortical-cortical network communication (Helfrich et al, 2021)."

Additionally, we now provide descriptive data of the adaptation and learning night (Table R3) in the Supplementary file – table 1 and explicitly mention the adaptation night in the results section, which was previously only mentioned in the method section(page 6, lines 101 – 105):.

"Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e. learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file – table 1 & 2)."

Reviewer #2 (Public Review):

In this study Hahn and colleagues investigate the role of Slow-oscillation spindle coupling for motor memory consolidation and the impact of brain maturation on these interactions. The authors employed a real-life gross-motor task, where adolescents and adults learned to juggle. They demonstrate that during post-learning sleep SO-spindles are stronger coupled in adults as compared to adolescents. The authors further show, that the strength of SO-spindle coupling correlates with overnight changes in the learning curve and task proficiency, indicating a role of SO-spindle coupling in motor memory consolidation.

Overall, the topic and the results of the present study are interesting and timely. The authors employed state of the art analyse carefully taking the general variability of oscillatory features into account. It also has to be acknowledged that the authors moved away from using rather artificial lab-tasks to study the consolidation of motor memories (as it is standard in the field), adding ecological validity to their findings. However, some features of their analyses need further clarification.

We thank the reviewer for their positive assessment of our manuscript. Incorporating the encouraging and helpful feedback, we believe that we substantially improved the clarity and robustness of our analyses.

1) Supporting and extending previous work of the authors (Hahn et al, 2020), SO-spindle coupling over centro-parietal areas was stronger in adults as compared to adolescents. Despite these differences in the EEG results the authors collapsed the data of adults and adolescents for their correlational analyses (Fig. 4a and 4b). Why would the authors think that this procedure is viable (also given the fact that different EEG systems were used to record the data)?

We thank the reviewers for the opportunity to clarify why we think it is viable to collapse the data of adolescents and adults for our correlational analyses. In the following we split our answers based on the two points raised by the reviewers: (1) electrophysiological differences (i.e. coupling strength) between the groups and (2) potential signal differences due to different EEG systems.

1. Electrophysiological differences

Upon inspecting the original Figure 4, it is apparent that the coupling strength of the combined sample does not form isolated clusters for each age group. In other words, while adult coupling strength is on the higher and adolescent coupling on the lower end due to the developmental increase in coupling strength we reported in the original Figure 3F, both samples overlap forming a linear trend. Second, when running the correlational analyses between coupling strength and task proficiency as well as learning curve separately for each age group, we found that they follow the same direction (Figure R3). Adolescents with higher coupling strength show better task proficiency (Figure R3A, rhos = 0.66, p = 0.005). This effect was also present when using robust regression (b = 109.97, t(15)=3.13, rho = 0.63, p = 0.007). Like adolescents, adults with higher coupling strength at C4 displayed better task proficiency after sleep (Figure R3B, rhos = 0.39, p = 0.053). This relationship was stronger when using robust regression (b = 151.36, t(23)=3.17, rho =0.56, p = 0.004). For learning curves, we found the expected negative correlation at C4 for adolescents (Figure R3C, rhos = -0.57, p = 0.020) and adults (Figure R3D, rhos = -0.44, p = 0.031). Results were comparable when using robust regression (adolescents: b = -59.58, t(15) = -2.94, rho = -0.60, p = 0.010; adults: b = -21.99, t(23 )= -1.71, rho = -0.37, p = 0.101).

Taken together, these results demonstrate that adolescents and adults show the effects and the same direction at the same electrode, thus, making it highly unlikely that our results are just by chance and that our initial correlation analyses are just driven by one group.

Additionally, we already controlled for age in our original analyses using partial correlations (also refer to our answer to issue #6). Hence, our additional analyses provide additional support that it is viable to collapse the analyses across both age groups even though they differ in coupling strength.

1. Different EEG-systems

   The reviewers also raise the question whether our analyses might be impacted by the different EEG systems we used to record our data. This is an important concern especially when considering that cross-frequency coupling analyses can be severely confounded by differences in signal properties (Aru et al, 2015). In our sample, the strongest impact factor on signal properties is most likely age, given the broadband power differences in the power spectrum we found between the groups (original Figure 3A). Importantly, we also found a similar systematic power difference in our longitudinal study using the same ambulatory EEG system for both data recordings (Hahn et al, 2020). This is in line with numerous other studies demonstrating age related EEG power changes in broadband- as well as SO and sleep spindle frequency ranges (Campbell & Feinberg, 2016; Feinberg & Campbell, 2013; Helfrich et al, 2018; Kurth et al, 2010; Muehlroth et al, 2019; Muehlroth & Werkle-Bergner, 2020; Purcell et al, 2017). Therefore, we already had to take differences in signal property into account for our cross-frequency analyses. Regardless whether the underlying cause is an age difference or different signal-to-noise ratios of different EEG systems.

To mitigate confounds in the signal, we used a data-driven and individualized approach detecting SO and sleep spindle events based on individualized frequency bands and a 75-percentile amplitude criterion relative to the underlying signal. Additionally we z-normalized all spindle events prior to the cross-frequency coupling analyses (Figure R3E). We found no amplitude differences around the spindle peak (point of SO-phase readout) between adolescents that were recorded with an ambulatory amplifier system (alphatrace) and adults that were recorded with a stationary amplifier system (neuroscan) using cluster-based random permutation testing. This was also the case for the SO-filtered (< 2 Hz) signal (Figure R3E, inset). Critically, the significant differences in amplitude from -1.4 to -0.8 s (p = 0.023, d = -0.73) and 0.4 to 1.5 s (p < 0.001, d = 1.1) are not caused by age related differences in power or different EEG-systems but instead by the increased coupling strength (i.e. higher coupling precision of spindles to SOs) in adults giving rise to a more pronounced SO-wave shape when averaging across spindle peak locked epochs.

Consequently, our analysis pipeline already controlled for possible differences in signal property introduced through different amplifier systems. Nonetheless, we also wanted to directly compare the signal-to-noise ratio of the ambulatory and stationary amplifier systems. However, we only obtained data from both amplifier systems in the adult sleep first group, because we recorded EEG during the juggling learning phase with the ambulatory system in addition to the PSG with the stationary system. First, we computed the power spectra in the 1 to 49 Hz frequency range during the juggling learning phase (ambulatory) and during quiet wakefulness (stationary) for every subject in the adult sleep first group in 10-seconds segments. Next, we computed the signal-to-noise ratio (mean/standard deviation) of the power spectra per frequency across all segments. We only found a small negative cluster from 21.9 to 22.5 Hz (p = 0.042, d = 0.53; Figure R3F), which did not pertain our frequency-bands of interest. Critically, the signal-to-noise ratio of both amplifiers converged in the upper frequency bands approaching the noise floor, therefore, strongly supporting the notion that both systems in fact provided highly comparable estimates.

In conclusion, both age groups display highly similar effects and direction when correlating coupling strength with behavior. Further, after individualization and normalization the analytical signal, we found no differences in signal properties that would confound the cross-frequency analysis. Lastly, we did not find systematic differences in signal-to-noise ratio between the different EEG-systems. Thus, we believe it is justified to collapse the data across all participants for the correlational analyses, as it combines both, the developmental aspect of enhanced coupling precision from adolescence to adulthood and the behavioral relevance for motor learning which we deem a critical research advance from our previous study.

Figure R3

(A) Cluster-corrected correlations (right) between individual coupling strength and overnight task proficiency change (post – pre retention) for adolescents of the sleep-first group (left, spearman correlation at C4, uncorrected). Asterisks indicate cluster-corrected two-sided p < 0.05. Grey-shaded area indicates 95% confidence intervals of the robust trend line. Participants with a more precise SO-spindle coordination show improved task proficiency after sleep. (B) Cluster-corrected correlation of coupling strength and overnight task proficiency change) for adults. Same conventions as in (A). Similar trend of higher coupling strength predicting better task proficiency after sleep (C) Cluster-corrected correlation of coupling strength and overnight learning curve change for adolescents. Same conventions as in (A). Higher coupling strength related to a flatter learning curve after sleep. (D) Cluster-corrected correlation of coupling strength and overnight learning curve change for adults. Same conventions as in (A). Higher coupling strength related to a flatter learning curve after sleep. (E) Spindle peak locked epoch (NREM3, co-occurrence corrected) grand averages (mean ± SEM) for adolescents (red) and adults (black). Inset depicts the corresponding SO-filtered (2 Hz lowpass) signal. Black lines indicate significant clusters. Note, we found no difference in amplitude after normalization. Significant differences are due to more precise SO-spindle coupling in adults. Spindle frequency is blurred due to individualized spindle detection. (F) Signal-to-noise ratio for the stationary EEG amplifier (green) during quiet wakefulness and for the ambulatory EEG amplifier (purple) during juggling training. Grey shaded area denotes cluster-corrected p < 0.05. Note that signal-to-noise ratio converges in the higher frequency ranges.

We have now added Figure R3E as Figure 3B to the revised version of the manuscript to demonstrate that there were no systematic differences between the two age groups in the analytical signal due to the expected age related power differences or EEG-systems. Specifically, we now state in the results section (page 13 – 14, lines 282 – 294):

"We assessed the cross frequency coupling based on z-normalized spindle epochs (Figure 3B) to alleviate potential power differences due to age (Figure 3 – figure supplement 1A) or different EEG-amplifier systems that could potentially confound our analyses (Aru et al, 2015). Importantly, we found no amplitude differences around the spindle peak (point of SO-phase readout) between adolescents and adults using cluster-based random permutation testing (Figure 3B), indicating an unbiased analytical signal. This was also the case for the SO-filtered (< 2 Hz) signal (Figure 3B, inset). Critically, the significant differences in amplitude from -1.4 to -0.8 s (p = 0.023, d = -0.73) and 0.4 to 1.5 s (p < 0.001, d = 1.1) are not caused by age related differences in power or different EEG-systems but instead by the increased coupling strength (i.e. higher coupling precision of spindles to SOs) in adults giving rise to a more pronounced SO-wave shape when averaging across spindle peak locked epochs."

Further, we added the correlational analyses that we computed separately for the age groups (Figure R3A-D) to the revised manuscript (Figure 3 – figure supplement 2CD) as they further substantiate our claims about the relationship between SO-spindle coupling and gross-motor learning.

We now refer to these analyses in the results section (page 16, lines 338 – 343):

"Critically, when computing the correlational analyses separately for adolescents and adults, we identified highly similar effects at electrode C4 for task proficiency (Figure 3 – figure supplement 2C) and learning curve (Figure 3 – figure supplement 2D) in each group. These complementary results demonstrate that coupling strength predicts gross-motor learning dynamics in both, adolescents as well as adults, and further show that this effect is not solely driven by one group."

2) The authors might want to explicitly show that the reported correlations (with regards to both learning curve and task proficiency change) are not driven by any outliers.

We thank the reviewers for their suggestion. We agree that when inspecting the scatter plots it looks like that the correlations could be severely influenced by two outliers in the adult group. Because this is an important matter, we recalculated all previously reported correlations without the two outliers (Figure R4, left column) and followed the reviewer’s suggestion to also compute robust regression (Figure R4, right column) and found no substantial deviation from our original results.

In more detail, increase in task proficiency resulted in flattening of the learning curve when removing outliers (Figure R4A, rhos = -0.70, p < 0.001) and when applying robust regression analysis (Figure R4B, b = -0.30, t(67) = -10.89, rho = -0.80, p < 0.001). Likewise, higher coupling strength still predicted better task proficiency (mean rho = 0.35, p = 0.029, cluster-corrected) and flatter learning curves after sleep (rho = -0.44, p = 0.047, cluster-corrected) when removing the outliers (Figure R4CE) and when calculating robust regression (Figure R4DF, task proficiency: b = 82.32, t(40) = 3.12, rho = 0.45, p = 0.003; learning curve: b = -26.84, t(40) = -2.96, rho = -0.43, p = 0.005). Furthermore, we calculated spearman rank correlations and cluster-corrected spearman rank correlations in our original manuscript, to mitigate the impact of outliers, even though Pearson correlations are more widely used in the field. Therefore, we still report spearman rank correlations for single electrodes instead of robust correlations as it is more consistent with the cluster-correlation analyses.

We now use robust trend lines instead of linear trend lines in our scatter plots. Further, we added the correlations without outliers (Figure R4ACE) to the supplements as Figure 2 – figure supplement 1D and Figure 3 – figure supplement 2 FG. These additional analyses are now reported in the results section of the revised manuscript (page 9, lines 186 – 191):

"[…] we confirmed a strong negative correlation between the change (post retention values – pre retention values) in task proficiency and the change in learning curve after the retention interval (Figure 2F; rhos = -0.71, p < 0.001), which also remained strong after outlier removal (Figure 2 – figure supplement 1D). This result indicates that participants who consolidate their juggling performance after a retention interval show slower gains in performance."

And (page 16, lines 343 – 346):

"[…] Furthermore, our results remained consistent when including coupled spindle events in NREM2 (Figure 3 – figure supplement 2E) and after outlier removal (Figure 3 – figure supplement 2FG)."

Furthermore, we now state that we specifically utilized spearman rank correlations to mitigate the impact of outliers in our analyses in the method section (page 35, lines 808 – 813)::

"For correlational analyses we utilized spearman rank correlations (rhos; Figure 2F & Figure 3DE) to mitigate the impact of possible outliers as well as cluster-corrected spearman rank correlations by transforming the correlation coefficients to t-values (p < 0.05) and clustering in the space domain (Figure 3DE). Linear trend lines were calculated using robust regression."

Figure R4:

(A) Spearman rank correlation between task proficiency change and learning curve change collapsed across adolescents (red dot) and adults (black diamonds) after removing two outlier subjects in the adult age group. Grey-shaded area indicates 95% confidence intervals of the robust trend line. (B) Robust regression of task proficiency change and learning curve change of the original sample. (C) Cluster-corrected correlations (right) between individual coupling strength and overnight task proficiency change (post – pre retention) after outlier removal (left, spearman correlation at C4, uncorrected). Asterisks indicate cluster-corrected two-sided p < 0.05. (D) Robust regression of coupling strength at C4 and task proficiency of the original sample. (E) Same conventions as in (C) but for overnight learning curve change. (F) Same conventions as in (D) but for overnight learning curve change.

3) The sleep data of all participants (thus from both sleep first and wake first) were used to determine the features of SO-spindle coupling in adolescents and adults. Were there any differences between groups (sleep first vs. wake first)? This might be in interesting in general but especially because only data of the sleep first group entered the subsequent correlational analyses.

We thank the reviewers for their remark. We agree that adding additional information about possible differences between the sleep first and wake first groups would allow for a more comprehensive assessment of the reported data. We did not explain our reasoning to include only the sleep first groups for the correlation analyses clearly enough in the original manuscript. Unfortunately, we can only report data for the adolescents in our sample, because we did not record polysomnography (PSG) for the adult wake first group. This is also one of the two reasons why we focused on the sleep first groups for our correlational analyses.

Adolescents in the sleep first group did not differ from adolescents in the wake first group in terms of sleep architecture (except REM (%), which did not correlate with behavior [task proficiency: rho = -0.17, p = 0.28; learning curve: -0.02, p = 0.90]) as well as SO and sleep spindle event descriptive measures (see Table R2). Importantly, we found no differences in coupling strength between the two groups (Figure R2A).

Table R2. Summary of sleep architecture and SO/spindle event descriptive measures (at electrode C4) of adolescents in the sleep first and wake first group (mean ± standard deviation). Independent t-tests were used for comparisons

The second reason why we focused our analyses on sleep first was that adolescents in the wake first group had higher task proficiency after the sleep retention interval than the sleep first group (Figure R2A; t(23) = -2.24, p = 0.034). This difference in performance is directly explained by the additional juggling test that the wake first group performed at the time point of their learning night, which should be considered as additional training. Therefore, we excluded the wake first group from our correlational analyses because sleep and wake first group are not comparable in terms of juggling training during the night when we assessed SO-spindle coupling strength.

Figure R2

(A) Comparison of SO-spindle coupling strength in the adolescent sleep first (blue) and wake first (green) group using cluster-based random permutation testing (Monte-Carlo method, cluster alpha 0.05, max size criterion, 1000 iterations, critical alpha level 0.05, two-sided). Left: exemplary depiction of coupling strength at electrode C4 (mean ± SEM). Right: z-transformed t-values plotted for all electrodes obtained from the cluster test. No significant clusters emerged. (B) Comparison of task proficiency between sleep first and wake first group after the sleep retention interval (mean ± SEM). Adolescents in the wake first group had higher task proficiency given the additional juggling performance test, which also reflects additional training.

These additional analyses (Figure R2) and the summary statistics of sleep architecture and SO/spindle event descriptives of adolescents in the sleep first and wake first group (Table R2), are now reported in the revised version of the manuscript as Figure 3 – figure supplement 2AB and Supplementary file – table 7. We now explicitly explain our rationale of why we only considered participants in the sleep first group for our correlational analyses in the results section (page 6, lines 101 – 105):

"Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e. learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file – table 1 & 2)"

And (page 15, lines 311 – 320):

"[…] Furthermore, given that we only recorded polysomnography for the adults in the sleep first group and that adolescents in the wake first group showed enhanced task proficiency at the time point of the sleep retention interval due to additional training (Figure 3 – figure supplement 2A), we only considered adolescents and adults of the sleep-first group to ensure a similar level of juggling experience adolescents and adults of the sleep-first group to ensure a similar level of juggling experience (for summary statistics of sleep architecture and SO and spindle events of subjects that entered the correlational analyses see Supplementary file – table 6). Notably, we found no differences in electrophysiological parameters (i.e. coupling strength, event detection) between the adolescents of the wake first and sleep first group (Figure 3 – figure supplement 2B & Supplementary file – table 7)."

4) To allow a more comprehensive assessment of the underlying data information with regards to general sleep descriptives (minutes, per cent of time spent in different sleep stages, overall sleep time etc.) as well as related to SOs, spindles and coupled events (e.g. number, density etc.) would be needed.

We agree with the reviewers that additional information about sleep architecture and SO as well as sleep spindle characteristics are needed for a more comprehensive assessment of our data. We now added summary tables for sleep architecture and SO/spindle event descriptive measures for the whole sample (Table R4) and for the sleep first groups that we used for our correlational analyses (Table R5) to the supplementary material in the updated manuscript. It is important to note, that due to the longer sleep opportunity of adolescents that we provided to accommodate the overall higher sleep need in younger participants, adolescents and adults differed in most general sleep architecture markers and SO as well as sleep spindle descriptive measures. In addition, changes in sleep architecture are prominent during the maturational phase from adolescence to adulthood, which might introduce additional variance between the two age groups.

Table R4. Summary of sleep architecture and SO/spindle event descriptive measures (at electrode C4) of adolescents and adults across the whole sample (mean ± standard deviation) in the learning night. Independent t-tests were used for comparisons

Table R5. Summary of sleep architecture and SO/spindle event descriptive measures (at electrode C4) of adolescents and adults in the sleep first group (mean ± standard deviation) in the learning night. Independent t-tests were used for comparisons

In order to ensure that our correlational analyses are not driven by these systematic differences between the two age groups, we used cluster-corrected partial correlations to control for sleep architecture markers (Figure R7) and SO/spindle descriptive measurements (Figure R8A). Critically, none of these possible confounders changed the pattern of our initial correlational analyses of coupling strength and task proficiency/learning curve. Additionally, we also controlled for differences in spindle event number by using a bootstrapped resampling approach. We randomly drew 200 spindle events in 100 iterations and subsequently recalculated the coupling strength for each subject. We found that resampled values and our original observation of coupling strength are almost perfectly correlated, indicating that differences in event number are unlikely to have an impact on coupling strength as long as there are at least 200 events (Figure R8B). Combined these analyses demonstrate that our correlations between coupling strength and behavior are not influenced by the reported differences in sleep architecture and SO/spindle descriptive measures.

Figure 7R

Summary of cluster-corrected partial correlations of coupling strength with task proficiency (left) and learning curve (right) controlling for possible confounding factors. Asterisks indicate location of the detected cluster. The pattern of initial results remained highly stable.

Figure R8

(A) Summary of cluster-corrected partial correlations of coupling strength with task proficiency (left) and learning curve (right) controlling SO/spindle descriptive measures at critical electrode C4. Asterisks indicate location of the detected cluster. The pattern of initial results remained highly stable. (B) Spearman correlation between resampled coupling strength (N = 200, 100 iterations) and original observation of coupling strength for adolescents (red circles) and adults (black diamonds), indicating that coupling strength is not influenced by spindle event number if at least 200 events are present. Grey-shaded area indicates 95% confidence intervals of the robust trend line.

We now provide general sleep descriptives (Table R4 & R5) in the revised version of the manuscript as Supplementary file – table 2 & table 6. These data are referred to in the results section (page 6, lines 101 – 105):

"Polysomnography (PSG) was recorded during an adaptation night and during the respective sleep retention interval (i.e. learning night) except for the adult wake-first group (for sleep architecture descriptive parameters of the adaptation night and learning night as well as for adolescents and adults see Supplementary file – table 1 & 2)."

And (page 15, lines 311 – 318):

"Furthermore, given that we only recorded polysomnography for the adults in the sleep first group and that adolescents in the wake first group showed enhanced task proficiency at the time point of the sleep retention interval due to additional training (Figure 3 – figure supplement 2A), we only considered adolescents and adults of the sleep-first group to ensure a similar level of juggling experience (for summary statistics of sleep architecture and SO and spindle events of subjects that entered the correlational analyses see Supplementary file – table 6)."

The additional control analyses (Figure R7 & R8) are also now added to the revised manuscript as Figure 3 – figure supplement 3 & 4 in the results section (page 16, lines 356 – 360):

"For a summary of the reported cluster-corrected partial correlations as well as analyses controlling for differences in sleep architecture see Figure 3 – figure supplement 3. Further, we also confirmed that our correlations are not influenced by individual differences in SO and spindle event parameters (Figure 3 – figure supplement 4)."

5) The authors used a partial correlations to rule out that age drove the relationship between coupling strength, learning curve and task proficiency. It seems like this analysis was done specifically for electrode C4, after having already established that coupling strength at electrode C4 correlates in general with changes in the learning curve and task proficiency. I think the claim that results were not driven by age as confounding factor would be stronger if the authors used a cluster-corrected partial correlation in the first place (just as in the main analysis).

The reviewers are correct that initially we only conducted the partial correlation for electrode C4. Following the reviewers suggestion we now additionally computed cluster-corrected partial correlations similar to our main analysis. Like in our original analyses, we found a significant positive central cluster (Figure R6A, mean rho = 0.40, p = 0.017) showing that higher coupling strength related to better task proficiency after sleep and a negative cluster-corrected correlation at C4 showing that higher coupling strength was related to flatter learning curves after sleep (Figure R6B, rho = -0.47, p = 0.049) also when controlling for age.

Figure R6

(A) Cluster-corrected partial correlation of individual coupling strength in the learning night and overnight change in task proficiency (post – pre retention) collapsed across adolescents and adults, controlling for age. Asterisks indicate cluster-corrected two-sided p < 0.05. A similar significant cluster to the original analysis (Figure 4A) emerged comprising electrodes Cz and C4. (B) Same conventions as in A. Like in the original analysis (Figure 4B) a negative correlation between coupling strength at C4 and learning curve change survived cluster-corrected partial correlations when controlling for age.

We now always report cluster-corrected partial correlations when controlling for possible confounding variables in the updated version of the manuscript (also see answer to issue #7). A summary of all computed partial correlations including Figure R6 can now be found as Figure 3 – figure supplement 3 & 4 in the revised manuscript.

Specifically we now state in the results section (page 16 – 17, lines 347 – 360):

"To rule out age as a confounding factor that could drive the relationship between coupling strength, learning curve and task proficiency in the mixed sample, we used cluster-corrected partial correlations to confirm their independence of age differences (task proficiency: mean rho = 0.40, p = 0.017; learning curve: rhos = -0.47, p = 0.049). Additionally, given that we found that juggling performance could underlie a circadian modulation we controlled for individual differences in alertness between subjects due to having just slept. We partialed out the mean PVT reaction time before the juggling performance test after sleep from the original analyses and found that our results remained stable (task proficiency: mean rho = 0.37, p = 0.025; learning curve: rhos = -0.49, p = 0.040). For a summary of the reported cluster-corrected partial correlations as well as analyses controlling for differences in sleep architecture see Figure 3 – figure supplement 3. Further, we also confirmed that our correlations are not influenced by individual differences in SO and spindle event parameters (Figure 3 – figure supplement 4)."

And in the methods section (page 35, lines 813 – 814):

"To control for possible confounding factors we computed cluster-corrected partial rank correlations (Figure 3 – figure supplement 3 and 4)."

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Author Response:

Reviewer #1:

Maimon-Mor et al. examined the control of reaching movement of one-handers, who were born with a partial arm, and amputees, who lost their arm in adulthood. The authors hypothesized that since one-handers started using their artificial arm earlier in life then amputees, they are expected to exhibit better motor control, as measured by point-to-point reaching accuracy. Surprisingly, they found the opposite, that the reaching accuracy of one-handers is worse than that of amputees (and control with their non-dominant hand). This deficit in motor control was reflected in an increase in motor noise rather than consistent motor biases.

Strengths:

• I found the paper in general very well and clearly written.
• The authors provide detailed analyses to examine various possible factors underlying deficits in reaching movements in one-handers and amputees, including age at which participants first used an artificial arm, current usage of the arm, performance in hand localization tasks, and statistical methods that control for potential confounding factors.
• The results that one handers, who start using the artificial arm at early age, show worse motor control than amputees, who typically start using the arm during adulthood, are surprising and interesting. Also intriguing are the results that reaching accuracy is negatively correlated with the time of limbless experience in both groups. These results suggest that there is a plasticity window that is not anchored to a certain age, but rather to some interference (perhaps) from the time without the use of artificial arm. In one-handers these two time intervals are confounded by one another, but the amputees allow to separate them. I think that the results have implications for understanding plasticity aspects of acquiring skills for using artificial limbs.

Weaknesses:

• While I found that one of the main conclusion from the paper is that the main factor that is related to increased motor noise is the time spent without the artificial arm, it felt that this was not emphasized as such. These results are not mentioned in the abstract and the correlation for amputees is not shown in a figure.

We thank the reviewer for their comment. While it is true that motor noise correlated with time of limbless experience in both groups, we were hesitant to highlight the results found in amputees, considering the small number of participants, and lack of converging evidence (e.g., contrary to the congenital group, we did not find a strong main effect). For these reasons, we have chosen to include it in the manuscript but not highlight it or base our main conclusions on it. Following the reviewer’s comment, the correlation of the amputees’ data is now visualised in Figure 3. Moreover, while the behavioural correlation might be similar in both groups, from a neural standpoint, the limbless experience of a toddler with a developing brain is qualitatively different to that of an adult, with a fully developed brain, who has lost a limb. As such, we were hesitant to link these two findings into a single framework, however in the revised manuscript we highlight this tentative link.

Discussion (4th paragraph):

“In both the congenital and acquired groups, artificial arm reaching motor noise correlated with the amount of time they spent using only their residual limb. It is therefore tempting to link these two results under a unifying interpretation; however, this requires further research, considering the neural differences between the two groups.”

Figure 3. Years of limbless experience before first artificial arm use in the acquired group. (A) Relationship between years of limbless experience and (A) artificial arm reaching errors or (B) artificial arm motor noise in the acquired group.

• The suggested mechanism of a deficit in visuomotor integration is not clear, and whether the results indeed point to this hypothesis. The results of the reaching task show that the one-handers exhibit higher motor noise and initial error direction than amputees. The results of the 2D localization task (the same as the standard reaching task but without visual feedback) show no difference in errors between the groups. First, it is not clear how the findings of the 2D localization task are in line with the results that one-handers show larger initial directional errors.

We fully take on the reviewer’s comment regarding the vague use of the term visuomotor integration. In the revised manuscript, we have opted instead for a much broader term, suggesting a deficit in visual-based corrective movements, considering we are limited in our ability to infer the specific underlying mechanism from our result. We have also made changes to the abstract based on the reviewer’s comment (see below).

With regards to discussing how the various results fit together, in the revised manuscript, these are now discussed more at length. In short, in the 2D localisation task (reaching without visual feedback), participants were not instructed to perform fast ballistic movements. Instead, participants were instructed that they could perform movements to correct for their initial aiming error (using proprioception). Together with the similar performance observed for the proprioceptive task, this strengthens our suggestion that the deficit in the congenital group is triggered by visual-driven corrections. These various considerations are now detailed as follows:

Abstract:

“Since we found no group differences when reaching without visual feedback, we suggest that the ability to perform efficient visually-based corrective movements, is highly dependent on either biological or artificial arm experience at a very young age.”

Result (section 7, 1st paragraph):

“From these results, we infer that early-life experience relates to a suboptimal ability to reduce the system’s inherent noise, and that this is possibly not related to the noise generated by the execution of the initial motor plan. Early life experience might therefore relate to better use of visual feedback in performing corrective movements. The continuous integration of visual and sensory input is at the heart of visually- driven corrective movements. Therefore, one possibility is that limited early life experience, results in suboptimal integration of information within the sensorimotor system.”

Discussion (2nd paragraph):

“When performing reaching movements without visual feedback (2D localisation task), the congenital group did not differ from the acquired or control group. This begs the question, if the congenital group has a deficit in motor planning why was it not evident in this task as well? In the 2D localisation task, unlike the main task, participants were allowed to make corrective movements. While they did not receive visual feedback, the proprioceptive and somatosensory feedback from the residual limb appears to be enough to allow them to correct for initial reaching errors and perform at the same level as the acquired and control group. Moreover, we did not find strong evidence for an impaired sense of localisation of either the residual or the artificial arm in the congenital group. As such, by elimination, our evidence suggests that the process of using visual information to perform corrective movements isn’t as efficient in the congenital group.”

Discussion (2nd paragraph):

“Lack of concurrent visual and motor experience during development might therefore cause a deficit in the ability to form the computational substrates and thus to efficiently use visual information in performing corrective movements.”

Discussion (last paragraph):

“By the process of elimination, we have nominated suboptimal visual feedback-based corrections to be the most likely cause underlying this motor deficit.”

Second, I think that these results suggest that the deficiency in one-handers is with feedback responses rather than feedforward. This may also be supported by the correlation with age: early age is correlated with less end-point motor noise, rather than initial directional error. Analyses of feedback correction might help shedding more light on the mechanism. The authors mention that the participants were asked to avoid doing corrective movement and imposed a limit of 1 sec per reach to encourage that. But it is not clear whether participants actually followed these instructions. 1 sec could be enough time to allow feedback responses, especially for small amplitude movements (e.g., <10 cm).

Please see below our response to the feedback correction analysis suggestion. Regarding corrective movements, we had the same concern as the reviewer which led us to use hand velocity data to identify first movement termination. We apologise if the experimental design and pre-processing procedures were not clear.

In short, a 1 sec trial duration was imposed on all trials to generate a sense of time- pressure and encourage participants to perform fast ballistic movements. As we were worried that participants might still perform secondary corrective movements within this 1 sec window, for each trial, we used the hand velocity profile to identify the end of the first movement. Below, we have plotted the arm velocity from a single trial to illustrate this procedure. For this trial, the timepoint indicated by the circular marker has been identified as the time of the end of the first movement (See Methods for further information). For each trial, endpoint location was defined as the location of the arm at the movement termination timepoint defined by the kinematic data and not the endpoint at the 1 sec timepoint. It is worth noting that performing the same analysis using the end- points recorded at the 1 sec timepoint did not generate different statistical results.

This has now been further clarified in the text.

Results (section 1, 1st paragraph):

“Reaching performance was evaluated by measuring the mean absolute error participants made across all targets (see Figure 1C). The absolute error refers to the distance from the cursor’s position at the end of the first reach (endpoint) to the centre of the target in each trial. The endpoint of each trial was set as the arm location at the end of the first reaching movement, identified using the trial’s kinematic data (See Methods).”

Methods (section: Data processing and analysis – main task):

“Within the 1 sec movement time constraint, in some trials, participants still performed secondary corrective movements. We therefore used the tangential arm velocities to identify the end of the first reach in each trial (i.e., movement termination).”

Reviewer #2:

This is a broad and ambitious study that is fairly unique in scope - the questions it seek to answer are difficult to answer scientifically, and yet the depth of the questions it seeks to answer and the framework in which it is founded seem out of place in a clinical journal.

And yet, as a scientist and clinician, I found myself objecting to the claims of the authors, only have them to address my objection in the very next section. The results are surprising, but compelling - the authors have done an excellent job of untangling a very complicated question, and they have tested (for our field) a large number of subjects.

The main two results of the paper, from my perspective, are as follows:

1) Persons with an amputation can form better models of new environments, such as manipulandums, than can those with congenital deficiencies. This result is interesting because a) the task did not depend on significant use of the device (they were able to use their intact musculature for the reaching-based task), and b) the results were not influenced by the devices used by the subjects (cosmetic, body-powered, or myoelectric).

2) Persons with congenital deficiency fit earlier in life had less error than those fit later in life.

Taken together, these results suggest that during early childhood the brain is better able to develop the foundation necessary to develop internal models and that if this is deprived early in childhood, it cannot be regained later in life - even if subjects have MORE experience. (E.g., those with congenital deficiencies had more experience using their prosthetic arm than those with amputation, and yet scored worse).

The questions analyzed by the researchers are excellent and the statistical methods are generally appropriate. My only minor concern is that the authors occasionally infer that two groups are the same when a large p-value is reported, whereas large p-values do not convey that the groups are the same; only that they cannot be proven to be different. The authors would need to use a technique such as ICC or analysis of similarities to prove the groups are the same.

We appreciate the reviewer’s concern about inferring the null from classical frequentist statistics. In this manuscript, we have opted to using Bayesian statistics as a measure of testing the significance of similarity across groups (See Methods: Statistical analysis) as opposed to the frequentist methods suggested by the reviewer. This approach is equivalent to the ones proposed by the reviewer and are widely used in our field. A Bayesian Factor (BF) smaller than 0.33 is regarded as sufficient evidence for supporting the null hypothesis that is, that there are no differences between the groups.

This approach is described in detail in the methods and is introduced in the first section of the results as well.

Results (1st section 2nd paragraph):

“To further explore the non-significant performance difference between amputees and controls, we used a Bayesian approach (Rouder et al., 2009), that allows for testing of similarities between groups (the null hypothesis). In this analysis, the smaller effect size of the two reported here (1.39) was inputted as the Cauchy prior width. The resulting Bayesian Factor (BF10=0.28) provided moderate support to the null hypothesis (i.e., smaller than 0.33).”

Methods (Statistical analysis section):

“In parametric analyses (ANCOVA, ANOVA, Pearson correlations), where the frequentist approach yielded a non-significant p-value, a parallel Bayesian approach was used and Bayes Factors (BF) were reported (Morey & Rouder, 2015; Rouder et al., 2009, 2012, 2016). A BF<0.33 is interpreted as support for the null-hypothesis, BF > 3 is interpreted as support for the alternative hypothesis (Dienes, 2014). In

Bayesian ANOVAs and ANCOVA’s, the inclusion Bayes Factor of an effect (BFIncl) is reported, reflecting that the data is X (BF) times more likely under the models that include the effect than under the models without this predictor. When using a Bayesian t-test, a Cauchy prior width of 1.39 was used, this was based on the effect size of the main task, when comparing artificial arm reaches of amputees and one- handers. Therefore, the null hypothesis in these cases would be there is no effect as large as the effect observed in the main task.”

Following the reviewer’s comment, we have carefully scanned through the manuscript to make sure no equivalence claims are made without the support of a significant BF. In one instance that has been the case and has been rectified.

Results (3rd section, 2nd paragraph):

“We compared artificial arm and nondominant arm biases (distance from the centre of the endpoint to the target) across groups, using intact arm biases as a covariate. The ANCOVA resulted in no significant (inconclusive) group differences (F(2,47)=2.40, p=0.1, BFIncl=0.72; see Figure 2A).”

Author Response

Reviewer #1 (Public Review):

Redox signaling is a dynamic and concerted orchestra of inter-connected cellular pathways. There is always a debate whether ROS (reactive oxygen species) could be a friend or foe. Continued research is needed to dissect out how ROS generation and progression could diverge in physiological versus pathophysiological states. Similarly, there are several paradoxical studies (both animal and human) wherein exercise health benefits were reported to be accompanied by increases in ROS generation. It is in this context, that the present manuscript deserves attention.

Utilizing the in-vitro studies as well as mice model work, this manuscript illustrates the different regulatory mechanisms of exercise and antioxidant intervention on redox balance and blood glucose level in diabetes. The manuscript does have some limitations and might need additional experiments and explanation.

The authors should consider addressing the following comments with additional experiments.

1) Although hepatic AMPK activation appears to be a central signaling element for the benefits of moderate exercise and glucose control, additional signals (on hepatic tissue) related to hepatic gluconeogenesis such as Forkhead box O1 (FoxO1), phosphoenolpyruvate carboxykinase (PEPCK), and GLUT2 needs to be profiled to present a holistic approach. Authors should consider this and revise the manuscript.

We appreciate the constructive suggestion. Besides glycolysis, gluconeogenesis and glucose uptake are critical in maintaining liver and blood glucose homeostasis.

FoxO1 has been tightly linked with hepatic gluconeogenesis through inhibiting the transcription of gluconeogenesis-related PEPCK and G6Pase expression (1, 2). Herein, we found the expression of FoxO1 increased in the diabetic group but reduced in the CE, IE and EE groups (Fig. X1A, Fig.5E-F in manuscript). Meanwhile, the mRNA level of Pepck and G6PC (one of the three G6Pase catalytic-subunit-encoding genes) also decreased in the CE, IE, and EE groups (Fig. X1B-1C, Fig.5H-I in manuscript). These results indicates that these three modes of exercise all inhibited gluconeogenesis through down-regulating FoxO1.

For the glucose uptake, we detected the protein expression of GLUT2 in the liver tissue. Glut2 helps in the uptake of glucose by the hepatocytes for glycolysis and glycogenesis. Accordingly, we found GLUT2,a glucose sensor in liver, was up-regulated in diabetic rats, but down-regulated by the CE and IE intervention. However, GLUT2 didn’t decrease in the EE group, which is consistent with the results of the unimproved blood glucose by EE intervention (Figure X1A, Fig.5E and 5G in manuscript).

Taken together, moderate exercise could benefits glucose control through increasing glycolysis and decreasing gluconeogenesis. We added this part in Page 9 line 251-263 and Figure 5E-5I in this version.

Figure X1. A. Representative protein level and quantitative analysis of FOXO1 (82 kDa), GLUT2 (60-70 kDa) and Actin (45 kDa) in the rats in the Ctl, T2D, T2D + CE, T2D + IE and T2D + EE groups. C-D. Expression of hepatic Pepck and G6PC mRNA in the Ctl, T2D, T2D + CE, T2D + IE and T2D + EE groups were evaluated by real-time PCR analysis. Values represent mean ratios of Pepck and G6PC transcripts normalized to GAPDH transcript levels.

2) Very recently sestrin2 signaling is assumed significant attention in relation to exercise and antioxidant responses. Therefore, authors should profile the sestrin2 levels as it is linked to several targets such as mTOR, AMPK and Sirt1. Additionally, the levels of Nrf2 should be reported as this is the central regulator of the threshold mechanisms of oxidative stress and ROS generation.

We appreciate reviewer’s expert comments. Nrf2 is an important mediator of antioxidant signaling, playing a fundamental role in maintaining the redox homeostasis of the cell. Under unstressed conditions, Nrf2 activity is suppressed by its innate repressor Kelch-like ECH-associated protein 1 (Keap1) (3). With the increase of ROS level in the development of diabetes, Nrf2 was activated to induce the transcription of several antioxidant enzymes (4, 5).

Nrf2 expression level has been reported to increase in HFD mice or diabetic patients (6, 7). It has been found from in vitro studies that NRF2 activation is achieved with acute exposure to high glucose, whereas longer incubation times or oscillating glucose concentration failed to activate Nrf2 (8, 9). These suggest that the increase of ROS in diabetes can cause compensatory upregulation of Nrf2. In our study, we found that Nrf2 increased in diabetic rats, which can further initiate the expression of antioxidant enzymes. As shown in Fig.X2A (Fig.2H-2K in manuscript), Grx and Trx involved in thioredoxin metabolism were up-regulated accordingly like Nrf2. After CE intervention, the level of Nrf2 increased further more (Fig.2E-2F), suggesting that CE intervention could activate antioxidant system to achieve a high-level redox balance. We have added these new results into Figure 2.

On the other hand, the expression level of Sestrin2 and Nrf2 decreased after antioxidant supplement. Our results suggest that the antioxidant treatment improved the diabetes through inhibiting ROS level to achieve a low-level redox balance, but moderate exercise enhanced ROS tolerance to achieve a high-level balance (Fig.X2D-F, Fig.3E-3G in manuscript).

We added the new data in “Page 5 line 147-153 and Page 7 line 183-186” and Figure 2-3 in current version.

Figure X2. A-C. Representative protein level and quantitative analysis of Nrf2 (97 kDa), Sestrin2 (57 kDa) and Actin (45 kDa) in the rats in the Ctl, T2D and T2D + CE groups. D-F. Representative protein level and quantitative analysis of Nrf2 (97 kDa), Sestrin2 (57 kDa) and HSP90 (90 kDa) in the rats in the Ctl, T2D and T2D + APO groups.

3) Authors should discuss the exercise-associated hormesis curve. They should discuss whether moderate exercise could decrease the sensitivity to oxidative stress by altering the bell-shaped dose-response curve.

We thank the reviewer’s valuable comments. According to literatures, Zsolt Radak et al proposed a bell-shaped dose-response curve between normal physiological function and level of ROS in healthy individuals, and suggested that moderate exercise can extend or stretch the levels of ROS while increases the physiological function (10). Our results validated this hypothesis and further proposed that moderate exercise could produce ROS meanwhile increase antioxidant enzyme activity to maintain high level redox balance according to the Bell-shaped curve, whereas excessive exercise would generate a higher level of ROS, leading to reduced physiological function. In this study, we found the state of diabetic individuals is more applicable to the description of a S-shaped curve, due to the high level of oxidative stress and decreased reduction level in diabetic individuals (Fig.8B). With the increase of ROS, the physiological function of diabetic individuals gradually decreases and enters a state of redox imbalance. Moderate exercise shifts the S-shaped curve into a bell-shaped dose-response curve, thus reducing the sensitivity to oxidative stress in diabetic individuals and restoring redox homeostasis. However, with excessive exercise, ROS production increases beyond the threshold range of redox balance, resulting in decreased physiological function (Fig.8B, see the decreasing portion of the bell curve to the right of the apex).

Nevertheless, the antioxidant intervention increased physiological activity by reducing ROS levels in diabetic individuals, restoring a bell-shaped dose-response curve at low level of ROS (Fig.8B). Therefore, redox balance could be achieved either at low level of ROS mediated by antioxidant intervention or at high level of ROS mediated by moderate exercise, both of which were regulated by AMPK activation. Therefore, both high and low levels of redox balance can lead to high physiological function as long as they are in the redox balance threshold range. Then, the activation of AMPK is an important sign of exercise or antioxidant intervention to obtain redox dynamic balance which helps restore physiological function. Accordingly, we speculate that the antioxidant intervention based on moderate exercise might offset the effect of exercise, but antioxidants could be beneficial during excessive exercise. The human study also supports that supplementation with antioxidants may preclude the health-promoting effects of exercise (11). Therefore, personalized intervention with respect to redox balance will be crucial for the effective treatment of diabetes patients.

We added this part into “Discussion” in this version (Page 13-14 line 389-418).

4) It would not be ideal to single-out AMPK as a sole biomarker in this manuscript. Instead, authors should consider AMPK activation and associated signaling in relation to redox balance. This should also be presented in Fig 7.

We thank reviewer’s critical comments. According to the comments, we have discussed the AMPK signaling in the discussion part (Page 13, line 373-384) and added the AMPK signaling in Fig.8A.

Reference:

1. R. A. Haeusler, K. H. Kaestner, D. Accili, FoxOs function synergistically to promote glucose production. J Biol Chem 285, 35245-35248 (2010).
2. J. Nakae, T. Kitamura, D. L. Silver, D. Accili, The forkhead transcription factor Foxo1 (Fkhr) confers insulin sensitivity onto glucose-6-phosphatase expression. J Clin Invest 108, 1359-1367 (2001).
3. M. McMahon, K. Itoh, M. Yamamoto, J. D. Hayes, Keap1-dependent proteasomal degradation of transcription factor Nrf2 contributes to the negative regulation of antioxidant response element-driven gene expression. J Biol Chem 278, 21592-21600 (2003).
4. R. S. Arnold et al., Hydrogen peroxide mediates the cell growth and transformation caused by the mitogenic oxidase Nox1. Proc Natl Acad Sci U S A 98, 5550-5555 (2001).
5. J. M. Lee, M. J. Calkins, K. Chan, Y. W. Kan, J. A. Johnson, Identification of the NF-E2-related factor-2-dependent genes conferring protection against oxidative stress in primary cortical astrocytes using oligonucleotide microarray analysis. J Biol Chem 278, 12029-12038 (2003).
6. T. Jiang et al., The protective role of Nrf2 in streptozotocin-induced diabetic nephropathy. Diabetes 59, 850-860 (2010).
7. X. H. Wang et al., High Fat Diet-Induced Hepatic 18-Carbon Fatty Acids Accumulation Up-Regulates CYP2A5/CYP2A6 via NF-E2-Related Factor 2. Front Pharmacol 8, 233 (2017).
8. T. S. Liu et al., Oscillating high glucose enhances oxidative stress and apoptosis in human coronary artery endothelial cells. J Endocrinol Invest 37, 645-651 (2014).
9. Z. Ungvari et al., Adaptive induction of NF-E2-related factor-2-driven antioxidant genes in endothelial cells in response to hyperglycemia. Am J Physiol Heart Circ Physiol 300, H1133-1140 (2011).
10. Z. Radak et al., Exercise, oxidants, and antioxidants change the shape of the bell-shaped hormesis curve. Redox Biol 12, 285-290 (2017).
11. M. Ristow et al., Antioxidants prevent health-promoting effects of physical exercise in humans. Proc Natl Acad Sci U S A 106, 8665-8670 (2009).

Author Response

Reviewer #1 (Public Review):

In one of the most creative eDNA studies I have had the pleasure to review, the authors have taken advantage of an existing program several decades old to address whether insect declines are indeed occurring - an active area of discussion and debate within ecology. Here, they extracted arthropod environmental DNA (eDNA) from pulverized leaf samples collected from different tree species across different habitats. Their aim was to assess the arthropod community composition within the canopies of these trees during the time of collection to assess whether arthropod richness, diversity, and biomass were declining. By utilizing these leaf samples, the greatest shortcoming of assessing arthropod declines - the lack of historical data to compare to - was overcome, and strong timeseries evidence can now be used to inform the discussion. Through their use of eDNA metabarcoding, they were able to determine that richness was not declining, but there was evidence of beta diversity loss due to biotic homogenization occurring across different habitats. Furthermore, their application of qPCR to assess changes in eDNA copy number temporally and associate those changes with changes to arthropod biomass provided support to the argument that arthropod biomass is indeed declining. Taken together, these data add substantial weight to the current discussion regarding how arthropods are being affected in the Anthropocene.

Thank you very much for the positive assessment of our work.

I find the conclusions of the paper to be sound and mostly defensible, though there are some issues to take note of that may undermine these findings.

Firstly, I saw no explanation of the requisite controls for such an experiment. An experiment of this scale should have detailed explanations of the field/equipment controls, extraction controls, and PCR controls to ensure there are no contamination issues that would otherwise undermine the entirety of the study. At one point in the manuscript the presence of controls is mentioned just once, so I surmise they must exist. Trusting such results needs to be taken with caution until such evidence is clearly outlined. Furthermore, the plate layout which includes these controls would help assess the extent of tag-jumping, should the plate plan proposed in Taberlet et al., 2018 be adopted.

Second, without the presence of adequate controls, filtering schemes would be unable to determine whether there were contaminants and also be unable to remove them. This would also prevent samples from being filtered out should there be excessive levels of contamination present. Without such information, it makes it difficult to fully trust the data as presented.

Finally, there is insufficient detail regarding the decontamination procedures of equipment used to prepare the samples (e.g., the cryomil). Without clear explanations of the steps the authors took to ensure samples were handled and prepared correctly, there is yet more concern that there may be unseen problems with the dataset.

We are well aware of the potential issues and consequences of contamination in our work. However, we are also confident that our field and laboratory procedures adequately rule out these issues. We agree with the reviewer that we should expand more on our reasoning. Hence, we have now significantly expanded the Methods section outlining controls and sample purity, particularly under “Tree samples of the German Environmental Specimen Bank – Standardized time series samples stored at ultra-low temperatures” (lines 303-304), “Test for DNA carryover in the cryomill” (lines 448-464) and “Statistical analysis” (lines 570-575).

We ran negative control extractions as well as negative control PCRs with all samples. These controls were sequenced along with all samples and used to explore the effect of experimental contamination. With the exception of a few reads of abundant taxa, these controls were mostly clean. We report this in more detail now in the Methods under “Sequence analysis” (lines 570-575). This suggests that our data are free of experimental contamination or tag jumping issues.

We have also expanded on the avoidance of contamination in our field sampling protocols. The ESB has been set up for monitoring even the tiniest trace amounts of chemicals. Carryover between samples would render the samples useless. Hence, highly clean and standardized protocols are implemented. All samples are only collected with sterilized equipment under sterile conditions. Each piece of equipment is thoroughly decontaminated before sampling.

The cryomill is another potential source of cross-contamination. The mill is disassembled after each sample and thoroughly cleaned. Milled samples have already been tested for chemical carryover, and none was found. We have now added an additional analysis to rule out DNA carryover. We received the milling schedule of samples for the past years. Assuming samples get contaminated by carryover between milling runs, two consecutive samples should show signatures of this carryover. We tested this for singletaxon carryover as well as community-wide beta diversity, but did not find any signal of contamination. This gives us confidence that our samples are very pure. The results of this test are now reported in the manuscript (Suppl. Fig 12 & Suppl. Table 3).

Reviewer #2 (Public Review):

Krehenwinkel et al. investigated the long-term temporal dynamics of arthropod communities using environmental DNA (eDNA) remained in archived leave samples. The authors first developed a method to recover arthropod eDNA from archived leave samples and carefully tested whether the developed method could reasonably reveal the dynamics of arthropod communities where the leave samples originated. Then, using the eDNA method, the authors analyzed 30-year-long well-archived tree leaf samples in Germany and reconstructed the long-term temporal dynamics of arthropod communities associated with the tree species. The reconstructed time series includes several thousand arthropod species belonging to 23 orders, and the authors found interesting patterns in the time series. Contrary to some previous studies, the authors did not find widespread temporal α-diversity (OTU richness and haplotype diversity) declines. Instead, β-diversity among study sites gradually decreased, suggesting that the arthropod communities are more spatially homogenized in recent years. Overall, the authors suggested that the temporal dynamics of arthropod communities may be complex and involve changes in α- and β-diversity and demonstrated the usefulness of their unique eDNA-based approach.

Strengths:

The authors' idea that using eDNA remained in archived leave samples is unique and potentially applicable to other systems. For example, different types of specimens archived in museums may be utilized for reconstructing long-term community dynamics of other organisms, which would be beneficial for understanding and predicting ecosystem dynamics.

A great strength of this work is that the authors very carefully tested their method. For example, the authors tested the effects of powdered leaves input weights, sampling methods, storing methods, PCR primers, and days from last precipitation to sampling on the eDNA metabarcoding results. The results showed that the tested variables did not significantly impact the eDNA metabarcoding results, which convinced me that the proposed method reasonably recovers arthropod eDNA from the archived leaf samples. Furthermore, the authors developed a method that can separately quantify 18S DNA copy numbers of arthropods and plants, which enables the estimations of relative arthropod eDNA copy numbers. While most eDNA studies provide relative abundance only, the DNA copy numbers measured in this study provide valuable information on arthropod community dynamics.

Overall, the authors' idea is excellent, and I believe that the developed eDNA methodology reasonably reconstructed the long-term temporal dynamics of the target organisms, which are major strengths of this study.

Thank you very much for the positive assessment of our work.

Weaknesses:

Although this work has major strengths in the eDNA experimental part, there are concerns in DNA sequence processing and statistical analyses.

Statistical methods to analyze the temporal trend are too simplistic. The methods used in the study did not consider possible autocorrelation and other structures that the eDNA time series might have. It is well known that the applications of simple linear models to time series with autocorrelation structure incorrectly detect a "significant" temporal trend. For example, a linear model can often detect a significant trend even in a random walk time series.

We have now reanalyzed our data controlling for autocorrelation and for non-linear changes of abundance and recover no change to our results. We have added this information to the manuscript under “Statistical analysis” (lines 629-644).

Also, there are some issues regarding the DNA sequence analysis and the subsequent use of the results. For example, read abundance was used in the statistical model, but the read abundance cannot be a proxy for species abundance/biomass. Because the total 18S DNA copy numbers of arthropods were quantified in the study, multiplying the sequence-based relative abundance by the total 18S DNA copy numbers may produce a better proxy of the abundance of arthropods, and the use of such a better proxy would be more appropriate here. In addition, a coverage-based rarefaction enables a more rigorous comparison of diversity (OTU diversity or haplotype diversity) than the readbased rarefaction does.

We did not use read abundance as a proxy for abundance, but used our qPCR approach to measure relative copy number of arthropods. While there are biases to this (see our explanations above), the assay proved very reliable and robust. We thus believe it should indeed provide a rough estimate of biomass. As biomass is very commonly discussed in insect decline (in fact the first study on insect decline entirely relies on biomass; Hallmann et al. 2017), we feel it is important go include a proxy for this as well. However, we also discuss the alternative option that a turnover of diversity is affecting the measured biomass. A pattern of abundance loss for common species has been described in other works on insect decline.

We liked the reviewer’s suggestion to use copy number information to perform abundance-informed rarefaction. We have done this now and added an additional analysis rarefying by copy number/biomass. A parallel analysis using this newly rarefied table was done for the total diversity as well as single species abundance change. Details can be found in the Methods and Results section of the manuscript. However, the result essentially remains the same. Even abundance-informed rarefaction does not lead to a pattern of loss of species richness over time (see “Statistical analysis”).

The overall results are supporting a scenario of no overall loss of species richness over time, but a loss of abundance for common species. And we indeed see the pattern of declining abundance for once-common species in our data, for example the loss of the Green Silver-Line moth, once a very common species in beech canopy (Suppl. Fig. 10). We have added details on this to the Discussion (lines 254-260).

These points may significantly impact the conclusions of this work.

Reviewer #3 (Public Review):

The aim of Weber and colleagues' study was to generate arthropod environmental DNA extracted from a unique 30-year time series of deep-frozen leaf material sampled at 24 German sites, that represent four different land use types. Using this dataset, they explore how the arthropod community has changed through time in these sites, using both conventional metabarcoding to reconstruct the OTUs present, and a new qPCR assay developed to estimate the overall arthropod diversity on the collected material. Overall their results show that while no clear changes in alpha diversity are found, the βdiversity dropped significantly over time in many sites, most notable in the beech forests. Overall I believe their data supports these findings, and thus their conclusion that diversity is becoming homogenized through time is valid.

Thank you for the positive assessment.

While overall I do not doubt the general findings, I have a number of comments. Firstly while I agree this is a very nice study on a unique dataset - other temporal datasets of insects that were used for eDNA studies do exist, and perhaps it would be relevant to put the findings into context (or even the study design) of other work that has been done on such datasets. One example that jumps to my mind is Thomsen et al. 2015 https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2656.12452 but I am sure there are others.

We have expanded the introduction and discussion on this citing this among other studies now (lines 71-72, 276-278).

From a technical point of view, the conclusions of course rely on several assumptions, including (1) that the biomass assay is effective and (2) that the reconstructed levels of OTU diversity are accurate,

With regards to biomass although it is stated in the manuscript that "Relative eDNA copy number should be a predictor for relative biomass ", this is in fact only true if one assumes a number of things, e.g. there is a similar copy number of 18s rDNA per species, similar numbers of mtDNA per cell, a similar number of cells per individual species etc. In this regard, on the positive side, it is gratifying to see that the authors perform a validation assay on 7 mock controls, and these seem to indicate the assay works well. Given how critical this is, I recommend discussing the details of this a bit more, and why the authors are convinced the assay is effective in the main text so that the reader is able to fully decide if they are in agreement. However perhaps on the negative side, I am concerned about the strategy taken to perform the qPCR may have not been ideal. Specifically, the assay is based on nested PCR, where the authors first perform a 15cycle amplification, this product is purified, then put into a subsequent qPCR. Given how both PCR is notorious for introducing amplification biases in general (especially when performed on low levels of DNA), and the fact that nested PCRs are notoriously contamination prone - this approach seems to be asking for trouble. This raises the question - why not just do the qPCR directly on the extracts (one can still dilute the plant DNA 100x prior to qPCR if needed). Further, given the qPCRs were run in triplicate I think the full data (Ct values) for this should be released (as opposed to just stating in the paper that the average values were used). In this way, the readers will be able to judge how replicable the assay was - something I think is critical given how noisy the patterns in Fig S10 seem to be.

We agree with this point, and this is why we do not want to overstate the decline in copy number. This is an additional source of data next to genetic and species diversity. We have added to our discussion of turnover as another potential driver of copy number change (lines 257-260). We have also added text addressing the robustness of the mock community assay (lines 138-141).

However, we are confident of the reliability and robustness of our qPCR assay for the detection of relative arthropod copy number. We performed several validations and optimizations before using the assay. We have added additional details to the manuscript on this (see “Detection of relative arthropod DNA copy number using quantitative PCR”, lines 548-556). We got the idea for the nested qPCR from a study (Tran et al.) showing its high accuracy and reproducibility. We show that our assay has a very high replicability using triplicates of each qPCR, which we will now include in the supplementary data on Dryad. The SD of Ct values is very low (~ 0.1 on average). NTC were run with all qPCRs to rule out contamination as an issue in the experiments. We also find a very high efficiency of the assay. At dilutions far outside the observed copy number in our actual leaf data, we still find the assay to be accurate. We found very comparable abundance changes across our highly taxonomically diverse mock communities. This also suggests that abundance changes are a more likely explanation than simple turnover for the observed drop in copy number. A biomass loss for common species is well in line with recent reports on insect decline. We can also rely on several other mock community studies (Krehenwinkel et al. 2017 & 2019) where we used read abundance of 18S and found it to be a relatively good predictor of relative biomass.

The pattern in Fig. S10 is not really noisy. It just reflects typical population fluctuations for arthropods. Most arthropod taxa undergo very pronounced temporal abundance fluctuations between years.

Next, with regards to the observation that the results reveal an overall decrease in arthropod biomass over time: The authors suggest one alternate to their theory, that the dropping DNA copy number may reflect taxonomic turnover of species with different eDNA shedding rates. Could there be another potential explanation - simply be that leaves are getting denser/larger? Can this be ruled out in some way, e.g. via data on leaf mass through time for these trees? (From this dataset or indeed any other place).

This is a very good point. However, we can rule out this hypothesis, as the ESB performs intensive biometric data analysis. The average leaf weight and water content have not significantly changed in our sites. We have addressed this in the Methods section (see ”Tree samples of the German Environmental Specimen Bank – Standardized time series samples stored at ultra-low temperatures”, lines 308-311).

With regards to estimates of OTU/zOTU diversity. The authors state in the manuscript that zOTUs represent individual haplotypes, thus genetic variation within species. This is only true if they do not represent PCR and/or sequencing errors. Perhaps therefore they would be able to elaborate (for the non-computational/eDNA specialist reader) on why their sequence processing methods rule out this possibility? One very good bit of evidence would be that identical haplotypes for the individual species are found in the replicate PCRs. Or even between different extractions at single locations/timepoints.

We have repeated the analysis of genetic variation with much more stringent filtering criteria (see “Statistical analysis”, lines 611-615). Among other filtering steps, this also includes the use of only those zOTUs that occur in both technical replicates, as suggested by the reviewer. Another reason to make us believe we are dealing with true haplotypic variation here is that haplotypes show geographic variation. E.g., some haplotypes are more abundant in some sites than in others. NUMTS would consistently show a simple correlation in their abundance with the most abundant true haplotype.

With regards to the bigger picture, one thing I found very interesting from a technical point of view is that the authors explored how modifying the mass of plant material used in the extraction affects the overall results, and basically find that using more than 200mg provides no real advantage. In this regard, I draw the authors and readers attention to an excellent paper by Mata et al. (https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14779) - where these authors compare the effect of increasing the amount of bat faeces used in a bat diet metabarcoding study, on the OTUs generated. Essentially Mata and colleagues report that as the amount of faeces increases, the rare taxa (e.g. those found at a low level in a single faeces) get lost - they are simply diluted out by the common taxa (e.g those in all faeces). In contrast, increasing biological replicates (in their case more individual faecal samples) increased diversity. I think these results are relevant in the context of the experiment described in this new manuscript, as they seem to show similar results - there is no benefit of considerably increasing the amount of leaf tissue used. And if so, this seems to point to a general principal of relevance to the design of metabarcoding studies, thus of likely wide interest.

Thank you for this interesting study, which we were not aware of before. The cryomilling is an extremely efficient approach to equally disperse even traces of chemicals in a sample. This has been established for trace chemicals early during the operation of the ESB, but also seems to hold true for eDNA in the samples. We have recently done more replication experiments from different ESB samples (different terrestrial and marine samples for different taxonomic groups) and find that replication of extraction does not provide much more benefit than replication of PCR. Even after 2 replicates, diversity approaches saturation. This can be seen in the plot below, which shows recovered eDNA diversity for different ESB samples and different taxonomic groups from 1-4 replicates. A single extract of a small volume contains DNA from nearly all taxa in the community. Rare taxa can be enriched with more PCR replicates.

Author response

Reviewer #1 (Public Review):

This careful study reports the importance of Rab12 for Parkinson's disease associated LRRK2 kinase activity in cells. The authors carried out a targeted siRNA screen of Rab substrates and found lower pRab10 levels in cells depleted of Rab12. It has previously been reported that LLOMe treatment of cells breaks lysosomes and with time, leads to major activation of LRRK2 kinase. Here they show that LLOMe-induced kinase activation requires Rab12 and does not require Rab12 phosphorylation to show the effect.

We thank the reviewer for their comments regarding the carefulness and importance of our work and for their specific feedback which has substantially improved our revised manuscript.

1) Throughout the text, the authors claim that "Rab12 is required for LRRK2 dependent phosphorylation" (Page 4 line 78; Page 9 line 153; Page 22 line 421). This is not correct according to Figure 1 Figure Supp 1B - there is still pRab10. It is correct only in relation to the LLOMe activation. Please correct this error.

We appreciate the reviewer’s comment around the requirement of Rab12 for LRRK2-dependent phosphorylation of Rab10 and question regarding whether this is relevant under baseline conditions or only in relation to LLOMe activation. Using our MSD-based assay to quantify pT73 Rab10 levels under basal conditions, we observed a similar reduction in Rab10 phosphorylation when we knockdown Rab12 as we also observed with LRRK2 knockdown (Figure 1A). Further, we see comparable reduction in Rab10 phosphorylation in RAB12 KO cells as that observed in LRRK2 KO cells using our MSD-based assay (Figure 2A and B). Based on this data, we believe Rab12 is a key regulator of LRRK2 activation under basal conditions without additional lysosomal damage. However, as the reviewer noted, we do observe some residual Rab10 phosphorylation upon Rab12 knockdown when assessed by western blot analysis (Figure 1D and Figure 1- figure supplement 1). A similar signal is observed upon LRRK2 knockdown, which may suggest that some small amount of Rab10 phosphorylation may be mediated by another kinase in this cell model. Nevertheless, we appreciate this reviewer’s point and have therefore modified the text to remove any reference to Rab12 being required for LRRK2-dependent Rab phosphorylation and now instead refer to Rab12 as a regulator of LRRK2 activity.

As noted by the reviewer, our data does suggest that Rab12 is required for the increase in Rab10 phosphorylation observed following LLOMe treatment to elicit lysosomal damage, and we now refer to this appropriately throughout the text.

2) The authors conclude that Rab12 recruitment precedes that of LRRK2 but the rate of recruitment (slopes of curves in 3F and G) is actually faster for LRRK2 than for Rab12 with no proof that Rab12 is faster-please modify the text-it looks more like coordinated recruitment.

The reviewer raises an excellent point regarding our ability to delineate whether Rab12 recruitment precedes that of LRRK2 on lysosomes following LLOMe treatment. As noted by the reviewer, we do see both the recruitment of Rab12 and LRRK2 to lysosomes increase on a similar timescale, so we cannot truly resolve whether Rab12 recruitment precedes LRRK2 recruitment in our studies. Based on this, we have modified the text to emphasize that this data supports coordinated recruitment, as suggested, and we have further removed any mention of Rab12 preceding LRRK2. The specific change is as follows “Rab12 colocalization with LRRK2 increased over time following LLOMe treatment, supporting potential coordinated recruitment of these proteins to lysosomes upon damage (Figure 3I). Together, these data demonstrate that Rab12 and LRRK2 both associate with lysosomes following membrane rupture.” and can be found on lines 460-463 of the updated manuscript.

3) The title is misleading because the authors do not show that Rab12 promotes LRRK2 membrane association. This would require Rab12 to be sufficient to localize LRRK2 to a mislocalized Rab12. The authors DO show that Rab12 is needed for the massive LLOME activation at lysosomes. Please re-word the title.

To address the reviewer’s concern regarding the title of our manuscript, we have modified the title from “Rab12 regulates LRRK2 activity by promoting its localization to lysosomes” to “Rab12 regulates LRRK2 activity by facilitating its localization to lysosomes” to soften the language around the sufficiency of Rab12 in regulating the localization of LRRK2 to lysosomes. We show that Rab12 deletion significantly reduces LRRK2 activity (as assessed by Rab10 phosphorylation on lysosomes) and significantly increases the localization of LRRK2 to lysosomes upon lysosomal damage. The updated title better reflects the regulatory role of Rab12 in modulating LRRK2 activity, and we thank the reviewer for their suggestion to modify this accordingly.

Reviewer #2 (Public Review):

This study shows that rab12 has a role in the phosphorylation of rab10 by LRRK2. Many publications have previously focused on the phosphorylation targets of LRRK2 and the significance of many remains unclear, but the study of LRRK2 activation has mostly focused on the role of disease-associated mutations (in LRRK2 and VPS35) and rab29. The work is performed entirely in an alveolar lung cell line, limiting relevance for the nervous system. Nonetheless, the authors take advantage of this simplified system to explore the mechanism by which rab12 activates LRRK2. In general, the work is performed very carefully with appropriate controls, excluding trivial explanations for the results, but there are several serious problems with the experiments and in particular the interpretation.

We appreciate the reviewer’s comments regarding the rigor of our work and the potential impact of our studies to address a key unanswered question in the field regarding the mechanisms by which LRRK2 activation is mediated. Our studies focused on the A549 cell model given its high endogenous expression of LRRK2 and Rab10, and this cell line provided a simple system to investigate the mechanism and impact of Rab12-dependent regulation of LRRK2 activity. We agree with the reviewer that future studies are warranted to understand whether similar Rab12-dependent regulation of LRRK2 occurs in relevant CNS cell types.

First, the authors note that rab29 appears to have a smaller or no effect when knocked down in these cells. However, the quantitation (Fig1-S1A) shows a much less significant knockdown of rab29 than rab12, so it would be important to repeat this with better knockdown or preferably a KO (by CRISPR) before making this conclusion. And the relationship to rab29 is important, so if a better KD or KO shows an effect, it would be important to assess by knocking down rab12 in the rab29 KO background.

The reviewer raises a good point regarding the importance of confirming that loss of Rab29 has no effect on Rab10 phosphorylation. To address potential concerns about insufficient Rab29 knockdown, we measured the levels of pT73 Rab10 in RAB29 KO A549 cells by MSD-based analysis. RAB29 deletion had no effect on Rab10 phosphorylation, confirming findings from our RAB siRNA screen and the observations of Dario Alessi’s group reported previously (Kalogeropulou et al Biochem J 2020; PMID: 33135724). We have included this new data into our updated manuscript in Figure 1- figure supplement 1 and comment on it on page 6 in the updated Results section.

Secondly, the knockdown of rab12 generally has a strong effect on the phosphorylation of the LRRK2 substrate rab10 but I could not find an experiment that shows whether rab12 has any effect on the residual phosphorylation of rab10 in the LRRK2 KO. There is not much phosphorylation left in the absence of LRRK2 but maybe this depends on rab12 just as much as in cells with LRRK2 and rab12 is operating independently of LRRK2, either through a different kinase or simply by making rab10 more available for phosphorylation. The epistasis experiment is crucial to address this possibility. To establish the connection to LRRK2, it would also help to compare the effect of rab12 KD on the phosphorylation of selected rabs that do or do not depend on LRRK2.

The reviewer raises an interesting question regarding whether Rab12 can further reduce Rab10 phosphorylation independently of LRRK2. Using our quantitative MSD-based assay, we observe that pRab10 levels are at the lower limits of detection of the assay in LRRK2 KO A549 cells. Unfortunately, this means that we are unable to detect whether there might be any additional minor reduction in Rab10 phosphorylation with Rab12 knockdown in LRRK2 KO cells. We cannot rule out that Rab12 may play a LRRK2-independent role in regulating Rab10 phosphorylation in other cell lines, and future studies are warranted to explore whether Rab12 knockdown can further reduce Rab10 phosphorylation in other systems, including in CNS cells.

Regarding exploring the effects of RAB12 knockdown on the phosphorylation of other Rabs, we also assessed the impact of RAB12 KO on phosphorylation of another LRRK2-Rab substrate, Rab8a. We observed a strong reduction in pT72 Rab8a levels in RAB12 KO cells compared to wildtype cells, suggesting the impact of RAB12 deletion extends beyond Rab10 (see representative western blot in Author response image 1). Due to potential concerns with the selectivity of the pT72 Rab8a antibody (potentially detecting the phosphorylation of other LRRK2-Rabs), we cannot definitively demonstrate that Rab12 mediates the phosphorylation of other Rabs. This question should be revisited when additional phospho-Rab antibodies become available that enable us to selectively detect LRRK2-dependent phosphorylation of additional Rab substrates under endogenous expression conditions.

Author response image 1.

A strength of the work is the demonstration of p-rab10 recruitment to lysosomes by biochemistry and imaging. The demonstration that LRRK2 is required for this by biochemistry (Fig 4A) is very important but it would also be good to determine whether the requirement for LRRK2 extends to imaging. In support of a causal relationship, the authors also state that lysosomal accumulation of rab12 precedes LRRK2 but the data do not show this. Imaging with and without LRRK2 would provide more compelling evidence for a causative role.

We thank the reviewer for their suggestion to assess Rab12 recruitment to damaged lysosomes with and without LRRK2 using imaging-based analyses to add confidence to our findings from biochemical approaches. To address this comment, we have imaged the recruitment of mCherry-tagged Rab12 to lysosomes (as assessed using an antibody against endogenous LAMP1) and observed a significant increase in Rab12 levels on lysosomes following LLOMe treatment. This occurs to a similar extent in LRRK2 KO A549 cells, suggesting that Rab12 is an upstream regulator of LRRK2 activity. This new data has been incorporated into the revised manuscript (Figure 3E) and is presented on page 20 of the updated manuscript.

Our conclusions on this are further strengthened by new data assessing Rab12 recruitment to lysosomes using orthogonal analysis of isolated lysosomes biochemically. Using the Lyso-IP method, we observed a strong increase in the levels of Rab12 on lysosomes following LLOMe treatment that was maintained in LRRK2 KO cells. These data have been added to the updated manuscript (new data added to Figure 3- figure supplement 1).

Together, these data support our hypothesis that Rab12 recruitment to damaged lysosomes is upstream, and independent, of LRRK2.

The authors also touch base with PD mutations, showing that loss of rab12 reduces the phosphorylation of rab10. However, it is interesting that loss of rab12 has the same effect with R1441G LRRK2 and D620N VPS35 as it does in controls. This suggests that the effect of rab12 does not depend on the extent of LRRK2 activation. It is also surprising that R1441G LRRK2 does not increase p-rab10 phosphorylation (Fig 2G) as suggested in the literature and stated in the text.

We agree with the reviewer that it is quite interesting that RAB12 knockdown significantly attenuates Rab10 phosphorylation in the context of PD-linked variants in addition to that observed in wildtype cells basally and after LLOMe treatment. As noted by the reviewer, we did not observe increased levels of phospho-Rab10 in LRRK2 R1441G KI A549 cells at the whole cell level (Figure 2G). However, we observed a significant increase in Rab10 phosphorylation on isolated lysosomes from LRRK2 R1441G KI cells compared to WT cells (Figure 4B). This may suggest that the LRRK2 R1441G variant leads to a more modest increase in LRRK2 activity in this cell model. Previous studies in MEFs from LRRK2 R1441G KI mice or neutrophils from human subjects that carry the LRRK2 R1441G variant showed a 3-4 fold increase in Rab10 phosphorylation (Fan et al Acta Neuropathol 2021 PMID: 34125248 and Karaye et al Mol Cell Proteomics 2020 PMID: 32601174), supporting that this variant does lead to increased Rab10 phosphorylation and that the extent of LRRK2 activation may vary across different cell types.

Most important, the final figure suggests that PD-associated mutations in LRRK2 and VPS35 occlude the effect of lysosomal disruption on lysosomal recruitment of LRRK2 (Fig 4D) but do not impair the phosphorylation of rab10 also triggered by lysosomal disruption (4A-C). Phosphorylation of this target thus appears to be regulated independently of LRRK2 recruitment to the lysosome, suggesting another level of control (perhaps of kinase activity rather than localization) that has not been considered.

The reviewer suggests an interesting hypothesis around the existence of additional levels of control beyond the lysosomal levels of LRRK2 to lead to increased Rab10 phosphorylation of lysosomes. Given the variability we have observed in measuring endogenous LRRK2 levels on lysosomes, we performed two additional replicates to assess lysosomal LRRK2 levels in LRRK2 R1441G KI and VPS35 D620N KI cells at baseline and after treatment with LLOMe. We observed a significant increase in LRRK2 levels on lysosomes in cells expressing either PD-linked variant and a trend toward a further increase in the levels of LRRK2 on lysosomes after LLOMe treatment in these cells (Figure 4D in the updated manuscript). We have updated the text on page 24 to reflect this change, suggesting that the PD-linked variants do not fully occlude the effect of lysosomal disruption on the lysosomal recruitment of LRRK2.

LLOMe treatment leads to a stronger increase in Rab10 phosphorylation on lysosomes from LRRK2 R1441G and VPS35 D620N cells compared to the modest increase in LRRK2 levels observed. This could suggest that, as the reviewer noted, additional mechanisms beyond increased lysosomal localization of LRRK2 may be driving the robust increase in Rab10 phosphorylation observed. We have modified the results section on lines 548-551 to highlight this possibility: “Rab10 phosphorylation showed a more significant increase in response to LLOMe treatment than LRRK2 on lysosomes from LRRK2 R1441G and VPS35 D620N KI cells, suggesting that there may be more regulation beyond the enhanced proximity between LRRK2 and Rab that contribute to LRRK2 activation in response to lysosomal damage.”

Reviewer #3 (Public Review):

Increased LRRK2 kinase activity is known to confer Parkinson's disease risk. While much is known about disease-causing LRRK2 mutations that increase LRRK2 kinase activity, the normal cellular mechanisms of LRRK2 activation are less well understood. Rab GTPases are known to play a role in LRRK2 activation and to be substrates for the kinase activity of LRRK2. However, much of the data on Rabs in LRRK2 activation comes from over-expression studies and the contributions of endogenously expressed Rabs to LRRK2 activation are less clear. To address this problem, Bondar and colleagues tested the impact of systematically depleting candidate Rab GTPases on LRRK2 activity as measured by its ability to phosphorylate Rab10 in the human A549 type 2 pneumocyte cell line. This resulted in the identification of a major role for Rab12 in controlling LRRK2 activity towards Rab10 in this model system. Follow-up studies show that this role for Rab12 is of particular importance for the phosphorylation of Rab10 by LRRK2 at damaged lysosomes. Increases in LRRK2 activity in cells harboring disease-causing mutants of LRRK2 and VPS35 also depend (at least partially) on Rab12. Confidence in the role of Rab12 in supporting LRRK2 activity is strengthened by parallel experiments showing that either siRNA-mediated depletion of Rab12 or CRISPR-mediated Rab12 KO both have similar effects on LRRK2 activity. Collectively, these results demonstrate a novel role for Rab12 in supporting LRRK2 activation in A549 cells. It is likely that this effect is generalizable to other cell types. However, this remains to be established. It is also likely that lysosomes are the subcellular site where Rab12-dependent activation of LRRK2 occurs. Independent validation of these conclusions with additional experiments would strengthen this conclusion and help to address some concerns that much of the data supporting a lysosome localization for Rab12-dependent activation of LRRK2 comes from a single method (LysoIP). Furthermore, there is a discrepancy between panel 4A versus 4D in the effect of LLoMe-induced lysosome damage on LRRK2 recruitment to lysosomes that will need to be addressed to strengthen confidence in conclusions about lysosomes as sites of LRRK2 activation by Rab12.

We thank the reviewer for their comments regarding our work that identifies Rab12 as a novel regulator of LRRK2 activation and the appreciation of the parallel approaches we employed to add confidence in this effect.

As suggested by the reviewer, we have updated our manuscript to now include independent validation of our conclusions using imaging-based analyses to complement our data from biochemical analyses using the Lyso-IP method. Specifically, we have included new imaging data that confirms that Rab12 levels are increased on lysosomes following membrane permeabilization with LLOMe treatment and demonstrates that this occurs independent of LRRK2, providing additional support that Rab12 is an upstream regulator of LRRK2 activity (Figure 3E in the updated manuscript).

Regarding the reviewer’s comment on a discrepancy between our findings in Figure 4A and Figure 4D, we have performed additional independent replicates in Figure 4D to assess the impact of lysosomal damage on the lysosomal levels of LRRK2 at baseline or upon the expression of genetic variants. We observed a significant increase in LRRK2 levels on lysosomes following LLOMe treatment in our set of experiments included in Figure 4A and a non-significant trend toward an increase in LRRK2 levels on isolates lysosomes in Figure 4D. As described in more detail below (in response to the second point raised by this reviewer), we think this variability arises because of a combination of low levels of LRRK2 on lysosomes with endogenous expression and variability across experiments in the efficiency of lysosomal isolation. Our observations of increased recruitment of LRRK2 to lysosomes upon damage are further supported by parallel imaging-based studies (Figure 3F-I) and are consistent with previous studies using overexpression systems.

We thank the reviewer for all of the suggestions which have added further confidence to our conclusions and substantially improved the manuscript.

Author Response:

Reviewer #1:

This work is aiming at the characterization of the molecular and kinetic mechanism of how three members of the SLC6 family of transporters, namely for dopamine (DAT), norepinephrine (NET), and serotonin (SERT), transport substrate across the membrane, and how the transport process is affected by cations. The authors use electrophysiology and sophisticated rapid solution exchange methods, in conjunction with fluorescence recordings from single cells, to correlate flux (from fluorescence) with electrical activity (from currents).

The strength of the methods is based on the application of a kinetic method with high time resolution, allowing the isolation of fast processes in the transport mechanism, and their modeling using a kinetic multistep scheme. In particular useful is the combination with fluorescence recording from single cells, which allows the authors to measure flux and current in the same cell under voltage clamp conditions. This is an elegant approach to get information on the voltage dependence of substrate flux, which is difficult to obtain with other methods. As to the strength of the results, the data are generally of high quality, showing the kinetic and mechanistic similarities and differences between the three transporters under observation. Another strength is that the results are quantitatively represented by kinetic simulations, which appear to fit the experimental data well.

The major weakness of the research is related to interpretation of the experimental results. While the authors propose a unified K+ interaction mechanism for the three transporters, DAT, NET and SERT, the proposed K+ association/dissociation mechanism is 1) highly unusual, and 2) not unique in the ability to explain the experimental data. As to point 1), the DAT mechanism (Fig. 7A) proposes a sequence of intracellular K+ association and dissociation steps. Since the intracellular [K+] remains constant, such a sequence requires a change of affinity for K+, which is initially high when K+ associates (33 microM according to the provided rate constants) and then has to be low for K+ dissociation (3.3 mM). Such an affinity change requires input of free energy, to promote K+ dissociation. From the provided rate constants and at room temperature this free energy change can be approximated as 11.4 kJ/mol. This is a large energy amount, in fact larger than what is stored in the physiological concentration gradient for one Na+ ion as a driving force for transport. It appears that the transporter would waste a lot of energy for no apparent benefit, with a futile K+ association/dissociation cycle, that would just generate heat.

Therefore, while the authors have achieved their aim of quantitatively assessing transporter function and thorough description by a kinetic mechanism, their final proposed mechanism does not support all of the conclusions because it is by far from unique in being able to explain the data (point 2) above). While this may be true for other transport mechanisms proposed in the past, the mechanism proposed here is somewhat odd with respect to energy requirements. Thus, it would require extraordinary experimental proof to propose it in exclusion of other, maybe more plausible mechanisms.

Despite these shortcomings, the potential impact of the work is high, because a unifying theory of cation interaction and stoichiometry of the monoamine transporter members of the SLC6 family has been missing in the literature. In addition, the elegant method of combining single cell electrophysiology and fluorescence flux measurements is impactful, especially in the whole cell recording method, allowing the control of intracellular ionic composition.

We thank reviewer 1 for his comments on the kinetic modelling. We do not claim that the mechanism, which we propose, is unique in its ability to explain the data. However, we should like to argue that the proposed mechanism is plausible and parsimonious. We, much like reviewer 1, initially asked the question, whether a mechanism requiring an ion such as potassium to associate and subsequently dissociate from the same side of the transporter was energetically feasible. In fact, one of the main reasons for employing kinetic models was to address this specific issue.

If detailed balance in a kinetic model is maintained (i.e., the product of the rates in the forward direction of a loop equals the product of the rates in the reverse direction), the model is energetically sound (i.e., such a model does not violate the laws of thermodynamics). It is true that for a spontaneous reaction to occur, the Gibbs free energy has to be negative. In a multistep process, however, this consideration only pertains to the “initial” and the “final” state. As long as the Gibbs free energy between these two states is negative the reaction will proceed, even if the Gibbs free energy between “intermediate” states is positive. This point is illustrated in the schemes below.

Scheme (A) maps out the Gibbs free energy of the outer loop of the kinetic model of DAT (i.e., this path describes the conformational trajectory, which the transporter takes in the presence of intracellular K+- see scheme in Fig.7A of the manuscript). For calculating the Gibbs free energy of this loop, we assumed a pre-equilibrium condition (i.e., an extracellular and intracellular substrate concentration that we arbitrarily set to 10 μM and 100 nM, respectively) and the membrane voltage as 0 mV. As shown in the scheme, the Gibbs free energy between the “initial-left” and the “final-right” state is negative. Accordingly, the multistep reaction can proceed spontaneously.

In scheme (B), we mapped out the Gibbs free energy for the same path and the same pre-equilibrium condition as shown in scheme (A); the only difference is that the membrane potential was now assumed to be -60 mV. This is to show that voltage is also a determining factor of the extent by which the Gibbs free energy changes.

In Scheme (C), we mapped out the Gibbs free energy at equilibrium (the difference in Gibbs free energy between the “initial” and the “final” state is zero). This condition is met when the intracellular substrate concentration is 155 μM. At this intracellular substrate concentration, the energy stored in the substrate gradient notably matches exactly the energy of the Na+ gradient. The model therefore predicts that no energy is dissipated as heat, an observation that is in contrast to the concern raised by reviewer 1. We admit that the model can be criticized on this ground, because arguably, a realistic process is expected to dissipate energy as heat even if it involves a microscopic system (as is the case here). Determination of how much heat is generated in a transport cycle is, however, beyond the scope of the present manuscript and warrants a detailed study. In such a study, one could investigate if any heat loss generated can be compensated by, for instance, the occasional antiport of K+ by DAT, which, as we point out in the discussion, is possible. In this context, we stress that the energetic costs would have been much higher, if we had assumed non-obligatory antiport of K+ through DAT. Such a mechanism predicts that the K+ gradient is constantly dissipated in the absence of the substrate, which would indeed create the futile heat loss reviewer 1 is concerned about.

An alternate hypothesis to the actions of intracellular K+ on the DAT transport cycle would be to propose the presence of a regulatory K+ binding site. We are reluctant to assume this mechanism for the simple reason that there is little evidence for such sites from the available crystal structures. The view that K+ binds to Na2 site in DAT, NET and SERT is consistent with our data (see Fig.5). These observations are aided by a previous study that shows K+ can bind to the Na2 site in DAT, as determined by extensive molecular dynamic simulations (Razavi et al., 2017, cited in the manuscript). By its very nature, the Na2 site cannot serve as a regulatory K+ binding site; for the transporter to proceed in the transport cycle, K+ must at some point dissociate from the Na2 site.

On further scrutiny of our model for DAT, NET and SERT, we noticed that the extra and intracellular affinities for Na+ were set too high. We regret this oversight that arose because we had only simulated experiments in which the intracellular Na+ concentrations had been zero. The selected Na+ affinities would not have allowed the transporter to function properly at a physiological intracellular Na+ concentration (which is ~10 mM). We now rectified this problem by lowering the inner and outer Na+ affinity by a factor of 10. In Fig.7 of the main manuscript and supplementary figure 6, we have now replaced all previous simulations of the three transporters with the predictions of the newly amended model. As seen, the changes in the binding parameters for Na+ in the model could still account for the key findings of this study.

Reviewer #2:

Bhat et al. study transport mechanism of three members of the SLC6 family, i.e. DAT, NET and SERT, using a combination of cellular electrophysiology, fluorescence measurements - taking advantage of a fluorescent substrate (APP+) that can be transported by each of these different transporters - and kinetic modelling. They find that DAT, NET and SERT differ in intracellular K+ binding. In DAT and NET, intracellular K+ binding is transient, resulting in voltage-dependent transport. In contrast, SERT transports K+, and the addition of a charged substrate to the transport cycle makes serotonin transport voltage-independent.

This is an extremely nice and interesting manuscript, based on a series of beautifully designed and executed experiments that are convincingly analyzed via a kinetic model. I have only some suggestions:

1) Fig. 4: I find the description of Fig. 4 extremely difficult to understand. In clear contrast to the introductory sentence "Previous studies showed that Kin+ was antiported by SERT, but not by NET or DAT (Rudnick & Nelson, 1978; Gu et al., 1996; Erreger et al.,2008), SERT appears to be able to transport APP+ without K+ in Fig. 4. I was trying to understand this obvious discrepancy for a long time, until I found the authors coming back to this point in the discussion "However steady-state assessment of transporter mediated substrate uptake is hindered by the fact that all three monoamine transporters can also transport substrate in the absence of Kin+". This is a little late, and the author should address this point more explicitly in the result section, close to the description of Fig. 4.

We agree with reviewer 2’s comments pertaining to the SERT data represented in Fig.4C. The observations made from this dataset seem confusing in the absence of any relevant context. We have added the following statements to clarify any discrepancy arising from Fig. 4 (lines 266-273): “Owing to the instrumental role of Kin+ in the catalytic cycle of SERT, the observed lack of difference in APP+ uptake profiles by SERT-expressing cells in the presence or absence of Kin+ seem contradictory. This discrepancy can be explained as follows: 1) SERT can alternatively antiport protons to complete its catalytic cycle (Keyes and Rudnick, 1982; Hasenhuetl et al. 2016) and 2) APP+ is a poor SERT substrate (as determined by lack of APP+ induced steady state currents, Fig. 2F and 3F) that may be shuttled into SERT-expressing cells at rates slower than the rate limiting isomerization of SERT from inward open to outward open state.”

2) Throughout the whole manuscript I am missing statistical details in comparisons.

Statistical details for comparisons, which were done on some data sets in Fig. 4, Fig.5 and Fig.6, have now been incorporated in the manuscript text.

3) Since APP+ might also only bind to the transporter or even only bind to the cell membrane, the authors might want to look at how the time course of the cellular APP+ signal depends on the size of the cells or on the ratio of transport currents and capacitance. It is of course possible that the tested cells do not differ sufficiently in size to permit such comparison. The authors should at least comment on this possibiliy.

We are working on monoclonal lines. Thus, the differences in cell size are small (between 25- 30 pF). In the new supplementary figure 1, we show that our (previously held) conjecture that the fast component represents membrane binding was wrong. In fact, analysis of the APP+ fluorescence in control cells (supplementary figure 1D) suggests that APP+ adherence to the plasma membrane does not contribute to significant fluorescence signal. We apologize for this misinterpretation and please refer to the responses to reviewer 1 for more details.

4) Another set of results one might look at are the time courses of fluorescence decay after the end of the APP+ perfusion (Fig. 2 and 4). Substrate (APP+) outward transport should have a comparable voltage dependence as substrate uptake, moreover it should depend on the amount of substrate that entered to the cell before. Could the authors provide such result and use them to exclude specific/unspecific APP+ binding?

In supplementary figure 1 (panel, A and C) and video files 1 and 2, we show that APP+ adheres to intracellular membranes of organelles. This has also been shown previously by others (Solis Jr. et al., 2012; Karpowicz Jr et al., 2013; Wilson et al., 2014, cited in the manuscript). Because these structures serve as sinks, there is no (or only little) free APP+, which is available for outward transport.

Reviewer #3:

The sodium-coupled biogenic transporters DAT, NET and SERT, terminate the synaptic actions of dopamine, norepinephrine and serotonin, respectively. They belong to the family of Neurotransmitter:sodium:symporters. These transporters have very similar sequences and this is reflected at the structural level as judged by similarity of the crystal structures of the outward-facing conformations DAT and SERT. However, earlier functional studies indicated that transport by SERT is electroneutral because the charges sodium ions and substrate moving into the cell are compensated by the outward movement of potassium ions (or protons) to complete the transport cycle. On the other hand, DAT and NET are electrogenic. Moreover, potassium ions are not extruded by these transporters and the Authors set out to investigate if the electrogenicity is related to difference in potassium handling between SERT and the two other biogenic transporters. This was done by analyzing the role of intracellular cations and voltage on substrate transport by the three biogenic amine transporters. This was achieved by the simultaneous recording of uptake of the fluorescent substrate APP+ and the current induced by this process under voltage-clamp conditions by single HEK293 cells expressing the transporters. The Authors found that even though uptake by NET and DAT did not require internal potassium, these transporters could actually interact with internal potassium as judged by the voltage dependence of the so-called peak current. This voltage dependence was very steep in the absence of both sodium and potassium. However, in the presence of either cation this voltage dependence became less steep when either of these cations was present in the internal milieu, indicating that not only sodium but also potassium could bind from the inside. The same result was obtained with SERT. However, uptake by SERT was found to be much less dependent on the membrane voltage than that by DAT and NET and was stimulated by internal potassium, consistent with the proposed electroneutrality of the former. The observations indicate that the structural similarity of the three biogenic amine transporters is also reflected in their ability to bind potassium, even though this cation can translocate to the outside only in SERT.

Strengths:

Development of a sophisticated technique to interrogate the mechanism of sodium coupled biogenic amine transport in single cells. Rigorous analysis of the data. Conclusions supported by the data. The methodology can be used to obtain novel insights into the mechanism of other transporters.

Weaknesses:

The presentation could be made more "user friendly" by explaining in more detail what is happening as we go through the data. For instance, peak and steady state currents are shown already in Figure 1, but an (too brief) explanation is only provided when describing Figure 5. A schematic in the first part of the Results would be useful. Some information of on the structural background should be provided as well as a full description of the transport cycle, namely the number of sodium ions translocated per cycle and the argument why chloride remains bound to the transporter throughout the cycle. The control that in contrast to potassium, lithium is inert should be performed not only for DAT, but also for the two other transporters.

We thank Dr. Kanner for these recommendations. Regarding the role of Na+ and Cl- in the transport cycle of the monoamine transporters, we have briefly mentioned the same in the introduction as follows: “The crystal structure of both hSERT and dDAT show two bound Na+ ions. However, only one Na+ ion is thought to be released on the intracellular side in both transporters (Rudnick & Sandtner, 2019). Cl-, on the other hand, has been shown to play a modulatory role in the transport cycle of SERT and DAT, but Cl- is not essential for the transport stoichiometry (Erreger et al., 2008; Hasenhuetl et al., 2016).”

As for the control experiments with Li+, we are very grateful to Dr. Kanner for his suggestions. En route to extending the observations, which we obtained with DAT in the presence of high intracellular Li+, to NET and SERT, we stumbled upon some unexpected results: while IV relations of peak currents with high intracellular Li+ or NMDG+ in NET were identical (similar to DAT), SERT gave us exactly the opposite profiles. IV relations of high intracellular Li+ in SERT were as shallow as those in the presence of high +++ intracellular K or high intracellular Na . This is indicative of intracellular Li binding to SERT, an observation not previously reported that further highlights the differences in DAT/NET and SERT in cation binding. We believe that our observations with Li+ and SERT could be expanded on in a separate story. We have accordingly changed the manuscript text in the Results and Discussion as follows:

Results (lines 320-337):

“Because the absence of Kin+ affected the slope of the IV-relation of the peak current, we surmised that potassium bound from the intracellular side not only to SERT but also possibly to DAT and NET. We explored this conjecture by determining the IV relation of peak currents through all three +++ transporters in the presence of lithium (Liin = 163 mM) instead of Kin . Li is believed to be an inert cation, because it does not support substrate translocation by SLC6 transporters. As expected, the IV relation of peak currents through DAT and NET were similar in the presence of 163 mM Lin+ to those recorded in the absence of Kin+ (cf., diamond and triangle symbol in Fig. 5J and 5K). These observations clearly indicate that Kin+ binds to both DAT and NET and rule out an alternative explanation, i.e. that the effect can be accounted for water and monovalent cations briefly occupying a newly available space in the inner vestibule. SERT, on the hand, show shallow IV relations of peak currents with high Liin+ when compared to those acquired in the absence of Kin+ (cf., diamond and triangle symbol in Fig. 5L). This is indicative of Liin+ binding to SERT on the intracellular side. The exact nature of Liin+ binding to SERT has not been reported previously and warrants further investigation. The IV relations of peak currents are similar in the presence of 163 mM Kin+ (Fig. 5A-C) and of 163 mM Nain+ (Fig. 5G-I) in DAT, NET and SERT (cf. circle and square symbols in Fig. 5J-L). This is consistent with the idea that Nain+ and Kin+ bind to overlapping sites in these transporters. “

Discussion (lines 524-527):

“Interestingly, differences between DAT/NET and SERT are further substantiated by the ability of SERT+ to bind to intracellular Li . The exact nature of this interaction is unknown and necessitates an in-depth investigation that is beyond the scope of this study.”

Author Response

Reviewer #1 (Public Review):

This paper is a follow-up of the authors previous paper (2018), in which they carefully described the organisation of the junctions between cells of the adult Drosophila midgut epithelium and their control from the basal side by integrin signalling. Here, the authors used state-of-the art imaging and genetics to unravel step-by-step the events leading from an initially unpolarised cell to an epithelial cell that integrates into the existing epithelium. Many of the images are accompanied by cartoons, which help the reader to better understand the images and follow the conclusions. It would have been helpful yet, in particular with respect to the mutant phenotypes described later, if they would have named each of the steps/stages. In addition, mentioning the timescale would give an idea about the temporal frame in which this process elapses.

We have used terms such as “unpolarised cells, polarised Actin/Cno” to label different stages in Figure 6, since this sequence of steps is inferred from results obtained from fixed samples with still images. We have illustrated the septate junction mutant phenotype in Figure 8I.

We have also performed a new experiment to estimate the time taken for an activated EB to form a PAC and to become a mature enterocyte using overexpressing Sox21a with esg[ts]>GFP to induce enteroblast differentiation. Counting the number of GFP+ve cells without PAC, with a PAC and with full apical domain at different time points suggests that activated EBs take about a day to form a PAC and another day to form a fully-integrated enterocyte. We have summarised the results in Figure 5-figure supplement 1C.

We have also included this result in the main-text as “ To estimate the time taken for enteroblasts to progress to pre-enterocytes with a PAC, and for pre-enterocytes become to enterocytes, we induced enterocyte differentiation by over-expressing UAS-Sox21a under the control of esg[ts]-Gal4 and counted the number of GFP+ve cells without a PAC or apical domain, with a PAC and with a full apical domain at different time points after induction (Chen et al., 2016; Meng and Biteau, 2015; Zhai et al., 2017). 17 hours after shifting the flies to 25ºC to inactivate Gal80ts, almost no GFP+ve cells had progressed to pre-EC with a PAC (0.1%) or EC (1%), and these few cells probably started to differentiate before Sox 21a induction. 24 hours later, 10% of the GFP+ve cells had developed into pre-ECs with a PAC and 20% had become ECs (Figure 5-figure supplement 1B-C). After an additional 24 hours, the number of cells with a PAC fell to 1%, whereas 50% were ECs. Assuming that it takes 12-17 hours to induce high levels of Sox21a expression, these results suggest that most activated EBs take about 24 hours to develop into a pre-EC with a PAC and a further 24 hours to differentiate into a mature EC, although some cells differentiate faster. This time frame is in agreement with a previous study using similar approaches to accelerate differentiation (Rojas Villa et al., 2019) and a recent live imaging study tracing the enteroblast to enterocyte transition (Tang et al., 2021). These results also indicate that down-regulation of Sox21a is not essential for enteroblast to pre-enterocyte differentiation, since enteroblasts overexpressing Sox21a still from a PAC (Figure 5-figure supplement 1B).

The authors convincingly show that septate junctions are instrumental for proper polarisation and integration of the enteroblast. However, while they nicely showed that Canoe in neither required in the enteroblast nor in the enterocytes for this process, it remains unclear whether septate junction proteins are required in enteroblast or in enterocytes or in both and at which particular step the process fails in the mutant.

Early stage enteroblasts neither express or require septate junction proteins, whereas late stage enteroblasts and pre-enterocytes do (Chen et al., 2020; Hung et al., 2020; Izumi et al., 2019; Xu et al., 2019). Since cells mutant for septate junction proteins do not develop into mature enterocytes with an apical domain facing the gut lumen, we cannot answer the reviewer’s question of whether septate junction proteins are required in enterocytes.

As we discussed in the paper, we think that “differentiating enteroblasts only require a basal cue to establish their initial apical-basal polarity, whereas the formation of the pre-assembled apical compartment also requires a junctional cue. The septate junctions are not necessary for apical domain formation per se, however, as mesh mutant enteroblasts form a full-developed apical domain with a brush border inside the cell. This suggests that septate junctions define the site of apical domain formation by delimiting the region where apical membrane proteins are secreted to assemble the brush border, but do not control the process of apical domain formation directly.”

Reviewer #2 (Public Review):

The authors recently showed the polarization of the cells of the adult Drosophila midgut does not require any of the canonical epithelial polarity factors, and instead depend on basal cues from adhesion to the ECM, as well as septate junction proteins (Chen et al, 2018). Here they extend this research to examine in greater detail precisely how midgut epithelial cells integrate in the pre-exisiting epithelium and become polarized. Surprisingly, they show that enteroblasts form an apical membrane initiation site prior to polarizing. Furthermore, they show that this develops into a pre-apical compartment containing fully-formed brush border. This is a very interesting finding - it explains how integrating enteroblasts can integrate into a pre-existing epithelium without disrupting barrier function. The conclusions of this paper are mostly well supported by data, but some aspects could do with being clarified and extended as outlined below.

Model presented in Figure 6

While the separation of membranes indicated in Figure 6 steps 3-5 can be seen in the image shown in Figure 3B, this is one of the only images which supports the idea that there is a separation of membranes between the enteroblast and overlying enterocytes during PAC formation. Is the model in Figure 6 supported by EM data - can you see a region where there is brush border and separation of cells? Supplementing Figure 3 with corresponding EM images would greatly aid the reader in interpreting the data and strengthen the model.

We think that AJ clearing and membrane separation is a brief process that is quickly followed by the separation of the apical and junctional proteins and apical secretion at the AMIS to form the PAC. We have not captured this stage in our EM images, but have many other examples that show this step (e.g Figure 4C and Figure 8F). Another example is shown below.

A key step in the model is that the clearance of E-Cadherin from the apical membrane leads to a loss of adhesion between the enteroblast and the overlying enterocytes. This would need to be supported by functional data such as overexpression of E-Cad or E-CadDN in enteroblasts or by generating shg mutant clones. If the model is correct, perturbing E-Cad levels in enteroblasts should lead to defects in PAC formation, such as loss of de-adhesion/early de-adhesion/excessive de-adhesion.

We think it is the local clearance of ECad from the apical membrane, not the downregulation of total level of ECad that is important for the local membrane separation and future PAC formation. The experiment of overexpressing ECad or ECad-DN proposed by the reviewer might be crucial to demonstrate the importance of total amount of ECad, but might not be very helpful in determining the importance of membrane separation in the PAC formation. Moreover, AJ formation in fly midgut epithelium does not depend on ECad, suggesting that ECad and NCad act redundantly which further complicates this approach (Choi et al., 2011; Liang et al., 2017).

Role for the septate junction proteins

Septate junction proteins were previously shown by these authors to be required for enteroblast polarization and integration into the midgut epithelium (Chen et al, 2018). Here they extend this by examining enteroblasts mutant for septate junction proteins, and conclude that septate junction proteins are required for normal PAC formation. However, it is not clear what aspect of the polarization of the enteroblasts is disrupted, because a number of mesh mutant cells (albeit a lower proportion than in wildtype) do form PACs. The main phenotype seems to be that cells fail to polarize (as previously reported) or have internalised PACs. It is hard to know what to conclude from this data about the role of the septate junction components in PAC formation.

The major phenotype of the septate junction mutants is the loss of polarity, i.e. an inability to form an apical domain and integrate into the epithelial layer as shown in Figure 8. Neither mesh or Tsp2a mutants can form a PAC, even though mesh mutant cells have higher propensity to form an internal PAC-like structure (Figure 8B,C,E,G,H, Figure 8-figure supplement 1L). Thus, we think that septate junctions are required for AMIS and PAC formation. What complicates the interpretation is that some (6-20%) septate junction mutant cells do form an AMIS like structure (Figure 8D-F, Figure 8-figure supplement 1F&K). The simplest explanation for this result is that this is due to perdurance of the wild-type proteins after clone induction, with the weaker phenotype of ssk mutants being due to longer perdurance of this protein. However, we cannot rule out the alternative explanation that AMIS and PAC formation is facilitated by the septate junction proteins, but that they can still form very inefficiently in their absence.

We realise that this section was quite confusing in the orginal version of the manuscript and have now re-written it to make this interpretation clearer.

Coracle is used as a readout for the localization of septate junction components, yet the staining for Cora in Figure S3B looks quite different to Mesh in S3D. If Cora is to be used as a readout for the localization of septate junction components, then staining for Cora/Mesh and/or Cora/SSk or Tsp2a should be shown.

When discussing the requirement for septate junctions for enteroblast integration - Coracle and Mesh are used interchangeably - but as mentioned before, it is not clear if they colocalize, or if their localization is interdependent (as demonstrated for Mesh, Tsp2a and Ssk in Figure 7). What is the phenotype of enteroblasts mutant for cora?

Following from the previous point - while it is clear that Coracle is apical early during AMIS formation, it is not clear if Mesh, Tsp2a and Ssk also are, yet these are the mutants that are examined for a role in AMIS/PAC formation. It would be good to know whether the loss of cora would lead to defects in AMIS formation.

The reason we used mainly Coracle as a marker for the septate junctions is that Mesh and Tsp2A localise to the basal labyrinth as well as to the septate junctions which could confuse the reader. We have now added new panels to Figure 3-figure supplement 3E&F showing the colocalization of Cora with Mesh/Tsp2a at the septate junctions and during the crucial stages of PAC formation.

Additional Results:

"Coracle is a peripheral septate junction protein whose localisation depends on the structural septate junction components such as Mesh/Ssk/Tsp2a (Chen et al., 2018; Izumi et al., 2016, 2012). Cora antibody staining provides a clearer marker for the septate junctions than Mesh or Tsp2a antibody staining, because the latter also label the basal labyrinth (Figure 3-figure supplement 1E&F). To determine whether Cora is required for PAC formation or epithelial polarity in the adult midgut, we generated a null mutant allele with a premature stop codon in FERM domain using CRISPR. Cells mutant for this allele, corajc, or a second cora null allele, cora5, can form a PAC, septate junctions and a full apical domain, indicating that Cora is also not required for enteroblast integration or enterocyte polarity (Figure 7F&G, Figure 7-figure supplement 1E-H).

Additional Materials and Methods:

We used the CRISPR/Cas9 method (Bassett and Liu, 2014) to generate null alleles of canoe and coracle. sgRNA was in vitro transcribed from a DNA template created by PCR from two partially complementary primers:

forward primer:

For coracle:

5′-GAAATTAATACGACTCACTATAGAAGCTGGCCATGTACGGCGGTTTTAGAGCTAGAAATAGC-3′;

The sgRNA was injected into…Act5c-Cas9 embryos to generate coracle null alleles (Port et al., 2014). Putative…coracle mutants in the progeny of the injected embryos were recovered, balanced, and sequenced. …The coraclejc allele contains a 2bp deletion around the CRISPR site, resulting in a frameshift that leads to stop codon at amino acid 225 in the middle of the FERM domain, which is shared by all isoforms. No Coracle protein was detectable by antibody (DSHB C615.16) staining in both midgut and follicle cell clones. The coraclejc allele was recombined with FRT G13 to make the FRTG13 coraclejc flies.

It is unclear what is happening in Figure 8A,C,E, S7D. Is that a detachment phenotype or an integration phenotype? Are the majority of cells unpolarised due to loss of integrin attachment rather than failure to form an AMIS/PAC?

Cells mutant for septate junction proteins do not detach from the basement membrane and still localise Talin basally, as illustrated by the new panel we have added (Figure 8-figure supplement 1N), showing Talin localisation in Tsp2a mutant cell.

However, because the mutant cells cannot integrate and remain stuck beneath the septate junctions between the enterocytes, they sometimes become displaced from a portion of the basement membrane by younger EBs that derive from the same mutant ISC, leading to a pile up of cells in the basal region of the epithelium (e.g. Figure 8A, E and H).

We have added the following sentences to the Results, explaining these points:

"Because the mutant cells remain trapped beneath enterocyte-enterocyte septate junctions, they accumulate in the basal region of the epithelium, with new EBs derived from the same mutant ISC forming beneath them and reducing their contact with the basement membrane (Figure 8A)."

" The majority of cells mutant for septate junction components fail to polarise or form an AMIS, although they form normal lateral and basal domains, as the basal integrin signalling component, Talin, localises normally (Figure 8-figure supplement 1N)."

It is unclear whether enteroblasts really pass through an 'unpolarized stage'. In Figure 6, when they are described as 'unpolarised', they clearly have distinct basal and AJ domains. In septate junction mutants, when cells are classified as unpolarized, do they still have distinct regions of integrin/E-Cad expression?

This is a semantic question. We agree that they have distinct lateral and basal domains, but they do not have an apical domain. In this respect, these "unpolarised" cells are similar to a mesenchymal fibroblast migrating on a substrate, which has a distinct basal side contacting the substrate that is different from the non-contacting regions of the cell surface. They also match the description of the migratory, "mesenchymal" enteroblasts (Antonello et al., 2015). To make this clearer, we have added the following notes to the legend for Figure 6: “Unpolarised” in the second panel of this figure indicates that the enteroblast has not formed a distinct apical domain. At this stage, no marker is clearly apically localised. “unpolarised” or “polarised” in the third and fourth panels describe the localisation of marker proteins, such as Actin and Cno."

Author Response

eLife assessment

This important paper exploits new cryo-EM tomography tools to examine the state of chromatin in situ. The experimental work is meticulously performed and convincing, with a vast amount of data collected. The main findings are interpreted by the authors to suggest that the majority of yeast nucleosomes lack a stable octameric conformation. Despite the possibly controversial nature of this report, it is our hope that such work will spark thought-provoking debate, and further the development of exciting new tools that can interrogate native chromatin shape and associated function in vivo.

We thank the Editors and Reviewers for their thoughtful and helpful comments. We also appreciate the extraordinary amount of effort needed to assess both the lengthy manuscript and the previous reviews. Below, we provide our provisional responses in bold blue font. The majority of the comments are straightforward to address. We have taken a more conservative approach with the subset of comments that would require us to speculate because we either lack key information or we lack technical expertise. Instead of adding the speculative replies to the main text, we think it will be better to leave them in the rebuttal for posterity. Readers will therefore have access to our speculation and know that we did not feel confident enough to include these thoughts in the Version of Record.

Reviewer #1 (Public Review):

This manuscript by Tan et al is using cryo-electron tomography to investigate the structure of yeast nucleosomes both ex vivo (nuclear lysates) and in situ (lamellae and cryosections). The sheer number of experiments and results are astounding and comparable with an entire PhD thesis. However, as is always the case, it is hard to prove that something is not there. In this case, canonical nucleosomes. In their path to find the nucleosomes, the authors also stumble over new insights into nucleosome arrangement that indicates that the positions of the histones is more flexible than previously believed.

We want to point out that canonical nucleosomes are there in wild-type cells in situ, albeit rarer than what’s expected based on our HeLa cell analysis. The negative result (absence of any canonical nucleosome classes in situ) was found in the histone-GFP mutants.

Major strengths and weaknesses:

Personally, I am not ready to agree with their conclusion that heterogenous non-canonical nucleosomes predominate in yeast cells, but this reviewer is not an expert in the field of nucleosomes and can't judge how well these results fit into previous results in the field. As a technological expert though, I think the authors have done everything possible to test that hypothesis with today's available methods. One can debate whether it is necessary to have 35 supplementary figures, but after working through them all, I see that the nature of the argument needs all that support, precisely because it is so hard to show what is not there. The massive amount of work that has gone into this manuscript and the state-of-the art nature of the technology should be warmly commended. I also think the authors have done a really great job with including all their results to the benefit of the scientific community. Yet, I am left with some questions and comments:

Could the nucleosomes change into other shapes that were predetermined in situ? Could the authors expand on if there was a structure or two that was more common than the others of the classes they found? Or would this not have been found because of the template matching and later reference particle used?

Our best guess (speculation) is that one of the class averages that is smaller than the canonical nucleosome contains one or more non-canonical nucleosome classes. We do not feel confident enough to single out any of these classes precisely because we do not yet know if they arise from one non-canonical nucleosome structure or from multiple – and therefore mis-classified – non-canonical nucleosome structures (potentially with other non-nucleosome complexes mixed in). We feel it is better to leave this discussion out of the manuscript, or risk sending the community on wild goose chases.

Our template-matching workflow uses a low-enough cross-correlation threshold that any nucleosome-sized particle (plus minus a few nanometers) would be picked, which is why the number of hits is so large. So unless the noncanonical nucleosomes quadrupled in size or lost most of their histones, they should be grouped with one or more of the other 99 class averages (WT cells) or any of the 100 class averages (cells with GFP-tagged histones). As to whether the later reference particle could have prevented us from detecting one of the non-canonical nucleosome structures, we are unable to tell because we’d really have to know what an in situ non-canonical nucleosome looks like first.

Could it simply be that the yeast nucleoplasm is differently structured than that of HeLa cells and it was harder to find nucleosomes by template matching in these cells? The authors argue against crowding in the discussion, but maybe it is just a nucleoplasm texture that side-tracks the programs?

Presumably, the nucleoplasmic “side-tracking” texture would come from some molecules in the yeast nucleus. These molecules would be too small to visualize as discrete particles in the tomographic slices, but they would contribute textures that can be “seen” by the programs – in particular RELION, which does the discrimination between structural states. We do not know the inner-workings of RELION well enough to say what kinds of density textures would side-track its classification routines.

The title of the paper is not well reflected in the main figures. The title of Figure 2 says "Canonical nucleosomes are rare in wild-type cells", but that is not shown/quantified in that figure. Rare is comparison to what? I suggest adding a comparative view from the HeLa cells, like the text does in lines 195-199. A measure of nucleosomes detected per volume nucleoplasm would also facilitate a comparison.

Figure 2’s title is indeed unclear and does not align with the paper’s title and key conclusion. The rarity here is relative to the expected number of nucleosomes (canonical plus non-canonical). We have changed the title to “Canonical nucleosomes are a minority of the expected total in wild-type cells”. We would prefer to leave the reference to HeLa cells to the main text instead of as a figure panel because the comparison is not straightforward for a graphical presentation. Instead, we will report the total number of nucleosomes estimated for this particular tomogram (~7,600) versus the number of canonical nucleosomes classified (297; 594 if we assume we missed half of them).

If the cell contains mostly non-canonical nucleosomes, are they really non-canonical? Maybe a change of language is required once this is somewhat sure (say, after line 303).

This is an interesting semantic and philosophical point. From the yeast cell’s “perspective”, the canonical nucleosome structure would be the form that is in the majority. That being said, we do not know if there is one structure that is the majority. From the chromatin field’s point of view, the canonical nucleosome is the form that is most commonly seen in all the historical – and most contemporary – literature, namely something that resembles the crystal structure of Luger et al, 1997. Given these two lines of thinking, we will add the following clarification after line 303:

“At present, we do not know what the non-canonical nucleosome structures are, meaning that we cannot even determine if one non-canonical structure is the majority. Until we know what the family of non-canonical nucleosome structures are, we will use the term non-canonical to describe the nucleosomes that do not have the canonical (crystal) structure”.

The authors could explain more why they sometimes use conventional the 2D followed by 3D classification approach and sometimes "direct 3-D classification". Why, for example, do they do 2D followed by 3D in Figure S5A? This Figure could be considered a regular figure since it shows the main message of the paper.

Because the classification of subtomograms in situ is still a work in progress, we felt it would be better to show one instance of 2-D classification for lysates and one for lamellae. While it is true that we could have presented direct 3-D classification for the entire paper, we anticipate that readers will be interested to see what the in situ 2-D class averages look like.

The main message is that there are canonical nucleosomes in situ (at least in wild-type cells), but they are a minority. Therefore, the conventional classification for Figure S5A should not be a main figure because it does not show any canonical nucleosome class averages in situ.

Figure 1: Why is there a gap in the middle of the nucleosome in panel B? The authors write that this is a higher resolution structure (18Å), but in the even higher resolution crystallography structure (3Å resolution), there is no gap in the middle.

There is a lower concentration of amino acids at the middle in the disc view; unfortunately, the space-filling model in Figure 1A hides this feature. The gap exists in experimental cryo-EM density maps. See below for an example. The size of the gap depends on the contour level and probably the contrast mechanism, as the gap is less visible in the VPP subtomogram averages. To clarify this confusing phenomenon, we will add the following lines to the figure legend:

“The gap in the disc view of the nuclear-lysate-based average is due to the lower concentration of amino acids there, which is not visible in panel A due to space-filling rendering. This gap’s size may depend on the contrast mechanism because it is not visible in the VPP averages.”

Reviewer #2 (Public Review):

Nucleosome structures inside cells remain unclear. Tan et al. tackled this problem using cryo-ET and 3-D classification analysis of yeast cells. The authors found that the fraction of canonical nucleosomes in the cell could be less than 10% of total nucleosomes. The finding is consistent with the unstable property of yeast nucleosomes and the high proportion of the actively transcribed yeast genome. The authors made an important point in understanding chromatin structure in situ. Overall, the paper is well-written and informative to the chromatin/chromosome field.

We thank Reviewer 2 for their positive assessment.

Reviewer #3 (Public Review):

Several labs in the 1970s published fundamental work revealing that almost all eukaryotes organize their DNA into repeating units called nucleosomes, which form the chromatin fiber. Decades of elegant biochemical and structural work indicated a primarily octameric organization of the nucleosome with 2 copies of each histone H2A, H2B, H3 and H4, wrapping 147bp of DNA in a left handed toroid, to which linker histone would bind.

This was true for most species studied (except, yeast lack linker histone) and was recapitulated in stunning detail by in vitro reconstitutions by salt dialysis or chaperone-mediated assembly of nucleosomes. Thus, these landmark studies set the stage for an exploding number of papers on the topic of chromatin in the past 45 years.

An emerging counterpoint to the prevailing idea of static particles is that nucleosomes are much more dynamic and can undergo spontaneous transformation. Such dynamics could arise from intrinsic instability due to DNA structural deformation, specific histone variants or their mutations, post-translational histone modifications which weaken the main contacts, protein partners, and predominantly, from active processes like ATP-dependent chromatin remodeling, transcription, repair and replication.

This paper is important because it tests this idea whole-scale, applying novel cryo-EM tomography tools to examine the state of chromatin in yeast lysates or cryo-sections. The experimental work is meticulously performed, with vast amount of data collected. The main findings are interpreted by the authors to suggest that majority of yeast nucleosomes lack a stable octameric conformation. The findings are not surprising in that alternative conformations of nucleosomes might exist in vivo, but rather in the sheer scale of such particles reported, relative to the traditional form expected from decades of biochemical, biophysical and structural data. Thus, it is likely that this work will be perceived as controversial. Nonetheless, we believe these kinds of tools represent an important advance for in situ analysis of chromatin. We also think the field should have the opportunity to carefully evaluate the data and assess whether the claims are supported, or consider what additional experiments could be done to further test the conceptual claims made. It is our hope that such work will spark thought-provoking debate in a collegial fashion, and lead to the development of exciting new tools which can interrogate native chromatin shape in vivo. Most importantly, it will be critical to assess biological implications associated with more dynamic - or static forms- of nucleosomes, the associated chromatin fiber, and its three-dimensional organization, for nuclear or mitotic function.

Thank you for putting our work in the context of the field’s trajectory. We hope our EMPIAR entry, which includes all the raw data used in this paper, will be useful for the community. As more labs (hopefully) upload their raw data and as image-processing continues to advance, the field will be able to revisit the question of non-canonical nucleosomes in budding yeast and other organisms.

Author Response:

Reviewer #1:

In this paper, Wammes et al. used fMRI to investigate changes in representational similarity of temporally paired images in hippocampal subfields. The stimuli were designed to parametrically vary in their visual similarity so that individual pairs covered the entire range of visual overlap, which was behaviourally validated by a separate sample of participants. The authors compared the neural patterns evoked by these pairs of stimuli before and after participants completed a statistical learning task. The findings showed that pre- to post-learning, representations in the dentate gyrus reconfigured to fit a cubic model, consistent with the non-monotonic plasticity hypothesis (NMPH).

This is an interesting, novel approach with a clever stimulus manipulation which addresses a gap in the current literature. The study is well-motivated by theory, the analyses are appropriate and clearly described, the implemented controls are carefully designed, and the manuscript is well-written. However, it is unclear whether the same principles necessarily generalize beyond visual similarity, and whether these neural patterns meaningfully relate to behaviour.

1) The analytic approach is well-designed and the results clearly address the hypotheses. However, it seems like the conclusions might be dependent on this learning paradigm, which should be discussed in a bit more detail and made clearer. The present statistical learning approach is somewhat implicit in its nature and relies on the participants gradually recognizing the temporal links between stimuli. In contrast, in most prior studies cited in the present manuscript, participants were explicitly instructed to make associations between stimuli that either occurred on the screen simultaneously, or relatively far apart in time (i.e., not successively). This top-down influence likely plays an important role. Even beyond experimental paradigms - we often make connections between similar experiences that occurred far apart in time, and cannot always rely on temporal contingencies. The step between previous work and statistical learning needs to be made clearer and more explicit.

Although our current approach involves a more implicit statistical learning task, the hypothesized non-monotonic plasticity is a general mechanism that has been and can be applied across tasks. We used temporal contingency to create a situation where representations were concurrently active. However, prior work has used other manipulations, such as linking to a shared associate. We have modified and expanded both the Introduction and Conclusion to emphasize this broader context and highlight directions for future work.

See Introduction (p. 4, lines 60-74): “The NMPH has been put forward as a learning mechanism that applies broadly across tasks in which memories compete, whether they have been linked based on incidental co-occurrence in time or through more intentional associative learning (Ritvo et al., 2019). The NMPH can explain findings of differentiation in diverse paradigms (e.g., linking to a shared associate: Chanales et al., 2017; Favila et al., 2016; Schlichting et al., 2015; Molitor et al., 2020; retrieval practice: Hulbert & Norman, 2015; statistical learning: Kim, Norman, & Turk-Browne, 2017) by positing that these paradigms induced moderate coactivation of competing memories. Likewise, relying on the same parameter of coactivation, the NMPH can explain seemingly contradictory findings showing that shared associates (Collin et al., 2015; Milivojevic et al., 2015; Schlichting et al., 2015; Molitor et al., 2020) and co-occurring items (Schapiro et al., 2012; Schapiro, Turk-Browne, Norman, & Botvinick, 2016) can lead to integration by positing that — in these cases — the paradigms induced strong coactivation. Importantly, although the NMPH is compatible with findings of both differentiation and integration across several paradigms with diverse task demands, the explanations above are post hoc and do not provide a principled test of the NMPH’s core claim that there is a continuous, U-shaped function relating the level of coactivation to representational change.

See Introduction (p. 5, lines 83-86): “No existing study has demonstrated the full U- shaped pattern for representational change; that is what we set out to do here, using a visual statistical learning paradigm — specifically, we brought about coactivation using temporal co-occurrence between paired items, and we manipulated the degree of coactivation by varying the visual similarity of the items in a pair.”

See Conclusion (p. 18, lines 370-374): “From a theoretical perspective, these results provide the strongest evidence to date for the NMPH account of hippocampal plasticity. We expect that a similar U-shaped function relating coactivation and representational change will manifest in paradigms with different task demands and stimuli, but additional work is needed to provide empirical support for this claim about generality.”

2) Related to the point above - the timecourse over which such statistical learning occurs should be discussed. If I understood correctly, all of the learning occurred in the 6 scanned blocks between the two templating runs. Does the NMPH predict that the hippocampal patterns should immediately reconfigure depending on visual input, or only reconfigure once the participants encode the links between paired stimuli? If the pattern consistent with the NMPH is immediately evident, this would suggest that the present findings, while very convincing, might not be governed by the same mechanisms as integration/differentiation in memory. It seems unlikely that participants would immediately attempt to link these complex visual stimuli, especially as the cover task was orthogonal. To this end, it would be helpful to see any kind of analysis evaluating representations across the 6 statistical learning runs.

The reviewer correctly describes that learning took place over the six blocks between templating runs. We agree that observing the emergence of representational change across those runs would be ideal. Unfortunately, however, our design is not compatible with this analysis. Because the pairs were learned from deterministic transition probabilities, the onsets of the paired stimuli were correlated in time. When these correlated events are convolved with the slow hemodynamic response, the responses to the paired stimuli cannot be reliably distinguished. Also, the response to the second stimulus in a pair would be affected by visual similarity to its preceding stimulus as a result of adaptation/repetition suppression, confounding comparisons across conditions. These problems are precisely why we employed a pre/post design in which to-be/previously paired stimuli are presented independently in a random order. This allows for the assessment of representational similarity unconfounded with correlated onsets or adaptation.

Although we cannot provide a sense of the learning trajectory, we now highlight this design decision, acknowledge the limitation, and highlight this as an opportunity for future work with other more time-resolved modalities or with (random) representational assessments interdigitated with the learning blocks.

See Discussion (p. 17, lines 358-366): “Finally, although analyzing representational overlap in templating runs before and after statistical learning afforded us the ability to quantify pre-to-post changes, our design precluded analysis of the emergence of representational change over time. That is, we could not establish whether integration or differentiation occurred early or late in statistical learning. This is because, during statistical learning runs, the onsets of paired images were almost perfectly correlated, meaning that it was not possible to distinguish the representation of one image from its pairmate. Future work could monitor the time course of representational change, either by interleaving additional templating runs throughout statistical learning (although this could interfere with the statistical learning process), or by exploiting methods with higher temporal resolution where the responses to stimuli presented close in time can more readily be disentangled.”

3) In the Introduction and Discussion, the authors focus on learning and discuss the integration/differentiation of memories. To establish a link between the reported hippocampal representations and behaviour, it would be helpful to show evidence of a link between neural differentiation and measures of statistical learning such as priming.

As the reviewer alluded to earlier, our behavioral task is orthogonal to the manipulation of temporal co-occurrence. Accordingly, we do not have any behavioral data on which we could conduct such an analysis. We fully acknowledge the value of this suggestion and now describe this as a limitation and area for future research.

See Discussion (p. 17, lines 350-357): “Prior work in this area has demonstrated brain- behavior relationships (Favila et al., 2016; Molitor et al., 2020), so it is clear that changes in representational overlap (i.e., either integration or differentiation) can bear on later behavioral performance. However, in the current work, our behavioral task was intentionally orthogonal to the dimensions of interest (i.e., unrelated to temporal co- occurrence and visual similarity), limiting our ability to draw conclusions about potential downstream effects on behavior. We believe that this presents a compelling target for follow-up research. Establishing a behavioral signature of both integration and differentiation in the context of nonmonotonic plasticity will not only clarify the brain-behavior relationship, but also allow for investigations in this domain without requiring brain data.”

4) From the authors' predictions (and Fig 1), it might follow that participants who show steeper slopes in early visual regions (i.e., higher correspondence to stimulus similarity) pre-learning might also show a stronger cubic trend in the hippocampus. It would be useful to show within-participant analyses to link visual processing regions to hippocampal representations.

What a fantastic suggestion! To test this prediction, we extracted the linear coefficients in the visual similarity analysis from cortical ROIs (V1, V2, LO, IT, FG, PHC, PRC, and EC) and the cubic model fit in the representational change analysis from the key hippocampal ROI (DG). Linearity during the initial templating run in PRC was associated with stronger non-monotonicity in DG. The full reporting of these analyses is now included in the figure supplements and referenced in the main text.

See Results, subsection Representational Change (p. 12, lines 228-229): “Interestingly, in an exploratory analysis, we found that the degree of model fit in DG was predicted by the extent to which visual representations in PRC tracked model similarity (see Figure 4—figure supplement 2).”

Reviewer #2:

The authors apply neural network modeling and representational analysis of fMRI data to testing the ability of the theoretical framework under the "non-monotonic-plasticity hypothesis" to explain how hippocampal subdivisions represent similarity and distinctiveness between events. They suggest that the dentate gyrus subfield, in particular, was sensitive to the degree of overlap between experiences, and changes how it favored distinctiveness or similarity in its representation of associated stimuli in a non-monotonic manner.

Overall, the work builds logically on prior evidence from this group focused on how cortical representations influence memory, and leverages a compelling theoretical framework to reconcile some conflict in the literature on how hippocampal representations respond to overlap.

The primary confusion and concern with the current manuscript was on the theoretical side. It was not wholly clear from the literature review why DG was the predicted locus of the non-monotonic representational relationship observed, and how the findings fit with extant data from rodent work.

Thank you for providing an opportunity to better motivate our work. We have updated the paragraph justifying our focus on the hippocampus and on DG in particular.

See Introduction (p. 8, lines 122-147): “We and others have previously hypothesized that nonmonotonic plasticity applies widely throughout the brain (Ritvo et al., 2019), including sensory regions (e.g., Bear, 2003). In this study, we focused on the hippocampus because of its well-established role in supporting learning effects over relatively short timescales (e.g., Favila et al., 2016; Kim et al., 2017; Schapiro et al., 2012). Importantly, we hypothesized that, even if nonmonotonic plasticity occurs throughout the entire hippocampus, it might be easier to trace out the full predicted U-shape in some hippocampal subfields than in others. As discussed above, our hypothesis is that representational change is determined by the level of coactivation — detecting the U-shape requires sweeping across the full range of coactivation values, and it is particularly important to sample from the low-to-moderate range of coactivation values associated with the differentiation ‘dip’ in the U-shaped curve (i.e., the leftmost side of the inset in Fig. 1). Prior work has shown that there is extensive variation in overall activity (sparsity) levels across hippocampal subfields, with CA2/3 and DG showing much sparser codes than CA1 (Barnes, McNaughton, Mizumori, Leonard, & Lin, 1990; Duncan & Schlichting, 2018). We hypothesized that regions with sparser levels of overall activity (DG, CA2/3) would show lower overall levels of coactivation and thus do a better job of sampling this differentiation dip, leading to a more robust estimate of the U-shape, compared to regions like CA1 that are less sparse and thus should show higher levels of coactivation (Ritvo et al., 2019). Consistent with this idea, human fMRI studies have found that CA1 is relatively biased toward integration and CA2/3/DG are relatively biased toward differentiation (Dimsdale-Zucker et al., 2018; Kim et al., 2017; Molitor et al., 2020). Zooming in on the regions that have shown differentiation in human fMRI (CA2/3/DG), we hypothesized that the U-shape would be most visible in DG, for two reasons: First, DG shows sparser activity than CA3 (Barnes et al., 1990; GoodSmith et al., 2017; West, Slomianka, & Gundersen, 1991) and thus will do a better job of sampling the left side of the coactivation curve. Second, CA3 is known to show strong attractor dynamics (‘pattern completion’; McNaughton & Morris, 1987; Rolls & Treves, 1998; Guzowski, Knierim, & Moser, 2004) that might make it difficult to observe moderate levels of coactivation. For example, rodent studies have demonstrated that, rather than coactivating representations of different locations, CA3 patterns tend to sharply flip between one pattern and the other (e.g., Leutgeb, Leutgeb, Moser, & Moser, 2007; Vazdarjanova & Guzowski, 2004).”

Additionally, the theoretical model (nicely illustrated in the manuscript) is considered in a somewhat biological-network-agnostic level. Some assumption for how context changes over time, how prior representations are maintained over time, etc., are important for non-monotonic relationships between representations and memory to manifest in the model, but the manuscript does not provide much discussion of their plausibility. This was particularly notable in terms of the emphasis given in the fMRI data to different hippocampal subfields, but not much discussion given on whether/why the model framework is static across subfields (in terms of how context and item information are represented and connected).

We appreciate this nudge to discuss these additional subfield-specific factors; we have added a paragraph to the Discussion that addresses these issues.

See Discussion (p. 16, lines 318-336): “Although we focused above on differences in sparsity when motivating our predictions about subfield-specific learning effects, there are numerous other factors besides sparsity that could affect coactivation and (through this) modulate learning. For example, the degree of coactivation during statistical learning will be affected by the amount of residual activity of the A item during the B item’s presentation in the statistical learning phase. In Figure 1, this residual activity is driven by sustained firing in cortex, but this could also be driven by sustained firing in hippocampus; subfields might differ in the degree to which activation of stimulus information is sustained over time (see, e.g., the literature on hippocampal time cells: Eichenbaum, 2014; Howard & Eichenbaum, 2013), and activation could be influenced by differences in the strength of attractor dynamics within subfields (e.g., Neunuebel & Knierim, 2014; Leutgeb et al., 2007). Also, in Figure 1, the learning responsible for differentiation was shown as happening between ‘perceptual conjunction’ neurons and ‘context’ neurons in the hippocampus. Subfields may vary in how strongly these item and context features are represented, in the stability/drift of the context representations (DuBrow, Rouhani, Niv, & Norman, 2017), and in the interconnectivity between item and context features (Witter, Wouterlood, Naber, & Van Haeften, 2000). It is also likely that some of the relevant plasticity between item and context features happens across, in addition to within, subfields (Hasselmo & Eichenbaum, 2005). For these reasons, exploring the predictions of the NMPH in the context of biologically detailed computational models of the hippocampus (e.g., Schapiro, Turk-Browne, Botvinick, & Norman, 2017; Frank, Montemurro, & Montaldi, 2020; Hasselmo & Wyble, 1997) will help to sharpen predictions about what kinds of learning should occur in different parts of the hippocampus.

As such, this review was very positive, and found the methods to be sound and the conclusions to be solid. There was some room for improvement in how the theoretical foundation was presented for the hippocampal subregion fMRI predictions and for the conceptualization of the neural network memory model.

We agree with the reviewer that more justification of our specific hippocampal predictions was required and we are grateful for their suggestions.

Author Response

Reviewer #1 (Public Review):

This study explores the mechanisms responsible for reduced steroidogenesis of adrenocortical cells in a mouse model of systemic inflammation induced by LPS administration. Working from RNA and protein profiling data sets in adrenocortical tissue from LPS-treated mice they report that LPS perturbs the TCA cycle at the level of succinate dehydrogenase B (SDHB) impairing oxidative phosphorylation. Additional studies indicate these events are coupled to increased IL-1β levels which inhibit SDHB expression through DNA methyltransferase-dependent DNA methylation of the SDHB promoter.

In general, these are interesting studies with some novel implications. I do, however, have concerns with some of the author's rather broad conclusions given the limitations of their experimental approach. The paper could be improved by addressing the following points:

1) The limitations of using LPS as the model for systemic inflammation need to be explicitly described.

We thank the Reviewer for this suggestion. Indeed, the LPS model has several limitations as a preclinical model of sepsis, which are outlined in the revised Discussion. Despite its limitations, we chose this model over other models of sepsis, such as the cecal slurry model, due to its high reproducibility, which enabled the here presented mechanistic studies.

2) The initial in vivo findings, which support the proposed metabolic perturbation, are based on descriptive profiling data obtained at one time point following a single dose of LPS. The author's conclusion that the ultimate transcriptional pathway identified hinges critically on knowledge of the time course of this effect following LPS, which is not adequately addressed in the paper. How was this time and dose of LPS established and are there data from different dose and time points?

We thank the Reviewer for raising this question, which we indeed addressed at the beginning of our studies in order to determine a suitable time point and dose of LPS treatment. We chose 6 h as a suitable starting time point to perform transcriptional analyses, based on the fact that LPS triggers transcriptional changes in the adrenal gland and other tissues within the range of few hours (1-3). Confirming our expectations we found 2,609 differentially expressed genes (Figure 1a) in the adrenal cortex of LPS-treated mice among which many were involved in cellular metabolism (Figure 1d,e, 2a-e, Table 1, Table 2). Acute transcriptional changes, which are more likely to reflect direct effects of inflammatory signals compared to changes occurring at later time points (for instance in the range of days), would allow us to mechanistically investigate the effects of inflammation in the adrenal gland, which was the purpose of our studies. Hence, we were guided by the transcriptional changes observed at 6 h of LPS treatment and established the hypothesis that disruption of the TCA cycle in adrenocortical cells is key in the impact of inflammation on adrenal function. Along this line, we analyzed the metabolomic profile of the adrenal gland at 6 and 24 h of LPS treatment. At 6 h succinate levels as well as the succinate / fumarate ratio remained unchanged (Author response image 1A), while at 24 h post-injection these were increased by LPS (Author response image 1B, Figure 2l,o,q). The time delay of the increase in succinate levels (observed at 24 h) following downregulation of Sdhb mRNA expression (at 6 h) can be explained by the time required for reduction of SDHB protein levels, which is dependent on the protein turnover suggested to be approximately 12 h in HeLa cells (4). Based on these findings, all further metabolomic analyses were performed at 24 h of LPS treatment.

Author response image 1. LPS increases the succinate/fumarate ratio at 24 but not 6h. Mice were i.p. injected with 1 mg/kg LPS and 6 h (A) and 24 h (B) post-injection succinate and fumarate levels were determined by LC-MS/MS in the adrenal gland. n=8-10; data are presented as mean ± s.e.m. Statistical analysis was done with two-tailed Mann-Whitney test. *p < 0.05.

Having established the most suitable time points of LPS treatments to observe induced transcriptional and metabolic changes, we set out to define the LPS dose to be used in subsequent experiments. The data shown in Author response image 1, were acquired after treatment with 1 mg/kg LPS. This is a dose that was previously reported to cause transcriptional re-profiling of the adrenal gland (1, 2). However, 5 mg/kg LPS, similarly to 1 mg/kg LPS, also reduced Sdhb, Idh1 and Idh2 expression at 4 h (Author response image 2A) and increased succinate and isocitrate levels at 24 h (Author response image 2B) in the adrenal gland. Given that the effects of 1 and 5 mg/kg LPS were similar, for animal welfare reasons we continued our studies with the lower dose.

Author response image 2. Five mg/kg LPS downregulate Sdhb, Idh1 and Idh2 expression and increase succinate and isocitrate levels in the adrenal gland of mice. Sdhb, Idh1 and Idh2 expression (A) and succinate and isocitrate levels (B) were assessed in the adrenal gland of mice treated with 5 mg/kg LPS for 4 h (A) and 24 h (B). n=5; data are presented as mean ± s.d. Statistical analysis was done with two-tailed Mann-Whitney test. p < 0.05, *p < 0.01.

3) Related to the point above, the authors data supporting a break in the TCA cycle would be strengthened direct biochemical assessment (metabolic flux analysis) of step kin the TCA cycle process impacted.

We entirely agree with the Reviewer and considered performing TCA cycle metabolic flux analyses in adrenocortical cells. Unfortunately, the low yield of adrenocortical cells per mouse (approx. 3,000- 6,000) does not allow the performance of metabolic flux experiments, which require higher cell numbers per sample, several time points per condition and an adequate number of replicates per experiment. Moreover, NCI-H295R cells being adrenocortical carcinoma cells are expected to have substantially altered metabolic fluxes compared to normal cells. Since we wouldn’t have the capacity to confirm findings from metabolic flux experiments in NCI-H295R cells in primary adrenocortical cells, as we did for the rest of the experiments, we decided to not perform metabolic flux experiments in NCI-H295R cells. However, performing metabolic flux analyses in adrenocortical cells under inflammatory or other stress conditions remains an important future task that we will pursue upon establishment of a more suitable cell culture system.

4) The proposed connection of DNMT and IL1 signaling to systemic inflammation and reduced steroidogenesis could be more firmly established by additional studies in adrenal cortical cells lacking these genes.

We thank the Reviewer for this excellent suggestion. In the revised manuscript we strengthened the evidence for an IL-1β –DNMT1 link and show that DNMT1 deficiency blocks the effects of IL-1β on SDHB promoter methylation (Figure 6k), the succinate / fumarate ratio (Figure 6m), the oxygen consumption rate (Figure 6n) and steroidogenesis (Figure 6o-q) in adrenocortical cells. In order to validate the role of IL-1β in vivo, mice were simultaneously treated with LPS and Raleukin, an IL-1R antagonist. Treatment with Raleukin increased the SDH activity (Figure 6r), reduced succinate levels and the succinate / fumarate ratio (Figure 6s,t) and increased corticosterone production in LPS-treated mice (Figure 6u).

Reviewer #2 (Public Review):

The present manuscript provides a mechanistic explanation for an event in adrenal endocrinology: the resistance which develops during excessive inflammation relative to acute inflammation. The authors identify disturbances in adrenal mitochondria function that differentiate excessive inflammation. During severe inflammation the TCA in the adrenal is disrupted at the level of succinate production producing an accumulation of succinate in the adrenal cortex. The authors also provide a mechanistic explanation for the accumulation of succinate, they demonstrate that IL1b decreases expression of SDH the enzyme that degrades succinate through a methylation event in the SDH promoter. This work presents a solid explanation for an important phenomenon. Below are a few questions that should be resolved experimentally.

1) The authors should confirm through direct biochemical assays of enzymatic activity that steroidogenesis enzyme activity is not impaired. Many of these enzymes are located in the mitochondria and their activity may be diminished due to the disturbed, high succinate environment of the cortical cell as opposed to the low ATP production.

We thank the Reviewer for this question. The activity of the first and rate-limiting steroidogenic enzyme, cytochrome P450-side-chain-cleavage (SCC, CYP11A1) which generates pregnenolone from cholesterol, was recently shown to require intact SDH function (5). In agreement with this report we show that production of progesterone, the direct derivative of pregnenolone, is impaired upon SDH inhibition (Figure 5b,e,h). In addition, we assessed the activity of CYP11B1 (steroid 11β-hydroxylase), the enzyme catalyzing the conversion of 11-deoxycorticosterone to corticosterone, i.e. the last step of glucocorticoid synthesis, by determining the corticosterone and 11-deoxycorticosterone levels by LC-MS/MS and calculating the ratio of corticosterone to 11-deoxycorticosterone in ACTH-stimulated adrenocortical cells and explants. The corticosterone / 11-deoxycorticosterone ratio was not affected by Sdhb silencing in adrenocortical cells (Figure 5- Supplement 2g) nor did it change upon LPS treatment in adrenal explants (Figure 5- Supplement 2h), suggesting that CYP11B1 activity may not be altered upon SDH blockage. Hence, we propose that upon inflammation impairment of SDH function may disrupt at least the first steps of steroidogenesis (producing pregnenolone/progesterone), thereby diminishing production of all downstream adrenocortical steroids. This is now discussed in the revised manuscript.

2) What is the effect of high ROS production? Is steroidogenesis resolved if ROS is pharmacologically decreased even if the reduction of ATP is not resolved?

We thank the Reviewer for this suggestion, which helped us to broaden our findings. Indeed, ROS scavenging by the vitamin E analog Trolox (Figure 5n) partially reversed the inhibitory effect of DMM on steroidogenesis (Figure 5o,p), suggesting that impairment of SDH function impacts steroidogenesis also via enhanced ROS production (Figure 4g).

3) Does increased intracellular succinate (through cell permeable succinate treatment) inhibit steroidogenesis even if there is not a blockage of OXPHOS?

We suggest that SDH inhibition and succinate accumulation lead to reduced steroidogenesis due to impaired oxidative phosphorylation (Figure 4c,e, 5i), reduced ATP synthesis (Figure 4d, 5j-m) and increased ROS production (Figure 4g, 5o,p). Since SDH is part (complex II) of the electron chain transfer it cannot be decoupled from oxidative phosphorylation, thereby limiting the experimental means for addressing this question.

4) It should be demonstrated the genetic loss of IL1 signaling in adrenal cortical cells results in a loss of the effect of LPS on reduced steroidogenesis and increased succinate accumulation.

We thank the Reviewer for this suggestion. Development of a mouse line with genetic loss of Il-1r in adrenocortical cells was rather impossible during the short time of revisions. Instead, mice under LPS treatment were treated with the IL-1R antagonist, Raleukin, to study the in vivo effects of IL-1β in the adrenal gland. IL-1R antagonism increased SDH activity in the adrenal cortex (Figure 6r), decreased succinate levels and the succinate/fumarate ratio in the adrenal gland (Figure 6s,t) and enhanced corticosterone production (Figure 6u) in LPS-treated mice, supporting our hypothesis that IL-1β mediates the effects of systemic inflammation in the adrenal cortex.

5) It should be demonstrated the genetic loss of IL1 signaling in adrenal cortical cells results in a loss of the effect of LPS on SDH activity and ATP production and SDH promoter methylation

As outlined above, Raleukin treatment increased SDH activity in the adrenal cortex (Figure 6r) and decreased succinate levels and the succinate/fumarate ratio in the adrenal gland (Figure 6s,t) of mice treated with LPS. Furthermore, IL-1β reduced the ATP/ADP ratio (Figure 6e) and enhanced SDHB promoter methylation in NCI-H295R cells (Figure 6k).

6) It should be shown that the silencing of DNMT eliminates or diminishes the effect of LPS on reduced steroidogenesis and increased succinate accumulation.

We thank the Reviewer for this suggestion, which prompted us to strengthen the evidence for the implication of DNMT1 in the effects of LPS on adrenocortical cell metabolism and function. As mentioned above, development of a new mouse line, in this case bearing genetic loss of DNMT1 in adrenocortical cells, was considered impossible during the short time of revisions. Therefore, we assessed the role of DNMT1 by silencing it via siRNA transfections in primary adrenocortical cells and NCI-H295R cells. We show that DNMT1 silencing inhibits the effect of IL-1β on SDHB promoter methylation (Figure 6k), restores Sdhb expression (Figure 6l) and reduces the succinate/fumarate ratio in IL-1β treated adrenocortical cells (Figure 6m). Accordingly, DNMT1 silencing restores ACTH-induced production of corticosterone, 11-deoxycorticosterone and progesterone in IL-1β treated adrenocortical cells (Figure 6o-q). We chose to stimulate adrenocortical cells with IL-1β instead of LPS, as in vitro the effects of IL-1β were more robust than these of LPS (possibly due to a reduction of TLR4 expression or function in cultured adrenocortical cells) and in order to show the link between IL-1β and DNMT1.

7) Does silencing of DNMT reduce OXPHOS in adrenal cortical cells?

We measured the oxygen consumption rate in NCI-H295R cells, which were transfected with siRNA against DNMT1 and treated or not with IL-1β. IL-1β reduced the OCR in cells transfected with control siRNA, while DNMT1 silencing blunted the effect of IL-1β (Figure 6n).

8) The effects of LPS on reduced adrenal steroidogenesis are not elaborated at the physiological level. The manuscript should demonstrate the ramifications of the adrenal function decreasing after LPS. Does CORT release become less pronounced after subsequent challenges? Does baseline CORT decrease at some point? No physiological consequences are shown. Similarly, these physiological consequences of decreased adrenal function should be dependent on decreased SDH activity and OXPHOS in adrenal cells and this should be demonstrated experimentally.

We thank the Reviewer for raising this excellent question. Inflammation is a potent inducer of the Hypothalamus-Pituitary-Adrenal gland (HPA) axis, causing increased glucocorticoid production, a stress response leading to vital immune and metabolic adaptations. Accordingly, LPS treatment rapidly increases glucocorticoid production in mice (1, 6, 7). Reduced adrenal gland responsiveness to ACTH associates with decreased survival of septic mice (8). These preclinical findings stand in accordance with observations in septic patients, in which impairment of adrenal function correlates with high risk for death (9). Along this line, ACTH test was suggested to have prognostic value for identification of septic patients with high mortality risk (9, 10).

In order to confirm impairment of the adrenal gland function in septic mice, animals were subjected to sepsis via administration of a high LPS dose (10 mg / kg) and treated with ACTH 24 h later. Indeed, the ACTH-induced increase in corticosterone levels was diminished in LPS-treated mice (Author response image 3). This finding was further confirmed in adrenal explants, in which LPS pre-treatment also blunted ACTH-stimulated corticosterone production (Figure 5s).

Author response image 3. High LPS dose blunts the ACTH response in mice. C57BL/6J mice were i.p. injected with 10 mg/kg LPS or PBS and 24 h later they were i.p. injected with 1 mg/kg ACTH. One hour after ACTH administration blood was retroorbitally collected and corticosterone plasma levels were determined by LC-MS/MS. n=4-5; data are presented as mean ± s.d. Statistical analysis was done with two-tailed Mann-Whitney test. *p < 0.05.

Given that purpose of our studies was to dissect the mechanisms underlying adrenal gland dysfunction in inflammation rather than analyzing the physiological consequences thereof, we chose not to follow these lines of investigations and concentrate on the role of cell metabolism in adrenocortical cells in the context of inflammation.

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Author Response:

Reviewer #1:

The manuscript by Jasmien Orije and colleagues has used advanced Diffusion Tensor and Fixel-Based brain imaging methods to examine brain plasticity in male and female European starlings. Songbirds provide a unique animal model to interrogate how the brain controls a complex, learned behaviour: song. The authors used DT imaging to identify known and uncover new structural changes in grey and white matter in male and female brains. The choice of the European starling as a model songbird was smart as this bird has a larger brain to facilitate anatomical localization, clear sex differences in song behavior and well-characterized photoperiod-induced changes in reproductive state. The authors are commended for using both male and female starlings. The photoperiodic treatment used was optimal to capture the key changes in physiological state. The high sampling frequency provides the capability to monitor key changes in physiology, behaviour and brain anatomy. Two exciting findings was the increased role of cerebellum and hippocampal recruitment in female birds engaged in singing behaviour. The development of non-invasive, multi-sampling brain imaging in songbirds provides a major advancement for studies that seek to understand the mechanism that control the motivation and production of singing behavior. The methods described herein set the foundation to develop targeted hypotheses to study how the vocal learning, such as language, is processed in discrete brain regions. Overall, the data presented in the study is extensive and includes a comprehensive analyses of regulated changes in brain microstructural plasticity in male and female songbirds.

Reviewer #2:

Orije et al. employed diffusion weighted imaging to longitudinally monitor the plasticity of the song control system during multiple photoperiods in male and female starlings. The authors found that both sexes experience similar seasonal neuroplasticity in multisensory systems and cerebellum during the photosensitive phase. The authors' findings are convincing and rely on a set of well-designed longitudinal investigations encompassing previously validated imaging methods. The authors' identification of a putative sensitive window during which sensory and motor systems can be seasonally re-shaped in both sexes is an interesting finding that advances our understanding of the neural basis of seasonal structural neuroplasticity in songbirds.

Overall, this is a strong paper whose major strengths are:

1) The longitudinal and non-invasive measure of plasticity employed

2) The use of two complementary MR assays of white matter microplasticity

3) The careful experimental design

4) The sound and balanced interpretation of the imaging findings

I do not have any major criticism but just a few minor suggestions:

1) Pp 6-7. While the comparative description of canonical DTI with respect to fixel-based analysis is well written and of interest to readers with formal training in MR imaging, I found this entire section (and especially the paragraphs in page 7) too technical and out of context in a manuscript that is otherwise fundamentally about neuroplasticity in song birds. The accessibility of this manuscript to non-MR experts could be improved by moving this paragraph into the methods section, or by including it as supplemental material.

The main purpose of this section was to introduce and explain the diffusion parameters which are used throughout the rest of the paper. Furthermore, we wanted to familiarize the reader with the concept of the population based template and the different structures that can be visualized by them. We agree that the technical details might have distracted from this main message. Therefore, we have trimmed the technical details out of this section and left a short explanation of the biological relevance of the different diffusion parameters and the anatomical structures visible on the population template. The technical details that were taken out are now a part of the material and methods section.

The section now reads as follows:

In the current study, we analyzed the DWI scans in two distinct ways: 1) using the common approach of diffusion tensor derived metrics such as fractional anisotropy (FA) and; 2) using a novel method of fiber orientation distribution (FOD) derived fixel-based analysis. Both techniques infer the microstructural information based on the diffusion of water molecules, but they are conceptually different (table 1). Common DTI analysis extracts for each voxel several diffusion parameters, which are sensitive to various microstructural changes in both grey and white matter specified in table 1. Fixel-based analysis on the other hand explores both microscopic changes in apparent fiber density (FD) or macroscopic changes in fiber-bundle cross-section (log FC) (table 1). Positive fiber-bundle cross-section values indicate expansion, whereas negative values reflect shrinkage of a fiber bundle relative to the template (Raffelt, Tournier et al. 2017).

A population-based template created for the fixel-based analysis can be used as a study based atlas in which many of the avian anatomical structures can be identified (figure 2). We recognize many of the white matter structures such as the different lamina, occipito-mesencephalic tract (OM) and optic tract (TrO) among others. Interestingly, many of the nuclei within the song control system (i.e. HVC, robust nucleus of the arcopallium (RA), lateral magnocellular nucleus of the anterior nidopallium (LMAN), and Area X), auditory system (i.e. intercollicular nucleus complex, nucleus ovoidalis) and visual system (i.e. entopallium, nucleus rotundus) are identified by the empty spaces between tracts. The applied fixel-based approach is inherently sensitive to changes in white matter and cannot report on the microstructure within grey matter like brain nuclei; but rather sheds light on the fiber tracts surrounding and interconnecting them. As such, it provides an excellent tool to investigate neuroplasticity of different brain networks, and in the case of a nodular song control system focusing on changes in the fibers surrounding the song control nuclei, referred to as HVC surr, RA surr and Area X surr.

2) Similarly, many sections, especially results, are in my opinion too detailed and analytical. While the employed description has the benefit of being systematic and rigorous, the ensuing narrative tends to be very technical and not easily interpretable by non experts. I think the manuscript may be substantially shortened (by at least 20% e.g. by removing overly technical or analytical descriptions of all results and regions affected) without losing its appeal and impact, but instead gaining in strength and focus especially if the new result narrative were aimed to more directly address the interesting set of questions the authors define in the introductory sections.

We rewrote the result section, taking out the statistic reporting when it was also reported in a figure to reduce the bulk of this section and make it more readable. We made some of the descriptions of the regions affected more approachable by replacing it with parts of the discussion. This way we incorporated some of the explanations why certain findings are unexpected or relevant, as suggested by reviewer #3. Parts of text that were originally in the discussion are indicated in purple.

3) The possible effect of brain size has been elegantly controlled by using a medial split approach. Have the authors considered using tensor-based morphometry (i.e. using the 3D RARE scans they acquired) to account for where in the brain the small differences in brain size occur? That could be more informative and sensitive than a whole-brain volume quantification.

We have taken into consideration to add tensor-based morphometry, but we feel that log FC calculated with MrTrix can provide a similar account of the localization of these brain differences. Both methods are based on the Jacobean warps created between the individual images and the population template. They only differ in the starting images they use (3D RARE images in tensor-based morphometry or diffusion weighted images in log FC metric of MrTrix3) and the fact that MrTrix3 limits itself to the volume changes along a certain tract.

The log FC difference in figure 4 gives a similar account of the differences in brain size between both sexes. Additionally, figure 6 indicates the log FC differences between small and large brain birds.

4) I think Figures Fig. 3 and Fig. 4 may benefit from a ROI-based quantification of parameters of interests across groups (similar to what has been done for Fig. 7 and its related Fig. 8). This could help readers assess the biological relevance of the parameter mapped. For instance, in Fig. 3, most FA differences are taking place in low FA (i.e. gray matter dense?) regions.

We supplied the figures with extracted ROI-based parameters of figure 3 and figure 4. In line with this reasoning we also added the same kind of supplementary figures for figure 5 and 6.

Figure 3 - figure supplement 1: Overview of the fractional anisotropy (FA) changes over time extracted from the relevant ROI-based clusters with significant sex differences. The grey area indicates the entire photosensitive period of short days (8L:16D). Significant sex differences are reported with their p-value under the respective ROI-based cluster. Different letters denote significant differences by comparison with each other in post-hoc t-tests with p < 0.05 (Tukey’s HSD correction for multiple comparisons) comparing the different time points to each other. If two time points share the same letter, the fractional anisotropy values are not significantly different from each other.

Figure 4 – figure supplement 2: Overview of the fiber density (FD) changes over time extracted from the relevant ROI-based clusters with significant sex differences. The grey area indicates the entire photosensitive period of short days (8L:16D). Significant sex differences are reported with their p-value under the respective ROI-based cluster. Different letters denote significant differences by comparison with each other in post-hoc t-tests with p < 0.05 (Tukey’s HSD correction for multiple comparisons) comparing the different time points to each other. If two time points share the same letter, the FD values are not significantly different from each other. Abbreviations: surr, surroundings.

Figure 4 –figure supplement 3: Overview of the fiber-bundle cross-section (log FC) changes over time extracted from the relevant ROI-based clusters with significant sex differences. The grey area indicates the entire photosensitive period of short days (8L:16D). Significant sex differences are reported with their p-value under the respective ROI-based cluster. Different letters denote significant differences by comparison with each other in post-hoc t-tests with p < 0.05 (Tukey’s HSD correction for multiple comparisons) comparing the different time points to each other. If two time points share the same letter, the log FC values are not significantly different from each other. Abbreviations: surr, surroundings.

Figure 5 – figure supplement 1: Overview of the fractional anisotropy (FA) changes over time in extracted from the relevant ROI-based clusters with significant differences in brain size. The grey area indicates the entire photosensitive period of short days (8L:16D). Significant brain size differences are reported with their p-value under the respective ROI-based cluster. Different letters denote significant differences by comparison with each other in post-hoc t-tests with p < 0.05 (Tukey’s HSD correction for multiple comparisons) comparing the different time points to each other. If two time points share the same letter, the fractional anisotropy values are not significantly different from each other. Abbreviations: C, caudal; surr, surroundings.

Figure 6- figure supplement 2: Overview of the fiber density (FD) changes over time in extracted from the relevant ROI-based clusters with significant differences in brain size. The grey area indicates the entire photosensitive period of short days (8L:16D). Significant brain size differences are reported with their p-value under the respective ROI-based cluster. Different letters denote significant differences by comparison with each other in post-hoc t-tests with p < 0.05 (Tukey’s HSD correction for multiple comparisons) comparing the different time points to each other. If two time points share the same letter, the FD values are not significantly different from each other. Abbreviations: C, caudal; surr, surroundings.

Figure 6- figure supplement 3: Overview of the fiber-bundle cross-section (log FC) changes over time in extracted from the relevant ROI-based clusters with significant differences in brain size. The grey area indicates the entire photosensitive period of short days (8L:16D). Significant brain size differences are reported with their p-value under the respective ROI-based cluster. Different letters denote significant differences by comparison with each other in post-hoc t-tests with p < 0.05 (Tukey’s HSD correction for multiple comparisons) comparing the different time points to each other. If two time points share the same letter, the log FC values are not significantly different from each other. Abbreviations: C, caudal; surr, surroundings.

5) In Abstract: "We longitudinally monitored the song and neuroplasticity in male.." Perhaps something should be specified after the "the song"? Did the authors mean "the neuroplasticity of song system"?

No, this is not what we meant, we monitor song behavior and neuroplasticity independently. In our study, we do not limit ourselves to the neuroplasticity of the song system, but instead use a whole brain approach. The monitoring of the song behavior in itself might be useful for other songbird researchers.

We clarified this in the abstract as follows:

We longitudinally monitored the song behavior and neuroplasticity in male and female starlings during multiple photoperiods using Diffusion Tensor and Fixel-Based techniques.

Reviewer #3:

In their paper, Orije et al used MRI imaging to study sexual dimorphisms in brains of European starlings during multiple photoperiods and how this seasonal neuroplasticity is dependent in brain size, song rates and hormonal levels. The authors main findings include difference in hemispheric asymmetries between the sexes, multisensory neuroplasticity in the song control system and beyond it in both sexes and some dependence of singing behavior in females with large brains. The authors use different methods to quantify the changes in the MRI data to support various possible mechanisms that could be the basis of the differences they see. They also record the birds' song rates and hormonal levels to correlate the neural findings with biological relevant variables.

The analysis is very impressive, taking into account the massive data set that was recorded and processed. Whole-brain data driven analysis prevented the authors from being biased to well-known sexually dimorphic brain areas. Sampling of a large number of subjects across many time points allowed for averaging in cases where individual measurements could not show statistical significance. The conclusions of the paper are mostly well supported by data (except of some confounds that the authors mention in the text). However, the extensive statistically significant results that are described in the paper, make it hard to follow at times.

1) In the introduction the authors mention the pre optic area as a mediator for increase singing and therefore seasonal neuroplasticity. Did the authors find any differences in that area or other well know nuclei that are involved in courtship (PAG for example)?

Interestingly, we did not detect any seasonal changes in the pre-optic area or PAG. Whereas prior studies reported volume changes in the POM within 1-2 days after testosterone administration in canaries (Shevchouk, Ball et al. 2019). In male European starlings, POM volumes changed seasonally, although this seems to depend on whether or not the males possessed a nest box (Riters, Eens et al. 2000). In our setup, our starlings are not provided with nest boxes. The lack of seasonal change in POM could have a biological reason, besides the limitations of our methodology. Since these are small regions and are grey matter like structures, they are less likely to be picked up with our diffusion MRI methods.

2) Following the first comment, what is the minimum volume of an area of interest that could be detected using the voxel analysis?

The up-sampled voxel size is (0.1750.1750.175) mm3. In the voxel-based statistical analysis a significance threshold is set at a cluster size of minimum 10 voxels: 0.05 mm3.

3) It would be useful to have a figure describing the song system in European starlings and how the auditory areas, the cerebellum and the hippocampus are connected to it, before describing the results. It would make it easier for the broader community to make a better sense of the results.

An additional figure was added to the introduction to give a schematic overview of the song control system, the auditory system and the proposed cerebellar and hippocampal projections. This scheme includes both a 2D, and a 3D representation as well as a movie of the 3D representation of the different nuclei and the tractography.

Figure 1: Simplified overview of the experimental setup (A), schematic overview of the song control and auditory system of the songbird brain and the cerebellar and hippocampal connections to the rest of the brain (B) and unilateral DWI-based 3D representation of the different nuclei and the interconnecting tracts as deduced from the tractogram (C). Male and female starlings were measured repeatedly as they went through different photoperiods. At each time point, their songs were recorded, blood samples were collected and T2-weighted 3D anatomical and diffusion weighted images (DWI) were acquired. The 3D anatomical images were used to extract whole brain volume (A). The song control system is subdivided in the anterior forebrain pathway (blue arrows) and the song motor pathway (red arrows). The auditory pathway is indicated by green arrows. The orange arrows indicate the connection of the lateral cerebellar nucleus (CbL) to the dorsal thalamic region further connecting to the song control system as suggested by (Person, Gale et al. 2008, Pidoux, Le Blanc et al. 2018) (B,C). Nuclei in (C) are indicated in grey, the tractogram is color-coded according to the standard red-green-blue code (red = left-right orientation (L-R), blue = dorso-ventral (D-V) and green = rostro-caudal (R-C)). For abbreviations see abbreviation list.

Figure 1 – figure supplement 1: Movie of the unilateral 3D representation of the different nuclei and the interconnecting tracts rotating along the vertical axis.

4) In the results section the authors clearly describe which brain areas are sexually dimorphic or change during the photoperiod and what is the underlying reason for the difference. However, only in the discussion section it is clearer why some of those differences are expected or surprising. It would be useful to incorporate some of those explanations in the results section other than just having a long list of brain areas and metrics. For example, I found the involvement of visual and auditory areas in the female brain in the mating season very interesting.

Next to the reductions in technical explanation suggested by reviewer #2, We replaced some of the description of significant regions with parts of the discussion and vice versa(indicated in purple). This way we incorporated some of the explanations why certain findings are unexpected or relevant. Furthermore, we added some extra info on the reason why these changes are relevant for the visual system and the cerebellum.

In line 420: Neuroplasticity of the visual system could be relevant to prepare the birds for the breeding season, where visual cues like ultraviolet plumage colors are important for mate selection (Bennett, Cuthill et al. 1997).

In line 424: This shows that multisensory neuroplasticity is not limited to the cerebrum, but also involves the cerebellum, something that has not yet been observed in songbirds.

Author Response

Reviewer #1 (Public Review):

[…] Overall, the results from these analyses are convincing and valuable, but still do not seem to be a big leap from their Unger 2021 paper […]. The methodology that they established should be described more clearly so that it can be shared with the research community. For example, they say cells how many donors were recruited for this experiment? are there differences in efficiency in B cell differentiation by individual?

Also, it would be important to assay for antibodies in the culture media. How would you suggest to improve the culture system to be used to model diseases?

We appreciate the reviewer's queries and the points raised. In response to the first set of comments, the reviewer has correctly observed that the methodology of the assay itself as employed in this paper is not new or superior to our previously published data in (Unger et al., Cells 2021), where we described a minimalistic in vitro system for efficient differentiation of human naive B cells into antibody-secreting cells (ASCs). However, the current study aims to elucidate a comprehensive evaluation of the phenotype of the cells in the in vitro system and their relationships in potential differentiation pathways. In addition, we aimed to elucidate how the detailed gene expression profiles of the differentiating cells in vitro compare to in vivo observed counterparts. In this way, we were able to uncover an antibody secreting cell phenotype in vivo that was not observed before and could only be uncovered due to our full transcriptome knowledge of these cells. In addition, we present novel findings that demonstrate that this culture system not only enables efficient ASCs generation but also recapitulates the entire in vivo B cell differentiation pathway, as evidenced by the presence of germinal-center (GC)-like and pre-memory B cells in the culture. These results have not been previously reported in the literature for human B cells in culture and represent a significant contribution to the field of human B cell biology.

In regards to the reviewer's inquiry about the cell culture protocol, its reproducibility, donors variability, and additional experimental applications, we refer to three additional recent publications from our group that have adopted the same in vitro B cell differentiation system and have provided extensive analysis of the immunoglobulin production, intracellular signaling pathways, as well as comparison with other culture systems in the field (Marsman et al., Cells 2020; Marsman et al., Eur. J. Immunol. 2022; Marsman et al., Front. Immunol. 2022). On top pf this, we now realize that the section that describes the culture system (MATERIAL AND METHODS - “In vitro naive B cell differentiation cultures”) was a bit too concise and we thank the reviewer for mentioning it. We have extended now on it and corrected an inconsistency at lines 125-127: “After six days, activated B cells were collected and co-cultured with 1 × 104 9:1 wild type (WT) to CD40L-expressing 3T3 cells that were irradiated and seeded one day in advance (as described above), together with IL-4 (100 ng/ml) and IL-21 (50 ng/ml; Invitrogen) for five days.”

As for the application of our in vitro system in disease modeling, as requested by the reviewer, this would require modifying the culture conditions to mimic the disease-specific biology background (if known). For instance, by inhibiting or enhancing specific transcriptional pathways that are known to be associated with the disease in question. However, it would also require the presence of antigen-specific B cells in the pool of naive B cells included in the culture, which can be difficult to achieve due to their low frequency. Alternatively, the system could be used to study antigen-specific recall responses using antigen-specific memory B cells as starting material. Our group has evaluated this approach in a recent publication (Marsman et al., Front. Immunol 2022).

[..] B cell differentiation may also influence to cell cycle regulation. Rather than normalize its effect, can authors analyze effect of cell cycle in B cell differentiation? [...]

We very much agree with the reviewer and know that the cell cycle plays a significant role in B cell differentiation output trajectories (Zhou et al, Front Immunol. 2018; Duffy et al., Science 2012). Preparing the manuscript, we have in fact performed a parallel analysis in which we compared both cell cycle regressed- and not cell cycle regressed-based clustering and marker gene selection. Concerning the clustering, other clusters were obtained using the not cell-cycle-regressed dataset compared to the cell-cycle-regressed dataset (figure below). However, when overlaying the clusters obtained with the cell cycle-regressed dataset, the extra clusters were the same cell population but now split based on cycling and not cycling cells: cluster 2 is now divided into the cycling cluster “c”, and the not-cycling cluster “d” while cluster 4 and 5 are now divided into the cycling clusters “e” and the not-cycling cluster “f”. A comprehensive examination of the expression of the top 50 genes associated with antibody-secreting cells in the (non)cycling clusters 4 and 5 reveals that these genes are expressed at a higher level in (non)cycling cluster 5 as compared to cluster 4. This suggests that the cells within cluster 5 are more advanced in their differentiation, regardless of their cell cycle state. This finding has led us to the decision to present the data that has undergone cell cycle regression in the manuscript. Should the reviewer so desire, we are very willing to include additional supplementary figures to the manuscript that include the un-regressed representation.

Figure legend: A-C) UMAP projection of single-cell transcriptomes of in vitro differentiated human naive B cells without cell cycle regression. Each point represents one cell, and colors indicate graph-based cluster assignments identified without cell-cycle regression (A), with cell cycle regression (B) or with cell cycle regression and additional subdivision in cycling and not cycling cells (C). D) Dotplot showing the top 50 differentially expressed genes in cycling and not-cycling cells from cluster 4 and 5. Point size indicate percentage of cell in the cluster expressing the gene, color indicates average expression

Author Response

Reviewer #3 (Public Review):

The manuscript by the Qiu and Lu labs investigates the mechanism of desensitization of the acid-activated Cl- channel, PAC. These trimeric channels reside in the plasma membrane of cells as well as in organelles and play important roles in human physiology. PAC channels, like many other ion channels, undergo a process known as desensitization, where the channel adopts a non-conductive conformation in the presence of a prolonged physiological stimulus. For PAC the mo-lecular mechanisms regulating this process are not well understood. Here the authors use a com-bination of electrophysiological recordings and MD simulations to identify several acidic residues and a conserved histidine side chain as important players in PAC desensitization. The results are overall interesting and clearly indicate a role for these residues in this process. However, there are several weaknesses in the experimental design, inconsistencies between the mutagenesis data and the MD results, as well as in the interpretation of the data. For these reasons I do not think the authors have made a convincing mechanistic case.

We thank the reviewer for the constructive comments and address the concerns point-by-point below.

Major weaknesses:

The underlying assumption in the interpretation of all the data is that the mutations stabilize or destabilize the desensitized conformation of the channel. However, none of the functional meas-urements provide direct evidence supporting this key assumption. Without direct evidence sup-porting the notion that the mutations specifically impact the rate of recovery from desensitiza-tion, I do not think the authors have made a convincing mechanistic case.

We agree with the reviewer that our functional data measure the degree and rate of the PAC channel entering desensitization from the activated state upon prolonged acid treatment. This is a common experimental procedure for research on desensitization/inactivation of ion channels. Fol-lowing the reviewer’s suggestion, we also sought to capture the kinetics from the desensitized state to the activated state by switching from more acidic pH to less acidic pH (for example 4.0 to 5.0) or neutral pH. However, we found that such experiments are not feasible partly because the kinetics of PAC desensitization is much slower compared to other channels, such as ASIC channels (see a recent study we cited: https://elifesciences.org/articles/51111). For the mutants with strong desensitization (E94R and D91R), it’s unclear whether the currents we recorded at pH 5.0 right after pH 4.0 representing the activated state or the desensitized state at pH 5.0. In other words, we don’t know if the PAC channel transitions from the desensitized state from a lower pH back to the activated state or rather directly to the desensitized state at a higher pH. For the mutants with reduced desensitization, the current amplitude at pH 4.0 were often similar to that at pH 5.0, which makes the recovery/transition variable. We also tried to switch the acidic pH to neutral pH. We found that the PAC channels (both WT and mutants) go back to the closed state from the desensitized state in seconds as limited by our perfusion speed. These data suggest that the desensitized state of PAC is no longer maintained after switching buffer from low pH to neutral pH. In summary, it’s technically infeasible, in our opinion, to measure the rate of recovery from desensitization to activation for the PAC channel. However, our data do support the con-clusion that the rates of entering desensitization from the activated state, a standard measurement of desensitization, change for various channel mutants we studied.

Overall, the agreement between the MD simulations, functional data, and interpretation are often weak and some issues should be acknowledged and addressed.

For example:

1) The experimental data suggests that H98, E107, and D109 play analogous roles in PAC desen-sitization. However, the MD simulations suggest that the H98-D109 interaction energy is ~4 times larger than that of H98-E107. This should lead to a much greater effect of the D109 muta-tion. How is this rationalized?

The purpose of quantifying the interaction between H/R98 with E107 and D109 is to better dis-sect the mechanism by which H/R98 interacts with the acidic pocket residues. The result suggests that R98 has a reduced association with E107/D109 when compared to H98. It also suggests that D109 makes a more direct interaction with H/R98 when compared to E107. We acknowledge that this is not clear in our initial manuscript and we have updated the text to better describe this result. However, this doesn’t imply that the desensitization phenotype of E107R should be less pronounced than D109R. Both E107R and D109R are expected to disrupt the integrity of the acidic pocket, thus resulting in diminished channel desensitization. It is worth pointing out that E107 played a more complex role as it was identified in our previous papers as one of the major proton sensors. The E107R mutant could allow the PAC channel to become more sensitive to ac-id-induced activation (Figure 4d-e in Ruan et al, Nature, 2020), further complicating its effect in desensitization. Taken together, we don’t think the E107/D109 and H/R98 interaction strength could have quantitative correlation with the desensitization phenotype of E107R and D109R.

2) The experimental data shows that E94 plays a key role in desensitization and the authors argue that this is due to the interactions of this residue with the β10-11 linker. However, the MD simu-lations show that these interactions happen for a small fraction, ~10%, of the time and with inter-action energies comparable to those of the H98-E107-D109 cluster. It is not clear how these sparse and transient interactions can play such a critical role in desensitization. Also, if the inter-action energies are of the same sign, how come one set of mutants favors desensitization and one does not?

The 10% value is the amount of time when at least a hydrogen bond forms between E94/R94 and the β10–β11 loop. It is NOT the amount of time that they form interactions, as there could be other types of non-bonded interactions such as Van der Waals interaction and Coulombic interaction. In fact, our non-bonded energy calculation clearly suggests that R94 interacts with the β10–β11 loop much more favorably than E94 (Figure 4C). The impact of E94R on β10–β11 loop is also reflected in the root-mean-square-fluctuation analysis, where the β10–β11 loop shows a reduced flexibility when R94 is present (Figure 4B).

Our central hypothesis is that PAC becomes more prone to desensitization when the desensitized conformation is stabilized. Two critical interactions are characteristic of the desensitized structure of PAC, including the association of the E94 with the β10–β11 loop, and H98 with E107/D109. Therefore, we expect mutations that alter these interactions to affect PAC channel desensitization. Based on the MD simulations, we observed the root-mean-square-fluctuation of β10–β11 loop are reduced for E94R when compared to WT (Figure 4B), suggesting that β10–β11 loop is stabilized when E94 is replaced by an arginine. The non-bonded interaction energy between E94 and the β10–β11 loop is also more negative for E94R when compared to WT (Figure 4C), another indicator of conformation stabilization. As a result, the E94R mutant favors desensitization. This is in sharp contract with the H98R data, in which H98R interact less favorably with E107/D109 (Figure 2F, G, H, I) when compared to WT. Although the interaction energies are of the same sign, it is the difference between WT and the mutants that will ultimately determine whether a certain mutation will favor desensitization or not.

The authors' MD analysis critically depends on assumptions on the protonation states of multiple residues, that are often located in close proximity to each other. In the methods, the authors state they use PropKa to estimate the pKa of residues and assigned the protonation states based on this. I have several questions about this procedure:

• What pH was considered in the simulations? I imagine pH 4.0 to match that of the electrophys-iological experiments.

The exact pH environment cannot be explicitly modeled in standard MD as the protonation state of an ionizable group is not allowed to change during the simulation. Therefore, in our simulation, we prepared the MD system by first predicting the pKa of titratable residues of PAC in the de-sensitized state, and then assign the protonation status of these residues based on the pKa values. We acknowledge that the description in this part is not very clear in our original manuscript. We have revised the method to better describe how the protonation status is assigned.

• Was the propKa analysis run considering how choices in the protonation state of neighboring residues affect the pKa of the other residues? This is critical because the interaction energies will greatly depend on the protonation state chosen.

The pKa analysis was done based on the WT structure and the residue protonation status was assigned based on the predicted value. It is possible that mutations on certain residues could change the pKa of neighboring residues. To evaluate this impact, we carried out pKa prediction for all the mutant structures that we used as input for simulation. This is summarized in the table below:

As shown in the table, although mutations will affect the pKa of neighboring residues, the impact is generally within 0.3 units. As our simulation is carried out based on a pH of 4.0, this variability will not affect how we assign the residue protonation status.

• Was the pKa for the mutant constructs re-evaluated? For example, does having a Gln or Arg in place of a His affect the pKa of nearby acidic residues?

We didn’t re-evaluate the pKa for each mutant in our initial manuscript. We have conducted such an analysis as indicated in the above table. The result suggests that arginine substitutions of H98/E94/D91 could have an impact on the pKa value of nearby residues. However, the differ-ence is relatively small and does not alter the predominant protonation status of these residues at pH 4.0.

• H98R and Q have the same functional effect. The MD partially rationalizes the effect of H98R, however, it is not clear how Q would have the same effect as R on the interaction energies.

Our analysis on H98R and H98Q serves two different purposes. H98 is expected to be protonat-ed at pH 4.0. The fact that H98Q mutant reduced PAC desensitization suggests that positive charge at the location is critical for PAC desensitization, which we attribute to the loss of favora-ble interaction between H98 and E107/D109. This is different from H98R mutant as arginine bears the same amount of charge as a protonated histidine. Our data suggest that the exact bio-chemical property, including its charge and side-chain flexibility, of H98 is crucial for PAC de-sensitization.

• Are 600 ns sufficient to evaluate sampling of the different conformations?

Our MD analysis doesn’t intend to sample large conformational transitions between different functional state. Instead, our analysis focused on local dynamics which allowed us to correlate the observation with electrophysiology data. During the revision, we have extended our simula-tion to 1 μs for each mutant. It is worth pointing out that because PAC protein is a trimer, and we performed all the calculations across three subunits. Therefore, the effective sampling time would become 3 μs in total. The new result remains the same as our initial analysis, suggesting that the sampling time is sufficient to evaluate the metrics reported in the study. We also acknowledged this limitation of our study in the discussion.

Author Response:

Reviewer #2 (Public Review):

The molecular mechanisms as well as the cellular players of colonization of the adult thymus are incompletely understood. In this manuscript, the authors investigate the role of the SIRPa-CD47 ligand pair in seeding of bone-marrow derived progenitors to the adult murine thymus. The study is based on the authors' earlier characterization of thymic portal endothelial cells, which have a role in mediating progenitor homing to the thymus (Shi et al., 2016). The authors show that loss of SIRPa or CD47 results in reduced frequencies and numbers of early T lineage progenitors (ETPs), but no substantial alterations in thymocyte numbers at later developmental stages and of bone-marrow precursors. Short-term homing assays suggest impaired colonization of the thymus. The authors further characterize cell biology and biochemistry of the SIRPa-CD47 system using peripheral lymphocyte co-cultures with genetically engineered MS1 endothelial cells. Finally, they assess the role of SIRPa-CD47 in thymus regeneration in combination with growth of a model tumor.

Strengths:

The authors describe a clear phenotype, consistent with the moderate effect size in ETP loss upon deletion of other homing mediators, such as PSGL-1 or individual chemokine receptors, such as CCR7, CCR9 or CXCR4.

The authors use multiple genetic models, including both, SIRPa and CD47 deficient mouse strains, to support their findings. Using the Tie2Cre model for endothelial cell-specific deletion is particularly informative and could have been used more extensively. Some data are further strengthened by the complementary use of inhibitory SIRPa-Ig fusion proteins.

In vitro analysis of the molecular mechanism and the role of signaling mediators using MS1 cells is well executed and conclusive.

Weaknesses:

Short-term homing assays suffer from the problem that the system is overwhelmed by an excessive number of donor cells (millions), whereas at steady state only a few hundred HPCs capable of colonizing the thymus circulate in peripheral blood, questioning the physiological relevance of this approach. The short-term nature of the experiments also precludes analysis, whether homed cells do in fact constitute T cell progenitors. More suitable experiments comprise mixed competitive bone marrow chimeras using congenically discernible donor cells or, even better, transfers into non-irradiated recipients of defined age as pioneered by the Goldschneider and Petrie labs. Thus, the conclusion that the SIRPa-CD47 system mediates homing of thymus seeding progenitors is not fully justified.

a) Thank you for the comments. To overcome the disadvantage of total bone marrow transfer, we sorted progenitor-containing lineage- bone marrow cells, which takes about 3% of the total bone marrow cells, by MACS enrichment followed by FACS. The amount of donor cells needed for transfer was therefore reduced from 5×10^7 total bone marrow cells per mouse to less than 1×10^6 lineagecells per mouse. This would prevent the overwhelming effect in the previous method. Result of short-term homing assay with 1×10^6 lineage- bone marrow cells confirmed the homing defect in the thymus of Sirpα^-/- mice (new Figure 2I), but not in the spleen (new Figure2—figure supplement 2J).

b) To track whether immigrated lineage^- progenitors actually develop into thymocytes, we conducted adoptive transfer of congenically marked (CD45.1) WT lineage^- into naïve non-irradiated WT or Sirpα^-/- (CD45.2) recipients. 3 weeks later, donor-derived cell subsets were detected. Significant defect of donor-derived thymocyte development, particularly at DN and DP stages, was found in Sirpα^-/- mice as shown in new Figure 2J,K. Therefore, the defective thymic homing of progenitor cells in Sirpα^-/- mice indeed influence following T cell development.

c) Mixed bone marrow chimera or mixed congenically discernible WT and CD47KO progenitor cell transfer into non-irradiated WT recipients is not applicable as has been explained in details in response to the 2nd point of Summary of Essential Revisions. This is probably due to rapid clearance of CD47-null cells from the system by phagocytosis(Jaiswal et al., 2009). Therefore, it currently remains a technical difficulty to address the role of CD47 on progenitor cells for thymic homing using mixed competitive bone marrow chimeras or mixed progenitor cell transfer in non-irradiated hosts. Instead, we have used cleaner in vitro transwell assay to confirm the role of CD47 on progenitor cells during TEM (new Figure 4F), as explained in more details just below.

While technically elegant and mechanistically conclusive, the in vitro studies using MS1 cells and peripheral lymphocytes are somewhat isolated from the original focus of the paper addressing the role of SIRPa-CD47 specifically in thymus seeding. It should be considered devising similar assays replacing lymphocytes with bone-marrow derived progenitors.

Major in vitro transendothelial migration assays have been repeated with FACS sorted lineage^- bone marrow progenitor cells (Lin^- BMCs). Lin^- BMCs showed significant defect of TEM on Sirpα^-/- ECs compared to that on WT ECs (new Figure 3F); Cd47^-/- Lin^- BMCs also showed significant defect of TEM compared with WT Lin^- BMCs (new Figure 4F). Therefore, the conclusion that progenitor CD47 - endothelial SIRPα signaling is required for TEM remains unchanged.

Analysis of thymus regeneration is interesting, but a number of open questions remain for this experimental setup, also in part raised by the authors in the discussion section. Most notably, during regeneration, the reduction in ETPs is accompanied by reduced numbers in more mature thymocyte subsets and peripheral T cells. Such a reduction was not observed at steady-state in KO models and it cannot be concluded from this experiment, that these observations are caused by a defect in thymus colonization. Notably, SL-TBI is associated with massive cell death and alterations in phagocytosis and many other factors may come into play here as well.

We agree with these comments. CV-1 treatment during SL-TBI induced thymic injury and regeneration is a complicated scenario. To make it cleaner, we did SL-TBI directly on Sirpα^-/- mice and control mice. Congenically marked bone marrow cells were also adoptively transferred for better monitoring. At 4 weeks after transfer, donor derived DN thymocyte subset was found defective in Sirpα^-/- recipients compared to that in control hosts (Figure R1). However, DP, SP subsets did not show difference, probably due to compensation effect.

Figure R1. Reconstitution of bone marrow-derived progenitors in Sirpα^-/- *mice. (A) Schematic view of the experiment. (B,C) Statistics of proportion (B) and cell number (C) of donor derived cells in the thymus 4 weeks after SL-TBI and adoptive transfer. n=6 in each group, unpaired t-test applied. *: p <0.01*

As the reviewer indicated, SB-TBI is associated with massive changes on many aspects. Therefore, we also tested the role of SIRPα on thymic homing and thymocyte development in steady state. First, we conducted short-term homing assay using sorted lineage- bone marrow progenitor cells instead of total bone marrow cells to avoid the overwhelming effect of massive number of cells used. Short-term homing assay with 1×10^6 lineage^- bone marrow progenitor cells showed similarly significant defect in Sirpα^-/- recipient thymus (new Figure 2I), but not in the spleen (new Figure2—figure supplement 2J). Second, we also examined following T cell development in this scenario. At 3 weeks after adoptive transfer of lineage^- bone marrow progenitor cells, significantly reduced population of donor-derived thymocytes (mainly DP subset) was found in Sirpα^-/- mice (new Figure 2J,K). However, it should be noted that, later stage of thymocyte development, such as SP, was not significantly impaired, although there is a trend to be reduced in Sirpα^-/- mice.

Thus, our data suggest that while SIRPα deficiency results in impaired thymic homing of progenitor cells and is accompanied with reduced ETP population and impaired early thymocyte development, later thymocyte development is less affected probably due to compensation effect. Whether this effect might be amplified at certain scenarios remains an intriguing open question.

Taken together, the study in its presents form contains the description of an interesting new phenotype, consistent with a role of the CD47-SIRPa interaction in colonization of the thymus by bone-marrow derived progenitors. However, at present, homing experiments lack sufficient rigor and experiments on thymus regeneration, while showing an interesting additional finding, do not justify to conclude homing as mechanistic explanation.

Thank you for the comment. With these new data, hopefully the role of SIRPα on thymic progenitor homing, T cell development during steady state and T cell regeneration at SL-TBI scenario has been made clearer. We agree that the causal relationship between thymic progenitor homing and thymus regeneration is still indirect and inconclusive, which may require further investigation in future. In this study, we would like to emphasize more on the novel role of CD47-SIRPα in controlling thymic progenitor homing, and the underlying molecular and biochemical mechanism. We hope these have been validated.

Reviewer #3 (Public Review):

The manuscript by Ren et al. seeks to describe a role for endothelial cell (EC) expression of Sirpα playing a role in the importation of hematopoietic progenitors from the circulation into the thymus. Specifically, the authors demonstrate that there is a reduction in the number of the earliest T lineage progenitors (ETPs) in the thymus in mice deficient for Sirpa or CD47 (its ligand), and through a series of elegant in vitro transendothelial migration studies, identify that intracellular Sirpα signaling mediates this process by regulating VE-Cadherin expression and thus EC tight junctions. In particular, the use of transwell assays modified to study TEM is particularly well utilized to tease apart the mechanisms. Overall, I found this to be an excellent manuscript. In fact, every time I had a critique developing in my head, the authors quickly dispensed of it by producing some follow up data that addressed my concern! My biggest concern with the manuscript is that it was difficult to determine exactly how many repeats of each experiment have been performed and what data is being presented in the figures (and being statistically analyzed). This should not change the conclusions of the manuscript but will make reading the figures and matching them with the legends easier. The following are a some major and minor concerns that should be addressed to strengthen the manuscript:

Major:

• My main concern is that there needs to be greater care taken with highlighting the number of repeats done for each individual study as it is not always clear. For instance, in Figure 2 the data are presented as being representative of three independent experiments with an n of 3 in each experiment but in 2B, D, and F there are 4 data points for the Sirpa-/- group. This is likely explained by there being 4 mice in that particular experiment, but that is why the numbers should be presented for each experiment rather than a general statement at the end. Another example of this is that in Figure 2 S1 the authors would like to claim that the only differences are in the DN1 subsets which contains the ETPs. However, it is likely this is just due to low numbers as it seems like there is a real decrease in the number of DN2, DN3, DN4 and even DP thymocytes (as well as total cellularity).

1. This should not change any conclusions of the paper but will aid in reader interpretation.

Thank you for your advice and we apologize for the negligence and have rechecked all figure legends and reported sample size for each panel individually. Furthermore, we repeated those experiments with too few samples in the group. For mouse experiments, we used littermates for detection which were not always have equal number of individual mouse in each group, now mouse used have been labeled specifically in each experiment. For thymic subset detection in Sirpα^-/- mice, we have increased sample size (n=5 for both Sirpα^-/- and control group as shown in Figure 2—figure supplement 1AE) and indeed found significant decrease of DN2, DN3 and DN4 subsets in Sirpα KO mice, though total cellularity was still not significantly changed. Overall, the conclusion of defective early thymocyte development in Sirpα^-/- mice retains valid.

2. In this manuscript the authors show that Sirpa expression by TPECs is critical for their capacity to guide the importation of HPCs, and in their previous work they have shown that lymphotoxin can regulate the importation capacity of these same TPECs. Therefore, it would be extremely interesting to know if LT signaling is regulating the expression of Sirpa. Furthermore, it would be important to at least comment on what may be influencing Sirpa expression. For instance, we know from the work of Petrie and others that DN niche availability can influence the ability of the thymus to import of progenitors. Similarly, after TBI the "gates" are let open and the capacity of the thymus to import progenitors increases. Do the authors know (or could they comment) on what happens to Sipra expression after TBI in ECs?

Thank you for your suggestion. It is an interesting and important question how SIRPα expression is regulated on TPECs. As the reviewer suggested, we examined SIRPα expression in different settings. Given the important role of LT-LTβR signaling on TPEC development and maintenance, we first tested whether LT-LTβR signal would be required for SIRPα expression. However, the remaining TPECs in Ltbr^-/- mice showed similar level of SIRPα expression compared to that in WT mice (new Figure 1—figure supplement 1C). Thymic stromal niche is another factor regulating thymic settling of progenitor cells (Krueger, 2018; Prockop and Petrie, 2004). Increased thymic stromal niche was found during irradiation (Zlotoff et al., 2011). We also detected SIRPα expression on TEPC at Day 14 after 5.5Gy total body sublethal irradiation and found no significant change in SIRPα expression (new Figure 1—figure supplement 1D). Whether SIRPα expression on TPECs is a constitutive event or regulatable upon thymic microenvironmental change remains to be tested in future.

3. The use of the in vitro TEM assays in transwell plates are a nifty way of interrogating and manipulating the effect of Sirpa in these conditions, however, the caveat is that these all use EC cell lines that do not correspond to the TPECs being described in vivo. This caveat should be acknowledged in the text.

Thank you for the advice, EC cell line we used is a pancreatic islet endothelial cell line (MS1), which is not derived from or corresponding to TPECs. We have mentioned this caveat in the text.

4. I am a little confused as to the interpretation of the final experiment looking at tumor clearance. The authors show that this could be clinically relevant as blockade of the CD47-Sirpa axis is becoming an increasingly attractive immunotherapy option but its use could preclude thymic recovery after damage and thus contribute toward poorer T cell responses against tumors. This last study is very interesting but also very hard to interpret given the likely positive effect of Sirpa-CD47 blockade on tumor clearance, in opposition to its potential effects hindering thymic repair. While it is notable that there is reduced clearance of tumor in mice treated with CV1, it is unclear why there does not seem to be any positive effect of CV1 on tumor clearance (is this because there are fewer T cells in the periphery as it is still early after damage?). On the thymic repair and reconstitution front, perhaps a cleaner way would be to look in Sirpa or CD47 deficient mice and without tumors.

We agree that the findings regarding tumor immunotherapy need further explanation on detailed mechanism, therefore this part of results was removed from this project. CV1 treatment in our approach is ahead of tumor inoculation, therefore, CV1 mediated blockaded of CD47 (which is the case in CV1 mediated tumor clearance) would not occur on tumor cells. However, we did not test for the mechanism behind, which is quite interesting and would be done in future study.

As to the suggestion of testing thymic regeneration in straightforward Sirpα or CD47 deficient mice, we have done this in Sirpα deficient mice. We conducted SL-TBI directly on Sirpα-/- mice and control mice. Congenically marked bone marrow cells were also adoptively transferred for better monitoring. At 4 weeks after transfer, donor derived DN thymocyte subset was found defective in Sirpα-/- recipients compared to that in control hosts (Figure R1). However, DP, SP subsets did not show difference, probably due to compensation effect. (Figure R1).

Minor Comments:

• In Fig. 2I (and Fig. 2S2I-J), it is difficult to determine how long after the chimera transplant the homing assays were performed. However, this approach has limitations as the process of creating those chimeras (conditioning such as irradiation etc.) will change the function and possibly the mechanisms of progenitor entry into the thymus. There is clearly still an effect of Sirpa in this context but it is possible (even likely) that the importation mechanisms in the thymus change after damage such as that caused by the conditioning required in the initial chimera generation.

For the study of short-term homing in bone marrow chimeric mice, we have updated legends for the related figure (which is now Figure 2G in the article). The homing assays were performed at 8 weeks after the chimeric reconstruction. Meanwhile, it is indeed possible that the changes of the thymic homing mechanisms may give rise to the abnormal progenitor cells entry. In order to exclude this potential effect, we conducted homing assays without irradiation. In this experiment, we also observed impaired shortterm homing (new Figure 2I) and following T cell development (new Figure 2J,K)

Furthermore, although using the Tie2-Cre strain will distinguish Sirpa on ECs and TECs, it will not distinguish between expression on other cells such as DCs (Tie2 will delete expression in both endothelial and hematopoietic lineages). Although the optimal experiment to address these concerns would be to delete Sirpa from ECs specifically (such as with Cdh5-CreERT2 mice), I am convinced by the preponderance of in vitro data that there is an EC-specific effect and therefore it is not necessary to perform this time-consuming, albeit interesting, potential experiment. However, these limitations should be acknowledged in the discussion or text.

Thank you for your kind suggestion, we have discussed this limitation in the text.

• As a technical note I am surprised that there was considerable reconstitution of naive T cells at day 21 after TBI (Fig.7G-H). In our experience that is very early for naïve T cells in the periphery which generally take about 4 weeks to start reconstituting in a real sense. Is it possible there are direct effects of this treatment on residual radio-resistant peripheral T cell numbers?

Thank you very much for sharing your information. Indeed, we cannot exclude the possibility of residual radio-resistant peripheral T cells. To better clarify this, we have performed SL-TBI (6 Gy) followed by adoptive transfer of congenically marked WT (CD45.1) total bone marrow cells into Sirpα^-/- or control mice (CD45.2) for better monitoring. In this situation, we found that at day 28, more that 97% of thymocytes were donor-derived in both groups and the thymus had been completely reconstituted (Figure R2). In addition, as have been shown in Figure R1, donor-derived DN thymocyte subset was found significantly reduced in Sirpα^-/- mice compared to that in control mice. However, no defect was found at later development stages of thymocytes.

Given the complication of the original experimental design, and as suggested by the reviewers, the original Fig. 7 was removed. The new data described above are hopeful informative to understand the role of SIRPα in a thymic regeneration scenario.

Figure R4. Chimerism detection at day 28 in host transferred with bone marrow cells. (A) Chimerism of thymic subsets, chimerism=CD45.1^+%/(CD45.1+ %+CD45.2^+ %). (B) Representative FACS of donor (CD45.1) and host (CD45.2) cells in total thymocyte (single and live cell gated). n=6 in each group, unpaired t-test applied. **: p<0.01

Author Response:

Reviewer #1 (Public Review):

This manuscript integrates conditional mouse models for TRAP, PAPERCLIP and FMRP-CLIP together with compartment specific profiling of mRNA in hippocampal CA1 neurons. Previously, similar approaches have been used to interrogate mRNA localization, differential regulation of 3'UTR isoforms, their local translation, and FMRP-dependent mRNA regulation. This study builds on these previous findings by combining all three approaches, together with analysis of mRNA dysregulation in Fmr1 KO neuron model of FXS. The strengths of the paper are the rich data sets and innovative integration of methods that will provide a valuable technical resource for the field. The weakness of the paper is the limited conceptual advance as well as lack of deeper mechanistic insights on FMRP biology over previous studies, although the present study validates and integrates past studies, adding some new information on 3'UTR isoforms.

We appreciate the Reviewer’s recognition that “the present study validates and integrates past studies, adding some new information on 3'UTR isoforms”. We also appreciate the Reviewer’s recognition that “The strengths of the paper are the rich data sets and innovative integration of methods that will provide a valuable technical resource for the field.”

We differ, however, with the concern that the work presents a “limited conceptual advance.” Specifically, we find, for the first time, that FMRP regulates two different biologically coherent sets of mRNAs in CA1 neuronal cell bodies and neurites. This provides a profound new insight into FMRP-RNA regulation, including the fact that these two different sets of mRNA targets (encoding chromatin-associated proteins and synaptic proteins, respectively) are both translationally regulated by FMRP and transcribed from genes implicated in autism.

We recognize that FMRP was known, by our own work and that of others (as noted by the Reviewer) to regulate specific targets “in bulk” in neuronal cell types, brain and even in CA1 neurons. What is most unexpected here? Among directly bound FMRP mRNAs in brain CA1 neurons, there is subcellular compartmentalization of this regulation. This is new for FMRP, and in fact is new for RNA binding proteins more generally (recognizing of course the extensive work on RNA localization in different compartments previously discovered by others, beginning with Rob Singer’s work on actin localization and up to the present in work on neurons).

We also think it is also important for readers to understand up-front the novelty in “combining approaches” referred to. We use cell-specific (cTag) CLIP to define direct FMRP interactions in subcompartments--dendrites vs cell bodies--of CA1 neurons within mouse brain hippocampus. We also normalize this data to ribosome-bound mRNAs in CA1 neurons, and validate observations by studying WT and FMRP-null brains. This set of complex mouse models and methods is completely new, and its application is what allowed us to make robust conclusions about FMRP translational regulation of different mRNAs in different cellular compartments.

We strongly disagree with the Reviewer’s comment that FMRP directly interacts with functional classes of mRNAs in different cellular compartments “has previously been shown in the field.” Compartment-specific FMRP-CLIP has not been reported that we’re aware of, much less in a cell-type specific manner. Our previous cell-type specific FMRP-CLIP experiments have been on bulk neuronal material (Sawicka et al. 2019; Van Driesche et al., n.d.). Although cell-type specific TRAP-seq has been performed on microdissected CA1 compartments (Ainsley et al. 2014), investigators were unable to isolate significant amounts of RNA from resting neurons, and degradation of the isolated RNAs did not allow the types of 3’UTR and alternative splicing analyses that were performed here. The Schuman group has performed extensive analysis of mRNAs from microdissected CA1 compartments (Cajigas et al. 2012a; Tushev et al. 2018), but have not performed FMRP-CLIP or any experiments using cell-type specific or direct protein-RNA regulatory methods. In vitro systems have been used to analyze mRNA localization in FMRP KO systems (i.e. (Goering et al. 2020)), but in vitro systems are unable to fully recapitulate the complexities of in vivo brain regions, and did not analyze direct RNA-protein interactions. As our work is on in vivo brain slices, is cell-type specific, and integrates TRAP-seq, PAPERCLIP and CLIP-seq datasets, we believe that our work is novel and will be of great interest to the field.

Despite the fact that FMRP targets are overrepresented in the dendritic transcriptome, it does not appear from this study that FMRP plays an active role in the mechanism of dendritic mRNA localization, at least under steady state conditions. One goal of the manuscript is to address a major question in the mRNA localization field, which is how FMRP may differentially modulate "localization" of functional classes of mRNAs such as those encoding transcriptional regulators and synaptic plasticity genes (Line 78-90). The data here indicate that FMRP directly interacts with functional classes of mRNAs in different cellular compartments, which has previously been shown in the field. However, no evidence is provided that mechanistically reveal a role for FMRP to promote subcellular localization of different functional classes of mRNAs. The correlative evidence presented in this manner does not add mechanistic insight.

We do recognize that the question of what localizes FMRP mRNA targets differentially in the dendrite (and cell body) is of great interest, and remains unanswered. We also appreciate that, despite the Reviewer’s comment above, they also recognize “it does not appear from this study that FMRP plays an active role in the mechanism of dendritic mRNA localization, at least under steady state conditions.”

We believe that some of the confusion here lies in the Reviewer’s comment “One goal of the manuscript is to address a major question in the mRNA localization field, which is how FMRP may differentially modulate "localization" of functional classes of mRNAs such as those encoding transcriptional regulators and synaptic plasticity genes (Line 78-90).” While this is a question of interest that has been studied, we think there is a major disconnect here in the Reviewer’s comments and our findings. To be clear, in the original manuscript, we did not find evidence, in WT vs KO CA1 neurons, that FMRP was acting to differentially localize mRNAs, including those mentioned by the Reviewer.

Nonetheless, to further address the issue of a possible role for FMRP in localizing the transcripts it regulates, we have now performed quantitative analysis of FMRP target mRNA localization in dendrites from WT vs. Fmr1 KO mice. These results are now presented in Supplemental Figures 9 and 10 of the manuscript, and which we present and summarize below.

Supplemental Figure 9. FMRP is not required for localization of its targets into the dendrites of CA1 neurons. A) Dendrite-enriched mRNAs were defined in FMRP KO mice (red) in the same manner as in Figure 1 for FMRP WT animals using bulk RNA-seq and TRAP-seq data. Overlap with dendrite-enriched mRNAs in WT (Figure 1, shown here in green) and CA1 FMRP targets (blue) in shown. 95.6% of dendrite-enriched FMRP targets in the WT were also found to be enriched in the dendrites of FMRP KO animals. B) Dendrite-present mRNAs were defined in FMRP KO. Overlap with dendrite-present mRNAs in WT (Figure 1) and CA1 FMRP targets is shown. 95.7% of dendrite-present FMRP targets in WT are also to be found as dendrite-present in KO animals. C-E) FISH was performed to assess FMRP target localization (Kmt2d (C) , Lrrc7 (D) and Map2 (E)) in FMRP KO mouse brain slices. Left panel shows the proportion of detected mRNAs that were detected in the neuropil (> 10 um from the predicted Cell bodies layer) in WT and KO animals. Wilcoxon ranked sum was performed to detect significance. Middle panel shows densitometry of 1000 spots samples from each picture analyzed. Distance from the CB was determined as described in methods and Figure 1. In the right panel, spots were binned into 15 groups according to the distance traveled from the CB, and the fraction of spots in each genotype in this range was analyzed by t-test to determined differences in the fraction of spots at each location in FMRP WT and KO animals (* indicates p-value < .05, ** is < .01).

Supplemental Figure 10. FMRP is not required for differential localization of 3’UTR isoforms of its targets. A) Differential 3’UTR usage was analyzed using DEXseq as described in Figure 2 to identify 3’UTRs whose ratio of usage between neuropil and CB in FMRP WT and KO animals were altered. Shown is results from DEXseq analysis showing the log2foldChange (neuropil vs cell bodies, KO vs WT) and -log10(p-value) of each 3’UTR. Gray spots indicate that all 3’UTRs analyzed have an FDR > .05, indicating no significant change in usage between FMRP KO and WT animals. B and C) FISH analysis of localization of 3’UTR isoforms of Cnksr2 (B) and Anks1b (C ) isoforms in FMRP WT and KO animals. These genes were found in Figure 2 to express 3’UTR isoforms that are differentially localized to dendrites. Sequestered isoforms are those that are significantly localized to cell bodies in FMRP WT, and Localized are those that are significantly used in the dendrites of WT CA1 neurons. Left panel, the fraction of spots that are found to be localized to the neuropil (> 10 um from the cell body layer) are shown for each isoform in FMRP WT and KO animals. Differences were assessed by wilcoxon ranked sum tests. Middle panel, densitometry of the distance traveled from the cell bodies for a representative 1000 spots from each picture that was analyzed. Right panel, as described in Supplemental Figure 9, detected mRNAs were binned into 15 bins according to the distance traveled from the cell bodies, and differences in the fractions of spots in each bin in FMRP WT and KO slices were analyzed. Significance indicates results of t-tests (* indicates p-value < .05).

In summary, we characterized the dendritic transcriptome in FMRP KO animals, and compared it to the FMRP WT results presented in Figures 1 and 2, as suggested by the Reviewers. We find that the dendritic transcriptome of FMRP KO animals is extremely similar to that of FMRP WT animals, with ~95% of mRNAs found to be dendrite-present or dendrite-enriched in WT also being found in FMRP KO animals (Figure S9). We validated these results with FISH and found no evidence for significant disruption in the localization of FMRP targets Kmt2d (Figure S9C), Lrrc7 (Figure S9D) or Map2 (Figure S9E) to the CA1 neuropil.

To detect FMRP-dependent changes in distribution of 3’UTR isoforms of FMRP targets, we first performed global analysis of 3’UTR usage in TRAP from FMRP KO animals, using the expressed 3’UTR isoforms that were found in Figure 2. DEXseq analysis on 3’UTR expression in CA1 neuropil vs cell bodies TRAP showed no significant instances of altered 3’UTR usage ratios in FMRP KO animals (Figure S10A). We validated these results by performing FISH on the sequestered and localized 3’UTR isoforms of Cnksr2 and Anks1b genes and show no significant changes in the localization of the 3’UTR isoforms in FMRP KO animals (Figure S10B-C). Taken together, this data suggests that FMRP is not significantly involved in localization of its targets in resting CA1 neurons, but rather shows remarkable selection for localized mRNA isoforms. Instead, we find evidence that FMRP regulates the ribosome association of its targets in a compartment-specific manner by showing an increase in ribosome association of a subset of FMRP targets in the dendrites of CA1 neurons (see Figure 7E).

Besides the addition of the figures described above, we have also now made corrections to the text of the manuscript, enumerated below, to address this.

First, we have, as much as possible, reduced our emphasis throughout the manuscript on the “localization” of mRNAs and rather point out that the study seeks to characterize the differences between the regulated transcriptomes in CA1 cell bodies and dendrites. For example, for Figure 4, instead characterizing the log2FoldChange (neuropil vs CA1 cell bodies) as “dendritic localization”, we change the wording to “relative dendritic abundance” to focus on changes in the abundance of these transcripts in the dendrite vs the cell bodies. We also changed the section heading in the results that describes analysis in the FMRP KO animal from “Dysregulation of mRNA localization in FMRP KO animals” to “FMRP regulates the ribosome association of its targets in dendrites”. We believe that these changes will help to clear up this confusion for the reader.

Second, we reformatted the model in Figure 7F. The new version of the model (shown here) emphasizes the point that our study reveals compartment-specific FMRP regulation of a subset of its targets without implying a role for FMRP in the mRNA localization of these transcripts. The text of the manuscript and figure legends have been updated accordingly.

Figure 7F Distinct, compartment-specific FMRP regulation of functionally distinct subsets of mRNAs in CA1 cell bodies and dendrites. In dendrites, the absence of FMRP increases the ribosome association of its targets; this finding is consistent with a model in which FMRP inhibits ribosomal elongation and thereby translation (J. C. Darnell et al. 2011). In resting neurons, the translation of FMRP-bound mRNAs encoding synaptic regulators (FM2 and FM3 mRNAs) is repressed. When FMRP is absent, due to either genetic alteration (FMRP KO or FXS) or neuronal activity-dependent regulation (e.g. FMRP calcium-dependent dephosphorylation (Lee et al. 2011; Bear, Huber, and Warren 2004), ribosome association and translation of targets are increased. In cell bodies, FMRP binds mRNAs that encode for chromatin regulators (the FM1 cluster of FMRP targets), as well as FM2/3 mRNAs (consistent with synapses forming on the cell soma). FM1 targets show patterns of mRNA regulation similar to what our group observed in bulk CA1 neurons: FMRP target abundance is decreased in FMRP KO cells, perhaps due to loss of FMRP-mediated block of degradation of mRNAs with stalled ribosomes (Sawicka et al. 2019; R. B. Darnell 2020).

Third, we have revised the Discussion in order to more completely discuss the model above and also emphasize the finding that FMRP was not found to be involved in the localization of its mRNA targets, but rather in the regulation of the local translation of its targets in a compartment-specific manner. We further speculate on the roles of FMRP in regulation of mRNA abundance and translation in these compartments.

We hope that these changes better reflect the interpretation and novelty of our findings for both the Reviewers and the readers.

Further related to a role of FMRP in mRNA localization, a recent paper in eLife reports that FMRP RGG box promotes mRNA localization of a set of FMRP targets through G-quadruplexes (Goering et al 2020). This relevant paper needs to be cited and discussed.

We apologize for this omission, and have now cited and discussed this paper in the Results and Discussion of the manuscript. Importantly, we find that dendrite-enriched mRNAs have high GC content (see figure below, which is now Supplemental Figure 5). This complicates the discovery of potential G-quadruplexes; put another way, G-rich mRNAs will therefore be enriched when compared to not-localized mRNAs, and this is also true for C-rich mRNAs. Dendrite-enriched FMRP directly-bound CA1 neuronal targets (defined by CLIP) are actually G-poor when compared to dendrite-enriched FMRP non-targets (see new Figure S5 and below).

Supplemental Figure 5A-D: Dendrite-enriched are GC rich and dendrite-enriched FMRP targets are GC poor compared to dendrite-enriched non FMRP targets. A) Schematic of the overlap between CA1 FMRP targets and dendrite-enriched mRNAs (defined in Main Figure 1) B) GC content, as defined by percent G + C for all CA1 mRNAs, dendrite enriched mRNAs (1211), dendrite-enriched FMRP targets (413), and dendrite-enriched non-FMRP targets (798, see A). Stars indicate significance in wilcoxon rank sum tests ( is p < .05, ** is p < .0001). C) G content, as defined by percent G, D) C content, as defined by percent C.

In light of these observations, analysis of G- or C- containing motifs needs to be examined in this context. To this end, we performed the experiments suggested here, but did so by searching for the prevalence of G-quadruplexes in dendrite-enriched FMRP targets versus dendrite-enriched FMRP non-targets (Figure S5A). To do this, we used both experimentally-defined G-quadruplexes (described in (Guo and Bartel 2016), Figure S5E), as well as motifs (described in (Goering et al. 2020), Figure S5F). We include the results below, and in a new Figure S5 in the paper.

Supplemental Figure 5E-F: mRNAs containing G-quadruplexes are not enriched in dendritic FMRP targets vs dendrite-enriched non-FMRP targets. E) The percent of all CA1 mRNAs, all dendrite-enriched mRNAs, dendrite-enriched FMRP-bound targets (413), and dendrite-enriched non-FMRP targets (798) that contain experimentally-defined G-quadruplexes is plotted. Shown are the results of chi-squared analysis comparing the enrichment of G-quadruplex containing mRNAs in dendrite-enriched FMRP targets vs dendrite-enriched non-FMRP targets. F) As in E, except looking for the presence of mRNAs with G-quadruplex motifs in 3’UTRs as described in (Goering et al. 2020)

Interestingly, we found no difference in the presence of G-quadruplex motifs in the 3’UTRs of these two sets (above and new Supplemental Figure 5). For example, of 413 dendrite-enriched FMRP targets, 100 (24%) had experimentally defined G-quadruplexes in the 3’UTRs, while 159 (22.5%) dendrite-enriched non-FMRP targets had experimentally defined G-quadruplexes. These differences were not significant (by chi-square test).

Searching the 3’UTR sequences of 413 dendrite-enriched FMRP targets above for G-quadruplex motifs (as described in (Goering et al. 2020), which searched for an empirically derived specific motif: GW--G, separated by 7nt), we only found 3 instances in dendrite-enchriched FMRP-bound target mRNAs. Similarly, we found out of 798 non-FMRP targets, only a small subset (6) contained this specific motif in their 3’UTRs. These results were not significant (chi-square test).

In summary, we do not find evidence in our data of G-quadruplexes playing a role in determination of FMRP binding in CA1 dendrites. This data is now included in the results and discussed in the Discussion of the paper.

Reviewer #2 (Public Review):

The authors performed transcriptomic analyses from compartment-specific, micro-dissected hippocampal CA1 region tissue from transgenic mice. One feature that distinguishes this work from previous studies is the use of conditional knock-in of tags (GFP or HA) and tissue specific expression of the Cre recombinase to target a very specific population of pyramidal neurons in the CA1 region--as well as the combined use of TRAPseq, PAPERCLIP and FMRP-CLIP. Also, central to this work are the analysis pipelines that look at large populations of mRNA with the goal of finding features shared by those mRNA that bind FMRP.

First, they established the identity of mRNAs that are dendritically enriched or/and alternatively polyadenylated (APA) by sequencing; followed by validation of a few candidates using smFISH. Next, the APA data was filtered through the rMATS statistical program to identify alternatively spliced (AS) mRNA variants within the APA population. The authors concluded that the majority of splicing events were of the exon-skipping type with NOVA2 as the likely culprit leading to this differential localization of AS isoforms. The authors then proceeded to perform FMRP-CLIP which was analyzed against the TRAP dataset. The (413) mRNAs that were shared by the two experiments (TRAP and FMRP-CLIP) exhibited two notable features: dendrite-enrichment and longer average transcript length. More importantly, They demonstrated that FMRP can preferentially bind to an AS isoform that is enriched in dendrites. Further analyses of FMRP CLIP targets showed that they shared a significant level of genes designated by gene set enrichment analysis (GSEA) as involved in ion transport and receptor signaling and similarly for ASD-related candidate genes.

Strengths: -The combined use of tissue-specific Cre and conditional tags for RPL22, PABPC1 and FMRP help make these pull-downs highly specific and robust. -RNA sequencing approach allows for identification and comparison of populations of ribosome-, PABPC1- and FMRP-associated mRNAs. -Preferential binding of FMRP to AS or APA isoforms in dendrites is an impactful and significant finding.

Weaknesses: -A caution in interpreting comparative or differential RNA-sequencing results as some are correlative.

We appreciate this concern, and agree that RNA-seq analysis alone can be difficult to interpret. However, we feel that our unique approach of combining multiple cell-type specific approaches, including CLIP-seq and PAPERCLIP along with TRAP-seq and RNA-seq result in stronger conclusions that are supported by multiple lines of evidence.

-Validation of FMRP interaction with AS or APA isoforms or ASD candidates by smFISH-IF is lacking.

We find that smFISH-IF in the CA1 neuropil is difficult to interpret in mouse brain slices due to dense networks of processes in addition to contaminating cell types, making IF signals dense, noisy and difficult to quantitate. Although we could theoretically attempt these experiments using an in vitro cell culture model, we believe that the novelty of our work is in a) the cell-type specific nature of our analyses and in b) the fact that our analysis and validation is all performed in vivo. We do not feel confident that in vitro systems are similar enough to our in vivo system to be relevant for this work. This is due not only to differences in their transcriptomes, but also due to the limited number of synapses in vitro cells make with other neurons when compared to CA1 neurons in the brain. Instead, we validate the interactions between FMRP and AS and APA isoforms by isolating junction reads among FMRP-CLIP tags isolated in a cell-type specific manner from intact mouse brains (Figure 5). In this manner, we find direct evidence of FMRP selectively binding to dendritic mRNA isoforms in vivo.

-Although hippocampal CA1 region is an excellent site to study FMRP-RNA interactome, are there other projection systems where altered FMRP-RNA interaction may lead to greater dysfunction?

We appreciate this point and now include this in the revised Discussion.

Author Response

Reviewer #1 (Public Review):

Identifying private peptides for generating personalised cancer vaccines is a promising approach to launch robust anti-tumor response; however, the challenges remain in developing an effective process to achieve that. In this manuscript, the authors present an interesting and powerful pipeline (PeptiCRAD) to achieve this goal by examining CT26 model. Overall, this manuscript is well written and presented. Despite that this work presents interesting findings and pipeline, I have the following concerns. I do feel that this manuscript will improve if these concerns can be addressed.

We thank the reviewer very much for having appreciated the quality and the originality of our work.

1. It will be critical to confirm TILs and T cells in draining lymph nodes indeed recognise the peptide used in Figure 7-8 by ELISPOT of IFNg.

We agree with the reviewer´s comment as regard to confirming that TILS and T cells in draining lymph nodes recognise the peptide used in Figure 7-8 by functional characterization in an ELISPOT IFN-γ assay. In our experience, the ELISPOT assay works at the best when fresh samples are employed; additionally, the splenocytes are source of enough cells to test individual mouse reactivity to single peptide. To this end, as the samples from figures 7 and 8 were frozen, we decided to repeat the animal experiment according to figure 7 schedule treatment to perform then the ELISPOT on splenocytes freshly harvested from mice. Following the previous results, we selected the best group (PeptiCRAd1) to further investigate the peptide response; untreated mice (Mock) and Virus alone (VALO-mD901) were used as control as well. Interestingly, the peptide deconvolution showed T cell reactivity to one peptide (RYLPAPTAL, peptide 2) (Figure 1A) in the PeptiCRAd1 group, in contrast no T-cell reactivity was observed for SYLPPGTSL (peptide 1) (Figure 1B). These data highlighted the role of an individual antigen in eliciting specific anti-tumor T cell response, appearing an interested candidate for further proof of concept in animal experimental setting.

Figure 1 Interferon-γ Elispot results Harvested splenocytes from the treatment groups (as indicated in the figure) were functional characterized in an IFN-γ ELISPOT assay; individual response to SYLPPGTSL A) and RYLPAPTAL B) for each mouse is reported as IFN-γ spot forming cells (SFC)/106 splenocytes. The data are depicted as single dots plot and mean + SEM is shown. (Virus=VALO-mD901, PC=PeptiCRAd).

1. It would be interesting to see if this pipeline can be used to identify human peptides in human melanomas.

We thank the reviewer for pointing out that this pipeline can be used to identify human peptide in human melanomas. Indeed, the work here described is a proof concept meant to be translated in human setting. To this end, in the lab we have two projects on-going that are exploiting the same pipeline to investigate the human epithelioid and human mesothelioma ligandome landscape. Regarding this latter, we are investigating four different human cell lines (H2B, MSTO211H, H2452 and JL1). As shown in the picture below (Figure 2), the peptide length distribution showed an enrichment in 9mres in both replicates (Rep1 and Rep2), in line with a ligandome profile. The analysis of the binders revealed that most of them were good binders (according to EL-Rank score) for at least one of alleles for each cell line. Following the pipeline reported in this manuscript, to select candidate peptides we applied two different approaches; the first approach relied on RNA seq analysis to check which source proteins of the peptides isolated in the ligandome analysis were reported as upregulated or downregulated in resected tumor compared to normal tissues.

(Fromhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51024 GSE51024). The second approach (analysis still on-going) will be the use of HEX software.

Regarding the epithelioid project, we have analysed the ligandome profile of two human cell lines: NEPS and VA-ES-BJ. Please find below the example of the ligandome analysis for VAES- BJ cell line (Figure 3). Overall, the analysis outcome was similar to published dataset (aminoacidic length distribution, Gibbs clustering profile, amount of binders) confirming the good quality of the ligandome landscape identified. Next, we applied HEX analysis to narrow down the list of peptide candidates for further test. We are currently in the stage of collecting more different epithelioid cell lines to expand our cohort of samples.

Figure 2 Mesothelioma project (data not published)

Figure 3 Epithelioid project (data not published)

Author Response:

Reviewer #2:

The SNX-BAR family of sorting nexin proteins is involved in the formation of tubular carriers at endosomes. The best characterized yeast sorting nexins form part of the retromer complex, which binds sorting signals on cargo proteins to direct their recycling. There is some debate as to the role of sorting nexins in mediating cargo recognition vs tubule formation, and it is unclear which (if any) other members of the sorting nexin family bind directly to cargo.

In this manuscript, the authors investigate the function of the yeast sorting nexin Mvp1. This protein was previously proposed to cooperate with retromer in the formation of recycling tubules, and to recruit the dynamin-like protein Vps1 to promote their scission (Chi et al, JCB 2014). Here, Suzuki et al find that Mvp1 has a cargo-sorting role that is distinct from that of other sorting nexins. They show that Mvp1 (but not retromer) is required for the correct localization of the membrane protein Vps55, and identify a cytosolically-exposed sequence in Vps55 required for its sorting. Using structurally-guided mutagenesis, they find that dimerization and membrane binding is important for Mvp1 function. They use live cell imaging to show that Vps55 is largely sorted into different tubules compared to the retromer cargo protein Vps10, and use fractionation of vesicle fusion-deficient cells to show these cargo are present in different vesicle populations, suggesting that Mvp1 and retromer form different classes of retrograde carriers. By surveying the trafficking of other membrane proteins, they show that in some cases Mvp1 acts redundantly with two other sorting nexin complexes (Snx4 and/or retromer) to recycle cargo at endosomes. Moreover, they find that loss of all three sorting nexin complexes perturbs endosome function, lipid asymmetry, and the endosomal recruitment of the scission factor Vps1. Although Mvp1 was previously implicated in Vps1 recruitment (Chi et al, 2014), Suzuki et al use a GTPase-defective form of Vps1 to provide the first evidence that Mvp1 physically interacts with Vps1 in vivo and in vitro. Taken together, these data suggest that Mvp1, retromer and Snx4 recognize distinct sets of cargo proteins and mediate independent recycling pathways at endosomes, and imply that each sorting nexin recruits Vps1 to complete tubule scission.

Overall, this manuscript presents a large number of experiments that are technically well executed and makes several novel observations. It should be noted that many experiments largely repeat previous work: this was not always clearly indicated in the manuscript. For the most novel observations, some weaknesses were noted. A key novel finding was that Mvp1 binds to and sorts the cargo protein Vps55 via recognition of a cytosolic motif. The supporting data do not provide the typical burden of proof for such experiments, because: (1) the identified sequence was shown to be necessary but not sufficient, thus the mutation could indirectly affect binding at another site, and (2) Mvp1 failed to coIP with the Vps55 mutant from cell lysates, but this could be an indirect effect of Vps55 missorting to the vacuole while Mvp1 remains at the endosome, and does not prove that Mvp1 binds directly to Vps55 via this motif.

Thank you for pointing this out. As mentioned above, to address your point, we examined the Mvp1-Vps55 interaction in cells lacking Vam3, required for endosome fusion with the vacuole. In this mutant, both WT and recycling mutants localize at the endosome (Fig. Rev. 1C). We confirmed that mutations in the recycling sequence altered the Mvp1-Vps55 interaction even in vam3Δ cells (Figure 3-figure supplement 1C was added to the revised manuscript). To address whether the recycling signal is sufficient for Mvp1-mediated recycling, we tried to generate several chimera constructs, but we did not obtain a construct recycled in Mvp1 dependent manner. Hence, we were not able to address this point.

A second key finding is that Mvp1 and retromer form distinct classes of tubular carriers at endosomes. While the manuscript does provide data to support this conclusion, I was disappointed that there was no discussion of the work of Chi et al, who showed through careful quantitative analysis that Mvp1 and retromer frequently label the same population of tubules.

Thank you for pointing this out. In the revised manuscript, we have also discussed the differences with Chi et al. in the text (Page 13, line 408).

Moreover, the authors claim that mvp1 mutants secrete little CPY, yet the literature indicates these mutants secrete ~65% of newly synthesized CPY (Ekena and Stevens, MCB 1995), suggesting a functional link between Mvp1 and Vps10 recycling. In fact, vps55 mutants themselves have a significant CPY missorting defect (~50% secreted) suggesting that some mvp1 phenotypes could be a secondary consequence of Vps55 mislocalization.

Thank you for pointing this out. We examined the CPY sorting in the recycling signal mutants. Strikingly, CPY was partially missorted to the extracellular space in vps55Y61A/T63A/F66A/M67A mutants (Fig. Rev. 6). Since Vps10 recycling was not altered in mvp1Δ cells (Figure 5A), we believe that the mislocalization of Vps55 causes the CPY sorting defect in mvp1Δ cells.

It was not mentioned that Vps55 interacts with the transmembrane protein Vps68: these proteins are interdependent for their stability and loss of Vps68 slows traffic out of the endosome (Schluter et al MBOC 2008). This provides a simple explanation for the observed ubiquitination and degradation of overexpressed Vps55, which presumably saturates available Vps68.

As suggested by the reviewer, we have revised the manuscript (Page 5, line 158). Also, as mentioned above, we observed that Vps55 missorting was suppressed by overexpression of Vps68 (Figure 3-supplement 1E was added to the revised manuscript), suggesting that Vps68 was saturated in this condition.

Other experiments in this manuscript were not completely novel, including: the demonstration that Mvp1 tubules bud from endosomes and that Mvp1 is important for Vps1 recruitment to endosomes (Chi et al, JCB 2014); that Vps1 GTPase mutants accumulate Mvp1 at endosomes (Ekena and Stevens, MCB 1995); that Mvp1 plays a role in Vps55 localization (Bean et al, Traffic 2017); and that GFP-SNX8 is present on endosomal tubules when expressed in mammalian cells (van Weering et al, Traffic 2012). While in most cases the experiments presented in this manuscript build on and extend previous work, I would like to see the earlier work fully acknowledged, and any discrepancies appropriately discussed. The fact that many of the experiments presented in this manuscript are not entirely novel detracts from the overall impact of the work. Despite this, key original findings presented in this paper - including the discovery that Mvp1 is required for sorting specific cargo and binds directly to the dynamin-like protein Vps1 - will be of broad interest to the trafficking field.

Thank you for pointing this out. In the revised manuscript, we have carefully revised the manuscript (Page 5, line 133; Page 8, line 236; Page 13, line 414; Page 12, line 377).

Author Response:

Reviewer #1 (Public Review):

This study demonstrates with analyical methods and simulations a new approach to estimate pairwise noise and signal correlations in two-photon calcium imaging data. This approach compensates for biases introduced by the dynamics of calcium signals, without deconvolution and for low trial numbers. Simulations based on idealized calcium signals demonstrate the efficiency of the method, and application to auditory cortex imaging data leads to mild changes in the results shown in the past based on less accurate estimates. This study has the merit to identify biases that can arise when evaluating noise and signal correlations across neurons with indirect signals. Moreover the solution provided, may become a useful addition to the neuroscientist's signal analysis toolbox. Noise and signal correlation are related to fonctional connectivity between neurons, and thereby give insights about the fonctional structure of the underlying network. They do not necessarily account for the full complexity of neural interactions but are used in numerous studies, which would be improved by this tool. A potential improvement of the study could be to indicate how this approach could be generalized to other neuron to neuron interaction measurements or data-driven neural network modeling.

We would like to sincerely thank Reviewer 1 for his supportive stance towards our work, and for providing helpful feedback to improve our manuscript

The main weakness of the study is that the efficency of the method is only assessed with simulated datasets. Finding real ground-truth data for a validation beyond that would be difficult if not impossible. However, authors could further convince the reader by showing the effect of relaxing certain assumptions of their surrogate data generation model (e.g. absence of temporal correlation in measurement noise), and show the robustness and limits of the methods.

Thank you for this suggestion. Motivated by this comment, and a related comment by Reviewer 2, we have now substantially enhanced our performance analyses in the revised manuscript and compiled them in a new subsection titled “Analysis of Robustness with respect to Modeling Assumptions” for better clarity and consistency. In summary:

1) We first examined the robustness of our proposed method with respect to model mismatch in the stimulus integration model. As suggested, we generated data according to a non-linear (i.e., quadratic sum of linear filters) receptive field model:

but assumed a linear stimulus integration model in our inference procedure

The comparison of the correlations estimated under this setting by each method are shown in Figure 2 – Figure Supplement 3. While the performance of our proposed signal correlation estimates under this setting degrade as compared to that in Figure 2 with no model mismatch, our proposed estimates still outperform the other methods and recovers the ground truth signal correlation structure reasonably well.

It is noteworthy that the model mismatch in the stimulus integration component does not affect the accuracy of noise correlation estimates in our method, as is evident from the noise correlation estimates in Figure 2 – Figure Supplement 3. In comparison, the biases induced in the other methods due to model mismatch and various other factors such as observation noise, temporal blurring, undermining non-linear mappings between spikes and underlying covariates, results in significantly larger errors in both signal and noise correlation estimates.

2) We incorporated our previous analysis of robustness with respect to calcium decay model mismatch in this subsection, which is shown in Figure 2 – Figure Supplement 4.

3) In response to a related comment by Reviewer 2, we then performed extensive simulations to evaluate the effects of SNR and firing rate on the performance of our method. Overall, while the performance of all algorithms degrades at low SNR or firing rate values (SNR < 10 dB, firing rate < 0.5 Hz), our algorithm outperforms the existing methods in a wide range of SNR and firing rate values considered. The results are summarized in Figure 2 – Figure Supplement 5.

4) Finally, we considered two observation noise model mismatch conditions, namely, white noise + low frequency drift and pink noise, similar to the treatment in Deneux et al. (2016). For each noise mismatch model, we also varied the SNR level and firing rate and compared the performance of the different algorithms as reported in Figure 2 – Figure Supplement 6. These new analyses demonstrate that our proposed estimates outperform the existing methods, under correlated generative noise models, and also with respect to varying levels of SNR and firing rate. As clearly evident in panels C and F of Figure 2 – Figure Supplement 6, even though the estimated calcium concentrations are contaminated by the temporally correlated fluctuations in observation noise, the putative spikes estimated as a byproduct of our iterative method closely match the ground truth spikes, which in turn results in accurate estimates of signal and noise correlations.

To address this comment, we performed extensive simulations to evaluate the robustness of different algorithms under model mismatch conditions induced by 1) non-linearity in the stimulus integration model, 2) calcium decay, 3) SNR and firing rate, and 4) temporal correlation of observation noise. We have now compiled these results in a new subsection called “Analysis of Robustness with respect to Modeling Assumptions” (Pages 6-7).

Also further intuitions about why this method outperform others would be of great help for the non-specialist readers.

Thank you for this suggestion. There are two sources for the performance gap between our proposed method and existing approaches:

1) Favorable soft decisions on the timing of spikes achieved by our method, as a byproduct of the iterative variational inference procedure: an accurate probabilistic decoding of spikes results in better estimates of the signal/noise correlations, and conversely having more accurate estimates of the signal/noise covariances improves the probabilistic characterization of spiking events. This is in contrast with both the Pearson and Two-Stage methods: in the Pearson method, spike timing is heavily blurred by the calcium decay; in the two-stage methods, erroneous hard (i.e., binary) decisions on the timing of spiking events result in biases that propagate to and contaminate the downstream signal and noise correlation estimation and thus result in significant errors.

2) Explicit modeling of the non-linear mapping from stimulus and latent noise covariates to spiking through a canonical point process model (which is in turn tied to a two-photon observation model in a multi-tier Bayesian fashion) results in robust performance under limited number of trials and observation duration. As we have shown in Appendix 1, as the number of trials L and trial duration T tend to infinity, conventional notions of signal and noise correlation indeed recover the ground truth signal and noise correlations, as the biases induced by non-linearities average out across trial repetitions. However, as shown in Figure 2 - Figure supplement 2, in order to achieve comparable performance to our method using 20 trials, the conventional correlation estimates require ~1000 trials.

To address this comment, we have now included the aforementioned items in the revised Discussion section, highlighting the key aspects of our method that makes it outperform existing approaches (Pages 17-18).

Reviewer #2 (Public Review):

This manuscript describes a new method for estimating signal and noise correlations from two-photon recordings of calcium activity in large neuronal networks. Unlike existing methods that first require inferring spikes from calcium transients before estimating the correlations, the proposed method performs the correlation estimation directly from the fluorescence traces. It treats the different inputs to each neuron as latent variables to be inferred from its observed fluorescence activity, and divides these inputs according to whether they are provided by stimulus-dependent (signal) or stimulus-independent (noise) inputs. The authors showed with simulations that proper definitions of signal and noise correlations based on these inferred variables converge with trial repetition much faster to the true correlations than conventional estimates. They are not sensitive to blurring produced by inaccurate spike deconvolution and are less prone to erroneously mixing the signal and noise components of the correlations. By applying this new method to real optical recordings from the auditory cortex of awake mice, the authors shed new light on the structure of the circuitry underlying the processing of sound information in this brain region. Circuits processing sound-related and sound-independent information appear to be more orthogonal than previously thought, with a spatial signature that changes between thalamorecipient layer 4 and supragranular layers 2/3.

This is a mathematical manuscript that introduces a promising new analysis approach. It is designed to be applied to two-photon experiments, that typically produce recordings of calcium activity of several hundred of neurons simultaneously. Because of their massive parallel recordings, which do not rely on spike sorting to identify single units, these optical techniques naturally provide access to correlation between units. They have given rise to a field of active research that attempts to link these correlations to elementary functional circuits in the brain. However, as the authors point out, the low efficiency of spike inference from calcium traces raises the need for correlation estimation approaches that circumvent this problem, as the method presented here does. As such, it could have a significant impact if the community succeeds in using it (see below).

We would like to sincerely thank Reviewer 2 for his/her supportive stance towards our work, and for providing helpful feedback to improve our manuscript.

Weaknesses and strengths

1) Public availability of the code implementing the new method is clearly necessary for the two-photon microscopy community to adopt it, and this is indeed the case at https://github.com/Anuththara-Rupasinghe/Signal-Noise-Correlation. However, it is also crucial that any end-user be able to get a clear picture of the conditions under which the method can or cannot be applied before diving in. The fact that such an applicability domain is not well defined is a major concern. Notably, each Real Data Study presented in the paper uses a preliminary selection of "highly active cells" (1rst study: N = 16; 2nd study: N = 10; 3rd study: N~20 per field), as the authors succinctly discuss that performance is expected to degrade "in the regime of extremely low spiking rate and high observation noise" (l. 518-519). But no precise criteria are provided to specify what is meant by "highly active cells". On the other hand, the authors also assume that there is at most one spiking event per time frame for each neuron, which seems to exclude bursting neurons. The latter assumption seems to be a challenge with respect to the example traces shown on Fig. 4C (F/F reaches 400%) and on Fig. 6C (F/F reaches 100%), considering that the GCaMP6s signal for a single spike is expected to peak below 10-20%. This forces the authors to take a scaling factor of the observations A = 1 x I (Real Data Study 1 and 3) or A = 0.75 x I (Real Data Study 2) compared to the A = 0.1 x I taken in the Simulation Studies. Therefore, it looks like if the Real Data Studies were performed on mainly bursting cells and each burst was counted as one spiking event. A detailed discussion of the usable range of firing rates, whether in spike or burst units, as well as the usable range of SNR should be added to the main text to allow future users to assess the suitability of their data for this analysis.

Thank you for pointing out the issues related to the applicability domain of our method. We agree that clarifying the rationale behind our model parameter choices is key to facilitating its usage by future users. In response to this comment, we have made three major revisions:

1) Adding a new subsection to the Methods and Materials called “Guidelines for model parameter settings” that includes our rationale and criteria for choosing the number of neurons (N), stim- ulus integration window length (R), observation noise covariance (Σ_w), scaling matrix A, state transition parameter (α), and mean of the latent noise process (μ_x);

2) Inspecting the capability of our proposed method in compensating for rapid increase of firing rate;

3) Performing extensive new simulations to evaluate the effect of SNR level and firing rate on the performance of our proposed method, included in a new subsection in the Results section called “Analysis of robustness with respect to modeling assumptions”.

We will next describe these changes in a point-by-point fashion.

-Criterion for selecting the number of neurons. While our proposed method scales-up well with the population size due to low-complexity update rules involved, including neurons with negligible spiking activity in the analysis would only increase the complexity and potentially contaminate the correlation estimates. Thus, we performed an initial pre-processing step to extract N neurons that exhibited at least one spiking event in at least half of the trials considered. This criterion is now clearly stated in the subsection “Guidelines for model parameter settings”. We have also reworded “highly active cells” to “responsive cells (according to the selection criterion described in Methods and Materials)” for clarity.

-Evaluating the effects of SNR level and firing rate. We had previously noted that the performance degrades at low SNR and firing rate values, with little quantitative justification. In response to this comment, and a related comment by Reviewer 1, we performed extensive simulations to evaluate the robustness of the different methods under varying SNR levels, firing rates, and observation noise model mismatch (including white noise + drift and pink noise models). These results are included in a new subsection called “Analysis of robustness with respect to modeling assumptions” and shown in Figure 2 – Figure Supplement 5 and 6.

While the performance of all methods (including ours) degrades at low SNR levels or firing rates (SNR < 10 dB, firing rate < 0.5 Hz), our proposed method outperforms the existing methods in a wide range of SNR and firing rate values and under the considered observation noise model mismatch conditions. To quantify this comparison, we have also indicated the mean and standard deviation of the relative performance gain of our proposed estimates across SNR levels and firing rates as insets in Figure 2 – Figure Supplement 5 and 6.

-Choosing the scaling matrix A. In each case, we set A=aI, and estimated a by considering the average increase in fluorescence after the occurrence of isolated spiking events. Specifically, we derived the average fluorescence activity of multiple trials triggered to the spiking onset and set a as the increment in the magnitude of this average fluorescence immediately following the spiking event.

-Compensation for rapid increase of firing rate. The comment of the reviewer regarding the sudden increase of ∆F/F in Fig. 4C prompted us to inspect the performance of the algorithm in such scenarios where the choice of A may underestimate the rapid increase of firing rate (e.g., A= I). In the new supplementary figure to Fig. 4, called Figure 4 – Figure Supplement 2, we show a zoomed-in view of the time-domain estimates of the latent processes obtained by our proposed method (replicated here for discussion):

Notably, the fluorescence activity rises up to a magnitude of ∼ 14, while we have set a=1. Thus, as the reviewer pointed out, this activity is induced by a burst-like event due to successive closely-spaced spikes. Due to the low firing rate of A1 neurons, we believe this is not a bursting event (in the electrophysiological sense), but a rapid increase in firing rate that may result in the occurrence of more than one spike per frame. From the estimates of the latent calcium concentration (purple) and putative spikes (green), we clearly see that our proposed method is still capable of matching the observed fluorescence activity through two mitigatory mechanisms that we describe next:

1) The proposed method predicts spiking events in adjacent time frames to compensate for rapid increase of firing rate (see the green trace following the vertical dashed line) and thus infers calcium concentration levels that match the observed fluorescence activity;

2) Even though our generative model assumes that there is only one spiking event in a given time frame, this assumption is implicitly alleviated in our inference framework by relaxing the constraint

as explained in the section Methods and Materials - Low-complexity parameter updates (Page 23). While this relaxation was performed in order to make the inverse problem tractable, we see that it in fact leads to improved estimation results under such settings, by allowing the putative spike magnitudes

to be greater than 1, as it is also evident in the magnitude of the inferred spikes right after the rise of fluorescence activity (the horizontal dashed line corresponds to spiking magnitude equal to 1).

We have now discussed this observation in the Results section (Page 10).

To address this comment, we have added a new subsection to Methods called “Guidelines for model parameter settings” that includes our rationale and criteria for choosing key model parameters (Page 24), have performed new simulation studies to evaluate the effects of SNR and firing rate on the performance of the proposed method (Pages 6-7), and closely inspected the performance of our method under rapid increase of firing rate (Page 10).

2) Another parameter seems to be set by the authors on a criterion that is unclear to me: the number of time lags R to be included in the sound stimulus vector st. It seems to act as a memory of the past trajectory of the stimulus and probably serves to enhance the effect of stimulus onset/offset relative to the rest of the sound presentation. It is consistent with the known tendency of neurons in the primary auditory cortex to respond to these abrupt changes in sound power. However, this R is set at 2 in the Simulation Study 1, whereas it is set at 25, in the Real Data Studies 1 and 3, and to 40 in the Real Data Study 2. What leads to these differences escaped to me and should be explained more clearly.

Thank you for pointing out this lack of clarity in explaining the rationale behind choosing R. In addressing this comment, we have now added an entry in the new subsection “Guidelines for model parameter settings”. Furthermore, we have unified our choice of R in the three real data studies. We will explain these changes in a point-by-point fashion next.

-Choice of R in simulation studies. The stimulus used in the simulation was a 6th-order autoregressive process whose present and immediate past values contributed to spiking in our generative model (i.e., R=2). Given that the ground truth value of R was known in the simulations, we used R=2 for inference as well.

-Choice of R for real data application. The number of lags R considered in stimulus integration is a key parameter that can be set through data-driven approaches or using prior domain knowledge. Examples of common data-driven criteria include cross-validation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which balance the estimation accuracy and model complexity.

To quantify the effect of R on model complexity, we first describe the stimulus encoding model in our framework. Suppose that the onset of the pth tone in the stimulus set (p=1,⋯,P , where P is the number of distinct tones) is given by a binary sequence

The choice of R implies that the response at time t post-stimulus depends only on the R most recent time lags. As such, the effective stimulus at time t corresponding to tone p is given by

By including all the P tones, the overall effective stimulus at the tth time frame is given by

The stimulus modulation vector d_j would thus be RP-dimensional. As a result, the number of parameters (M=RP) to be estimated linearly increases with R. By using additional domain knowledge, we chose R to be large enough to capture the stimulus effects, and at the same time to be small enough to control the complexity of the algorithm.

As an example, given that the typical response duration of mouse primary auditory neurons is < 1 s, with a sampling frequency of f_s=30 Hz, we surmised that a choice of R∼30 would suffice to capture the stimulus effects. We further examined the effect of varying R on the proposed correlation estimates in Figure 4 – Figure Supplement 1. As shown, small values of R (e.g., R = 1 or 10) may not be adequate to fully capture the effects of stimuli. By considering values of R in the range 25 − 50, we noticed that the correlation estimates remain stable. We thus chose R=25 for our real data analyses. Notably, the results of real data study 2 (that previously used R = 40) are nearly unchanged with the new choice of R=25, which is in accordance with our observation in Figure 4 – Figure Supplement 1.

To address this comment, we have added a new subsection to Methods called “Guidelines for model parameter settings” (Page 24) that includes our rationale for choosing the stimulus integration window length R and have performed a new analysis to evaluate the effect of R on the performance of the proposed method in real data study 1 (Page 10).

3) This memory of the past stimulus trajectory appears to be specific to the proposed method and is not accounted for in the 2-stage Pearson estimation, for example. Since it probably helps to reflect the common sensitivity of neurons to onset/offset, it alone provides an advantage to the proposed method over the 2-stage Pearson estimation. It would be instructive to also perform this comparison with R set to 1 to get an idea of the magnitude of this advantage.

We agree that explicit modeling of stimulus integration is a key advantage of our proposed method in comparison to the conventional ones. We have now explained this virtue in the discussion of the role of R in real data study 1 (Page 10). Additionally, as explained in our responses to the previous comment, we have included a new analysis of the sensitivity of our proposed estimates to the choice of R as a supplementary figure to Figure 4. As the reviewer suggested, we see that R=1 indeed fails to capture the underlying structure in the signal correlations. However, when R is sufficiently large (R>20), the estimates become stable.

To address this comment, we have now discussed the advantage of including the stimulus history in our model and probed the sensitivity of our estimates to the choice of R in Figure 4 – Figure Supplement 1 (Page 10).

4) Finally, although the example of ground truth signal and noise correlation matrices taken to illustrate the method in the simulation study on Fig. 2A have been chosen to be with almost no overlap in their non-zero coefficients, there is no fundamental reason why this separation should be the rule for real data. These coefficients reflect the patterns of stimulus-dependent and stimulus-independent functional connectivity in the recorded network. As such, these patterns could have different degree of overlap, depending on the brain areas recorded. It is therefore particularly striking that the authors find in their data a strong dissimilarity and almost no covariance between signal and noise correlation coefficients, throughout all the different sets of experiments they present here (Fig. 4E, Table 1, 2, 3, and Fig. 6A&B). This makes a strong and compelling statement on the likely separation of the corresponding circuits in the primary auditory cortex of the mouse.

We agree with the assessment of the reviewer. We suspect that some of the reported similari- ties between signal and noise correlations in existing literature could be due to leakage in estimating these two quantities, likely indued by limited number of trials, short observation duration, and undermining the effect of calcium dynamics and non-linearities.

Likely impact on the field

It is now well established that sound processing is modulated, even at the level of primary auditory cortex, by locomotion (Schneider et al. Nature 2018), task engagement (Fritz et al. Nat. Neurosci. 2003), or several other factors. Applying the proposed method to these situations could help understand how sound processing circuits are remodeled, without confounding other coexisting processes. In general, whenever a brain structure makes associations between multiple processes within the same network, the presence of multiple circuits makes the observation of correlations difficult to attribute to the signature of a single circuit. By significantly improving the estimation of signal and noise correlations, the proposed method should help distinguish the boundaries of these circuits as well as their intersections. The exploration of the role of many secondary sensory and associative cortical structures could be renewed by this work.

We would like to thank Reviewer 2 again for his/her supportive stance towards our work and for fairly summarizing our contributions

Author Response:

Reviewer #1:

The manuscript "Two different cell-cycle processes determine the timing of cell division in Escherichia coli" by Colin et al. presents an experimental approach to investigate the role of two governing cell-cycle processes, namely, DNA replication-segregation and cell division cycle, in size regulation. Authors tackle the problem by first decoupling these two cell-cycle process via sub-lethal dosages of A22, and then analyze the role of each process in the timing of cell division. Modern imaging and analysis techniques are used in this work to monitor cell division with single-cell resolution and chromosome replication with sub-cellular resolution. The large pool of data allows the authors to perform correlation analysis of cell-size and the cell cycle parameters, which led to the conclusion that the two processes have a "balanced contributions in non-perturbed cells."

The question studied in this manuscript is important and timely. The investigation of the two concurrent processes chosen by the authors is perhaps the right direction which may eventually lead to a complete understanding of the E. coli cell-cycle and size regulation. The high-resolution imaging and analysis accomplished in this work is also commendable. There is, however, a major concern about this manuscript, which is the entire conclusion is based on the cell-cycle and size perturbations by A22. The caveat of the A22 perturbations is that an aberrant cell shape could affect both of the cellular processes simultaneously. Even though the C-period and initiation size are largely unchanged, a possible, but unknown, cross-talk between the two processes may be affected by A22. Therefore, additional evidence is necessary to show whether the two processes independently determine cell division.

We agree that A22 treatment could possibly affect DNA replication or organization, e.g., indirectly through an effect of cell width on DNA organization. It would thus indeed be desirable to confirm our findings based on alternative perturbations. At the same time, our experiments clearly demonstrate that cell sizes at replication initiation and division are decreasingly correlated with increasing A22 concentration, which suggests that a process different from DNA replication is responsible for the timing of division.

Additionally, DNA replication could depend on cell division, which could possibly complicate the relationship between replication and division. We have now addressed the possibility of an influence on division on replication initiation in the Discussion, where we write ‘The concurrent-cycles framework assumes that replication initiation is independent of cell division or cell size at birth, [...]. However, we note that this is not the only possibility, and DNA replication may not be entirely independent of cell division. A complementary hypothesis \citep{Kleckner2018} posits a possible (additional or complementary) connection of initiation to the preceding division event. To test this hypothesis one could perturb specific division processes by titrating components involved in Z-ring assembly (e.g., titrating FtsZ \citep{Zheng2016}).’

Reviewer #2:

This is an interesting paper which makes important contributions to an interesting and highly controversial topic: how does an E.coli cell decide when to divide.

As the authors describe in clear and careful detail, two main camps have argued (often dogmatically) for "single process" models in which division is either a direct, downstream consequence of replication initiation (which is the regulated step) or of effects that act directly on division (irrespective of replication and, more generally, the chromosome cycle). The authors of this paper have, instead, proposed that both types of effects are important, in different proportions according to the circumstances. They refer to this idea as a "concurrent cycles" hypothesis. In previous work they have presented arguments and data which they interpret as being incompatible with any single process model and consistent with their alternative hypothesis.

This work now investigates the consequences of treatment with A22, a drug which inhibits MreB, with the result that it increases cell width and, concomitantly, increases the length of time between completion of a given round of DNA replication and the immediately ensuing cell division (an interval known as the "D period"). The idea to analyze this situation was motivated by the authors previous hypothesis: by the concurrent cycles idea, increasing the length of the D-period should prolong the replication-independent inter-division process such that it becomes rate limiting in determining the timing of division (relative to the replication-dependent process).

The data presented confirm the authors' expectation. They first show that progressively increasing the amount of A22 does not (dramatically) alter either: (i) the basic "adder" behavior in which a fixed amount of cell length is added irrespective of the length of the cell at birth or (ii) the finding that a fixed amount of cell length is added per replication origin during the period from one round of replication initiation to the next, which is consistent with (and generally considered to be supportive of) a role for a replication-dependent process.

However, they also discover an interesting additional effect by examining the amount of cell length added (per origin) during the entire period comprising replication plus the immediately ensuing division ("C+D"). In the unperturbed case, cells that are longer at the time of initiation of replication also add more length during the ensuing (C+D) period. In contrast, in the presence of increasing amounts of A22, this effect is progressively reversed such that, finally, at high drug levels, cells which are longer (per origin) at the time of initiation of replication add much less length during the ensuing (C+D) period. Since the length of the C period is essentially constant in all conditions, the relevant effect is the variation in the length of the D period. And since the observed effect becomes more and more prominent with increasing A22 concentration, variation in the D period dominates more and more as the length of that period gets longer and longer. The authors interpret this effect to mean that, with increasing D-period length, division timing is decreasingly dependent on replication initiation. They go on to infer that "with increasing average D period, a process different from DNA replication is likely increasingly responsible for division control". This is a sensible, relatively formal restatement of the finding. This statement allows for diverse specific interpretations. The authors focus on one possible interpretation: they show that their previously proposed concurrent cycles hypothesis can quantitatively explain these data. In essence, given a replication-independent and a replication-dependent process, the observed findings are explained by an increased contribution of the replication-independent process. This scenario also does a better job of explaining the presented data, as well as other findings, than other recent "single process" models, for reasons that are discussed in straightforward detail in the Discussion. The authors also do an excellent job of laying out the assumptions upon which their model (and other existing models) are based, thus laying open the possibility for future studies to consider other possible scenarios.

This work is important for four reasons. First, provides interesting new data which must be accommodated by any synthetic explanation for cell division control. Second, it makes it abundantly clear that the validity of any proposed single process model remains to be further substantiated. Third, it suggests an interesting alternative model which can accommodate a diversity of data, including that presented in the current work, and which has the potentially attractive feature of combining the two existing single-process models. Fourth, and perhaps most importantly, the authors discussion of the available data in this field clear, thoughtful and thought-provoking and leaves open the possibility of some as-yet unimagined mechanism. Overall, this work provides an important counterpoint to other published work and is a very valuable contribution to thinking and discussion in this field.

[It can also be noted specifically that this work provides an important counterpoint to the model proposed in a previous eLIFE paper on this topic by Witz et al., 2019 (eLife 2019;8:e48063 doi: 10.7554/eLife.48063).]

We thank the reviewer for her careful assessment and appreciation of our work.

Reviewer #3:

Colin, Micali et al. investigated slow-growing E. coli cells' division and replication over cell cycles at single cell level with the perturbed cellular dimension. They found that the time between replication termination and division increased by perturbing cell width as recently reported, and that chromosome replication became decreasingly limiting for cell division. These results well supported the 'concurrent-processes model' previously proposed by some of the authors.

1) Cell length can be used to represent the cell size (adder) only if the cell width keeps constant. In the current form of the manuscript, it is unknown whether or not the cell width varies significantly at single-cell level with A22 treatment (e.g., 1µg/ml A22). In this case, cell volume might not be nicely correlated with cell length. The interpretation of Figure 3 therefore would be devalued.

We now demonstrate in the new Figure 2–S2 that the coefficient of variations of cell width does not increase with A22 concentration (neither in snapshots from cells grown in liquid culture nore in the mother machine):

Figure: Variation of width at the single-cell level. Coefficient of variation of cell width as a function of mean cell with. Squares and triangles represent measurements done on cells grown in mother machine or in liquid culture respectively. Blue color represents wild-type cells. Grey color represents cells treated with different amounts of A22.

We also reference this figure in the main text, writing: ‘Increasing A22 concentration leads to increasing steady-state cell width both in batch culture and in the mother machine (Figure \ref{fig2}B), without affecting cell-to-cell width fluctuations (Figure \ref{CV_width}),} and without affecting doubling time (Figure \ref{fig2}C) or single-cell growth rate (Figure \ref{SI_Fig1}).’

2) The negative value of 𝜁C+D in Figure 3F (treated group) indicates that the division length is negatively correlated with the cell length at replication initiation. It is not obvious that this can rule out the possible contribution of DNA replication/segregation in offsetting the length difference at initiation and thus contribute to cell division. Since Figure 3F is the key observation to validate the model, more explanations are required to help readers understand how a negative 𝜁C+D can lead to a conclusion that a process different from DNA replication is likely responsible for division control with A22-treatment.

The negative value of zeta_CD actually corresponds to a lack of correlation between division size and size at initiation, typically predicted by the models where replication is never limiting for cell division (Micali et al 2018, Si et al. 2019). We have commented more explicitly on this point in the text, writing: ‘Note that the negative value of $zeta_{\rm CD}$ corresponds to a lack of correlation between division size and size at initiation (Figure \ref{fig3}G), typically predicted by the models where replication is never limiting for cell division~\cite{Micali2018,Micali2018b,Si2019}.’

3) As an important input for the model, the QC+D' is assumed to be equal to QC+D in unperturbed conditions and remains constant regardless of the A22 concentration (Line 548-554). This assumption is reasonable if the minimum time interval for segregation (D') is irrelevant to the change of cell width. But how D' and QC+D' changes with cell width are unknown. Earlier molecular studies revealed that the polymerization of MreB affects the activity of topoisomerase IV, an enzyme mediates the dimerization of sister chromosomes, which implies that changing cell width may affect D'. Given the importance of QC+D' to the model, it is vital for the authors to make this assumption clear in maintext and explain why such assumption is reasonable.

Q_CD’ (related to average growth in the CD’ period) is a parameter that we cannot measure, or bypass in the model. We have made this assumption more explicit in the text. While this question deserves further investigation in future studies, we know that D’ cannot increase too strongly with width, because otherwise it would leave replication/segregation limiting for division under A22 perturbations, contrary to our observation. This is the main reason to assume D’ constant in the model. A posteriori we can say that the loss of correlation between size at division and size at initiation observed under A22 treatment is in line with the hypothesis that D’ does not increase too much in order for the segregation process to interfere with cell division. We now write: ‘Note that neither the minimum completion time C+D' nor the coupling parameter $zeta_{CD’}$ can be measured experimentally, or bypassed in the model. In principle these parameters could change under A22 perturbations, since MreB affects the activity of topoisomerase IV \citep{madabhushi2009actin,kruse2003dysfunctional}, an enzyme that mediates the dimerization of sister chromosomes. However, constancy of $\zeta_{CD'}$ is supported by the constancy of the C period, and the minimum D' period cannot increase too strongly with width in the model, because otherwise it would render replication/segregation limiting for division under A22 perturbations, contrary to our experimental observation. Hence, for simplicity, we assumed $\zeta_{CD'}$ and the D' period to stay constant.’

Author Response

Reviewer #1 (Public Review):

The authors push a fresh perspective with a sufficiently sophisticated and novel methodology. I have some remaining reservations that concern the actual make-up of the data basis and consistency of results between the two (N=16) samples, the statistical analysis, as well as the “travelling” part.

I previously commented on the fact that findings from both datasets were difficult to discern and more effort should be made to highlight these. Also, a major conclusion “the directionality effect [effect of attention on forward waves] only occurs for visual stimulation” only rested on a qualitative comparison between studies. The authors have improved on this here, e.g., by toning down this conclusion. One thing that is still missing is a graphical representation of the data from Foster et al. (the second dataset analysed here) that would support the statistical results and allow the reader a visual comparison between the sets of findings.

We are glad that the reviewer recognizes the improvement in the presentation of the conclusions. According to the suggestions, we have modified figure 2, not only by including a third dataset (see point below), but also in a way that allows a direct comparison between the three datasets. Specifically, the results from the three datasets are now shown in three columns next to each other. The first row shows the FW and BW waves in contra and ipsilateral lines of electrodes for each dataset: our dataset and the one from Feldmann-Wustefeld and colleagues (the first and the second column in the figure, both with visual stimulation) shows a clear interaction between direction and laterality, as confirmed by the statistical analysis. The dataset from Foster and colleagues (the third column, no visual stimulation) shows a laterality effect only in the backward waves but not in the forward ones, in line with the hypothesis that FW waves are modulated only in the presence of visual stimulation. The second row shows a schematic representation of the task, and the third row illustrate the electrodes’ lines used in each dataset. We hope the reviewer will be satisfied with the current data presentation.

Also, for any naive reader, the concept of travelling waves may be hard to grasp in the way data are currently presented - only based on the results of the 2D-FFT. Can forward and backward-travelling waves be illustrated in a representative example to make this more intuitive?

We thank the reviewer for the suggestion. We included in figure 1 an additional panel E that represents a schematic example of forward and backward waves in the temporal domain (i.e., in the EEG data). We hope this example will provide a better understanding of the data and the traveling wave concept.

Finally, the way Bayes Factors from the Bayesian ANOVA are presented, especially with those close to the ‘meaningful boundaries’ ⅓ and 3, as defined in the ‘Statistical analysis’ section, requires some unification/revision. For example, here: “We found a positive correlation between contra- and ipsi- lateral backward waves, and occipital (all Pearson’s r~=0.4, all BFs 10 ~=3) and -to a smaller extent- frontal areas (all Pearson’s r~=0.3, all BFs 10 ~=2).”, where the second part should strictly be labelled as inconclusive evidence. In the same vein, there is occasional mention of “negative effects”, where it should say that evidence favours the absence of an effect.

We agree with the reviewer and apologize for the inaccuracies in reporting the statistical analysis. We corrected as suggested (see below), replacing ‘negative effects’ with ‘evidence favors the absence of an effect’.

From the updated manuscript :

"We found moderate evidence of a positive correlation between contra- and ipsi- lateral backward waves, and occipital (all Pearson’s r~=0.4, all BFs10~=3) but inconclusive evidence in the frontal areas (all Pearson’s r~=0.3, all BFs10~=2)."

From the revised ‘Results’ section, now it reads:

[…] whereas all other factors and their interactions revealed evidence in favor of the absence of an effect (BFs10<0.3).

[…] but not in the forward waves (BF10=0.231, error<0.01%, supporting evidence in favor of the absence of an effect).

Reviewer #2 (Public Review):

The present manuscript takes a new perspective and investigates the functional relevance of traveling alpha waves’ direction for visual spatial attention. While the modulation of alpha oscillatory power - and especially the lateralization of alpha power - has been associated with spatial attention in the literature, the present investigation offers a new perspective that helps understand and differentiate the functional roles of alpha oscillations in the ipsi- versus contralateral hemisphere for spatial attention.

The present study uses a straightforward approach and provides an analysis of two EEG datasets, which are convergingly in line with the authors’ claim that two patterns of travelling alpha waves need to be differentiated in visual spatial attention. First, backward waves in the ipsilateral hemisphere, and second, forward waves in the contralateral hemisphere, which are only observed during visual stimulation. Importantly, the authors test the relation of these patterns of traveling waves to the overall power of alpha oscillations and to the hemispheric lateralization of alpha power. Furthermore, to test the functional significance, the authors demonstrate that the pattern of forward and backward waves around stimulus onset differentiates between hits and misses in task performance.

Although the results are in line with the conclusions drawn, some questions remain. The authors investigate the relationship between traveling alpha waves and the hemispheric lateralization of alpha power, which is a well-established neural signature of spatial attention. Surprisingly, the lateralization of alpha power shown in Figure 3B appears relatively weak in the present dataset (by visual inspection), which raises the question of whether the investigation of a relation between lateralized alpha power and alpha traveling waves is warranted in the first place.

We agree with the reviewer that the effect seems reduced compared to other studies, despite the topography of alpha-band lateralization in our data is in line with the literature. In order to quantify the effect, we performed an analysis similar to (Thut et al., 2006), defining a laterality index as:

We computed such index for occipital electrodes and their average (in red in figure R1). The results reveal that for most electrodes, including their average, the laterality index is significantly larger than 0, confirming the presence of alpha-band lateralization. However, we also note that the amplitude of the effect (~0.04) is reduced compared to the study by Thut and colleagues, which was between 0.05 and 0.10.

Figure R1 – Laterality index for occipital electrodes, quantifying alpha-band lateralization during attention allocation. All electrodes go in the expected direction, revealing an increase of alpha-band power in the ipsilateral occipital hemisphere.

Furthermore, the authors employ between-subject correlations (with N = 16) to test the relationship between alpha traveling waves and (lateralized) alpha power. However, as inter- individual differences in patterns of travelling waves are not the main focus here, within- subject analyses of the same relations would be able to test the authors’ hypotheses much more directly.

As suggested, we included the recommended within-subject analysis in the revised manuscript by computing a trial-by-trial correlation between alpha power and traveling waves for each participant. First, we obtained a correlation coefficient and a p-value for each subject. Then, we tested whether the correlation coefficients had an overall positive or negative distribution (i.e., according to our previous results, we expected a positive correlation between backward waves and alpha power). Additionally, we combined the p-values to test for overall significance (using the Fisher method, see Methods section below). Our results corroborate the between-subject correlation, supporting the conclusion that alpha-band power correlates mostly with backward waves (especially contro-lateral to the attended location). The other correlations (i.e., forward waves and alpha power) were statistically inconclusive. We included in the revised manuscript these new results, as shown in the following.

From the Results section:

“To further investigate the relation between alpha-band travelling waves and alpha power, we performed the same analysis focusing on the correlation within each participant. In particular, we correlated trial-by-trial forward and backward waves with alpha-band power for each subject, obtaining correlation coefficients ‘r’ and their respective p-values. As in the previous analysis, we correlated forward and backward waves with frontal and occipital electrodes in both contro- and ipsilateral hemispheres. We applied the Fisher method (Fisher, 1992, see Methods for details) to combine all subjects' p-values in every conditions. Overall, we found a significant effect of all combined p-values (p<0.0001), except in the lateralization condition (contra- minus ipsilateral hemisphere), similar to our previous analysis. Additionally, we tested for a consistent positive or negative distribution of the correlation coefficients. As shown in figure 3C, the results support a significant correlation between backward waves and alpha- power in the hemisphere contralateral to the attended location (BF10=10.7 and BF10=7.4 for occipital and frontal regions, respectively; all other BF10 were between 1 and 2, providing inconclusive evidence). Interestingly, this analysis also revealed a small but consistent effect in the correlation between lateralization effects, as we reported a consistently positive correlation in the contra- minus ipsilateral difference between forward waves and alpha power (BF10~5 for both frontal and occipital electrodes). However, it’s important to notice that the combined p-values obtained using the Fisher method did not reach the significance threshold in the lateralization condition, reducing the relevance of this specific result.“

From the Methods section:

“Additionally, we computed trial-by-trial correlations between waves and alpha power for all participants. First, we tested the correlation coefficient against zero in all conditions. Then, we obtained a combined p-value per condition using the log/lin regress Fisher method (Fisher, 1992), as shown in (Zoefel et al., 2019). Specifically, we computed the T value of a chi- square distribution with 2*N degrees of freedom from the pi values of the N participants as:

It needs to be appreciated that the authors analyze two datasets in the present study. However, the question remains whether the absence of the forward waves effect in paradigms without visual stimulation is a general one and would replicate in other datasets. Moreover, the manuscript would benefit from a discussion of the potential implications of traveling waves for functional connectivity between posterior and anterior regions.

We have now included a third dataset in the paper. In this dataset, from (Feldmann-Wüstefeld & Vogel, 2019), participants performed a visual working memory task by attending either the left or the right side of the screen where a stimulus was displayed. We analyzed the amount of waves during stimulus presentation, and we found the same results as in our own dataset: very strong evidence in favor of an interaction between LATERALITY (contra- and ipsilateral) and DIRECTION (FW and BW). We now included the results in figure 2 (see point above) and in the results section of the manuscript. Unfortunately, we couldn't find any other publicly available EEG dataset in which participants attend to either side of the screen without ongoing visual stimulation.

In addition, we re-analyzed our main findings (i.e. the interaction between LATERALITY and DIRECTION) in all three datasets using a classic ANOVA to report the effect size as 𝜂2 (see point above). Unlike the Bayesian ANOVA (which -in JASP- is based on linear mixed models), the classic one does not model the slope of the random effects. Yet, we observed that the LATERALITY x DIRECTION interaction in the Foster dataset proved very significant, with a large effect size (F(1,16)=9.81, p=0.003, 𝜂2=0.13). Supposedly, modeling the slope of the random effects in the Bayesian ANOVA lowered its statistical sensitivity. For the sake of completeness, we reported both results in the manuscript.

Concerning the potential implications of traveling waves on functional connectivity, we consider the interpretation based on the Predictive Coding scheme in the one before the last paragraph of the discussion (reported below for the reviewer’s convenience). In this framework, top-down connections have inhibitory functions, suppressing the predicted activity in lower regions. These interpretations align with our findings, relating the inhibitory role of backward travelling waves to visual attention. Similarly, in the same paragraph, we refer to the work of Spratling, which extensively investigates the relationship between selective attention and Predictive Coding.

From the Results section:

"To confirm our previous results, we replicated the same traveling waves analysis on two publicly available EEG datasets in which participants performed similar attentional tasks (experiment 1 of Foster et al., 2017 and experiment 1 of Feldmann-Wüstefeld and Vogel, 2019). In the first experiment from the Feldmann-Wüstefeld and Vogel dataset, participants were instructed to perform a visual working memory task in which, while keeping a central fixation, they had to memorize a set of items while ignoring a group of distracting stimuli. We focused our analysis on those trials in which the visual items to remember were placed either to the right or the left side of the screen, while the distractors were either in the upper or lower part of the screen (we pulled together the trials with either 2 or 4 distractors, as this factor was irrelevant for the purposes of our analysis). The stimuli were shown for 200ms, and we computed the amount of forward and backward waves in the 500ms following stimulus onset. As shown in figure 2 (central column), the analysis confirmed our previous results, demonstrating a strong interaction between the factors DIRECTION and LATERALITY (BF10=667, error~2%; independently, the factors DIRECTION and LATERALITY had BF10=0.2 and BF10=0.4, respectively). These results confirmed that, in the presence of visual stimulation, spatial attention modulates both forward and backward waves. Next, we analyzed another publicly available dataset from Foster et al., 2017. [...]"

"Remarkably, as shown in figure 2 (right panel), our analysis demonstrated an effect of the lateralization (LATERALITY: BF10=3.571, error~1%), revealing more waves contralateral to the attended location, but inconclusive results regarding the interaction between DIRECTION and LATERALITY (BF10=2.056, error~1%). However, using a classical ANOVA (i.e., without modeling the slope of the random terms), the interaction between DIRECTION and LATERALITY proved significant (F(1,16)=9.81, p=0.003, 𝜂2=0.13)."

From the Methods section:

"We included two additional datasets in this study. In both studies, participants performed a visual attention task while keeping their fixation in the center of the screen. Regarding the Feldmann-Wüstefeld and Vogel, 2019 study, participants were asked to memorize the colors of two stimuli while ignoring a set of distractors stimuli. We analyzed uniquely those trials in which the visual stimuli were presented to the left or right side of the screen, while the distractors were placed above or below the fixation cross. After 500ms of the fixation cross, two colored 'target' stimuli were presented for 200ms. Participants were asked to memorize these stimuli, and a new 'probe’ stimulus was shown after an additional second. Participants reported whether the probe matched the target stimuli or not. We analyzed the traveling waves in the 500ms following the target stimulus onset. Participants performed a spatial attention task in the second dataset from Foster et al. 2017. First, the fixation cross cued participants to covertly attend one of eight possible spatial positions uniformly distributed around the center of the screen. After one second, a digit was displayed either in the cued location or in any other one. The remaining locations were filled with letters. Participants were instructed to report the only displayed digit. We analyzed the waves the second before the stimuli onset when participants attended to the locations cued to the left or right side of the screen (we discarded trials in which participants attended locations above or below the fixation cross). For additional details about both experimental procedures, we refer the reader to Foster et al., 2017 and Feldmann-Wüstefeld and Vogel, 2019.”

From the discussion:

"Our previous work proposed an alternative cause for the generation of cortical waves (Alamia and VanRullen, 2019). We demonstrated that a simple multi-level hierarchical model based on Predictive Coding (PC) principles and implementing biologically plausible constraints (temporal delays between brain areas and neural time constants) gives rise to oscillatory traveling waves propagating both forward and backward. This model is also consistent with the 2-dipoles hypothesis (Zhigalov and Jensen, 2022), considering the interaction between the parietal and occipital areas (i.e., a model of 2 hierarchical levels). However, dipoles in parietal regions are unlikely to explain the observed pattern of top-down waves, suggesting that more frontal areas may be involved in generating the feedback. This hypothesis is in line with the PC framework, in which top-down connections have an inhibitory function, suppressing the activity predicted by higher-level regions (Huang and Rao, 2011). Interestingly, Spratling proposed a simple reformulation of the terms in the PC equations that could describe it as a model of biased competition in visual attention, thus corroborating the interpretation of our finding within the PC framework (Spratling, 2008, 2012)."

Author Response

Reviewer #1 (Public Review):

Point 1) There is affluent evidence that the cortical activity in the waking brain, even in head restrained mice, is not uniform but represents a spectrum of states ranging from complete desynchronization to strong synchronization, reminiscent of the up and down states observed during sleep (Luczak et al., 2013; McGinley et al., 2015; Petersen et al., 2003). Moreover, awake synchronization can be local, affecting selective cortical areas but not others (Vyazovskiy et al., 2011). State fluctuations can be estimated using multiple criteria (e.g., pupil diameter). The authors consider reduced glutamatergic drive or long-range inhibition as potential sources of the voltage decrease but do not attempt to address this cortical state continuum, which is also likely to play a role. For example: does the voltage inactivation following ripples reflect a local downstate? The authors could start by detecting peaks and troughs in the voltage signal and investigate how ripple power is modulated around those events.

Our study is correlational, and hence, we cannot speak as to any casual role that the awake hippocampal ripples may play in the post-ripple hyperpolarization observed in aRSC. It is indeed possible that the post-awake-ripple neocortical hyperpolarization is independent of ripples and reflects other mechanisms that our experiments have possibly been blind to. One such mechanism is neocortical synchronization in the awake state. As reviewer 1 pointed out, it is possible that a proportion of hippocampal ripples occur before neocortical awake down-states. To test this hypothesis, we triggered the ripple power signal by the troughs (as proxies of awake down-states) and peaks (as proxies of awake up-states) of the voltage signals, captured from different neocortical regions, during periods of high ripple activity when the probability of neocortical synchronization is highest (McGinley et al., 2015; Nitzan et al., 2020). According to this analysis (see the figure below), the ripple power was, on average, higher before troughs of aRSC voltage signal than before those of other regions. On the other hand, the ripple power, on average, was not higher after the peaks of aRSC voltage signal than after those of other regions. This observation supports the hypothesis that a local awake down-state could occur in aRSC after the occurrence of a portion of hippocampal ripples. However, a recent work whose preprint version was cited in our submission (Chambers et al., 2022, 2021) reported that, out of 33 aRSC neurons whose membrane potentials were recorded, only 1 showed up-/down-states transitions (bimodal membrane potential distribution). Still, a portion (10 out of 30) of the remaining neurons showed an abrupt post-ripple hyperpolarization. In addition, they reported a modest post-ripple modulation of aRSC neurons’ membrane potential (~ %20 of the up/down-states transition range). Hence, these results suggest that the post-ripple aRSC hyperpolarization is not necessarily the result of down-states in aRSC. A paragraph discussing this point was added to the discussion lines 262-279.

Mean ripple power triggered by troughs and peaks of voltage signal captured from aRSC, V1, and FLS1. Zero time represents the timestamp of neocortical troughs/peaks. The shading represents SEM (n = 6 animals).

Point 2) Ripples are known to be heterogeneous in multiple parameters (e.g., power, duration, isolated events/ ripple bursts, etc.), and this heterogeneity was shown to have functional significance on multiple occasions (e.g. Fernandez-Ruiz et al., 2019 for long-duration ripples; Nitzan et al., 2022 for ripple magnitude; Ramirez-Villegas et al., 2015 for different ripple sharp-wave alignments). It is possible that the small effect size shown here (e.g. 0.3 SD in Fig. 2a) is because ripples with different properties and downstream effects are averaged together? The authors should attempt to investigate whether ripples of different properties differ in their effects on the cortical signals.

The seeming small effect size (e.g. 0.3 SD in Fig. 2a) is because the individual peri-ripple voltage/glutamate traces were z-scored against a peri-non-ripple distribution and then averaged. Alternatively, the peri-ripple traces could have been averaged first, and the averaged trace could have been z-scored against a sampling distribution constructed from the abovementioned peri-non-ripple distribution where the sample size would have been the number of ripples detected for a specific animal. In the latter case, the standard deviation of the sampling distribution would have been used as the divisor in the z-scoring process as opposed to the former case where the standard deviation of the original peri-non-ripple distribution would have been used. Since the standard deviation of the sampling distribution is smaller than the standard deviation of the original distribution by a factor of √(sample size), the final z-scored values in the latter would be higher than those in the former case by a factor of √(sample size). For instance, if the sample size in Fig. 2A (number of ripples) was 100, the mean z-scored value would be 0.3*10 = 3. In any case, it is of interest to investigate the relationship between the ripple and neocortical activity features.

To investigate the relationship between the hippocampal ripple power and the peri-ripple neocortical voltage activity, we focused on the agranular retrosplenial cortex (aRSC) as it showed the highest level of modulation around ripples. To get an idea of what features of the aRSC voltage activity might be correlated with the ripple power, the ripples were divided into 8 subgroups using 8-quantiles of their power distribution, and the corresponding aRSC voltage traces were averaged for each subgroup (similar to the work of Nitzan et al. (Nitzan et al., 2022)). The results of this analysis are summarized in the figure below.

Left: peri-ripple aRSC voltage trace was triggered on ripples in the odd-numbered ripple power subgroups for each animal and then averaged across 6 animals. The standard errors of the mean were not shown for the sake of simplicity. Right: the same as the left panel but for only lowest and highest power subgroups. The shading represents the standard error of the mean.

These results suggested that there might be a positive correlation between the ripple power and the pre-ripple and post-ripple aRSC voltage amplitude. To test this possibility, Pearson’s correlation between the ripple power and pre-/post-ripple aRSC amplitude was calculated for each animal separately. The ripple power for each detected ripple was defined as the average of the ripple-band-filtered, squared, and smoothed hippocampal LFP trace from -50 ms to +50ms relative to the ripple's largest trough timestamp (ripple center). The pre- and post-ripple aRSC amplitude for each ripple was calculated as the average of the aRSC voltage trace over the intervals [-200ms, 0] and [0, 200ms], respectively. The results come as follows.

Top: the scatter plots of the ripple power and pre-ripple aRSC voltage amplitude for individual animals. The black lines in each graph represent the linear regression line. The blue circles in each graph are associated with one ripple. The Pearson’s correlation values (ρ) and the p-value of their corresponding statistical significance are represented on top of each graph. Bottom: the same as top graphs but for post-ripple aRSC amplitude.

According to this analysis, 4 out of 6 animals showed a weak positive correlation (ρ = 0.0806 ± 0.0115; mean ± std), 1 animal showed a negative correlation (ρ = -0.20183), and 1 animal did not show a statistically significant correlation (p-value > 0.05) between ripple power and pre-ripple aRSC voltage amplitude. Moreover, 2 out of 6 animals showed a negative correlation (ρ = -0.1 and -0.14), and 4 animals did not show a statistically significant correlation (p-value > 0.05) between ripple power and post-ripple aRSC voltage amplitude.

To check that the correlation results were not influenced by the extreme values of the ripple power and aRSC voltage, we repeated the same correlation analysis after removing the ripples associated with top and bottom %5 of the ripple power and aRSC voltage values. According to this analysis, 1 out of 6 animals showed a negative correlation (ρ = -0.13), and 5 animals did not show a statistically significant correlation (p-value > 0.05) between ripple power and pre-ripple aRSC voltage amplitude. Moreover, 2 out of 6 animals showed a negative correlation (same animals that showed negative correlation before removing the extreme values; ρ = -0.12 and -0.14), 1 animal showed a positive correlation (ρ = 0.1), and 3 animals did not show a statistically significant correlation (p-value > 0.05) between ripple power and post-ripple aRSC voltage amplitude.

Based on these results, we cannot conclude that there is a meaningful correlation between the ripple power and amplitude of aRSC voltage activity before and after the ripples. It is noteworthy to mention that Nitzan et al. (see Fig S6 in (Nitzan et al., 2022)) did not report a statistically significant correlation between ripple power octile number (by discretizing a continuous-valued random variable into 8 subgroups) and pre-ripple firing rate of the mouse visual cortex. However, they reported a statistically significant negative correlation (ρ = -0.13) between the ripple power octile number and post-ripple firing rate of the mouse visual cortex. It appears that their reported negative correlation was influenced by the disproportionately larger values of the firing rate associated with the first ripple power octile compared to the other octiles. Therefore, repeating their analysis after removing the first octile would probably lead to a weak correlation value close to 0.

Next, we investigated the relationship between ripple duration and aRSC voltage activity. To get an idea of what features of the aRSC voltage activity might be correlated with the ripple duration, the ripples were divided into 8 subgroups using 8-quantiles of their duration distribution, and the corresponding aRSC voltage traces were averaged for each subgroup. The results of this analysis are summarized in the figure below.

Left: peri-ripple aRSC voltage trace was triggered on ripples in the odd-numbered ripple duration subgroups for each animal and then averaged across 6 animals. The standard errors of the mean were not shown for the sake of simplicity. Right: the same as the left panel but for only lower and highest duration subgroups. The shading represents standard error of the mean.

These results do not reveal a qualitative difference between the patterns of aRSC peri-ripple voltage modulation and ripple duration. However, the same correlation analysis performed for the ripple power was also conducted for the ripple duration. Only 1 animal out of 6 showed a statistically significant correlation (ρ = 0.08) between pre-ripple aRSC voltage amplitude and ripple duration.

Moreover, only 1 animal out of 6 showed a statistically significant correlation (ρ = -0.08) between post-ripple aRSC voltage amplitude and ripple duration. In conclusion, there does not seem to be a meaningful linear relationship between peri-ripple aRSC voltage amplitude and ripple duration.

Next, we investigated whether the peri-ripple aRSC voltage modulation differs depending on whether a single or a bundled ripple occurs in the dorsal hippocampus. The bundled ripples were detected following the method described in our previous work (Karimi Abadchi et al., 2020). We found that 9.4 ± 3.5 (mean ± std across 6 animals) percent of the ripples occurred in bundles. Then, the aRSC voltage trace was triggered by the centers of the single as well as centers of the first/second ripples in the bundled ripples, averaged for each animal, and averaged across 6 animals. The results of this analysis are represented in the following figure.

Left: animal-wise average of mean peri-ripple aRSC voltage trace triggered by centers of the single and centers of the first ripple in the bundled ripples. Right: Same to the left but triggered by the centers of the second ripple in the bundled ripples.

These results suggest that the amplitude of aRSC voltage activity is larger before bundled than single ripples, and the timing of aRSC voltage activity is shifted to the later times for bundled versus single ripples. The pre-ripple larger depolarization might signal the occurrence of a bundled ripple (similar to larger pre-bundled- than pre-single-ripple deactivation observed during sleep (Karimi Abadchi et al., 2020)).

Point 3) The differences between the voltage and glutamate signals are puzzling, especially in light of the fact that in the sleep state they went hand in hand (Karimi Abadchi et al., 2020, Fig. 2). It is also somewhat puzzling that the aRSC is the first area to show voltage inactivation but the last area to display an increase in glutamate signal, despite its anatomical proximity to hippocampal output (two synapses away). The SVD analysis hints that the glutamate signal is potentially multiplexed (although this analysis also requires more attention, see below), but does not provide a physiologically meaningful explanation. The authors speculate that feed-forward inhibition via the gRSC could be involved, but I note that the aRSC is among the two major targets of the gRSC pyramidal cells (the other being homotypical projections) (Van Groen and Wyss, 2003), i.e., glutamatergic signals are also at play. To meaningfully interpret the results in this paper, it would be instrumental to solve this discrepancy, e.g., by adding experiments monitoring the activity of inhibitory cells.

Observing that glutamate and voltage signals do not go hand-in-hand in awake versus sleep states was surprising for us as well, and it was the main reason that SVD analysis was performed. Especially that a portion of aRSC excitatory neurons showed elevated calcium activity despite the reduction of voltage and delayed elevation of glutamate signals in aRSC at the population level. At the time of initial submission, pre-ripple reduction and post-ripple elevation of calcium activity in a portion of three subclasses of the superficial aRSC inhibitory neurons were reported (Chambers et al., 2022, 2021), and it was the basis of our speculation on the potential involvement of feed-forward inhibition in the post-ripple voltage reduction. We speculated that the source of this potential feed-forward inhibition could stem from gRSC excitatory neurons, as the reviewer 1 pointed out, or from other neocortical or subcortical regions projecting to aRSC. It is also possible that feedback inhibition would be involved where the principal aRSC neurons that are excited by gRSC (as reviewer 1 pointed out) or any other region, including aRSC itself, excite aRSC inhibitory neurons.

Point 4) I am puzzled by the ensemble-wise correlation analysis of the voltage imaging data: the authors point to a period of enhanced positive correlation between cortex and hippocampus 0-100 ms after the ripple center but here the correlation is across ripple events, not in time. This analysis hints that there is a positive relationship between CA1 MUA (an indicator for ripple power) and the respective cortical voltage (again an incentive to separate ripples by power), i.e. the stronger the ripple the less negative the cortical voltage is, but this conclusion is contradictory to the statements made by the authors about inhibition.

A closer look at Figure 2B iv reveals that elevation of the cross-correlation function between peri-ripple aRSC voltage and hippocampal MUA starts with a short delay (~20 ms) and peaks around 75 ms after the ripple centers. It means the maximum correlation between the two signals occurs at point (75ms, 75ms) on the MUA time-voltage time plane whose origin (i.e. the point (0, 0)) is the ripple centers in the hippocampal MUA and corresponding imaging frame in the voltage signal. Reviewer 1’s interpretation would be correct if the maximum correlation occurred at the point (0, 0) not at the point (75ms, 75 ms). It is because the MUA value at the time of ripple centers (t = 0) is the indicator of the ripple power not at the time t = 75ms. Figure 2B iii shows that the amplitude of hippocampal MUA is more than 2 dB less at t = 75ms than at t = 0 which is a reflection of the fact that ripples are often short-duration events. Instead, if the maximum correlation occurred at the point (0, 100ms) where the ripples had maximum power and aRSC voltage was at its trough (Figure 2B iii), it could have been concluded that “the stronger the ripple the less negative the cortical voltage”.

Point 5) Following my previous point, it is difficult to interpret the ensemble-wise correlation analysis in the absence of rigorous significance testing. The increased correlation between the HPC and RSC following ripples is equal in magnitude to the correlation between pre-ripple HPC MUA and post-ripple cortical activity. How should those results be interpreted? The authors could, for example, use cluster-based analysis (Pernet et al., 2015) with temporal shuffling to obtain significant regions in those plots. In addition, the authors should mark the diagonal of those plots, or even better compute the asymmetry in correlation (see Steinmetz et al., 2019 Extended Fig. 8 as an example), to make it easier for the reader to discern lead/lag relationships.

The purpose of calculating the ensemble-wise correlation coefficient was to provide further information about the relationship between the two random processes peri-ripple HPC MUA and peri-ripple neocortical activity. In general, the correlation between the two random processes cannot be inferred from the temporal relationship between their mean functions. In other words, there are infinitely many options for the shape of the correlation function between two random processes with given mean functions. Moreover, the point was to compare the correlation of peri-ripple neocortical activity and HPC MUA across neocortical regions. The fact that mean peri-ripple activity in, for example, RSC and FLS1 are different does not necessarily mean their correlation functions with peri-ripple HPC MUA are also different.

As requested, we performed cluster-based significant testing via temporal shuffling for each individual VSFP (n = 6), iGluSnFR Ras (n = 4), and iGluSnFR EMX (n = 4) animals. The following figures summarize the number of animals showing significant regions in their correlation functions between peri-ripple HPC MUA and different neocortical regions. The diagonal of the correlation functions is marked; however, the temporal lead/lag should not be inferred from these results mainly because the temporal resolution of the two signals, one electrophysiological and one optical, are not the same.

Point 6) For the single cell 2-photon responses presented in Fig. 3, how should the reader interpret a modulation that is at most 1/20 of a standard deviation? Was there any attempt to test for the significance of modulation (e.g., by comparing to shuffle)? If yes, what is the proportion of non-modulated units? In addition, it is not clear from the averages whether those cells represent bona fide distinct groups or whether, for instance, some cells can be upmodulated by some ripples but downmodulated by others. Again, separation of ripples based on objective criteria would be useful to answer this question.

As explained in response to point 2, the seeming small modulation size (e.g. 0.05 SD in Fig. 3b) is because the individual peri-ripple calcium traces were z-scored against a peri-non-ripple distribution and then averaged. Alternatively, the peri-ripple traces could have been averaged first, and the averaged trace could have been z-scored against a sampling distribution constructed from the abovementioned peri-non-ripple distribution where the sample size would have been the number of ripples detected for a specific animal. In this latter case, the standard deviation of the sampling distribution would have been used as the divisor in the z-scoring process as opposed to the former case where the standard deviation of the original peri-non-ripple distribution would have been used. Since the standard deviation of the sampling distribution is smaller than that of the original distribution by a factor of √(sample size), the final z-scored values in the latter would be higher than those in the former case by a factor of √(sample size).

As suggested by the reviewer and to make our results more comparable with those of electrophysiological studies, we deconvolved the calcium traces and tested for the significance of the modulation of each neuron by comparing its mean peri-ripple deconvolved trace with a neuron-specific shuffled distribution (see the methods section for details). We found %8.46 ± 3 (mean ± std across 11 mice) of neurons were significantly modulated over the interval [0, 200ms] and %81.08 ± 8.91 (mean ± std across 11 mice) of which were up-modulated. If the criterion of being distinct is being significantly up- or down-modulated, these two groups could be considered distinct groups. The following figures show mean peri-ripple calcium and deconvolved traces, averaged across up- or down-modulated neurons for each mouse and then averaged across 11 mice.

Point 7) Fig. 3: The decomposition-based analysis of glutamate imaging using SVD needs to be improved. First, it is not clear how much of the variance is captured by each component, and it seems like no attempt has been made to determine the number of significant components or to use a cross-validated approach. Second, the authors imply that reconstructing the glutamate imaging data using the 2nd-100th components 'matches' the voltage signal but this statement holds true only in the case of the aRSC and not for other regions, without providing an explanation, raising questions as to whether this similarity is genuine or merely incidental.

The first 100 components explained about %99.9 of the variance in the concatenated stack of peri-ripple neocortical glutamate activity for each animal which is practically equivalent to the entire variance in the data. Our goal was not to obtain a low-rank approximation of the data for which the number of significant components had to be determined. Instead, we decomposed the data into the activity along the first principal component for which there was no noticeable topography among neocortical regions and the activity along the rest of the components for which there was a noticeable topography among neocortical regions. The first component explained %83.11 ± 6.75 (mean ± std across 4 iGluSnFR Ras mice) and %83.3 ± 5.07 (mean ± std across 4 iGluSnFR EMX mice) of variance in the concatenated stack of peri-ripple neocortical glutamate activity.

As we discussed in the discussion section of the manuscript, SVD is agnostic about brain mechanisms and only cares about capturing maximum variance. Specifically, it is not designed to capture the maximum similarity between glutamate and voltage activity in the brain. Therefore, the only thing we can say with certainty comes as follows: when the activity along the axis with maximum co-variability (1st principal component) across the neocortical regions’ glutamate activity is removed, only aRSC, and no other regions, show a post-ripple down-modulation, whose timing matches that of aRSC post-ripple voltage down-modulation. Moreover, the timing of activity of 1st principal component matches better with that of calcium activity among the up-modulated portion of aRSC neurons. Even though the genuineness of these results is not guaranteed, the similarity between the timing of SVD output in aRSC glutamatergic activity with that in two independently collected signals in aRSC, i.e. voltage and calcium, could support the idea that peri-ripple aRSC glutamatergic activity is likely a mixture of up- and down-modulated components.

Point 8) The estimation of deep pyramidal cells' glutamate activity by subtracting the Ras group (Fig. 4d) is not very convincing. First, the efficiency of transgene expression can vary substantially across different mouse lines. Second, it is not clear to what extent the wide field signal reflects deep cells' somatic vs. dendritic activity due to non-linear scattering (Ma et al., 2016), and it is questionable whether a simple linear subtraction is appropriate. The quality of the manuscript would improve substantially if the authors probe this question directly, either by using deep layer specific line/ 2-P imaging of deep cells or employing available public datasets.

Simulation studies have suggested that the signal, captured by wide-field imaging of voltage-sensitive dye, can be modeled as a weighted sum of voltage activity across neocortical layers (Chemla and Chavane, 2010; Newton et al., 2021). Hence, modeling the glutamate signal as a weighted sum of the glutamate activity across neocortical layers is a good starting point. Future studies would be needed to improve this starting point by imaging glutamate activity in a cohort of mice with iGluSnFR expression in only deep layers’ neurons. Moreover, Ma et al. (Ma et al. 2016) stated that “This means that signal detected at the cortical surface (in the form of a two-dimensional image) represents a superficially weighted sum of signals from shallow and deeper layers of the cortex”.

Reviewer #2 (Public Review):

Point 1) The authors throughout the manuscript compare the correlation between hippocampal MUA and the imaged cortical ensemble activity (Example: Lines 120-122). There is a potential time lag in signal detection with regard to the two detection methods. While the time lag using electrophysiological recording is at the scale of milliseconds, the glutamate-sensitive imaging might take several 100s of ms to be detected. It is not clear in the manuscript how the authors considered this problem during the analysis.

The ensemble-wise correlation analysis characterizes the relationship between two random processes, peri-ripple HPC MUA and peri-ripple neocortical activity (please see the response to reviewer 1’s major point 5). Although it is a valid point that the temporal resolution of the two signals is not the same which could introduce an error in the exact timing of the relationship between the two processes, we did not draw any conclusion based on the exact timing of the elevated correlation between the two processes. Moreover, we smoothed (equivalent to low-pass filtering) and down-sampled the MUA signal (please see the methods section) to bring the temporal scale of the two processes closer to each other. We also want to clarify that the temporal resolution of voltage and glutamate imaging is in the range of 10s of ms (Xie et al., 2016).

Point 2) In the results section "The peri-ripple glutamatergic activity is layer dependent", are the Ras and EMX expressed in two different experimental animal groups? If yes, and there was a time lag between the two groups, is it valid to estimate the deeper layer activity using a scaled version of the Ras from the EMX signal?

This comment is addressed in response to reviewer 1’s major point 8.

Point 3) The authors did not discuss the results adequately in the discussion section. Since there is no behavioral paradigm and no behavioral read-out to induce or correlate it with possible planning and future decision-making process, the significance of the paper will be enhanced by discussing the possible underlying circuitry mechanism that might cause the reported observations. With no planning periods in the task (instead just sitting on a platform), it is actually quite unclear what the purpose of wake ripples should be. For example, the authors discuss the superficial and deep layer responses and their relation to the memory index theory. However, the RSC possesses different groups of excitable neurons in different layers. Specifically, three excitable neurons are found within the different layers of the RSC; the intrinsically bursting neurons (IB), regular spiking (RS), and low-rheobase (LR) neurons. These neurons are distributed heterogeneously within the RSC cortical layer. Although the RS are abundant in the deeper layers of the RSC, they occupy 40% of the total amount of excitable neurons found in layers II/III. On the other hand, the LR is the dominant excitable neuron in the superficial layers. It will add to the significance of the work if the authors discussed the results in the context of the cellular structure of the RSC and how would that impact the observed inhibition in the peri-ripple time window. It would be helpful for the readers and the reviewers to add a schematic diagram to the discussion section.

The goal of our study was to characterize the patterns of neocortical activity around hippocampal ripples in the awake state and not shed light on the function (purpose) of awake ripples. However, we speculated about what our results could mean in the discussion section. To address the reviewer’s comment on the differences across RSC layers, the following paragraph was added to the discussion section lines 342-353.

“Our results suggest that dendrites of deep pyramidal neurons, arborized in the superficial layers of the neocortex, receive glutamatergic modulation earlier than those of the superficial ones. However, the results do not provide a mechanistic explanation of the phenomenon. It is possible that the observed layer-dependency of the glutamatergic modulation would partially result from the heterogeneity of the excitatory as well as inhibitory neurons across aRSC layers. But, the question is how this heterogeneity may lead to the above-mentioned layer-dependency to which our data does not provide an answer. It could be speculated that the difference in the dendritic morphology and firing type of different types of RSC excitatory neurons (Yousuf et al., 2020) or the difference in connectivity of different RSC layers with other brain regions would play a role (Sugar et al., 2011; van Groen and Wyss, 1992; Whitesell et al., 2021). This is a complicated problem and could only be resolved by conducting experiments specifically designed to address this problem.”

Point 4. A general issue (in addition to the missing behaviour), is the mix of the methods. On one side this makes the article very interesting since it highlights that with different methods you actually observe different things. But on the other side, it makes it very difficult to follow the results. It would be a major improvement of the article if the authors could include (as mentioned above) a schematic of the results and their theory, especially highlighting how the different methods would capture different parts of the mechanism. Finally, the authors should not use calcium signals as a direct measure of neuronal firing. Calcium influx is only seen in bursts of firing, not with individual spikes. It is a plasticity signal and therefore should be treated and discussed as such. Just recently it was shown by Adamantidis lab that the calcium signal changes between wake and sleep and this change does not parallel changes in neuronal firing/spikes.

We agree with the reviewer that the calcium signal is biased toward burst of spikes (Huang et al., 2021). To address this concern, the term “spiking activity” was replaced with “calcium activity” throughout the manuscript. Moreover, the calcium signal was deconvoled to get a better estimate of the spiking activity (please refer to our response to the reviewer 1’s point 6).

Point 5. In the discussion section, the authors focus their discussion on the connectivity between the CA1 area and the RSC. Although it is an important point, since the authors are examining the peri-ripple cortical dynamics, it is critical to discuss other possible connectivity effects. Furthermore, the hippocampal input preferentially targets the granular RSC, how would that impact the results and the interpretation of the authors? Additionally, a previous study reported the suppression of the thalamic activity during hippocampal ripples (Yang et al., 2019). Importantly, the thalamic inputs to the RSC target the superficial layers. It will add to the value of the paper if the authors expanded the discussion section and elaborated further on the possible interpretation of the results.

At the time of our initial submission, pre-ripple reduction and post-ripple elevation of calcium activity in a portion of three subclasses of the superficial aRSC inhibitory neurons were reported (Chambers et al., 2022, 2021), and it was the basis of our speculation on the potential involvement of feed-forward inhibition in the post-ripple voltage reduction. We speculated that the source of this potential feed-forward inhibition could stem from gRSC excitatory neurons or other neocortical or subcortical regions projecting to aRSC (please see the discussion section). However, the source being from the thalamus is less likely because multiple studies have observed the suppression of the majority of thalamic neurons during awake ripples (Logothetis et al., 2012; Nitzan et al., 2022; Yang et al., 2019). Moreover, peri-awake-ripple suppression of thalamic axons projecting to the first layer of aRSC is reported (Chambers et al., 2022). On the other hand, it is also possible that feedback inhibition would be involved where the excitatory aRSC neurons that are excited by gRSC (as reviewer 1 pointed out) or any other region, including aRSC itself, excite aRSC inhibitory neurons which in turn inhibit pyramidal cells. To address this comment, the following paragraph was added to the discussion section in lines 323-328.

“Thalamus is another source of axonal projections to aRSC (Van Groen and Wyss, 1992). However, it is less likely that thalamic projections contribute to the peri-awake-ripple aRSC activity modulation because multiple studies have observed the suppression of the majority of thalamic neurons during awake ripples (Logothetis et al., 2012; Nitzan et al., 2022; Yang et al., 2019). Moreover, peri-awake-ripple suppression of thalamic axons projecting to the first layer of aRSC is reported (Chambers et al., 2022).”

Author reponse

Reviewer #1 (Public Review):

In their paper, Kroell and Rolfs use a set of sophisticated psychophysical experiments in visually-intact observers, to show that visual processing at the fovea within the 250ms or so before saccading to a peripheral target containing orientation information, is influenced by orientation signals at the target. Their approach straddles the boundary between enforcing fixation throughout stimulus presentation (a standard in the field) and leaving it totally unconstrained. As such, they move the field of saccade pre-processing towards active vision in order to answer key questions about whether the fovea predicts features at the gaze target, over what time frame, with what precision, and over what spatial extent around the foveal center. The results support the notion that there is feature-selective enhancement centered on the center of gaze, rather than on the predictively remapped location of the target. The results further show that this enhancement extends about 3 deg radially from the foveal center and that it starts ~ 200ms or so before saccade onset. They also show that this enhancement is reinforced if the target remains present throughout the saccade. The hypothesized implications of these findings are that they could enable continuity of perception trans-saccadically and potentially, improve post-saccadic gaze correction.

Strengths:

The findings appear solid and backed up by converging evidence from several experimental manipulations. These included several approaches to overcome current methodological constraints to the critical examination of foveal processing while being careful not to interfere with saccade planning and performance. The authors examined the spatial frequency characteristics of the foveal enhancement relative, hit rates and false alarm rates for detecting a foveal probe that was congruent or incongruent in terms of orientation to the peripheral saccade target embedded in flickering, dynamic noise (i/f )images. While hit rates are relatively easy to interpret, the authors also reconstructed key features of the background noise to interpret false alarms as reflecting foveal enhancement that could be correlated with target orientation signals. The study also - in an extensive Supplementary Materials section - uses appropriate statistical analyses and controls for multiple factors impacting experimental/stimulus design and analysis. The approach, as well as the level of care towards experimental details provided in this manuscript, should prove welcome and useful for any other investigators interested in the questions posed.

Weaknesses:

I find no major weaknesses in the experiments, analyses or interpretations. The conclusions of the paper appear well supported by the data. My main suggestion would be to see a clearer discussion of the implications of the present findings for truly naturalistic, visually-guided performance and action. Please consider the implication of the phenomena and behaviors reported here when what is located at the gaze center (while peripheral targets are present), is not a noisy, relatively feature-poor, low-saliency background, but another high-saliency target, likely crowded by other nearby targets. As such, a key question that emerges and should be addressed in the Discussion at least is whether the fovea's role described in the present experiments is restricted to visual scenarios used here, or whether they generalize to the rather different visual environments of everyday life.

This is a very interesting question. While we cannot provide a definite answer, we have added a paragraph discussing the role of foveal prediction in more naturalistic visual contexts to the Discussion section (‘Does foveal prediction transfer to other visual features and complex natural environments?’). We pasted this paragraph in response to another comment in the ‘Recommendations for the authors’ section below. We suggest that “the pre-saccadic decrease in foveal sensitivity demonstrated previously[9] as well as in our own data (Figure 2B) may boost the relative strength of fed-back signals by reducing the conspicuity of foveal feedforward input”, presumably allowing the foveal prediction mechanism to generalize to more naturalistic environments with salient foveal stimulation.

Reviewer #2 (Public Review):

Human and primates move their eyes with rapid saccades to reposition the high-resolution region of the retina, the fovea, over objects of interest. Thus, each saccade involves moving the fovea from a pre-saccadic location to a saccade target. Although it has been long known that saccades profoundly alter visual processing at the time of saccade, scientists simply do not know how the brain combines information across saccades to support our normal perceptual experience. This paper addresses a piece of that puzzle by examining how eye movements affect processing at the fovea before it moves. Using a dynamic noise background and a dual psychophysical task, the authors probe both the performance and selectivity of visual processing for orientation at the fovea in the few hundred milliseconds preceding a saccade. They find that hit rates and false alarm rates are dynamically and automatically modulated by the saccade planning. By taking advantage of the specific sequence of noise shown on each trial, they demonstrate that the tuning of foveal processing is affected by the orientation of the saccade target suggesting foveal specific feedback.

A major strength of the paper is the experimental design. The use of dynamic filtered noise to probe perceptual processing is a clever way of measuring the dynamics of selectivity at the fovea during saccade preparation. The use of a dual-task allows the authors to evaluate the tuning of foveal processing as well and how it depends on the peripheral target orientation. They show compellingly that the orientation of the saccade target (the future location of the fovea) affects processing at the fovea before it moves.

There are two weaknesses with the paper in its current form. The first is that the key claim of foveal "enhancement" relies on the tuning of the false alarms. A more standard measure of enhancement would be to look at the sensitivity, or d-prime, of the performance on the task. In this study, hits and false alarms increase together, which is traditionally interpreted as a criterion shift and not an enhancement. However, because of the external noise, false alarms are driven by real signals. The authors are aware of this and argue that the fact that the false alarms are tuned indicates enhancement. But it is unclear to me that a criterion shift wouldn't also explain this tuning and the change in the noise images. For example, in a task with 4 alternative choices (Present/Congruent, Present/Incongruent, Absent/Congruent, Absent/Incongruent), shifting the criterion towards the congruent target would increase hits and false alarms for that target and still result in a tuned template (because that template is presumably what drove the decision variable that the adjusted criterion operates on). I believe this weakness could be addressed with a computational model that shows that a criterion shift on the output of a tuned template cannot produce the pattern of hits and false alarms.

We thank the reviewer for this comment. We will present three arguments, each of which suggests that our effects are perceptual in nature and cannot be explained by a shift in decision criterion: (1) the temporal specificity of the difference in Hit Rates (HRs), (2) the spatial specificity of the difference in HRs and (3) the phenomenological quality of the foveally predicted signal. In general, a criterion shift would indeed affect hits and false alarms alike. Nonetheless, the difference in HRs only manifested under specific and meaningful conditions:

First, the increase in congruent as compared to incongruent HRs, i.e., enhancement, was temporally specific: congruent and incongruent HRs were virtually identical when the probe appeared in a baseline time bin or one (Figure 2B) or even two (Figure 4A) early pre-saccadic time bins. Based on another reviewer’s comment, we collected additional data to measure the time course and extent of foveal enhancement during fixation. While pre-saccadic enhancement developed rapidly, enhancement started to emerge 200 ms after target onset during fixation. Crucially, these time courses mirror the typical temporal development of visual sensitivity during pre-saccadic attention shifts and covert attentional allocation, respectively[8,33]. We are unaware of data demonstrating similar temporal specificity for a shift in decision criterion. One could argue that a template of the target orientation needs to build up before it can influence criterion. Nonetheless, this template would be expected to remain effective after this initial temporal threshold has been crossed. In contrast, we observe pronounced enhancement in medium but not late stages of saccade preparation in the PRE-only condition (Figure 4A).

Second, it has been argued that a defining difference between innately perceptual effects and post-perceptual criterion shifts is their spatial specificity[53]: in opposition to perceptual effects, criterion shifts should manifest in a spatially global fashion. Due to a parafoveal control condition detailed in our reply to the next comment, we maintain the claim that enhancement is spatially specific: congruent HRs exceeded incongruent ones within a confined spatial region around the center of gaze. We did not observe enhancement for probes presented at 3 dva eccentricity even when we raised parafoveal performance to a foveal level by adaptively increasing probe contrast. The accuracy of saccade landing or, more specifically, the mean remapped target location (Figure 3B) influenced the spatial extent of the enhanced region in a fashion that is reconcilable with previous findings[30]. A criterion shift that is both spatially and temporally selective, follows the time course of pre-saccadic or covert attention depending on observers’ oculomotor behavior, does not remain effective throughout the entire trial after its onset, is sensitive to the mean remapped target location across trials, and does not apply to parafoveal probes even after their contrast has been increased to match foveal performance, would be unprecedented in the literature and, even if existent, appear just as functionally meaningful as sensitivity changes occurring under the same conditions.

Lastly and on a more informal note, we would like to describe a phenomenological percept that was spontaneously reported by 6 out of 7 observers in Experiment 1 and experienced by the author L.M.K. many times. On a small subset of trials, participants in our paradigms have the strong phenomenological impression of perceiving the target in the pre-saccadic center of gaze. This percept is rare but so pronounced that some observers interrupt the experiment to ask which probe orientation they should report if they had perceived two on the same trial (“The orientation of the normal probe or of the one that looked exactly like the target”). Interestingly, the actual saccade target and its foveal equivalent are perceived simultaneously in two spatiotopically separate locations, suggesting that this percept cannot be ascribed to a temporal misjudgment of saccade execution (after which the target would have actually been foveated). We have no data to prove this observation but nonetheless wanted to share it. Experiencing it ourselves has left us with no doubt that the fed-back signal is truly – and almost eerily – perceptual in nature.

The analysis suggested by the reviewer is very interesting. Yet for several reasons stated in the ‘Suggestions to the authors’ section, our dataset is not cut out for an analysis of noise properties at this level of complexity. We had always planned to resolve these concerns experimentally, i.e., by demonstrating specificity in HRs. We believe that our arguments above provide a strong case for a perceptual phenomenon and have incorporated them into the Discussion of our revised manuscript.

The second weakness is that the author's claim that feedback is spatially selective to the fovea is confounded by the fact that acuity and contrast sensitivity are higher in the fovea. Therefore, the subject's performance would already be spatially tuned. Even the very central degree, the foveola, is inhomogeneous. Thus, finding spatially-tuned sensitivity to the probes may simply indicate global feature gain on top of already spatially tuned processing in the fovea. Another possible explanation that is consistent with the "no enhancement" interpretation is that the fovea has increased. This is consistent with the observation that the congruency effects were aligned to the center of gaze and not the saccade endpoint. It looks from the Gaussian fits that a single gain parameter would explain the difference in the shape of the congruent and incongruent hit rates, but I could not figure out if this was explicitly tested from the existing methods. Additional experiments without prepared saccades would be an easy way to address this issue. Is the hit rate tuned when there is no saccade preparation? If so, it seems likely that the spatial selectivity is not tuned feedback, but inhomogeneous feedforward processing.

We fully agree. We do not consider a fixation condition diagnostic to resolve this question since, as of now, correlates of foveal feedback have exclusively been observed during fixation. In those studies, it was suggested that the effect, i.e., a foveal representation of peripheral stimuli, reflects the automatic preparation of an eye movement that was simply not executed[11,12,14]. To address another reviewer’s comment, we collected additional data in a fixation experiment. The probe stimulus could exclusively appear in the screen center (as in Experiment 1) and observers maintained fixation throughout the trial. While pre-saccadic congruency effects were significantly more pronounced and developed faster, congruency effects did emerge during fixation when the probe appeared 200 ms after the target. If pre-saccadic processes indeed spill over to fixation tasks to some extent and trigger relevant neural mechanisms even when no saccade is executed, we could expect a similar feedback-induced spatial profile during fixation. Since this matches the reviewer’s prediction if the pre-saccadic profiles resulted from inhomogeneous feedforward processing, we do not consider a fixation condition suitable to distinguish between both hypotheses.

To test whether the tuning of enhancement is effectively a consequence of declining visual performance in the parafovea/periphery, we instead raised parafoveal performance to a foveal level by adaptively increasing the opacity of the probe: while leaving all remaining experimental parameters unchanged, we presented the probe in one of two parafoveal locations, i.e., 3 dva to the left or right of the screen center. Observers were explicitly informed about the placement of the probe. We administered a staircase procedure to determine the probe opacity at which performance for parafoveal target-incongruent probes would be just as high as foveal performance had been in the preceding sessions. While the foveal probe was presented at a median opacity of 28.3±7.6%, a parafoveal opacity of 39.0±11.1% was required to achieve the same performance level. As a result, the gray dot at 0 dva in the figure below represents the incongruent HR in the center of gaze and ranges at 80% on the y-axis. The gray dots at ±3 dva represent incongruent parafoveal HRs and also range at ~80% on the y-axis. Using the reviewer’s terminology, we effectively removed the influence of acuity- (or contrast-sensitivity-) dependent spatial tuning. If the spatial profiles had indeed been the result of “global feature gain on top of already spatially tuned processing“, this manipulation should render parafoveal feature gain just as detectable as foveal feature gain. Instead, congruent and incongruent parafoveal HRs were statistically indistinguishable (away from the saccade target: p = .127, BF10 = 0.531; towards the saccade target: p = .336, BF10 = 0.352), inconsistent with the idea of a spatially global feature gain.

We had included these data in our initial submission. They were collected in the same observers that contributed the spatial profiles (Experiment 2). The data points at 0 dva in the reduced figure above correspond to the foveal probe location in Figure 2D. The data points at ±3 dva had been plotted and discussed in our initial submission, yet only very briefly. Based on this and another reviewer’s comment, we realize that we should have explained this condition more extensively in the main text rather than in the Methods and have added a dedicated paragraph to the Results section.

This paper is important because it compellingly demonstrates that visual processing in the fovea anticipates what is coming once the eyes move. The exact form of the modulation remains unclear and the authors could do more to support their interpretations. However, understanding this type of active and predictive processing is a part of the puzzle of how sensory systems work in concert with motor behavior to serve the goals of the organism.

Reviewer #3 (Public Review):

This manuscript examines one important and at the same time little investigated question in vision science: what happens to the processing of the foveal input right before the onset of a saccade. This is clearly something of relevance as humans perform saccades about 3 times every second. Whereas what happens to visual perception in the visual periphery at the saccade goal is well characterized, little is known about what happens at the very center of gaze, which represents the future retinal location where the saccade target will be viewed at high resolution upon landing. To address this problem the authors implemented an elegant experiment in which they probed foveal vision at different times before the onset of the saccade by using a target, with the same or different orientation with respect to the stimulus at the saccade goal, embedded in dynamic noise. The authors show that foveal processing of the saccade target is initiated before saccade execution resulting in the visual system being more sensitive to foveal stimuli which features match with those of the stimuli at the saccades goal. According to the authors, this process enables a smooth transition of visual perception before and after the saccade. The experiment is well designed and the results are solid, overall I think this work represents a valuable contribution to the field and its results have important implications. My comments below:

1. The change in the overall performance between the baseline condition and when the probe is presented after the saccade target is large, but I wonder if there are other unrelated factors that contribute to this difference, for example, simply presenting the probe after vs before the onset of a peripheral stimulus, or the fact that in the baseline the probe is presented right after a fixation marker, but in the other condition there was a longer time interval between the presentation of the marker and the probe transient. The authors should discuss how these confounding factors have been accounted for.

We thank the reviewer for this helpful comment. We would like to clarify that the probe was never presented right after the fixation dot. In the baseline condition, fixation dot and target were separated by 50 ms, i.e., the duration of one noise image. Since the fixation dot was an order of magnitude smaller than the probe (0.3 vs 3 dva in diameter) and since two large-field visual transients caused by the onset of a new background noise image occurred between fixation dot disappearance and probe appearance, we consider it unlikely that the performance difference was caused by any kind of stimulus interaction such as masking. Nonetheless, we had been puzzled by this difference already when inspecting preliminary results and wondered if it may reflect observers’ temporal expectations about the trial sequence. We therefore explicitly instructed and repeatedly reminded observers that the probe could appear before the peripheral target. Since the difference persisted, we ascribed it to a predictive remapping of attention to the fovea during saccade preparation, as we had stated in the Discussion.

Another contributing factor may be that observers approached the oculomotor and perceptual detection tasks sequentially. In early trial phases, they may have prioritized localizing the target and programming the eye movement. After motor planning had been initiated, resources may have been freed up for the foveal detection task. Since on the majority of probe-present trials, the probe appeared after the saccade target, this strategy would have been mostly adaptive. Crucially, however, observers yielded similar incongruent Hit Rates in the baseline and last pre-saccadic time bin (70% vs 74%). While we observed pronounced enhancement in the last pre-saccadic bin, congruent and incongruent Hit Rates in the baseline bin were virtually identical. We therefore conclude that lower overall performance in the baseline bin did not prevent congruency effects from occurring. Instead, congruency effects started developing only after target appearance. We have added this potential explanation to the Results.

1. Somewhat related to point 3, the authors conclude that the effects reported here are the result of saccade preparation/execution, however, a control condition in which the saccade is not performed is missing. This leaves me wondering whether the effect is only present during saccade preparation or if it may also be present to some extent or to its full extent when covert attention is engaged, i.e when subjects perform the same task without making a saccade.

Foveal feedback has, as of now, exclusively been demonstrated during fixation (see references in Introduction and Discussion). In most of these studies, it was suggested that these effects (i.e., the foveal representation of a peripheral stimulus) may reflect the automatic preparation of an eye movement that was simply not executed[11,12,14]. Since foveal feedback has been demonstrated during fixation, and since eye movement preparation may influence foveal processing even when the eyes remain stationary, we considered it likely that congruency effects would emerge during fixation. Nonetheless, we agree with the reviewer that an explicit comparison between saccade preparation and fixation would enrich our data set and allow for stronger conclusions. We therefore collected additional data from seven observers. While all remaining experimental parameters were identical to Experiment 1, observers maintained fixation throughout each trial. We found that pre-saccadic foveal enhancement was more pronounced and emerged earlier than foveal enhancement during fixation. We present these data in the Results section (Figure 5) and have updated the Methods section to incorporate this additional experiment. We have furthermore added a paragraph to the Discussion which addresses potential mechanisms of foveal enhancement during fixation and saccade preparation.

Furthermore, the reviewer’s comment helped us realize that we never stated a crucial part of our motivation explicitly. We now do so in the Introduction:

“Despite the theoretical usefulness of such a mechanism, there are reasons to assume that foveal feedback may break down while an eye movement is prepared to a different visual field location. First and foremost, saccade preparation is accompanied with an obligatory shift of attention to the saccade target[6-8] which in turn has been shown to decrease foveal sensitivity[9]. Moreover, the execution of a rapid eye movement induces brief motion signals on the retina[20] which may mask or in other ways interfere with the pre-saccadic prediction signal. On a more conceptual level, the recruitment of foveal processing as an ‘active blackboard’[21] may become obsolete in the face of an imminent foveation of relevant peripheral stimuli – unless, of course, foveal processing serves the establishment of trans-saccadic visual continuity.”

We believe that the additional data and the revisions to the Introduction and Discussion have strengthened our manuscript and thank the reviewer for this comment.

1. Differently from other tasks addressing pre-saccadic perception in the literature here subjects do not have to discriminate the peripheral stimulus at the saccade goal, and most processing resources are presumably focused at the foveal location. Could this have influenced the results reported here?

This is true. We intentionally made the features of the peripheral target as task-irrelevant as possible, contrary to previous investigations. We wanted to ensure that the enhancement we find would be automatic and not induced by a peripheral discrimination task, as we state in the Discussion and the Methods. We agree that the foveal detection task likely focused processing resources on the center of gaze in Experiment 1. In Experiment 2, however, we measured the spatial profile of enhancement which involved two different conditions:

1. In each observer’s first six sessions, the probe could be presented anywhere on a horizontal axis of 9 dva length. On a given trial, an observer could not predict where it would appear, and therefore could not strategically allocate their attention. Nonetheless, enhancement of target-congruent orientation information was tuned to the fovea.
2. In the final, seventh session, the probe appeared exclusively in one of two possible peripheral locations: 3 dva to the left or 3 dva to the right of the screen center. Observers were explicitly informed that the probe would never appear foveally, and processing resources should therefore have been allocated to the peripheral probe locations. The general performance level in this condition was comparable to performance in the fovea (see reply to the next comment). Nonetheless, we did not find peripheral enhancement of target-congruent information.

Importantly, the magnitude of the foveal congruency effect in the PRE-only condition of Experiment 1 (i.e., when the target disappeared before the eyes landed on it) was comparable to the foveal congruency effect in Experiment 2 (PRE-only throughout), suggesting that the format of the task – i.e., purely foveal detection or foveal and peripheral detection – did not alter our findings.

1. The spatial profile of the enhancement is very interesting and it clearly shows that the enhancement is limited to a central region. To which extent this profile is influenced by the fact that the probe was presented at larger eccentricities and therefore was less visible at 4.5 deg than it was at 0 deg? According to the caption, when the probe was presented more eccentrically the performance was raised to a foveal level by adaptively increasing probe transparency. This is not clear, was this done separately based on performance at baseline? Does this mean that the contrast of the stimulus was different for the points at +- 3 dva but the performance was comparable at baseline? Please explain.

Based on the previous comment and comments of Reviewer #2, we realize that we should have explained this condition more extensively in the main text rather than in the Methods and have adapted the manuscript accordingly. As stated in our reply to the previous comment, Experiment 2 involved one session in which we addressed whether the lack of parafoveal/peripheral enhancement could be due to a simple decrease in acuity as mentioned by the reviewer. Observers were explicitly informed that the to-be detected stimulus (the probe) would appear either 3 dva to the left or right but never in the screen center and were shown slowed-down example trials for illustration. Observers then performed a staircase procedure which was targeted at determining the probe contrast at which performance for parafoveal target-incongruent probes would be just as high as foveal performance for target-incongruent probes had been in the previous six sessions. While the foveal probe was presented at a median opacity of 28.3±7.6%, an opacity of 39.0±11.1% was required to achieve the same performance level at a 3 dva eccentricity. Therefore, the gray curve in Figure 2D that represents incongruent Hits reaches its peak just under 80% on the y-axis. The gray dots at ±3 dva also range at ~80% on the y-axis. The performance level for target-incongruent probes (‘baseline’ here) in the parafovea is thus equal to foveal performance for target-incongruent probes. Target-congruent parafoveal feature information had the same “chance” to be enhanced as foveal information in the preceding sessions. Despite an equation of performance, we found no parafoveal enhancement. This suggests that enhancement is a true consequence of visual field location and not simply mediated by visual acuity at that location.

1. The enhancement is significant within a region of 6.4 dva around the center of gaze. This is a rather large region, especially considering that it extends also in the direction opposite to the saccade. I was expecting the enhancement to be more confined to the central foveal region. Was the effect shown in Figure 2D influenced by the fact that saccades in this task were characterized by a large undershoot (Fig 1 D)? Did the effect change if only saccades landing closer to the target were included in the analysis? There may not be enough data for resolving the time course, but maybe there are differences in the size of the main effect.

Width of the profile: In general, the width of the enhancement profile is likely to be influenced by two experimental/analysis choices: the size of the probe stimulus presented during the experiment and the width of the moving window combining adjacent probe locations for analysis.

Probe size: Since the probe itself had a comparably large diameter of 3 dva, even the leftmost significant point at -2.6 dva could be explained by an enhancement of the foveal portion of the probe. We had mentioned this briefly in the Discussion but realize that this point is crucial and should be made more explicit. Moving window width: We designed the experiment with the intention to densely sample a range of spatial locations during data collection and combine a certain number of adjacent locations using a moving window during analysis (see preregistration: https://osf.io/6s24m). To ensure the reliability of every data point, the width of this window was chosen based on how many trials were lost during preprocessing. We chose a window width of 7 locations as this ensured that each data point contained at least 30 trials on an individual-observer level. Nonetheless, the width of the resulting enhancement profile depends on the width of the moving window:

We added these caveats to the Results section and incorporated the figure above into the Supplements. We now state explicitly that…

“the main conclusions that can be drawn are that enhancement i) peaks in the center of gaze, ii) is not uniform throughout the tested spatial range as, for instance, global feature-based attention would predict, and iii) is asymmetrical, extending further towards the saccade target than away from it.”

For the above reasons, the absolute width of the profile should be interpreted with caution.

Saccadic landing accuracy: To address the reviewer’s question, we inspected the spatial enhancement profile separately for trials in which the saccade landed on the target (i.e., within a radius of 1.5 dva from its center) or off-target but still within the accepted landing area. This trial separation criterion, besides appearing meaningful, ensured that all observers contributed trials to every data point. We had never resolved the time course in this experiment and could therefore not collapse across time points as suggested by the reviewer. To increase the number of trials per data point, we instead increased the width of the moving window sliding across locations from 6 to 9 neighboring locations (but see caveat above).

Considering only saccades that landed on the target (‘accurate’; A) yielded significant enhancement from -2.6 to 2.1 dva and from 3.2 dva throughout the measured range towards the saccade target. Saccades that landed off-target (‘inaccurate’; B) showed a more pronounced asymmetry. When only considering inaccurate saccades, enhancement reached significance between -1.1 and 4.4 dva.

The increased asymmetry for inaccurate saccades may be related to predictive remapping: since inaccurate saccades were hypometric on average, the predictively remapped location of the target was shifted towards the target by the magnitude of the undershoot. Asymmetric enhancement would therefore have boosted congruency at the remapped target location across all trials. In consequence, we inspected if aligning probe locations to the remapped target location on an individual-trial level would lead to a narrower profile for inaccurate saccades. This was not the case. Instead, we observed two parafoveal maxima (C). Their position on the x-axis equals the mean remapping-dependent leftwards (2.0 dva) and rightwards (1.9 dva) displacement across trials. In other words, they correspond to the pre-saccadic center of gaze. Note that these profiles could not be fitted with a mixture of Gaussians and were fitted using polynomials instead.  

In sum, while we do not observe a clear narrowing of the enhancement profile for accurate saccades, the profile’s asymmetry is more pronounced for inaccurate eye movements. An increase in asymmetry could bear functional advantages since it would boost congruency at the remapped target location across all trials. Importantly though, this adjustment seems to rely on an estimate of average rather than single-trial saccade characteristics: aligning probe locations to the remapped attentional locus on an individual trial level provides further evidence that, irrespective of individual saccade endpoints, enhancement was aligned to the fovea. We have added these analyses to the Results section (Figure 3). We have also added the remapped profiles for all saccades and accurate saccades only to the Supplements.

1. Is the size of the enhanced region around the center of gaze related to the precision of saccades? Presumably, if saccades are less precise a larger enhanced area may be more beneficial.

This is a very interesting point. To address this question, we estimated each observer’s saccadic precision by computing bivariate kernel densities from their saccade landing coordinates. As we measured the horizontal extent of enhancement in our experiment, we defined the horizontal bandwidth as an estimate of saccadic imprecision. To estimate the size of the enhanced region for each observer, we created 10,000 bootstrapping samples for each observer’s congruent and incongruent HRs (4 locations combined at each step) We then determined the difference between the bootstrapped congruent and incongruent HRs and defined significantly enhanced locations as all locations for which <= 5% of these differences fell below zero. We then defined the width of the enhancement profile as the maximum number of consecutive significant locations.

Instead of a positive correlation, we observed a negative correlation between the bandwidth of landing coordinates (i.e., saccadic imprecision) and the size of the enhanced window (r = -.56, p = .117). In other words, there was a non-significant tendency that the less precise an observer’s saccades, the narrower their estimated region of enhancement. We furthermore inspected the magnitude of enhancement per position within in the enhanced region. To do so, we computed the mean difference between congruent and incongruent HR across all positions in the enhanced region. The sizes of the orange circles in the figure above represent the resulting values (ranging from 2.9% to 13.3%). As saccadic precision decreases, the magnitude of enhancement per data point in the enhanced region tends to decrease as well. We therefore suggest that high saccadic precision is a sign of efficient oculomotor programming, which in turn allows peri-saccadic perceptual processes to operate more effectively. We added this analysis to the Supplements and refer to it in the Results section of the revised manuscript.

Author Response

Reviewer #1 (Public Review):

HCN channels are atypically opened by the downward movement of gating charges during hyperpolarisation and have such weak coupling between the VSD and pore domain, and in the absence of an open state structure, extracting mechanistic information has been difficult. This manuscript is a continuation of a previous study on HCN channel gating that revealed how hyperpolarisation causes a downward movement of the VSD's S4, with breakage into two helices. The authors explore gating motions and the coupling between VSD and the pore domain using atomistic simulations. This includes microsecond MD with and without very strong -1V applied potentials to try to drive VSD-TMD changes to open the channel. In the end, however, the authors used a biased simulation approach (adiabatic bias) to enforce conformational change from resting to an open homology model of HCN based on hERG/rEAG. This microsecond simulation followed three interaction distances that were suggested to change between resting and open states based on free MD. This simulation caused pore opening and allowed a description of changes that may occur during gating, including a competition of S5-S6 and S6-S6 contacts and lipid binding locations, which may suggest lipid-dependent function and explain an unexpected closed structure at 0mV in micelles. While I feel the manuscript is written for the HCN expert audience, the mechanistic information in terms of hyperpolarisation-induced voltage gating makes it of much interest. The manuscript is presented at a high level, though there are a couple of points to address, including reproducibility of simulations and potential for more relation to experimental findings.

We appreciate the comments, thank you, please find a detailed answer below.

The authors carried out 1μs-MD simulations of the resting, activated, and a Y289D mutant at 0 mV, and then tried to drive the conformational change with a very large -1V voltage (double that studied previously). In 1 us MD, is the membrane stable with such a big voltage, as it would likely not be experimentally? Even with a volt applied, there was incomplete activation of the voltage sensors, despite timescales approaching that of activation.

This reviewer is correct in cautioning against membrane rupturing effects in simulations with a voltage of this magnitude. We have indeed checked that the membrane and the protein remains intact under these conditions and can confirm that no poration occurs. As membrane poration is stochastic, it could indeed occur over microsecond timescales under 1V, but the probability remains low, and we were lucky to not face this situation herein. Note that whereas potentials of this magnitude could not be applied in experiments, they are relatively routinely used in MD simulations to speed up processes that are driven by changes in transmembrane potentials.

Interestingly, other work from our lab (Rems et al. Biophysical Journal 119 (1) 190-205 (2020)) has shown that HCN1 voltage sensor domains are less prone to poration than those from other voltage sensor domains, for reasons that remain to be determined.

Author Response Figure 1. Final snapshots from the simulations of the resting (blue), intermediate (yellow) and activated (red) states. The representation of the solvent (water+ions) in cyan showed no membrane poration at the end of the 1us simulations.

For the pulling/ driving simulations (adiabatic bias MD) to change suspected interaction distances (V390-I302, N300-W281, and D290-K412), it seems to be just 1 simulation, without reproducibility. One has to wonder, if the simulation was redone from a very different initial conformation, would the results be the same (in addition to the distances themselves that were enforced by the ABMD). Moreover, the authors had to model the open state, such that the results depend on a homology model based on other CNBD channels, hERG / rEAG. Although the model stayed open for a microsecond, what other measures of accuracy of the homology model are there, such as preserved distances according to mutants/double mutants?

The ABMD simulations were repeated, please refer to the response to essential revisions point 1 for details.

For reasons mentioned by the reviewer as well as a reconsideration of our strategy to model channel opening, we have decided to omit homology models from the revised version of the paper.

The authors find that activation involves hydrophobic forces that strengthen the intra-subunit S4/S5/S6 interface, as well as lipid headgroups that make contact with hydrophilic residues at this interface, with lipid tails also contributing to hydrophobic contacts. The authors see bending and rotation of the lower S4 and a displacement of S1 away from S4 that exposes the VSD-pore interface to lipids, with increased lipid contacts at S4 and S5 during activation. This indicates lipid tails may play a role in coupling in HCN1 and may explain the closed state micelle structure at 0mV. Two sites of lipid contact are identified, one engaging VSD residues and the other polar or charged residues on S5 and S6. No experiments are presented or proposed to test the predicted lipid sites. e.g. Mutation of key residues, such as the arginine and histidine seen binding lipid headgroups could be tested as proof of their involvement, or perhaps experiments with varied phosphate moieties? In the absence of new experiments, is there existing data that could help validate the findings?

We thank this reviewer for this comment. As noted in the response to essential revisions point 3, such experiments are challenging, and have not been reported so far in HCN channels. We do agree that aspects of the mechanism we propose remain hypothetical awaiting further work, but are happy to report that importance of lipid interactions with the crucial salt bridge pair mentioned in the response to essential revisions point 3 has been completely independently validated, thus strengthening our mechanistic hypothesis substantially.

During free MD simulation, the authors see tilting of S5 caused by activation of the Y289D mutation that brings D290 and K412 positions into proximity. How do we know that the adjacent mutant of Y289 to aspartate has not caused this, or was this interaction also seen in wild-type simulation? Fig.3c might suggest the wt activated simulation may see such an interaction, but it is unclear given the large C_alpha distances, as opposed to H-bonding distances.

Indeed, Figure 3 appears to indicate that this interaction between D290 and K412 is present in the activated state when the mutation is reverted to the WT sequence. We have recalculated the interaction propensity using all atoms of the residues and present an updated Figure 3c in response.

The authors predict that a D290-K412 salt bridge may be important for gating and sought to experimentally validate the interaction in the activated-open state using cysteine cross-bridging. As this is the only experimental backing in the paper, it is important to be able to judge its ability to report on the D290-K412 salt bridge. A comparison experiment demonstrating other crosslinks that do not favour the open state would have been helpful in this regard e.g. if crossbridging at similar locations (but not predicted to change interaction during gating) had little effect on I/Imax, then the result may be bolstered. Are there existing mutagenesis experiments that may suggest the importance of these residues (as well as for other key interaction distances identified)?

Negative results in cross bridging and cysteine accessibility studies in general are difficult to interpret as the lack of a cadmium-specific effect may be due to inaccessibility of the site to cadmium, pairwise distance too far to bridge by cadmium, or bridging or the specified site without a functional effect. However, as reviewer 2 pointed out below, the Yellen group has performed extensive cross bridging experiments in the S4-S5 to Clinker region in spHCN and in most of these positions, the pairs favoring the open state are closer together in our models than pairs favoring the closed state or those without functional effect. We have added Videos 1-6 to highlight this comparison on our open state models and describe in our updated discussion section.

Rotation of the V390 side chain from a position facing the pore lumen to a position facing I302 on S5 is coupled to an increase of the pore radius at V390, an increased hydration of the pore intracellular gate, and K+ ion movement. Perhaps 5 or 6 ions cross in that single simulation. As K channel ion permeation can depend critically on starting ion configs (as well as the model/force field), reproducibility of this finding is important but does not appear to have been tested. How can we be sure that periods of permeation or no permeation in individual simulations are reliable?

As mentioned in our response to essential revisions point 1, we have modified the collective variable set used in ABMD, and repeated the simulations in 4 replicates. Whereas the number of permeation events is low in each simulation (Figure 4 S1), the consistency across repeats indicates that these open pore models indeed represent conductive states. Given how short the simulations are, however, it appears unreasonable to infer conductance values from these observations.

Reviewer #3 (Public Review):

In this work, Elbahnsi and colleagues use enhanced sampling MD simulation, to recapitulate step by step, the electromechanical coupling between VSD and the pore in HCN1 channels. Building on the available cryoEM structures of HCN1 with the VSD in resting and active state, the authors characterize by MD a subset of interactions that seemingly stabilize the open channel. This subset is, in turn, used in enhanced-sampling simulations to guide channel opening. The main findings are that S4 movement induces a rearrangement of the hydrophobic interaction at the level of S1- S4- and S5 interfaces. Occupancy of lipids seems therefore statedependent and highlights their regulatory role in HCN gating.

The approach is rather innovative, and it apparently allows the reconstruction of the whole mechanism of gating, pushing the predictive power of MD simulation well beyond its actual temporal limitations. At the same time, the initial choice of interactions is crucial for this approach, because the result cannot differ from the inputs. And reading the paper it does not emerge clearly how the correctness of the reconstructed gating pathway can be verified, if not by functional validation.

We thank the reviewer for this thoughtful review. It has pushed us to reconsider our approach to enhance the sampling of channel activation and gating. Please refer to the detailed response below as well as the response in particular to essential revisions point 1.

Here are my comments on the main interactions that were used to feed the final MD simulation:

1) W281-N300: this interaction, previously identified and studied in SpH channels (Ramentol et al, 2020; Wu et al, 2021), has been elegantly confirmed in this paper. Its inclusion in the initial subset seems appropriate. In the other two cases, the choice of interactions requires further explanations and experimental validation.

2) D290 and K412: the validation of this interaction shown in Figure 3 and suppl Figure 1 is missing a control, i.e., the effect of the addition of Cd++ on the wt channel. Please add.

We have performed the control suggested. Please also refer to the answer to essential revisions point 2.

3) Modelling the open state of HCN1 pore (page 18), is done on the structure of the distantly related hERG rather than on the available open pore structure of HCN4. This choice is justified as follows by the authors:

a) "Available structures in the CNBD channel family for which representative structures have been solved in closed and open states".

b) "The structural mechanism of pore gating (i.e. the ⍺ to 𝜋 helix occurring at the glycine657 hinge in hERG) observed in rEAG/hERG may be a conserved gating transition in the CNBD family of channels"

I encourage the authors to consider the following:

a) The structure of hERG channel is not available in the closed/open configuration, indeed the comparison must be done with the closed configuration of the related channel rEAG. On the contrary, HCN4 is available in the closed/open configurations. Moreover, one of the open pore structures shows S4-S5-S6 in a very similar conformation to the lock open mutant (F186C/S264C) of HCN1 (Saponaro et al, 2021). With an available HCN4 open structure, forcing HCN1 to the open pore structure of hERG channel (which opens in depolarization and is not regulated by cAMP) seems not necessary.

In response to this point, we reconsidered our approach and chose to instead use a biasing distance that is consistently increased in CNBD channels of resolved structures, that between neighboring and cross-subunits V390. We have detailed our rationale in the response to essential revisions point 1.

To my knowledge, hERG is the only channel of the CNBD family for which the transition ⍺ to 𝜋 helix reported by the Authors, occurs in S6. It is not reported for other CNBD family members, in particular for the CNG channels mentioned by the Authors (Zheng et al., 2020; Xue et al., 2021, 2022). Task 4 (Zheng et al) does not show it. Its pore opens by a right-handed twist of S6 at glycine 399, a conserved glycine in all CNG. Human CNGA1 too, opens the pore by a rotational movement of S6 hinged at the equivalent glycine (glycine 385) (Xue et al, 2021). And the same occurs in the non-symmetrical channel CNGA1/B1 (Xue te al, 2022). So, it seems that CNG channels do not show the ⍺ to 𝜋 helix transition in the open pore. Moreover, hERG excluded, all other members of the CNBD family, CNG, EAG, and HCN4 included, do not bend at the hinge glycine 657 of hERG, but at another glycine (gly 648 in hERG numbering) located upstream. Further, their opening is due to a rotation of S6 associated with an outward movement, rather than to the lifting of the lower part of S6, as in hERG.

After considering this reviewer’s comment, we were surprised to see that HCN1 is apparently prone to secondary structure deformation in S6, even when biasing the aforementioned distances, and thus enforcing no rotation at all in S6. We are intrigued by this observation and eagerly await experimental validation or disproval.<br /> In the meantime, we have made clear in the text that this hypothesis remains based exclusively on modeling work.

4) V390-I302: this interaction is predicted to stabilize the open pore configuration and was included in the subset. The contact between V390 on S6 and I302 on S5 is observed in the homology model discussed above when the S6 is twisted at the glycine hinge, rotating the preceding residue (V390) out of its pore-lining position and is. Again, I can only disagree with this hypothesis because it has been experimentally demonstrated (Cheng et al, J Pharmacol Exp Ther. 2007 Sep;322(3):931-9) that the side chain of Valine390 is inside the cavity of the open pore of HCN1 channels as it controls the affinity for the pore blocker ZD7288.

In accordance with other comments above, we have eliminated the bias applied to the V390I302 distance. However, the new ABMD simulations with bias applied to encourage dilation at position 390 still involve rotation of V390 away from the central pore axis, albeit with bending of S6 at the upper glycine mentioned by this reviewer. The degree of rotation is lower than in our previous simulations so that V390 still lines the inner vestibule in the open state, consistent with the observation that this position influences the apparent affinity of open pore blockers.

In conclusion, modelling the open state pore of HCN1 on hERG rather than on that of HCN4 seems not justified based on accumulated evidence in the published literature. Therefore, the choice of the authors to use it as the open pore model of HCN1 channels needs to be experimentally validated. One possibility is to mutate the glycine hinge, gly391 in HCN1, into an Alanine in order to remove the flexible hinge. If this mutation alters pore gating, it will support the choice of the Authors.

Once more, we thank the reviewer for the comments, which have led us to reconsider a larg part of our modeling work.

Author Response

Reviewer #2 (Public Review):

There is emerging evidence that connexin43 hemichannels localized to mitochondria can influence their function. Here the authors demonstrated using an osteocyte cell model that connexin43 is localized to mitochondria and that this is enhanced in response to oxidative stress. Several lines of evidence were presented showing that mitochondrial connexin43 forms functional hemichannels and that connexin43 is required for optimal mitochondrial respiration and ATP generation. These aspects were major strengths of the study.

The authors also show that connexin43 is recruited to mitochondria in response to oxidant stress, as a cell protective mechanism. This was primarily done using hydrogen peroxide to generate oxidant stress; primary osteocytes from Csf-1+/- mice, which are prone to Nox4 induced oxidant stress, also show enhanced mitochondrial connexin43 when compared with wild type osteocytes.

Several approaches were used to demonstrate that connexin43 interacts with the ATP synthase subunit, ATP5J2, suggesting a direct role for connexin43 in the control of ATP synthesis by mediating mitochondrial ion homeostasis. Several experiments were done using a series of pHluorin fusion protein constructs as a proton sensor, these experiments hint at a potential role for connexin43 in regulating H+ permeability to support ATP production. However, the effects of inhibiting connexin43 on pH were modest, suggesting that additional roles for mitochondrial connexin43 in ATP generation should be considered.

Thank you for your positive and thoughtful comments. We agree that additional roles for mitochondrial Cx43 may be possible. As an example, we consider that there may be a change in the stability of ATP synthase that occurs after mtCx43 deficiency. This and other possible roles of mtCx43 ought to be investigated in the future.

Reviewer #3 (Public Review):

This manuscript should be of broad interest to readers not only in the field of gap junction (GJ) mediated cell-to-cell communication but also to scientists and clinicians working on the function of mitochondria and metabolism. Their data elucidates a new function of Cx43 in regulating the energy (ATP) generation of mitochondria, e.g., under oxidative stress.

The canonical function of gap junctions is in direct cell-to-cell communication by forming plasma membrane traversing channels that electrically and chemically connect the cytoplasms of adjacent cells. These channels are assembled from connexin proteins, connexin 43 (Cx43). However, more recently new, non-canonical cellular locations and functions of Cx43 have been discovered, e.g. mitochondrial Cx43 (mtCx43). However, very little is known about where Cx43 transported into mitochondria is derived from, how Cx43 is transported into mitochondria, where it is located in mitochondria, in which form Cx43 is present in mitochondria, (polypeptides, hemi-channels (HCs), complete GJ channels), and what the function of mtCx43 is. The authors addressed the latter question. The authors provide convincing evidence that mtCx43 modulates mitochondrial homeostasis and function in bone osteocytes under oxidative stress. Together, their study suggests that mtCx43 hemi-channels regulate mitochondrial ATP generation by mediating K+, H+, and ATP transfer across the mitochondrial inner membrane by directly interacting with mitochondrial ATP synthase (ATP5J2), leading to an enhanced protection of osteocytes against oxidative insult. These findings provide important information of a role of Cx43 functioning directly in mitochondria and not at the canonical location in the plasma membrane. While most of the functional assays presented in Figures 2-8 appear solid, the mitochondrial localization of Cx43, its translocation into mitochondria under oxidative stress, and its configuration as hemi-channels (Figure 1) is less convincing. I have five general comments that should be addressed:

1) This study was performed in MLO-Y4 osteocyte cells. Is the H2O2 induced increase of mitochondrial Cx43 MLO-Y4 cell type or osteocyte specific, or is Cx43 playing a more general role in mitochondrial function, e.g. under oxidative stress? Osteoblasts such as MC3T3-E1 and MG63, and many other cell types endogenously express Cx43, and oxidative stress is a general physiological stressor, not only for osteocytes and bone cells. Attending to this question would address the generality of the findings for mitochondrial function.

We thank the reviewer for bringing up these valid points; seeing the phenotype displayed in secondary cell types, such as osteoblasts, would be of great relevance and interest. To address this, we conducted new experiments on MC3T3-E1 cells (Figure 1-figure supplement 2). After 2 hrs of H2O2 treatment, Cx43 accumulated on the mitochondria, marked by Mitotracker. Statistical analysis also showed a significant increase of the localization between Cx43 and Mitotracker (Figure 1-figure supplement 2B). The colocalization coefficient is higher in the Ctrl group in MC3T3-E1 cells when compared with the MLO-Y4 Ctrl group, indicating a different response level in other cell lines. Osteoblasts seemed to be more sensitive to redox interference. Overall, proving the point that under oxidative stress, mtCx43 may display a similar phenotype, across multiple cell lines, although the degree of sensitivity may differ.

2) The images of MLO-Y4 cells (Figure 1A) and the primary osteocytes isolated from Csf-1+/- and control mice (Figure 8) do not show visible gap junctions. I guess this is due to the fact that slides were stained with the Cx43(E2) antibody. I feel, staining of these cells in addition with the Cx43(CT) antibody would be helpful to get a better understanding on the distribution of Cx43 in gap junctions and undocked/un-oligomerized Cx43 in these cells.

Thank you for the suggestion. To get a better understanding of the distribution of Cx43, either in GJ or HC form, we performed additional experiments in MLO-Y4 cells using the Cx43(CT) antibody and data are shown below. With Cx43(CT) staining, we observed more signals in the cells and on the plasma membrane. After H2O2 treatment, we observed increased and stronger signals localized on the mitochondria compared with the untreated control group. Stronger signals observed in the plasma membrane indicate the gap junction stained by Cx43(CT) antibody.

3) The images of cells presented in Figure 1A are quite fussy. No mitochondria are visible, and the Cx43 staining is hazy and does not localize to any subcellular structures. Also, it is not clear if the higher resolution image presented in Figure 1C actually represents a mitochondrion. A good DIC image, or co-staining with another mitochondrial marker such as MitoTracker (as shown in Figure 4-S1) would make the localization and translocation of Cx43 into mitochondria upon oxidative stress more convincing. This is especially important as the translocation, although statistically significant, increases only by about 10% or less (Figure 1B). Such a small difference (also represented in the Western analyses presented in Figure 1D) could easily be artefactual, depending on how the correlation coefficient was generated. Of note in this respect is that control cells in Figure 1A appear larger (compare the size of the nuclei) and are spread out more than the H2O2 treated cells. Better, more clear images would make the mitochondrial localization/translocation more convincing.

The reviewer made great points. To improve the image clarity, we redid the staining/imaging and determined the colocalization of SDHA and MitoTracker Deepred. The result (shown below) suggested that under normal conditions without H2O2 treatment, SDHA and MitoTracker merged perfectly, while after H2O2 treatment for 2 hrs, mitochondria became fragmented and the SDHA signal exhibited a more dotted pattern compared to the MitoTracker. Overall, we feel that MitoTracker represents the distribution of mitochondria better. SDHA is a subunit of mitochondrial complex II, and the images we presented in Figure 1C were captured from isolated mitochondria under a confocal microscope with SDHA and Cx43(CT) co-staining. Considering the specificity of SDHA (see images below), we believe the Cx43 signal we captured demonstrates the mitochondrial localization/translocation. After using MitoTracker as a mitochondrial marker and higher magnificent images, the correlation coefficient increased from 0.35 to 0.47, a 32% increment with statistical significance. As to the nuclei size, some cells indeed have smaller sizes, which may be affected by varied local cell density. The new images represented in Figure 1A are much more consistent in the nuclei size.

4) How pure are the mitochondria that were probed for Cx43 by Western shown in Figure 1D? The preparation method described is relatively simple, collecting the 10,000xg supernatant (here 9,000xg supernatant) as mitochondrial fraction. Is it possible that the Cx43 signal, at least in part, is derived from other, contaminating membranes, such as PM, Golgi, or ER? Testing the mitochondrial preparation by Western with marker proteins specific for these compartments would strengthen the author's results.

The reviewer made a great suggestion. To address this, we did a western blot to test the mitochondrial purity. Indeed, this method using centrifugation is simple, and as expected there were some contamination of ER (marked by PDI) and Golgi (marked by STX6). However, to further confirm the purity of the mitochondrial fraction, fluorescent dyes for mitochondria (MitoTracker Deepred), ER (ER-Tracker Blue-White), and nuclei (Hochest) were used. The organelle-specific dyes indicated most parts of the fraction were mitochondria. There were some contaminations with ER fragments and minimal nuclear contamination. Combining our western blot and immunofluorescence data, it can be concluded that our Cx43 signal is primarily derived from mitochondria.

5) The authors rely on previous studies to postulate that Cx43 in mitochondria forms hemichannels in their system, is localized in the inner membrane, and is oriented with the Cx43 C-termini facing the inter-membrane space (as schemed in Figure 8C). The authors use lucifer yellow (LY) dye transfer and carbenoxolone, but both are not hemi-channel specific probes. They are transferred by, and block GJ channels as well. Experiments, using hemi-channel specific probes would be more convincing. This is important, as the information cited is based on only two references (Boengler et al., 2009; Miro-Casas et al., 2009), and it still is highly unclear how a membrane protein that is co-translationally inserted into the ER membrane, then traffics through the Golgi to be inserted into the plasma membrane is actually imported into mitochondria and in which state (monomeric, hexameric). Why the Cx43(CT) specific antibody traverses the outer mitochondrial membrane and reaches the Cx43CT while the Cx43(E2) specific antibody is not described and clear either. Where are these mitochondria permeabilized with Triton X-100 as described in M&M?

We edited the Methods section. We did not use Triton X-100 to permeate mitochondria. PMP appeared to preserve mitochondrial inner membrane integrity allowing us to assess the localization of Cx43(CT) antibody on mitochondria. We showed these new immunofluorescence images in Figure 5- figure supplement 2. PMP used as a plasma membrane permeabilizer has a 6x affinity with MOM compared with MIM. Meanwhile, no Cx43(E2) Ab signal was detected in mitochondria, suggesting the extracellular loop of Cx43 faces the matrix and cannot be accessed by Cx43(E2) antibody.

The translocation of Cx43 to mitochondria was reported to involve the chaperone Hsp90-dependent TOM complex pathway (Rodriguez-Sinovas et al., 2006). After the translocation, if mtCx43 forms gap junctions in mitochondria is unclear. Lucifer yellow is widely used in hemichannel-mediated dye uptake or gap junction-mediated dye transfer. In our case, considering the channel orientation, mtCx43 should form hemichannels, and Cx43(CT) Ab could be used as a specific Cx43 HCs blocker like the study reported in cardiomyocytes (Lillo et al., 2019).

Author Response

Reviewer #3 (Public Review):

The authors showed that D2R antagonism did not affect the initial dip amplitude but shortened the temporal length of the dip and the rebound ACh levels. In addition, by using both ACh and DA sensors, the authors showed DA levels correlate with ACh dip length and rebound level, not the dip amplitude. Both pieces of evidence support their conclusion that DA does not evoke the dip but controls the overall shape of ACh dip. Overall the current study provides solid data and interpretation. The combination of D2R antagonist and CIN-specific Drd2 KO further support a causal relationship between DA and ACh dip. Overall, the experiments are well-designed, carefully conducted and the manuscript is well-written.

At the behavioral level, the author found a positive correlation between total AUC (of ACh signal dip) and press latency in Figure 10, indicating cholinergic levels contributes to the motivation. The next logic experiment would be to compare the press latency between control and ChAT-Drd2KO mice, since KO mice have smaller AUC while not affecting DA. However, this piece of information was missing in the manuscript. The author instead showed the correlation between AUC and latency was disrupted, which is indirectly related to the conclusion and hard to interpret. Figure 10 showed that eticlopride elongates the press latency, in a dose-dependent manner. However, it is not clear what this press latency means and how it was measured in this CRF task (Since there is no initial cue in the CRF test, how can we define the press latency?).

We did compare the press latency between control and ChATDrd2KO mice (Figure 10B). At baseline (saline), there is no difference between press latency between these two groups. We measured press latency as the time to press the lever after the lever has been extended. When the lever extends, it makes a sound (cue), which signals to the mice that a new trial has started. The fact that press latency is not enhanced in ChATDrd2KO mice was surprising to us. It is possibly due to compensation via other neuronal mechanisms that regulate press latency (see discussion to comment 6 of public review).

Pearson r<0.5 is normally defined as a weak correlation. It is better to state r values and discuss that in the manuscript.

A valid comment. We clarified our correlation analyses in the methods section (line 717):

“We used a variance explained statistical analysis (R2) to determine the % of variance in our correlation analyses (example: a correlation of 0.5 means 0.52 X 100= 25% of the variance in Y is “explained” or predicted by the X variable. When comparing correlation values, Fisher’s transformation was used to convert Pearson correlation coefficients to z-scores.”

We also added this to the result section: e.g., line 256: “which accounts for 22% of the variance in the ACh decrease explained by the DA peak.

Is there any correlation between ACh AUC and other behavior indexes such as press speed or the time between press and reward licking?

We don’t have the ability to measure press speed and there is no press rate because the lever retracts after the first lever press. We quantified the correlation between time to press until head entry (press to reward latency) and ACh AUC and the results are difficult to interpret. For Drd2f/fl control mice we determined a weak negative correlation (the larger the ACh dip the lower the press to reward latency). In contrast, in ChATDrd2KO mice we found a weak positive correlation between ACh AUC and press to reward latency (the smaller the dip, the lower the press to reward latency). Given these conflicting results, it is difficult to determine how the ACh AUC affects press to reward latency.

In figure 2B CS+ group, the author was focusing on the responses at CS+, however, the ACh dips at reward delivery seem to persist even after in this particular example. This might be an interesting phenomenon in which ACh got dissociated from DA signals, which needs further analysis from the author.

We see a persistent signal at reward delivery in both DA and ACh up to the 8 days of testing. However, 1 mouse lost its optical fiber for the GACh signal so the data from Days 6-8 is from 2 mice. We also measured the correlation between DA and ACh at reward delivery for all 8 days of testing (see below). The correlation data is variable with the strongest correlation being observed on Day 2. It is possible that these signals could get dissociated after even more days of testing, but we do not have this data available.

Author Response

Reviewer #2 (Public Review):

In general, the study has several novel comments, the experimental design is appropriate and the manuscript is well written. While the manuscript contains a lot of data, still it is a bit descriptive. There are also some issues, which should be addressed.

1) In Figure 1E, the authors demonstrate a small but significant decrease in body weight of mutant mice. The difference is not so drastic. They also mentioned that some mice showed kyphosis. Please provide data on what percentage of mutant mice showed kyphosis. Please also provide individual hind limb muscle weight normalized with body weight.

Thank you for your suggestions. The kyphosis was observed in some (more than one third of) Dst-b mutant mice as shown in the author response image 1. MRI or CT imaging of the skeleton is necessary to accurately diagnose kyphosis, however, the imaging was not performed in this paper. Therefore, we would like not to provide data on what percentage of mutant mice showed kyphosis.

We weighed the soleus of hind limb and demonstrated the data (lines 132-135).

2) There is a lot of variability in the age of the mice employed for this study. For example, in Figure 3, the authors mentioned 23 months old mice (Fig. 3a) and over 20 months old and over 18 months old mice. What was the exact age of the mice? Why three different age mice were used for the same set of experiments? The authors should also comment on whether the onset of myopathy in skeletal and cardiac muscle occurs at the same or different age in mutant mice.

According to the comments, we described exact ages in each figure legends. The reason for the variability in age of mice is that we performed a lot of different kinds of experiment at different time points. We described the myopathy phenotypes occurred around 16 months of age and older (lines 128-129). As for cardiomyopathy, fibrosis was observed around 16 months of age and older (Figure 3D,E).

3) Authors have studied protein aggregation only in the soleus muscle of mutant mice. Do the same types of aggregates also form in cardiomyocytes? They write that desmin aggregates were observed in cardiomyocytes of mutant mice. Please show those results in a supplemental figure.

According to the suggestion, we presented the data on desmin aggregates in the cardiomyocytes of Dst-bE2610Ter/E2610Ter mice (Figure 4-figure supplement 1).

4) In Figure 5, the authors suggest that mutant mice have mitochondrial abnormalities. However, this analysis is quite abstract and inconclusive. Immunohistochemical images show higher levels of CytoC and Tom20 whereas QRT-PCR demonstrates a significant decrease in mRNA levels of some of the mitochondria-related molecules. Authors should perform additional experiments to determine whether there is any difference in mitochondrial content between WT and mutant mice. In addition, they should perform some functional assays (i.e. OCR, seahorse experiment etc.) to measure mitochondria oxidative phosphorylation capacity is affected in mutant mice.

Thank you very much for the comment. Mitochondrial accumulation was a characteristic phenotype in Dst-bE2610Ter/E2610Ter muscle and also in other types of MFM. We performed quantitative analyses and added the data (Figure 5B). Mitochondrial accumulation was observed even in young stage when protein aggregates were not observed (Figure 3-figure supplement 1A). As the reviewer pointed out, it is important to demonstrate changes in mitochondrial function, but at this moment, we do not have that assay system and would like to present it as data for a future paper, including analysis on mitophagy.

5) The morphology of the mitochondria in TEM images shows features that are commonly observed during oxidative damage. Is there any evidence of oxidative stress in skeletal and cardiac muscle of mutant mice?

Thank you very much for the insightful comment. Gene ontology and KEGG pathway analysis on RNA-seq data did not show alterations of oxidative stress in the heart. We performed q-PCR for genes associated with oxidative stress in soleus (Figure 1-figure supplement 3), which did not show alterations in oxidative stress. In the future, we would to investigate on this point.

Reviewer #3 (Public Review):

This manuscript by Yoshioka et al. provides an extensive analysis of cardiac and skeletal muscle in a mouse model of Dst-b mutation. The authors have generated the mutant mouse model to selectively mutate Dst-b isoform of Dystonin and show that such a mutation leads to cardiomyopathy and late-onset myofibrillar myopathy. This is a novel discovery which adds valuable information to the genetic basis and molecular mechanism of MFM mediated by Dst-b. However, the manuscript needs substantial revision and additional feasible experiments.

In Figure3A, the authors suggest that there are smaller myofibers in the mutated mice however they do not provide enough data to support that. Cross-sectional areas between the mutant and WT have to be counted and represented as bins. This can better show the presence of smaller myofibers and muscle degeneration in the mutant mice.

Thank you for the helpful comment. We quantified distribution of cross-sectional area (CSA) in the soleus and then the data was indicated in Figure 3C. It indicates that there are smaller myofibers in the mutant mice.

In Figure 3A-B, the authors show that mutant mice have significantly more myofibers with centrally located myonuclei indicating the constant degeneration and regeneration in the mutant mice. Another indicator of this is the number of activated muscle stem cells. Under homeostasis, authors can compare the number of quiescent muscle stem cells and activated muscle stem cells. If there is constant degeneration and regeneration in the mutant muscle, there will be more cycling muscle stem cells and that will further prove such phenotype in question. Alternatively, they can use EdU water and quantify the number of EdU+/Pax7+ cells between the mutant and WT.

Thank you very much for the interesting comment. We agree that the subject of muscle regeneration in Dst-b mutant mice to be interesting. The authors tried to address this issue by making ISH probes for Pax7 and Emerin, which label muscle stem cells (image below). However, we were unable to reach a conclusion at this time. We intend to address this issue in the future.

In figure 2F, the authors show behavioral tests on the mutant mice of age 1 year. They do not show any significant difference in muscle strength. However, most of the myopathic phenotypes they observe are at 23 months of age, these behavioral tests can be repeated at that age to see if there is more muscle weakness in the mutant mice compared to the WT. Also, are these behavioral test readouts affected by the cardiomyopathy independent of skeletal muscle strength?

We have used rotarod test and wire hang test to evaluate motor coordination and have reported impairment of motor performance in dt mice (Horie et al., 2020). The purpose of these behavior tests in the present study was to evaluate motor coordination of Dst-b mutant mice compared to dt mice, not to address the skeletal muscle function. The text has been changed to clarify this point (lines 121-123).

Generally speaking, these behavioral tests, especially the rotarod test, may be affected by cardiac abnormalities. However, it is difficult to draw conclusions from the results of this study, since there were no significant differences in the behavioral experiments.

They show in Figure 3B that the number of CNF's are affected to a different extent in different muscles. These muscles have a different composition of myofibers, one consisting mostly of slow-type fibers while the other is mostly of fast-type. The question of whether Dst-b mutation effect of muscle fiber types is not clear. Is there a difference?

Thank you very much for insightful comment. We performed qPCR to evaluate whether Dst-b mutation affects the myofiber type of soleus muscle (Figure 1-figure supplement 3B). Expression levels of the genes did not change between WT and Dst-b mutant mice.

The cardiac myopathy phenotype that is clearly shown in figure 3 is shown in mice of 16 months of age whereas the skeletal muscle myopathy phenotype is shown in 23-month-old mice. The reason for the choice of the age of the mice should be discussed. Does the cardiac phenotype precede the skeletal muscle phenotype? Have they looked at the skeletal muscle phenotype at earlier ages? If so, that data should be provided as well and discussed.

Thank you for the comment. We analyzed myopathy and cardiomyopathy phenotypes in mice aged between 16-23 months and then have chosen histological photographs with the high quality. As shown in Figure 3B, CNFs increased in the soleus from all Dst-b mutant mice aged between 16-23 months. We added description that skeletal myopathy phenotypes occurred at 16-month-old mice.

The authors clearly show the formation of protein aggregates in the myofibers in the mutant mice. They further characterize the composition of these desmin aggregates by determining their co aggregates such as plectin and ab-Crystallin. Another component of the z-disk that has been shown to be involved in the aggregates in MFM is myotilin. The authors should also show the presence/ absence and co-aggregation of this protein with the desmin aggregates present in the mutant mice.

According with the suggestion, we performed immunohistochemistry of myotilin. Myotilin was abnormally accumulated in myofibers of the soleus from Dst-b mutant mice. We thank the nice comment and added the data in Figure 4-figure supplement 2.

The authors show abnormal accumulation of mitochondria through cyt c and Tom20 staining. The increased Tom20 levels in the mutant are shown in figure 5A which is from mice that are 23-month-old. However, in figure 3-figure supplement 1a they also show elevated Tom20 staining in the mutant mice that are only 1-2 months old. However, no other phenotype is observed at this age except for the disrupted mitochondria according to the data provided. This needs to be discussed and addressed.

We would like to correct that the data in figure 3-figure supplement 1a is 3-4 months old mutant mice. These data show that mitochondrial accumulation precedes CNF and desmin aggregation. We have described this point in the text (lines 206-209).

In Figure 5, the authors show changes in gene expression levels of genes involved in oxidative phosphorylation which supports the disrupted mitochondrial function. Additionally, ROS levels could be compared between the WT and mutant mice.

To address the involvement of oxidative stress, we performed q-PCR for genes associated with oxidative stress response in soleus (Figure 1-figure supplement 3C). qPCR data did not show alterations in such genes. In the future, we would like to investigate on this point.

In Figure 5 authors show disrupted oxidative phosphorylation in the mutant soleus muscle. Is this also associated with the fiber-type switch? Since mouse soleus muscle is a mix of fast and slow fiber types, they can look at differences in gene expression of key marker genes for slow and fast myofibers.

Thank you very much for the suggestion. We quantified expression levels of muscle fiber-type marker genes (Figure 1-figure supplement 3B). There is no data to suggest the fiber-type switch.

In figure 2, the authors show that mutant mice increase their body weight at a normal pace until 13 weeks of age after which the mutant mice become lighter than their WT counterparts. Is this suggestive of loss of muscle mass? If so, the authors show the muscle atrophy phenotype in 23-month-old mice with cross-sections. Does this mean muscle atrophy starts at an earlier age at 16 months in these mice? Please provide details on the age of the mice for each experiment. In addition, in the text (line 121) authors phrase that the mutant mice become leaner. Lean usually means a decrease in fat mass and an increase in muscle mass. Is this the case? If so, there is no data to support that and the phenotype in the mutant mice suggests there is muscle atrophy in these mice. Therefore, it would not be appropriate to suggest that these mice get lean. However, it is interesting that the bodyweight of the mutant mice gets significantly lighter after 13 weeks. EchoMRI analysis can be performed between these mice to see the total body composition to determine if there is a change in the different type of fat, lean or water composition.

Thank you for your comments. We provided exact ages in each figure legend. We described that skeletal myopathy phenotypes occur as early as 16-month-old mice, and CSA analysis showed that increased small caliber myofibers in the soleus of Dst-b mutant mice. However, muscle mass of the soleus normalized by body weight was not significantly different between control and Dst-bE2610Ter/E2610Ter mice. Therefore, muscle atrophy may be not significant enough to affect muscle weight.

Because we have not quantified the fat mass in Dst-b mutant mice, we changed the phrase from “the mutant mice become leaner” to “they become lower body weight compare with WT mice” (line 120).

Authors have performed RNA-Seq for the left ventricle from the mutant and the WT mice. Separate clustering of the WT and the mutant has to be shown at least through a PCA plot. Some IGV tracks to show the expression level changes in key genes between the mutant and WT should be shown as well. In addition, they could show how some of the genes involved in autophagy and protein degradation are affected since these are mainly the mechanism by which there is protein aggregation in MFM's.

Thank you for your helpful comment. We performed principal component analysis (PCA) and hierarchical clustering. The data showed that transcriptomic features of WT and Dst-b mutant hearts are separated (Figure 8-figure supplement 1A, B). To evaluate the change in expression level of genes, we also performed real time-PCR (Figure 8-figure supplement 1C). Our Gene ontology analysis and KEGG pathway analysis on RNA-seq data in the heart did not suggest the alterations in autophagy and protein degradation, while many genes responsible for unfolded protein response affected (Figure 8C, Figure 8-figure supplement 1C). Previous studies have reported that unfolded protein response is abnormal in several animal models for myofibrillar myopathy (Winter et al., 2014; Fang et al., J Clin Invest, 2017). We would like to investigate underlying mechanisms of protein aggregates in Dst-b mutant myofibers in the future.

Author response:

Reviewer #1 (Public Review):

This paper proposes a novel framework for explaining patterns of generalization of force field learning to novel limb configurations. The paper considers three potential coordinate systems: cartesian, joint-based, and object-based. The authors propose a model in which the forces predicted under these different coordinate frames are combined according to the expected variability of produced forces. The authors show, across a range of changes in arm configurations, that the generalization of a specific force field is quite well accounted for by the model.

The paper is well-written and the experimental data are very clear. The patterns of generalization exhibited by participants - the key aspect of the behavior that the model seeks to explain - are clear and consistent across participants. The paper clearly illustrates the importance of considering multiple coordinate frames for generalization, building on previous work by Berniker and colleagues (JNeurophys, 2014). The specific model proposed in this paper is parsimonious, but there remain a number of questions about its conceptual premises and the extent to which its predictions improve upon alternative models.

A major concern is with the model's premise. It is loosely inspired by cue integration theory but is really proposed in a fairly ad hoc manner, and not really concretely founded on firm underlying principles. It's by no means clear that the logic from cue integration can be extrapolated to the case of combining different possible patterns of generalization. I think there may in fact be a fundamental problem in treating this control problem as a cue-integration problem. In classic cue integration theory, the various cues are assumed to be independent observations of a single underlying variable. In this generalization setting, however, the different generalization patterns are NOT independent; if one is true, then the others must inevitably not be. For this reason, I don't believe that the proposed model can really be thought of as a normative or rational model (hence why I describe it as 'ad hoc'). That's not to say it may not ultimately be correct, but I think the conceptual justification for the model needs to be laid out much more clearly, rather than simply by alluding to cue-integration theory and using terms like 'reliability' throughout.

We thank the reviewer for bringing up this point. We see and treat this problem of finding the combination weights not as a cue integration problem but as an inverse optimal control problem. In this case, there can be several solutions to the same problem, i.e., what forces are expected in untrained areas, which can co-exist and give the motor system the option to switch or combine them. This is similar to other inverse optimal control problems, e.g. combining feedforward optimal control models to explain simple reaching. However, compared to these problems, which fit the weights between different models, we proposed an explanation for the underlying principle that sets these weights for the dynamics representation problem. We found that basing the combination on each motor plan's reliability can best explain the results. In this case, we refer to ‘reliability’ as execution reliability and not sensory reliability, which is common in cue integration theory. We have added further details explaining this in the manuscript.

“We hypothesize that this inconsistency in results can be explained using a framework inspired by an inverse optimal control framework. In this framework the motor system can switch or combine between different solutions. That is, the motor system assigns different weights to each solution and calculates a weighted sum of these solutions. Usually, to support such a framework, previous studies found the weights by fitting the weighed sum solution to behavioral data (Berret, Chiovetto et al. 2011). While we treat the problem in the same manner, we propose the Reliable Dynamics Representation (Re-Dyn) mechanism that determines the weights instead of fitting them. According to our framework, the weights are calculated by considering the reliability of each representation during dynamic generalization. That is, the motor system prefers certain representations if the execution of forces based on this representation is more robust to distortion arising from neural noise. In this process, the motor system estimates the difference between the desired generalized forces and generated generalized forces while taking into consideration noise added to the state variables that equivalently define the forces.”

A more rational model might be based on Bayesian decision theory. Under such a model, the motor system would select motor commands that minimize some expected loss, averaging over the various possible underlying 'true' coordinate systems in which to generalize. It's not entirely clear without developing the theory a bit exactly how the proposed noise-based theory might deviate from such a Bayesian model. But the paper should more clearly explain the principles/assumptions of the proposed noise-based model and should emphasize how the model parallels (or deviates from) Bayesian-decision-theory-type models.

As we understand the reviewer's suggestion, the idea is to estimate the weight of each coordinate system based on minimizing a loss function that considers the cost of each weight multiplied by a posterior probability that represents the uncertainty in this weight value. While this is an interesting idea, we believe that in the current problem, there are no ‘true’ weight values. That is, the motor system can use any combination of weights which will be true due to the ambiguous nature of the environment. Since the force field was presented in one area of the entire workspace, there is no observation that will allow us to update prior beliefs regarding the force nature of the environment. In such a case, the prior beliefs might play a role in the loss function, but in our opinion, there is no clear rationale for choosing unequal priors except guessing or fitting prior probabilities, which will resemble any other previous models that used fitting rather than predictions.

Another significant weakness is that it's not clear how closely the weighting of the different coordinate frames needs to match the model predictions in order to recover the observed generalization patterns. Given that the weighting for a given movement direction is over- parametrized (i.e. there are 3 variable weights (allowing for decay) predicting a single observed force level, it seems that a broad range of models could generate a reasonable prediction. It would be helpful to compare the predictions using the weighting suggested by the model with the predictions using alternative weightings, e.g. a uniform weighting, or the weighting for a different posture. In fact, Fig. 7 shows that uniform weighting accounts for the data just as well as the noise-based model in which the weighting varies substantially across directions. A more comprehensive analysis comparing the proposed noise-based weightings to alternative weightings would be helpful to more convincingly argue for the specificity of the noise-based predictions being necessary. The analysis in the appendix was not that clearly described, but seemed to compare various potential fitted mixtures of coordinate frames, but did not compare these to the noise-based model predictions.

We agree with the reviewer that fitted global weights, that is, an optimal weighted average of the three coordinate systems should outperform most of the models that are based on prediction instead of fitting the data. As we showed in Figure 7 of the submitted version of the manuscript, we used the optimal fitted model to show that our noise-based model is indeed not optimal but can predict the behavioral results and not fall too short of a fitted model. When trying to fit a model across all the reported experiments, we indeed found a set of values that gives equal weights for the joints and object coordinate systems (0.27 for both), and a lower value for the Cartesian coordinate system (0.12). Considering these values, we indeed see how the reviewer can suggest a model that is based on equal weights across all coordinate systems. While this model will not perform as well as the fitted model, it can still generate satisfactory results.

To better understand if a model based on global weights can explain the combination between coordinate systems, we perform an additional experiment. In this experiment, a model that is based on global fitted weights can only predict one out of two possible generalization patterns while models that are based on individual direction-predicted weights can predict a variety of generalization patterns. We show that global weights, although fitted to the data, cannot explain participants' behavior. We report these new results in Appendix 2.

“To better understand if a model based on global weights can explain the combination between coordinate systems, we perform an additional experiment. We used the idea of experiment 3 in which participants generalize learned dynamics using a tool. That is, the arm posture does not change between the training and test areas. In such a case, the Cartesian and joint coordinate systems do not predict a shift in generalized force pattern while the object coordinate system predicts a shift that depends on the orientation of the tool. In this additional experiment, we set a test workspace in which the orientation of the tool is 90° (Appendix 2- figure 1A). In this case, for the test workspace, the force compensation pattern of the object based coordinate system is in anti-phase with the Cartesian/joint generalization pattern. Any globally fitted weights (including equal weights) can produce either a non-shifted or 90° shifted force compensation pattern (Appendix 2- figure 1B). Participants in this experiment (n=7) showed similar MPE reduction as in all previous experiments when adapting to the trigonometric scaled force field (Appendix 2- figure 1C). When examining the generalized force compensation patterns, we observed a shift of the pattern in the test workspace of 14.6° (Appendix 2- figure 1D). This cannot be explained by the individual coordinate system force compensation patterns or any combination of them (which will always predict either a 0° or 90° shift, Appendix 2- figure 1E). However, calculating the prediction of the Re-Dyn model we found a predicted force compensation pattern with a shift of 6.4° (Appendix 2- figure 1F). The intermediate shift in the force compensation pattern suggests that any global based weights cannot explain the results.”

With regard to the suggestion that weighting is changed according to arm posture, two of our results lower the possibility that posture governs the weights:

(1) In experiment 3, we tested generalization while keeping the same arm posture between the training and test workspaces, and we observed different force compensation profiles across the movement directions. If arm posture in the test workspaces affected the weights, we would expect identical weights for both test workspaces. However, any set of weights that can explain the results observed for workspace 1 will fail to explain the results observed in workspace 2. To better understand this point we calculated the global weights for each test workspace for this experiment and we observed an increase in the weight for the object coordinates system (0.41 vs. 0.5) and a reduction in the weights for the Cartesian and joint coordinates systems (0.29 vs. 0.24). This suggests that the arm posture cannot explain the generalization pattern in this case.

(2) In experiments 2 and 3, we used the same arm posture in the training workspace and either changed the arm posture (experiment 2) or did not change the arm posture (experiment 3) in the test workspaces. While the arm posture for the training workspace was the same, the force generalization patterns were different between the two experiments, suggesting that the arm posture during the training phase (adaptation) does not set the generalization weights.

Overall, this shows that it is not specifically the arm posture in either the test or the training workspaces that set the weights. Of course, all coordinate models, including our noise model, will consider posture in the determination of the weights.

Reviewer #2 (Public Review):

Leib & Franklin assessed how the adaptation of intersegmental dynamics of the arm generalizes to changes in different factors: areas of extrinsic space, limb configurations, and 'object-based' coordinates. Participants reached in many different directions around 360{degree sign}, adapting to velocity-dependent curl fields that varied depending on the reach angle. This learning was measured via the pattern of forces expressed in upon the channel wall of "error clamps" that were randomly sampled from each of these different directions. The authors employed a clever method to predict how this pattern of forces should change if the set of targets was moved around the workspace. Some sets of locations resulted in a large change in joint angles or object-based coordinates, but Cartesian coordinates were always the same. Across three separate experiments, the observed shifts in the generalized force pattern never corresponded to a change that was made relative to any one reference frame. Instead, the authors found that the observed pattern of forces could be explained by a weighted combination of the change in Cartesian, joint, and object-based coordinates across test and training contexts.

In general, I believe the authors make a good argument for this specific mixed weighting of different contexts. I have a few questions that I hope are easily addressed.

Movements show different biases relative to the reach direction. Although very similar across people, this function of biases shifts when the arm is moved around the workspace (Ghilardi, Gordon, and Ghez, 1995). The origin of these biases is thought to arise from several factors that would change across the different test and training workspaces employed here (Vindras & Viviani, 2005). My concern is that the baseline biases in these different contexts are different and that rather the observed change in the force pattern across contexts isn't a function of generalization, but a change in underlying biases. Baseline force channel measurements were taken in the different workspace locations and conditions, so these could be used to show whether such biases are meaningfully affecting the results.

We agree with the reviewer and we followed their suggested analysis. In the following figure (Author response image 1) we plotted the baseline force compensation profiles in each workspace for each of the four experiments. As can be seen in this figure, the baseline force compensation is very close to zero and differs significantly from the force compensation profiles after adaptation to the scaled force field.

Author response image 1.

Baseline force compensation levels for experiments 1-4. For each experiment, we plotted the force compensation for the training, test 1, and test 2 workspaces.

Experiment 3, Test 1 has data that seems the worst fit with the overall story. I thought this might be an issue, but this is also the test set for a potentially awkwardly long arm. My understanding of the object-based coordinate system is that it's primarily a function of the wrist angle, or perceived angle, so I am a little confused why the length of this stick is also different across the conditions instead of just a different angle. Could the length be why this data looks a little odd?

Usually, force generalization is tested by physically moving the hand in unexplored areas. In experiment 3 we tested generalization using a tool which, as far as we know, was not tested in the past in a similar way to the present experiment. Indeed, the results look odd compared to the results of the other experiments, which were based on the ‘classic’ generalization idea. While we have some ideas regarding possible reasons for the observed behavior, it is out of the scope of the current work and still needs further examination.

Based on the reviewer’s comment, we improved the explanation in the introduction regarding the idea behind the object based coordinate system

“we could represent the forces as belonging to the hand or a hand-held object using the orientation vector connecting the shoulder and the object or hand in space (Berniker, Franklin et al. 2014).” The reviewer is right in their observation that the predictions of the object-based reference frame will look the same if we change the length of the tool. The object-based generalized forces, specifically the shift in the force pattern, depend only on the object's orientation but not its length (equation 4).

The manuscript is written and organized in a way that focuses heavily on the noise element of the model. Other than it being reasonable to add noise to a model, it's not clear to me that the noise is adding anything specific. It seems like the model makes predictions based on how many specific components have been rotated in the different test conditions. I fear I'm just being dense, but it would be helpful to clarify whether the noise itself (and inverse variance estimation) are critical to why the model weights each reference frame how it does or whether this is just a method for scaling the weight by how much the joints or whatever have changed. It seems clear that this noise model is better than weighting by energy and smoothness.

We have now included further details of the noise model and added to Figure 1 to highlight how noise can affect the predicted weights. In short, we agree with the reviewer there are multiple ways to add noise to the generalized force patterns. We choose a simple option in which we simulate possible distortions to the state variables that set the direction of movement. Once we calculated the variance of the force profile due to this distortion, one possible way is to combine them using an inverse variance estimator. Note that it has been shown that an inverse variance estimator is an ideal way to combine signals (e.g., Shahar, D.J. (2017) https://doi.org/10.4236/ojs.2017.72017). However, as we suggest, we do not claim or try to provide evidence for this specific way of calculating the weights. Instead, we suggest that giving greater weight to the less variable force representation can predict both the current experimental results as well as past results.

Are there any force profiles for individual directions that are predicted to change shape substantially across some of these assorted changes in training and test locations (rather than merely being scaled)? If so, this might provide another test of the hypotheses.

In experiments 1-3, in which there is a large shift of the force compensation curve, we found directions in which the generalized force was flipped in direction. That is, clockwise force profiles in the training workspace could change into counter-clockwise profiles in the test workspace. For example, in experiment 2, for movement at 157.5° we can see that the force profile was clockwise for the training workspace (with a force compensation value of 0.43) and movement at the same direction was counterclockwise for test workspace 1 (force compensation equal to -0.48). Importantly, we found that the noise based model could predict this change.

Author response image 2.

Results of experiment 2. Force compensation profiles for the training workspace (grey solid line) and test workspace 1 (dark blue solid line). Examining the force nature for the 157.5° direction, we found a change in the applied force by the participants (change from clockwise to counterclockwise forces). This was supported by a change in force compensation value (0.43 vs. -0.48). The noise based model can predict this change as shown by the predicted force compensation profile (green dashed line).

I don't believe the decay factor that was used to scale the test functions was specified in the text, although I may have just missed this. It would be a good idea to state what this factor is where relevant in the text.

We added an equation describing the decay factor (new equation 7 in the Methods section) according to this suggestion and Reviewer 1 comment on the same issue.

Reviewer #3 (Public Review):

The author proposed the minimum variance principle in the memory representation in addition to two alternative theories of the minimum energy and the maximum smoothness. The strength of this paper is the matching between the prediction data computed from the explicit equation and the behavioral data taken in different conditions. The idea of the weighting of multiple coordinate systems is novel and is also able to reconcile a debate in previous literature.

The weakness is that although each model is based on an optimization principle, but the derivation process is not written in the method section. The authors did not write about how they can derive these weighting factors from these computational principles. Thus, it is not clear whether these weighting factors are relevant to these theories or just hacking methods. Suppose the author argues that this is the result of the minimum variance principle. In that case, the authors should show a process of how to derive these weighting factors as a result of the optimization process to minimize these cost functions.

The reviewer brings up a very important point regarding the model. As shown below, it is not trivial to derive these weights using an analytical optimization process. We demonstrate one issue with this optimization process.

The force representation can be written as (similar to equation 6):

We formulated the problem as minimizing the variance of the force according to the weights w:

In this case, the variance of the force is the variance-covariance matrix which can be minimized by minimizing the matrix trace:

We will start by calculating the variance of the force representation in joints coordinate system:

Here, the force variance is a result of a complex function which include the joints angle as a random variable. Expending the last expression, although very complex, is still possible. In the resulted expression, some of the resulted terms include calculating the variance of nested trigonometric functions of the random joint angle variance, for example:

In the vast majority of these cases, analytical solutions do not exist. Similar issues can also raise for calculating the variance of complex multiplication of trigonometric functions such as in the case of multiplication of Jacobians (and inverse Jacobians)

To overcome this problem, we turned to numerical solutions which simulate the variance due to the different state variables.

In addition, I am concerned that the proposed model can cancel the property of the coordinate system by the predicted variance, and it can work for any coordinate system, even one that is not used in the human brain. When the applied force is given in Cartesian coordinates, the directionality in the generalization ability of the memory of the force field is characterized by the kinematic relationship (Jacobian) between the Cartesian coordinate and the coordinate of interest (Cartesian, joint, and object) as shown in Equation 3. At the same time, when a displacement (epsilon) is considered in a space and a corresponding displacement is linked with kinematic equations (e.g., joint displacement and hand displacement in 2 joint arms in this paper), the generated variances in different coordinate systems are linked with the kinematic equation each other (Jacobian). Thus, how a small noise in a certain coordinate system generates the hand force noise (sigma_x, sigma_j, sigma_o) is also characterized by the kinematics (Jacobian). Thus, when the predicted forcefield (F_c, F_j, F_o) was divided by the variance (F_c/sigma_c^2, F_j/sigma_j^2, F_o/sigma_o^2, ), the directionality of the generalization force which is characterized by the Jacobian is canceled by the directionality of the sigmas which is characterized by the Jacobian. Thus, as it has been read out from Fig*D and E top, the weight in E-top of each coordinate system is always the inverse of the shift of force from the test force by which the directionality of the generalization is always canceled.

Once this directionality is canceled, no matter how to compute the weighted sum, it can replicate the memorized force. Thus, this model always works to replicate the test force no matter which coordinate system is assumed. Thus, I am suspicious of the falsifiability of this computational model. This model is always true no matter which coordinate system is assumed. Even though they use, for instance, the robot coordinate system, which is directly linked to the participant's hand with the kinematic equation (Jacobian), they can replicate this result. But in this case, the model would be nonsense. The falsifiability of this model was not explicitly written.

As explained above, calculating the variability of the generalized forces given the random nature of the state variable is a complex function that is not summarized using a Jacobian. Importantly the model is unable to reproduce or replicate the test force arbitrarily. In fact, we have already shown this (see Appendix 1- figure 1), where when we only attempt to explain the data with either a single coordinate system (or a combination of two coordinate systems) we are completely unable to replicate the test data despite using this model. For example, in experiment 4, when we don’t use the joint based coordinate system, the model predicts zero shift of the force compensation pattern while the behavioral data show a shift due to the contribution of the joint coordinate system. Any arbitrary model (similar to the random model we tested, please see the response to Reviewer 1) would be completely unable to recreate the test data. Our model instead makes very specific predictions about the weighting between the three coordinate systems and therefore completely specified force predictions for every possible test posture. We added this point to the Discussion

“The results we present here support the idea that the motor system can use multiple representations during adaptation to novel dynamics. Specifically, we suggested that we combine three types of coordinate systems, where each is independent of the other (see Appendix 1- figure 1 for comparison with other combinations). Other combinations that include a single or two coordinate system can explain some of the results but not all of them, suggesting that force representation relies on all three with specific weights that change between generalization scenarios.”

Author Response:

Reviewer #1 (Public Review):

Garcia-Souto, Bruzos, and Diaz et al. analyzed hemic neoplasia in warty venus clams at multiple sites throughout Europe. They identified cases of disease in two locations, in Galicia and in the Mediterranean. They then use Illumina sequencing to discover that the samples with cancer DNA had reads which mapped to the mtDNA reference sequences from a different clam species in the same family, suggesting a cross-species transmissible cancer. By mapping reads to both the V. verrucosa and C. gallina mitogenomes they showed that more reads mapped to C. gallina in cancer samples compared to matched host tissue samples, and this was consistent across the whole mitogenome. Phylogenetic analysis of mtDNA genes of the host and cancer samples as well as identification of SNVs at a short region of one single-copy nuclear locus suggest that all cancer samples come from a single C. gallina transmissible cancer clone. All data agree that a single lineage of cancer from C. gallina is responsible for all identified cancers in V. verrucosa.

There are a few sections where there are either unclear methods or the methods do not quite match the descriptions of the results. 1. Regarding mapping of reads to different reference Cox1 sequences (for Figure 2a): "Then, we mapped the paired-end reads onto a dataset containing non-redundant mitochondrial Cytochrome C Oxidase subunit 1 (Cox1) gene references from 137 Vererid clam species." I do not see where this is explained anywhere in the methods, where this list of references comes from, or what is in it.

Answer: We retrieved a dataset of 3,745 sequences comprising all the barcode-identified venerid clam Cox1 fragments available from the Barcode of Life Data System (BOLD, http://www.boldsystemns.org/). Redundancy was removed using CD-HIT (Fu, et al. 2012), applying a cut-off of 0.9 sequence identity, and sequences were trimmed to cover the same region. Whole-genome sequencing data from both healthy and tumoral warty venus clams was mapped onto this dataset, containing 118 venerid species-unique sequences, using BWA-mem, filtering out reads with mapping quality below 60 (-q60) and quantifying the overall coverage for each sequence with samtools idxstats. PCR primers were designed with Primer3 v2.3.7 (Koressaar, et al. 2018) to amplify a fragment of 354 bp from the Cox1 mitochondrial gene of V. verrucosa and C. gallina (F: CCT ATA ATA ATT GGK GGA TTT GG, R: CCT ATA ATA ATT GGK GGA TTT GG). PCR products were purified with ExoSAP-IT and sequenced by Sanger sequencing.

Action: We have included this new information in the methods section.

1. Regarding de novo assembly of mitogenomes: "Hence, we employed bioinformatic tools to reconstruct the full mitochondrial DNA (mtDNA) genomes in representative animals from the two species involved....Then, we mapped the paired-end sequencing data from the six neoplastic specimens with evidence of interspecies cancer transmission onto the two reconstructed species-specific mtDNA genomes." In contrast to this, the methods say, "Then, we run MITObim v1.9.1 (Hahn, Bachmann, & Chevreux, 2013) to assemble the full mitochondrial genome of all sequenced samples, using gene baits from the following Cox1 and 16S reference genes to prime the assembly of clam mitochondrial genomes." It is unclear which method was used.

Answer: In total, we performed whole-genome sequencing on 23 samples from 16 clam specimens, which includes eight neoplastic and eight non-neoplastic animals by Illumina pairedend libraries of 350 bp insert size and reads 150 bp long. First we assembled the mitochondrial genomes of one V. verrucosa (FGVV18_193), one C. gallina (ECCG15_201) and one C. striatula (EVCS14_02) specimens with MITObim v1.9.1 (Hahn, et al. 2013), using gene baits from the 7 following Cox1 and 16S reference genes to prime the assembly of clam mitochondrial genomes: V. verrucosa (Cox1, with GenBank accession number KC429139; and 16S: C429301), C. gallina (Cox1: KY547757, 16S: KY547777) and C. striatula (Cox1: KY547747, 16S: KY547767). These draft sequences were polished twice with Pilon v1.23 (Walker, et al. 2014), and conflictive repetitive fragments from the mitochondrial control region were resolved using long read sequencing with Oxford Nanopore technologies (ONT) on a set of representative samples from each species and tumours. ONT reads were assembled with Miniasm v0.3 (Li 2016) and corrected using Racon v1.3.1 (Vaser, et al. 2017). Protein-coding genes, rDNAs and tRNAs were annotated on the curated mitochondrial genomes using MITOS2 web server (Bernt, et al. 2013), and manually curated to fit ORFs as predicted by ORF-FINDER (Rombel, et al. 2002). Then, we employed the entire mitochondrial DNAs of V. verrucosa (FGVV18_193) and C. gallina (ECCG15_201) as “references” to map reads from individuals with neoplasia, filter reads matching either mitogenome and assemble and polish their two (healthy and tumoral) mitogenomes individually as above. Further healthy individuals were later sequenced and their mitogenomes assembled, to further investigate the geographic and taxonomic spread of this neoplasia.

Action: We have included this information in the methods section (page 21-22), and in the results (pages 7 and 8). mtDNA annotations are now shown in Supplementary Figure 3. Nucleotide data for the mitochondrial DNA assemblies has been uploaded to GenBank under accession numbers MW662590-MW662611 and will be released upon publication or request.

There is one minor claim which may not be fully supported by the data: the statement that, "The analysis of mitochondrial and nuclear gene sequences revealed no nucleotide divergence between the seven tumours sequenced." If I am understanding the filtering of the SNVs from the nuclear gene correctly, only the presence or absence of the 14 SNVs that were fixed within each of the two species were analyzed. Therefore, it is unclear whether the authors looked for any additional somatic mutations within the cancer lineage that would have occurred at other positions. For mitochondria, the authors state that sequences were "extracted from paired-end sequencing data," but it is not explained how this was done. The data suggest that there are no differences between cancer samples in the 13 coding genes and 2 rDNA genes, but data on possible SNVs in the intergenic regions is not shown.

Answer: We obtained a preliminary nuclear assembly using short-reads only. Obviously, the resulting assemblies are fragmented and incomplete. This has limited the identification of candidate regions shared by the three genomes (V. verrucosa and both Chamelea clams). Out of the 44 candidate nuclear fragments we tested, only two (DEAH12 and TFHII) turned out to give good PCR products, adequate for Sanger sequencing. As mentioned above, we now provide additional data on a second gene (TFIIH), identified and selected on the same basis as DEAH12. We find 14 and 15 sites, respectively, for the DEAH12 and the TFIIH loci, with fixed SNVs (allele frequency >95%) that allowed to discriminate between the three relevant species (V. verrucosa, C. gallina and C. striatula) and the tumour. These diagnostic nucleotides were then used to filter the reads from individuals with neoplasia harbouring both DNA’s. Variation within the host lineage but not within the tumour was found along the nuclear DNA fragments employen in the ML phylogenies (see figure below).

Figure. Molecular phylogenies based on the two selected nuclear markers. (a) DEAH12 gene and (b) TFIIH gene, and diagnostic loci discriminating among species and tumour. Bootstrap support values (500 replicates) from ML analyses above 50 are shown above the corresponding branches. Note all diagnostic nucleotides are identical between tumours (black dots).

Regarding the mtDNA, firstly, we assembled the mitochondrial genomes of one V. verrucosa (FGVV18_193), one C. gallina (ECCG15_201) and one C. striatula (EVCS14_02) specimens with MITObim v1.9.1 (Hahn, et al. 2013). Then, we employed the entire mitochondrial DNAs from V. verrucosa (FGVV18_193) and C. gallina (ECCG15_201) as “references” to map reads from individuals with neoplasia, filter reads matching either mitogenome and assemble and polish their two (healthy and tumoral) mitogenomes individually as above. Further healthy individuals were later sequenced and their mitogenomes assembled, to further investigate the geographic and taxonomic spread of this neoplasia. Despite the usefulness of the mitochondrial control region (CR) to detect differences among lineages, we refrained from using it for two reasons. (1) The CR shows considerable variation in both length and sequence among the three species, making their alignment difficult (in fact, previous phylogenetic studies based on whole mitochondrial DNA sequences in Veneridae excluded the CR: https://doi.org/10.1111/zsc.12454), and (2) the CR contains quasi-but-not-identical tandem repeats, as a other mollusks (i.e., the Venerid Dosinia clams https://doi.org/10.1371/journal.pone.0196466 or the Littorina marine snails https://doi.org/10.1016/j.margen.2016.10.006). In our case, repeats are larger than the short-reads insert size, and even though we could infer them by means of long read sequencing, polishing the resulting consensus sequences to overcome the intrinsic error rate of those lectures would yield inconclusive results, hindering the comparison between normal and tumoral haplotypes.

Action: We updated the methods for the mitochondrial DNA analyses (pages 21-22, 24) and the nuclear DNA analyses (page 23). We now include new data in the results and discussion (pages 9-10).

Reviewer #2 (Public Review):

In rare but well-documented instances, certain types of cancers can transmit horizontally. These transmissible cancers have a clonal origin and have adapted to bypass allorecognition. A form of marine leukemia (hemic neoplasia or HM) belongs to this class of transmissible cancers and has been detected in several bivalve species (oysters, mussels, cockles and clams). Although HM mostly propagates within the same bivalve species, instances of cross-species transmission have been reported. To better understand the mode of transmission of HM, Garcia-Souto et al. analysed mitochondrial DNA (mtDNA) by next generation sequencing in different bivalve species collected in the Mediterranean Sea and the Atlantic Ocean. The authors found that HM isolated in Venus verrucosa contained mtDNA that actually matched Chamelea gallina. Analysis of the nuclear gene DEAH12 also showed single nucleotide polymorphisms (SNPs) matching C. gallina DNA. Based on mtDNA and DEAH12 sequences, the authors use Bayesian inference to generate phylogenetic trees showing that HM found in V. verrucosa is much closer to C. gallina than the host species. They conclude that HM propagated from C. gallina to V. verrucosa.

Overall, the study is well performed with enough samples analysed. The results are quite convincing but there are also some concerns.

1. Transmissible cancers are known to split into clades based on mtDNA differential rate of evolution and also to incorporate mtDNA from exogenous sources, so one has to be extra careful that the results prove cross-species transmission and not HM divergence into two clades and/or exogenous acquisition. Samples HM ERVV17-2997 and EMVV18-376, both at the N1 stage, appear devoid of C. gallinae mtDNA and do not appear to have been screened for DEAH12. One explanation for this result is that there are too few HM cells in the samples (but supplementary Figure 1 shows some HM cells in ERVV17-2997. However, a different explanation is that these samples contain V. verrucosae mtDNA. ERVV17-2997 and EMVV18-376 could have been analysed in greater depth to verify that they also contained C. gallinae mtDNA and typical DEAH12 SNPs.

Answer: Despite the high sequencing coverage obtained for the sequenced individuals, we did not find foreign reads in the N1 tumours (ERVV17-2997 and EMVV18-73) to mitochondrial nor nuclear (i.e., DEAH12, TFHII) level. This is most likely due to a very low proportion of neoplastic cells in their tissues.

Action: We have added a sentence on page 8 that discuss this issue.

1. To strengthen their argument, the authors could have analysed a few more nuclear genes for specific SNPs, although the sensitivity of this approach will depend on the depth of sequencing.

Answer: We obtained a preliminary nuclear assembly using short-reads only. Obviously, the resulting assemblies are fragmented and incomplete. This has limited the identification of candidate regions shared by the three genomes (V. verrucosa and both Chamelea clams). Out of the 44 candidate nuclear fragments we tested, only two (DEAH12 and TFHII) turned out to give good PCR products, adequate for Sanger sequencing. As mentioned above, we now provide additional data on a second gene (TFIIH), identified and selected on the same basis as DEAH12. Individual ML phylogenies for these two fragments evidenced that tumours cluster together and separately from the host species and, in the case of DEAH12, closer to C. gallina. The MSC phylogeny was rebuilt including this new nuclear fragment. 12 In addition, we conducted a comparative screening of tandem repeats on the genomes of C. gallina and V. verrucosa. Two DNA satellites, namely CL4 and CL17, of, respectively, 332 and 429 bp monomer size, were very abundant in C. gallina and in the tumoral animals, but absent from all healthy V. verrucosa specimens. FISH probes designed for these satellites mapped on the heterochromatic regions, mainly in subcentromeric and subtelomeric positions, of both C. gallina and the neoplastic metaphases found in V. verrucosa, but were absent from the normal metaphases of the host species V. verrucosa. These results were consistent with the genomic abundance of these satellites in the NGS data and strongly suggest that these chromosomes derive from C. gallina.

Action: We include the analysis of one additional nuclear locus, TFIIH (pages 9-10). We have obtained new ML and MSC phylogenies including this new locus (pages 9-10, figures 3b-c). Additional FISH approach looking for satellite DNA CL4 and CL11 was performed (page 10, figure 3d, supplementary figure 5). The methods section has been updated accordingly (pages 20- 21, 23-24).

1. It would have been interesting to have more information in the Discussion on the potential immunological barriers that this tumour needs to overcome for cross-species transmission.

Answer: At a glance, we could argue/discuss that this transmissibility, inside or cross-species, is prone to occur in bivalves due to their filtering feeding system and the fact that their immune system is not entirely developed and yet to be completely understood, as the reviewer may know. Also, it would be tempting to suggest that some genetic restrictions allowing for cancer contagion happening only between close taxa might be in place, but, unfortunately we do not have the means to state that with our current data.

Action: At this point, no specific action has been taken for this query. However, we are happy to include something in the discussion if the reviewer still thinks this is relevant for improving the manuscript.

Author Response:

Reviewer #1 (Public Review):

Jo et al. use a combination of micropatterned differentiation, single cell RNA sequencing and pharmacological treatments to study primordial germ cell (PGC) differentiation starting from human pluripotent stem cells. Geometrical confinement in conjunction with a pre-differentiation step allowed the authors to reach remarkable differentiation efficiencies. While Minn et al. already reported the presence of PGC-like cells in micropatterned differentiating human cultures by scRNA-Seq (as acknowledged by the authors), the careful characterization of the PGC-like population using immunostainings and scRNA-Seq is a strength of the manuscript. The attempt at mechanistically dissecting the signaling pathways required for PGC fate specification is somehow weaker. The authors do not present sufficient evidence supporting the ability to specify PGC fate in the absence of Wnt signaling and the importance of the relative signaling levels of BMP to Nodal pathways; the wording of the text should be amended to better reflect the presented evidence or the authors should perform additional experiments to support these claims.

We thank the reviewer for this comment. As described in more detail in the responses below, we have significantly strengthened the evidence for the rescue of Wnt inhibition by exogenous Activin treatment and have nuanced our interpretation. We believe that our data suggest low levels of Wnt may be required directly for PGC competence, while much higher levels are required indirectly to induce Nodal, with Nodal signaling being the limiting factor for PGC specification under the reference condition with BMP4 treatment only. We describe this in detail in the manuscript but summarize it here in a simplified diagram:

We have also carried out additional experiments that match model predictions demonstrating the importance of relative BMP and Nodal signaling levels and amended the text to reflect the evidence as suggested. More details are provided below.

The molecular characterization of why colonies confined to small areas differentiate much better would greatly increase the biological significance of the manuscript (the technical achievement of reaching such efficiency is impressive on its own).

We believe the mechanism by which cells confined to small colonies differentiate to PGCLCs more efficiently is explained by a larger fraction of the cells being exposed to the necessary levels of BMP and Nodal signaling. In large colonies BMP signaling was shown to be restricted to a distance of 50-100 um from the colony edge through receptor localization and secretion of inhibitors (Etoc et al, Dev Cell 2016). From this one would expect that BMP signaling extends a similar distance from the edge in small colonies, so that a larger fraction of cells are receiving the BMP signal needed to differentiate to PGCLCs. Because it was not previously shown that the length scale of BMP signaling and downstream signals are preserved as colony size is reduced, we have now included an analysis of BMP signaling (pSmad1 levels) and Nodal signaling (nuclear Smad2/3 levels) as a function of colony size (Figure 5i-k). This confirms our hypothesis and provides a potential mechanism.

The authors propose a mathematical model based on BMP and Nodal signaling that qualitatively recapitulate their experimental data. While the authors should be commended for providing examples of other simple models that do not fully recapitulate their data, it would have been nice to see an attempt at challenging quantitatively the model. In particular, the authors do not take advantage of the ability to explore in a more systematic manner the BMP/Nodal phase space with their system.

We thank the reviewer for this suggestion. Experimentally we have now tested the effect of 5x5 = 25 different combinations of BMP and Activin doses on PGCLC differentiation. We then challenged the mathematical model to predict the ‘phase diagram’ corresponding to this data with good agreement (Figure 6f). It is important to note here that the model was fit using only data with 50ng/ml of BMP, making this a true prediction. We also point out that the phase diagram predicted in this way is different from the one shown in Figure 6d, not only because of the lower resolution, but because Figure 6f shows the steady state after uniform stimulation in space and time (i.e. the response on the very edge), whereas the predicted phase diagram shows average expression at 42h in a 100um range from the colony edge using the previously measured spatiotemporal gradients of BMP and Activin response. Finally, the data in Figure 6f shows mean expression levels as opposed to the percentage double positive cells for the same data in Figure 4q because our model does not simulate individual cells and noise, only allowing us to compare mean expression. We explain all this in the text now. As a minor change to facilitate comparison of data and model we have now plotted the concentrations of BMP and Activin in Figure 6 rather than the scaled model parameters from 0 to 1, we also further optimized the model parameters without qualitative changes.

The authors' claim that PGCLC formation can be rescued by exogenous Activin when blocking endogenous Wnt production is surprising given the literature. The authors only show that they can restore a TFAP2C+SOX17+ population but do not actually stain for an established germ cell marker. It appears essential to perform a PRDM1 staining in these conditions (Figure 4A) to unambiguously identify this population.

We have significantly extended our analysis of the effect of WNT inhibition and subsequent rescue of PGCs by Activin treatment. This includes staining for TFAP2C,NANOG,PRDM1 and staining for LEF1 as a measure of WNT signaling. Figure 4 and Figure 4—figure supplement 1 now also include treatment with IWR-1, a different small molecule inhibitor of WNT signaling, as well inhibition by IWR-1 and IWP2 at different times and different doses.

The authors only provide weak evidence that the fates depend on the relative signaling levels of BMP and Nodal. Indeed, fewer cells acquire a fate the lower BMP concentration they use, including the fates marked by Sox17 expression. It would more convincing to show the assay of Figure 4F for a range of BMP concentrations at which the overall differentiation works sufficiently well.

As suggested, we have now included a range of BMP concentrations. The reduction in PGCs at lower BMP doses is in line with our model and does not contradict a dependence on the relative signaling levels of BMP and Nodal by which we mean that optimal dose of Activin for PGCLC specification depends on the level of BMP and vice versa. We have amended the text to state this more clearly.

References

Chen, Di, Na Sun, Lei Hou, Rachel Kim, Jared Faith, Marianna Aslanyan, Yu Tao, et al. 2019. “Human Primordial Germ Cells Are Specified From Lineage-Primed Progenitors..” Cell Reports 29 (13): 4568–4582.e5. doi:10.1016/j.celrep.2019.11.083.

Etoc, Fred, Jakob Metzger, Albert Ruzo, Christoph Kirst, Anna Yoney, M Zeeshan Ozair, Ali H Brivanlou, and Eric D Siggia. 2016. “A Balance Between Secreted Inhibitors and Edge Sensing Controls Gastruloid Self-Organization..” Developmental Cell 39 (3): 302–15. doi:10.1016/j.devcel.2016.09.016.

Kobayashi, Toshihiro, Haixin Zhang, Walfred W C Tang, Naoko Irie, Sarah Withey, Doris Klisch, Anastasiya Sybirna, et al. 2017. “Principles of Early Human Development and Germ Cell Program From Conserved Model Systems..” Nature 546 (7658): 416–20. doi:10.1038/nature22812.

Kojima, Yoji, Kotaro Sasaki, Shihori Yokobayashi, Yoshitake Sakai, Tomonori Nakamura, Yukihiro Yabuta, Fumio Nakaki, et al. 2017. “Evolutionarily Distinctive Transcriptional and Signaling Programs Drive Human Germ Cell Lineage Specification From Pluripotent Stem Cells..” Cell Stem Cell 21 (4): 517–532.e5. doi:10.1016/j.stem.2017.09.005.

Sasaki, Kotaro, Tomonori Nakamura, Ikuhiro Okamoto, Yukihiro Yabuta, Chizuru Iwatani, Hideaki Tsuchiya, Yasunari Seita, et al. 2016. “The Germ Cell Fate of Cynomolgus Monkeys Is Specified in the Nascent Amnion..” Developmental Cell 39 (2): 169–85. doi:10.1016/j.devcel.2016.09.007.

Tyser, R.C.V., Mahammadov, E., Nakanoh, S. et al. Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289 (2021). https://doi.org/10.1038/s41586-021-04158-y

Author Response

Reviewer #1:

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

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

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

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

Author response image 1.

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

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

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

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

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

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

Author response image 2.

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

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

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

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

And:

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

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

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

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

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

Author response image 3.

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

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

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

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

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

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

Reviewer #2:

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

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

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

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

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

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

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

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

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

See Author response image 2.

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

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

Author response image 4.

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

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

Reviewer #3 (Public Review):

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

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

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

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

Author Response

Reviewer #2 (Public Review):

I am not a specialist in cryo-EM, so cannot comment on the technicalities of the structure reconstruction or methods used. I thus focus on the conclusions and observations that the authors provide in the manuscript and their relevance to functional photosynthesis.

The authors attempt to resolve the structure of PSII from Dunaliella and noticed that three types of PSII could be identified: two conformational states, and a stacked configuration. There is no doubt that these structures add to our current knowledge of PSII and that they exist in abundance upon solubilisation of the sample. My main issue however is the relevance to in vivo conditions, and the efforts to exclude the possibility that pigment loss and conformational states and stacking are a reflection of ex-vivo manipulations.

Our compact model contains 202 Chls molecules while the stretched conformation contains 206 Chls. All of the differences in Chl binding are attributed to CP29. We have compiled a table enumerating the different CP29 structures currently available from plants and green alga at similar resolution to our work (Supplementary table 2). In the larger plant complexes (C2S2M2) CP29 contains 14 chls, while CP29 in smaller C2S2 complexes contains 10-13 chls, so it appears the some chl loss from CP29 is associated with the release of LHCIIM. In the green alga structures, CP29 contains less chls in general and shows a similar trend. The currently published structure most relevant to our work contains 8 chls (6KAC), a somewhat lower amount then both the compact and stretched models (9 and 11 chls, respectively). The stretched orientation, which is the closest match to the known PSII core arrangement, therefore contains more chls than comparable models. While the in-vivo configuration is not known in the sense that it could contain more chls, the current structure is apparently the closest representation of it.

The presence of CP29 with lower chls content in the chlamy C2S2 (6KAC, which is in a stretched orientation) supports a conclusion that pigment loss from CP29 alone is not sufficient to trigger the stretch to compact transition although it is associated with it. In general, the precise orientation of CP29 is variable and seem to depend on the binding of additional LHCII, it is possible that some chl loss is accompanied with these changes in vivo.

I see a number of questions pertaining to this work. Starting from the two conformations of PSII, compact and stretched, the authors say that both are highly active based on oxygen measurements at a saturating light intensity. In the meantime, they report large variations in the chl content and positions of the chlorophyll molecules in these structures (also compared to other known PSIIs). This gives the impression that one can lose two chlorophylls, and freely modify the distance between others without losing efficiency, certainly a risky conclusion. Are the samples highly active also in light-limiting conditions? It is thought that even tiny movements and alterations in chl-chl distances alter their coupling and spectral properties, how come the variations in this report are so huge? In other words, the assay tests the charge separation activity of the PSII RC in the preps, but not the light-harvesting efficiency.

The chl content differences reported in this work amounts to 2%. In our opinion this represents quite a low variation in pigment content, which exist in virtually any experiment involving large complexes. We agree that measurements of activity in limiting light conditions are interesting, however this goes beyond the scope of the current work. Light harvesting efficiency in PSII is known to vary substantially as a result of additional mechanisms (NPQ in some of its forms), not associated with chl loss or gain. While the formation of quenching centers is attributed to small structural changes within specific pigment protein complexes, what we are showing in this work are structural changes between pigment protein complexes. These can affect transfer rates between the different complexes but are distinct from the structural changes thought to accompany the formation of quenching centers within specific pigment protein complexes.

How does one ascertain that the lost chlorophyll molecules in CP29 are not a preparation error? Does slightly increasing the detergent concentration impact the proportion of stretched:compact forms?

The effect of detergent concentration on the proportion of the different forms was not tested directly. However, we do not detect many differences in lipids or bound detergent molecules content between the two conformations, suggesting that for these “ligands” the differences are not substantial. We can only distinguish these two forms at the very last stages of data processing, at the present state of cryoEM cost and time availability, mapping the effect of detergent concentration on the different orientations is outside our reach.

On a similar note, how do the authors exclude that a certain interaction with this type of grid impacts the distribution of these complexes? Is it identical to a biologically separate preparation of algae? In case of discoveries of this type, it is of high importance to exclude as many possibilities of non-native conditions or influences on the structure.

It’s hard to completely exclude grid and sample preparation issues. However, we employed relatively standard grids and vitrification conditions. The observed complexes are embedded in vitrified ice and do not interact with the grid directly. The differences we observed are mainly in the orientations of the PSII cores, all the interactions between PSII subunits within each core are preserved and agree with previously published structures. Since the interactions within the core and between cores involve the same physical principles, we think its fairly conservative to think that the observed core orientations are not an artefact of sample preparation.

I would further like to encourage the authors to elaborate on the CP29 phosphorylation. What is the proportion of PSIIcomp that are phosphorylated? I assume it is not 100%, as in this case, the authors would propose that this is the effect that modulates between compact and stretched architectures.

Its difficult to estimate the proportion of observed phosphorylation/sulfinylation. To be detected in maps, most of the residues (above 50%) are probably modified. We attempted to estimate this by refining the atom occupancies of the Pi molecule on Ser84 and the oxygens attached to Cys218, both values suggested that about 70% of the complexes are modified. With regards to the possibility that these modifications can promote the formation of the compact state, we think that this is certainly a possibility, since these modifications were detected in this state and are in close proximity to each other. However, this can also result from the resolution differences of the maps and the structural implications of both modifications are hard to predict. At this point we prefer to note their existence without further interpretations.

In line 290, the authors highlight the structural heterogeneity within the two groups' PSII conformations. I would like to see how does the distribution look like for all the structures together: are the two (stretched and compact) specifically forming two heterogenous distributions? Or is it possible that the distribution between the two is quasi-continuous? In other words, if the structures are not perfectly defined, how do the authors decide that two- and not more or less subtypes exist?

We went back and refined the initial particle group (containing both compact and stretched orientations) using multibody with masks defining the two PSII monomers. This analysis showed the expected two peaks only in the first Principal components which accounted for ~38% of the variance in the dataset.

Multibody refinement carried out on the combined particle dataset shows one very large PC accounting for about 38% of the variance and the presence of two distinct peaks in the particle distribution of the first PC.

From this analysis it’s clear that there are two distinct classes in this particle set (as expected), as none of the other PC’s shows any signs of multiple peaks, this analysis suggests that two distinct models are the best representation of this eukaryotic PSII. Whether these are quasi continuous or distinct is more complex. There is continuity in this representation (particle distributions along PC), a different picture may appear if characters such as CP29 state are considered, but the size of CP29 and the remaining heterogeneity does not provide enough signal to carry out this classification at the moment.

Considering the stacked PSII, I also have a few concerns. Contrary to previous studies the authors do not assign a functional role to the stacking beyond the structural aspect. This could be better backed by a discussion about the closest chlorophyll a molecules across the stacked PSII, which given the rather large distance shown in fig. 4L seems to be too large for any EET across the stromal gap.

The closest chl-chl distance that we can measure in the stacked PSII dimer is ~54 Å, with most distances at the ~70 Å range, making EET between staked complexes very slow. We have added a statement clarifying this to our manuscript. In our opinion a structural role for the staked PSII dimer is more likely.

There is a report that suggests the presence of some density between the stacked PSII - could the authors comment on the differences between it and their work? Are the angles and positions conserved between these types of stacks? https://doi.org/10.1038/s41598-017-10700-8

We referred to Albanese et al, in our manuscript. We isolated the C2S2 complex from green alga, the analysis in Albanese et al was done on C2S2M1 complexes from pea and this can account for some of the differences. At any rate, our conclusion that we don’t find any evidence for protein linkers in the stacked complex is stated clearly. The angles described in Albanese et al are consistent with our analysis.

Line 387, the authors state that due to the transient nature of the interactions across the stromal gap, the stacks could be "under-detected" in cryo-ET data. This statement is in my opinion misformulated. For once, the transient interaction argument would apply the same (if not more due to changing conditions induced by the purification process) to the single particle analysis performed in this paper. Second, tomographic volumes detect hundreds of PSII in a suspended state. Any transient interaction that adds up to 25% of particle population in a steady state cell should be clearly visible, while the in situ data suggests not more than random cross-stromal-gap orientations. Of course, this can be a specificity of Chlamydomonas or a particular growth condition. The statement used by the authors could be indeed converted into: the PSII stacks are over-detected in vitro, and it is certainly a simpler explanation for their presence. It is also important to mention that PSII stacking alone is not the only reason for grana architecture - stacking with the antenna of larger complexes, absent in the authors' preparation could also contribute to grana maintenance; and auxiliary proteins such as CURT help with this issue as well. Here a recent demonstration of the importance of minor antenna should probably be also cited: https://doi.org/10.1101/2021.12.31.474624

We used the term “flexible” rather than “transient” to describe the interactions within the stacked PSII dimer. Our data (and tomographic data) do not contain any temporal component. When we used the term under-detected we refer to the fact that PSII is mainly detected by the luminal extrinsic subunits. The flexibility detected in our analysis may affect the concurrent visibly of these features in the PSII complexes making up an individual PSII stack. Specifically, Wietrzynski et al mainly analyze C2S2M2L2 complexes while our analysis only contained C2S2 complexes. It is likely that the different amount of bound LHCII affect PSII stacking as well. For example, Wietrzynski et al, show some overlap between LHCII complexes and little overlap between cores in the larger complexes they analyzed. We observe mainly core to core overlap with little LHCII overlap in the smaller C2S2, although we did not observe any states where LHC’s were not included in what appear to be the binding interface. We agree with the reviewer on the relevance Lhcb’s and CURT contributions to stacking but prefer to focus on what was directly demonstrated in our data. We clearly note that we are discussing in-vitro results.

Taking these last thoughts, I would like to finish by mentioning one more thing - almost philosophical. The authors are certainly at the forefront of the booming cryoEM revolution in biology which is profoundly changing the way we understand the living. There is absolutely zero doubt that this powerful technique is of the highest interest. But a growing number of structures of photosynthetic complexes remain puzzling, in particular with regard to their abundance in vivo (such as the PSII stacks) and functional relevance. How do we ascertain that these interactions are not due to in vitro preparation (isolation from cells, solubilisation)? Which ways can we use to try to exclude this (simple) hypothesis? I suggest that at least a small extent of biological replicas - experiments performed on separate batches, in different technical conditions, with slightly altered solubilization conditions, and so on - could shed light on the nature of these structures and their occurrence in vivo. Technical reps of the freezing+analysis pipeline could also be tried to see the variability. This would strongly reinforce this manuscript and its conclusions, and while not completely unequivocal (the stacked PSII, for example, could form upon each purification), a quantification of the effects would be of high interest.

We certainly share the reviewer hope of being able to conduct cause and effect cryoEM experiments covering a complete set of experimental parameters. This is still beyond reach in terms of time and cost. Within each cryoEM experiment, however, all the analysis is consistent and, more importantly, transparent with regards to image analysis, which is the most important factor in our opinion. Preparation artefacts are always a possibility but, in our opinion, cryoEM is not affected by them differentially compared to other techniques. As we mentioned above, the particles are being observed suspended in vitreous ice, this is not different, and one can say even better, then numerous low temperature spectroscopic observations on samples suspended in glass state or crystals obtained in the presence of high concentrations of various agents. One thing that validates structural studies are the chemical details (bond lengths and angles etc…) underlying every model which are consistence with known values to close tolerances.

Reviewer #3 (Public Review):

In this manuscript, Caspy et al. present a detailed structural analysis of eukaryotic photosystem II (PSII) isolated from the green alga Dunaliella salina. By combining single-particle cryo-EM with multibody refinement, the authors not only reveal a high-resolution (2.4Å) structure of the eukaryotic PSII, but also demonstrate alternate conformations and intrinsic flexibility of the overall complex. Stretched and compact conformations of the PSII dimer were readily identified within the single-particle dataset. From this structural analysis, the authors propose that excitation energy transfer properties may be modulated by changes in transfer distance between key chlorophyll molecules observed in different conformational states of the PSII dimer. Due to the high resolution of the maps obtained, the authors identify post-translational modifications and a sodium binding site based on the observed cryo-EM maps. Additionally, the authors analyze PSII complexes in stacked and unstacked configurations, and find that compact and stretched states also exist within the stacked PSII complexes. From their cryo-EM maps, the authors demonstrate that there is no direct protein-protein interaction between stacked PSII complexes, and rather propose a model wherein long-range electrostatic interactions mediated by divalent cations such as magnesium, can facilitate PSII stacking.

The conclusions and models presented in the manuscript are mostly well justified by the data. The cryo-EM maps are high quality and the models appear generally well refined. However, some aspects of data processing and analysis, as well as the resultant conclusions need to be clarified.

1) In general, it is not clear from the cryo-EM processing workflow (suppl. Fig 1) or the methods section when exactly symmetry was applied during 3D classification and refinement. In the case of C2S2 unstacked particles, when was symmetry first applied in the overall processing workflow? To identify the compact and stretched configurations of C2S2, did the 3D classification without alignment (and/or the refinement preceding this classification) have C2 symmetry applied? If so, have you considered the possibility that some particles may actually be asymmetric in some regions?

We modified figure S1 to clearly indicate the use of symmetry and particle expansion. In general, we refined most of the particle sets without symmetry (C1). At the final processing stage of the unstacked PSII sets, after we separated both conformations, we used C2 symmetry to expand the data, this was followed by multibody refinement. No symmetry or symmetry expansion was used for the stacked PSII particle sets.

2) Following multibody refinement in Relion individual maps and half-maps for each body will be generated. There is no mention in the methods of how these individual maps for each C2S2 "monomer" were combined to produce an overall map of the dimer following multibody refinement. There are several methods currently used to combine such maps, including taking the maximum or average of the two maps or using a model-based approach in phenix. The authors should be explicit about the method they used, any potential artifacts that may develop from this map combination process, and/or the interface between masks used in multibody refinement.

We used phenix.combined_focused_maps to combine the maps. This is now indicated in the method section.

3) In addition to the point raised above, following multibody refinement there will be an individual FSC curve and resolution for each body. However, in supplemental figure 2 and supplemental table 1, only a single FSC curve and resolution are reported. Are these FSC curves/resolutions only reported for the better of the two bodies? If not, how was a single resolution calculated for the overall map of combined bodies?

Both FSC curves were calculated and were highly similar, as expected following C2 expansion. This can also be evaluated from the local resolution maps which are highly similar between the two bodies. The reported resolutions are all taken from the displayed FSC curves generated through relion PostProcess.

4) One of the major conclusions from the 3D classification and multibody refinement is that conformational changes and inherent flexibility of the PSII dimers have the potential to change distances between cofactors in the complex, ultimately leading to altered excitation energy transfer. However, it is unclear whether or not the authors believe one conformation over another may more readily support the evolution of oxygen. It would be nice if the authors could elaborate slightly upon this topic in the discussion.

As discussed above the structural changes associated with the formation of quenching centers are not expected to be detected in the current work. The changes we observe can however affect the transfer to such centers and by doing so can play an important part in PSII biology. We do not detect any changes around the OEC and we don’t find any reason to think the two conformations are different with respect to their ETC.

5) Along the lines of point 4 above, on line 95 the authors claim that "the high specific activity of 816 umol O2/ (mg Chl * hr) suggest that" both the C2S2 compact and stretched conformation are highly active. However, it is not clear to me why this measure of specific activity would suggest that both PSII conformations should have "high" activity. Maybe a reference here would help guide readers to previous measures of specific activity?

Looking at specific activity from previously published structural studies on eukaryotic PSII we find that Sheng et al, 2019 reported on a specific activity of 272 mol O2/ (mg Chl * hr), this difference can stem partially from the presence of larger complexes in their preparation and is comparable to the activity that we measured in our As fraction (276 mol O2/ (mg Chl * hr), Figure 1-figure supplement 9). Reported specific activity values from plants (Pisum sativum) are also similar, Su et al, reported on a maximal value of 288 mol O2/ (mg Chl * hr), again, for larger complexes which can explain some of the difference. However, the specific activity measured for the C2S2 PSII isolated in the current study is 2.8 X higher than this value, more than the differences in chl content which ranges between 1.5 X to 2 X in favor of the larger complexes. If either one of the conformations is not as active, it would only mean that the other conformation will display even higher specific activity which seems less likely. In addition, we find no difference around the oxygen evolution center or in the peripheral luminal subunits in both the shape or map strength so both orientations show highly similar structures around these regions which determine the oxygen evolution activity.

6) It is claimed that "more than 2100 water molecules were detected in the C2S2 compressed model", and the water distribution is shown in Figure 3. Obtaining resolutions capable of visualizing waters with cryo-EM is still a significant challenge. Upon visual inspection of the map supplied, it appears that several of the waters that were built into the atomic model simply do not have supporting peaks in the coulomb potential map above the level of noise. While some of the modeled waters are certainly supported by the map, in my opinion, there are many waters that simply are not, or at best are questionable. What method or tool was originally used to build waters into the model, and how were these waters subsequently validated during structure refinement?

We followed standard methods for water placement and refinement in the preparation of the model, in addition to manually curating the water structure. However, in light of the reviewer comment we undertook additional rounds of refinement and inspection of the water molecules in the model. We removed a few hundred water molecules so that the total number of water molecules is now around 1700. All the water molecules in the present model should be well supported at maps values higher then 2.5 sigma and in our opinion the current water model should be regarded as conservative and underestimates the number of bound water molecules. This also led to some improvements in additional validation statistics of the model which are listed in the Table 1. The new model has been deposited in the PDB and the new PDB validation report is included in our resubmission.

7) The authors claim to identify several unique map densities during model building. One of these is a sodium ion close to the OEC, which is coordinated by D1-His337, several backbone carbonyls, and a water molecule. When looking closely at the cryo-EM map supplied, it appears that the coulomb potential map is quite weak for this sodium, and is only visible at quite low contour levels. In fact, the features for the coordinating water, and chloride ions located ~7-9A away are much stronger than the sodium. Do the authors have any explanation for why the cryo-EM map is significantly weaker for the sodium compared to the coordinating water or chloride ions in the same general vicinity? Similar to what they did for the other post-translational modifications, the authors should consider showing the actual cryo-EM map for the bound sodium in supplemental Figure 10 a,b.

Our main support for the placement of a Na+ ion in this location stems from the analysis of Wang et al. Our maps show the presence of a density which is discernible at 4 σ with an elongated shape suggesting the presence of multiple atoms/waters. Although in principle positive ions should have very strong densities in cryoEM maps due to their interactions with electrons, other factors such as occupancy, coordination and b-factor also play a role making the distinction between water and sodium complicated and case specific. The sodium peak is not observed in unsharpened maps (as do most of the water molecules which occupy conserved positions).

  We collected a few examples from comparable cases (cryo-EM maps of similar resolution ranges) where the presence of sodium ions is highly probable based on additional evidence. These maps densities highlight the factors we discussed above. In cases ‘a’ (dual oxidase 1 prepared in high sodium conditions) and ‘b’ (human voltage-gated sodium channel), Na+ is observed in a highly coordinated states and especially in ‘a’ shows the expected increase density values compared to water molecules. However, cases ‘d’ (human Na+/K+ P type Atpase) and ‘e’ (voltage-gated sodium channel) appear very similar to the proposed Na+ assignment in PSII. We conclude that map density alone is not enough to distinguish between Na+ and water molecules and rely on the additional experiments described by Wang et al. which show increase PSII activity in elevated Na+ levels in basic conditions.

8) The cryo-EM maps showing CP29-Ser84 phosphorylation and CP47-Cys218 sulfinylation are quite convincing. However, it is interesting that these modifications are only observed in the compact conformation, and not in the stretched conformation. Can the authors elaborate on whether or not they believe the compact and stretched conformations could be a result of these posttranslational modifications, or vice versa?

This is an interesting suggestion. In our opinion it is less likely that the modification themselves trigger the transition between compact and stretched states. It is not clear how these modifications will stabilize the compact vs the stretched states. It is equally likely that these modifications are somehow triggered by the structural change. We cannot be certain that these modifications are not present in the stretched orientation as well but remain unobserved due to resolution differences. The correlation between the states and post translation modifications should be verified before a discussion on their possible roles in the transitions.

9) Do the authors believe that PSII dimers in the solution can readily interconvert between compact and stretched conformations? Or is the relative ratio of these conformations fixed at the time of membrane solubilization with decyl-maltoside?

We think that its more probable that the transition between these states occur in the membrane phase. The main reason for this will be that pigment loss and structural transitions in CP29 are more likely to occur in the membrane rather than in aqueous/micelle environments.

10) The model proposed for divalent cation-mediated stacking of PSII dimers is compelling, and seems to be in agreement with previous investigations that observed a lack of stacked dimers in cryo-EM preparations lacking calcium/magnesium. However, my understanding from reading the methods section is that the observed lack of density between the stacked PSII dimers was inferred from maps obtained after multibody refinement. Based on the way the masks to define bodies were created for multibody refinement (Fig. 4A), the region between stacked dimers would be highly prone to map artifacts following multibody refinement. Have the authors looked closely at the interfacial region between stacked dimers following conventional 3D classification/refinement to ensure that there are indeed no features observed in the interfacial region even at low contour levels?

We’ve made several attempts to resolve differences in the space between the stacked PSII dimer. These include focused classification with masks containing selected volumes from this regions and masks that include only one of the stacked PSII dimers to avoid signal subtraction in this region. All of these did not reveal any discernible features in this region. In addition, any stable binding of a bridging protein across the stacked dimer will probably be at least partially visible as additional density over the unstacked PSII. We searched for such features and found none.

Author Response:

Reviewer #1:

This manuscript by Gabor Tamas' group defines features of ionotropic and metabotropic output from a specific cortical GABAergic cell cortical type, so-called neurogliaform cells (NGFCs), by using electrophysiology, anatomy, calcium imaging and modelling. Experimental data suggest that NGFCs converge onto postsynaptic neurons with sublinear summation of ionotropic GABAA potentials and linear summation of metabotropic GABAB potentials. The modelling results suggest a preferential spatial distribution of GABA-B receptor-GIRK clusters on the dendritic spines of postsynaptic neurons. The data provide the first experimental quantitative analysis of the distinct integration mechanisms of GABA-A and GABA-B receptor activation by the presynaptic NGFCs, and especially gain insights into the logic of the volume transmission and the subcellular distribution of postsynaptic GABA-B receptors. Therefore, the manuscript provides novel and important information on the role of the GABAergic system within cortical microcircuits.

We have made all changes humanely possible under the current circumstances and we are open to further suggestions deemed necessary.

Reviewer #2:

The authors present a compelling study that aims to resolve the extent to which synaptic responses mediated by metabotropic GABA receptors (i.e. GABA-B receptors) summate. The authors address this question by evaluating the synaptic responses evoked by GABA released from cortical (L1) neurogliaform cells (NGFCs), an inhibitory neuron subtype associated with volume neurotransmission, onto Layer 2/3 pyramidal neurons. While response summation mediated by ionotropic receptors is well-described, metabotropic receptor response summation is not, thereby making the authors' exploration of the phenomenon novel and impactful. By carrying out a series of elegant and challenging experiments that are coupled with computational analyses, the authors conclude that summation of synaptic GABA-B responses is linear, unlike the sublinear summation observed with ionotropic, GABA-A receptor-mediated responses.

The study is generally straightforward, even if the presentation is often dense. Three primary issues worth considering include:

1) The rather strong conclusion that GABA-B responses linearly summate, despite evidence to the contrary presented in Figure 5C.

2) Additional analyses of data presented in Figure 3 to support the contention that NGFCs co-activate.

3) How the MCell model informs the mechanisms contributing to linear response summation.

These and other issues are described further below. Despite these comments, this reviewer is generally enthusiastic about the study. Through a set of very challenging experiments and sophisticated modeling approaches, the authors provide important observations on both (1) NGFC-PC interactions, and (2) GABA-B receptor mediated synaptic response dynamics.

The differences between the sublinear, ionotropic responses and the linear, metabotropic responses are small. Understandably, these experiments are difficult – indeed, a real tour de force – from which the authors are attempting to derive meaningful observations. Therefore, asking for more triple recordings seems unreasonable. That said, the authors may want to consider showing all control and gabazine recordings corresponding to these experiments in a supplemental figure. Also, why are sublinear GABA-B responses observed when driven by three or more action potentials (Figure 5C)? It is not clear why the authors do not address this observation considering that it seems inconsistent with the study's overall message. Finally, the final readout – GIRK channel activation – in the MCell model appears to summate (mostly) linearly across the first four action potentials. Is this true and, if so, is the result inconsistent with Figure 5C?

GABAB responses elicited by three and four presynaptic NGFC action potentials were investigated to have a better understanding about the extremities of NGFC-PC connection. Although, our spatial model suggests that in L1 in a single volumetric point one or two NGFCs could provide GABAB response with their respective volume transmission, it is still important that in the minority of the percentage three or more NGFCs could converge their output. The experiments in Fig 5 not only offer mechanistic understanding that possible HCN channel activation and GABA reuptake do not influence significantly the summation of metabotropic receptor-mediated responses, but also support additional information about the extensive GABAB signaling from more than two NGFC outputs. Interestingly in this experiment the summation until two action potentials show very similar linear integration as seen in the triplet recordings. This result suggests that the temporal and spatial summation is identical when limited inputs are arriving to the postsynaptic target cell. Similar summation interaction can be seen in our model until two consecutive GABA releases. Three or four consecutive GABA releases in our model still produces linear summation, our experiments show moderate sublinearity. One possible answer for this inconsistency is the vesicle depletion in NGFCs after multiple rapid release of GABA, which was not taken into account in our model.

Presumably, the motivation for Figure 3 is that it provides physiological context for when NGFCs might be coactive, thereby providing the context for when downstream, PC responses might summate. This is a nice, technically impressive addition to the study. However, it seems that a relevant quantification/evaluation is missing from the figure. That is, the authors nicely show that hind limb stimulation evokes responses in the majority of NGFCs. But how many of these neurons are co-active, and what are their spatial relationships? Figure 3D appears to begin to address this point, but it is not clear if this plot comes from a single animal, or multiple? Also, it seems that such a plot would be most relevant for the study if it only showed alpha-actin 2-positive cells. In short, can one conclude that nearby, presumptive NGFCs co-activate, and is this conclusion derived from multiple animals?

The aim of Fig. 3 D was to indicate that the active, presumably NGFCs are spatially located close to each other. The figure comes from a single animal. We agree with the reviewer, therefore changed the scatter plot figure in Fig. 3D to another one, that provides information about the molecular profiles of the active/inactive cells. We made an effort to further analyze our in vivo data and the spatial localization of the monitored interneurons (see Author response image 3.). The results are from 4 different animals, in these experiments numerous L1 interneurons are active during the sensory stimulus, as shown in the scatter plot. We calculated the shortest distance between all active cells and all ɑ-actinin2+ that were active in experiments. The data suggest that in the case of identified active ɑ-actinin2+ cells, the interneuron somas were on average 182.69+60.54 or 305.135+34.324 μm distance from each other. Data from Fig. 2D indicates that the average axonal arborization of the NGFCs is reaching ~200-250μm away. Taken these two data together, in theory it is probable that the spatial localization would allow neighboring NGFCs to directly interact in the same spatial point.

The inclusion of the diffusion-based model (MCell) is commendable and enhances the study. Also, the description of GABA-B receptor/GIRK channel activation is highly quantitative, a strength of the study. However, a general summary/synthesis of the observations would be helpful. Moreover, relating the simulation results back to the original motivation for generating the MCell model would be very helpful (i.e. the authors asked whether "linear summation was potentially a result of the locally constrained GABAB receptor - GIRK channel interaction when several presynaptic inputs converge"). Do the model results answer this question? It seems as if performing "experiments" on the model wherein local constraints are manipulated would begin to address this question. Why not use the model to provide some data – albeit theoretical – that begins to address their question?

We re-formulated the problem to be addressed in this Results section. We admit that our model is has several limitations in the Discussion and, consequently, we restricted its application to a limited set of quantitative comparisons paired to our experimental dataset or directly related to pioneering studies on GABAB efficacy on spines vs shafts. We believe that a proper answer to the reviewer’s suggestion would be worth a separate and dedicated study with an extended set of parameters and an elaborated model.

In sum, the authors present an important study that synthesizes many experimental (in vitro and in vivo) and computational approaches. Moreover, the authors address the important question of how synaptic responses mediated by metabotropic receptors summate. Additional insights are gleaned from the function of neurogliaform cells. Altogether, the authors should be congratulated for a sophisticated and important study.

Reviewer #3:

The authors of this manuscript combine electrophysiological recordings, anatomical reconstructions and simulations to characterize synapses between neurogliaform interneurons (NGFCs) and pyramidal cells in somatosensory cortex. The main novel finding is a difference in summation of GABAA versus GABAB receptor-mediated IPSPs, with a linear summation of metabotropic IPSPs in contrast to the expected sublinear summation of ionotropic GABAA IPSPs. The authors also provide a number of structural and functional details about the parameters of GABAergic transmission from NGFCs to support a simulation suggesting that sublinear summation of GABAB IPSPs results from recruitment of dendritic shaft GABAB receptors that are efficiently coupled to GIRK channels.

I appreciate the topic and the quality of the approach, but there are underlying assumptions that leave room to question some conclusions. I also have a general concern that the authors have not experimentally addressed mechanisms underlying the linear summation of GABAB IPSPs, reducing the significance of this most interesting finding.

1) The main novel result of broad interest is supported by nice triple recording data showing linear summation of GABAB IPSPs (Figure 4), but I was surprised this result was not explored in more depth.

We have chosen the approach of studying GABAB-GABAB interactions through the scope of neurogliaform cells and explored how neurogliaform cells as a population might give rise to the summation properties studied with triple recordings. This was a purposeful choice admittedly neglecting other possible sources of GABAB-GABAB interactions which possibly take place during high frequency coactivation of homogeneous or heterogeneous populations of interneurons innervating the same postsynaptic cell. We agree with the reviewer that the topic of summation of GABAB IPSPs is important and in-depth mechanistic understanding requires further separate studies.

2) To assess the effective radius of NGFC volume transmission, the authors apply quantal analysis to determine the number of functional release sites to compare with structural analysis of presynaptic boutons at various distances from PC dendrites. This is a powerful approach for analyzing the structure-function relationship of conventional synapses but I am concerned about the robustness of the results (used in subsequent simulations) when applied here because it is unclear whether volume transmission satisfies the assumptions required for quantal analysis. For example, if volume transmission is similar to spillover transmission in that it involves pooling of neurotransmitter between release sites, then the quantal amplitude may not be independent of release probability. Many relevant issues are mentioned in the discussion but some relevant assumptions about QA are not justified.

Indeed, pooling of neurotransmitter between release sites may affect quantal amplitude, therefore we examined quantal amplitude under low release probability conditions using 0.7- 1.5 mM [Ca]o to detect postsynaptic uniqantal events initiated by neurogliaform cell activation (Author response image 7). This way we measured similar quantal current amplitudes comparing with BQA method with no significant difference (4.46±0.83 pA, n=4, P=0.8, Mann-Whitney Test).

3) The authors might re-think the lack of GABA transporters in the model since the presence and characteristics of GATs will have a large effect on the spread of GABA in the extracellular space.

We agree that the presence of GAT could effectively shape the GABA exposure, e.g. (Scimemi 2014). During the development of the model, we took into consideration different possibilities and solutions to create the model’s environment. To our knowledge, there is no detailed electron microscopic study that would provide ultrastructural measurements of structural elements around the NGFC release sites and postsynaptic pyramidal cell dendrites in layer 1 while preserving the extracellular space. Moreover, quantitative information is scarce about the exact localization and density of the GATs along the membrane surface of glial processes around confirmed NGFC release sites. We felt that developing a functional environment that would contain GABA transporters without possessing such information would be speculative. Furthermore, during the development of the model it became clear that incorporating thousands of differentially located GABA transporters would massively increase the processing time of single simulations including monitoring each interaction between GATs and GABA molecules, and requiring computational power calculating the diffusion of GABA molecules in the extracellular space, even if GABA molecules are far from the postsynaptic dendritic site without any interaction.

As an admittedly simple and constrained alternative, we decided to set a decay half-life for the GABA molecules released. This approach allows us to mimic the GABA exposure time of 20-200 ms, based on experimental data (Karayannis et al 2010). In the model the GABA exposure time was 114.87 ± 2.1 ms with decay time constants of 11.52 ± 0.14 ms. After ~200 ms all the released GABA molecules disappeared from the simulation environment.

A detailed extracellular diffusion aspect was out of the scope of our model, we were interested in investigating how the subcellular localization of receptors and channels determine the summation properties.

4) I'm not convinced that the repetitive stimulation protocol of a single presynaptic cell shown (Figure 5) is relevant for understanding summation of converging inputs (Figure 4), particularly in light of the strong use-dependent depression of GABA release from NGFCs. It is also likely that shunting inhibition contributes to sublinear summation to a greater extent during repetitive stimulation than summation from presynaptic cells that may target different dendritic domains. The authors claim that HCN channels do not affect integration of GABAB IPSPs but one would not expect HCN channel activation from the small hyperpolarization from a relatively depolarized holding potential.

Use-dependent synaptic depression of NGFC induced postsynaptic responses was nicely documented by Karayannis and coworkers (2010) although they investigated the GABAA component of the responses and they found that the depression is caused by the desensitization of postsynaptic GABAA receptors. We are not aware of experiments published on the short term plasticity of GABAB responses. In our experiments represented in Fig 5 we found linearity in the summation of GABAB responses up to two action potentials and sublinearity for 3 and 6 action potentials. In fact, our results show that no synaptic depression is detectable in response to paired pulses since amplitudes of the voltage responses were doubled compared to a single pulse which means that the paired pulse ratio is around 1. To verify our result, we repeated our dual recording measurements with one, two, three and four spike initiation in the presynaptic neurogliaform cell (Author response image 6). Measuring both the amplitude and the overall charge of GABAB responses we again found linear relationship among one and two spike initiation protocol.

Author response image 6 - Integration of GABAB receptor-mediated synaptic currents (A) Representative recording of a neurogliaform synaptic inhibition on a voltage clamped pyramidal cell. Bursts of up to four action potentials were elicited in NGFCs at 100 Hz in the presence of 1 μM gabazine and 10 μM NBQX (B) Summary of normalized IPSC peak amplitudes (left) and charge (right). (C) Pharmacological separation of neurogliaform initiated inhibitory current.

Author Response:

Reviewer #1 (Public Review):

Overall the work is an impressive analysis of an understudied cell-type in human MS, and represents an important finding. The paper is well presented and the figures very clear. However, the manuscript is descriptive and, although this is not a problem by itself, the depth and limitations of the Cytof (only 37 markers) leaves the reader without a clear idea of what these cells could be doing.

Some single-cell RNAseq and other ways to interrogate potential mechanisms and function would be particularly helpful here, but is perhaps beyond the scope of the paper.

We thank the reviewer for this nice comment. We fully agree that a next informative step would be the investigation of the function and mechanisms of the NK cell populations in MS pathology. At this moment, that is indeed beyond the scope of the current manuscript. We do believe that our findings can guide future studies to explore potential mechanisms of NK cells in more depth.

At minimum more immunohistochemical and smFish or in situ hybridization to validate key findings (using the markers identified by CyTOF) and add to the spatial relationships of Nk Cells with other border and brain cells would be informative.

We appreciate this suggestion and have performed different immunohistochemical analysis to study the spatial relationship of NK cells and other immune and brain cells in the MS brain (Essential Revisions Fig. 1). We have stained the same cohort described in the manuscript for CD45, NKp46, GrB and Iba1 as well as CD45, NKp46, GrB and GFAP, to study the interaction of NK cells with microglia/macrophages and astrocytes, respectively, and with CD45+ immune cells in general. In MS lesions, we were able to detect a small but similar percentage of putative CD56bright NK cells (CD45+ NKp46+ GrB- cells) interacting with CD45+ Iba1- cells and with CD45+ Iba1+ cells (Essential Revisions Figure 1a-b). Due to astrogliosis, the processes of astrocytes densely populate the MS lesions and as such, we cannot infer if the interaction between NK cells and astrocytes is functional. Furthermore, the absolute number of NK cells in control brains is low, so we can only obtain reliable data from MS brains. As a result, we are unable to compare the observed interactions in MS lesions with a control condition. Of note, CD56bright NK cells are potent cytokine producers and their potential regulatory functions are not be limited to contact-dependent interactions.

Essential Revisions Fig. 1 cellular interactions of Granzyme B- NK cells (a) Representative immunohistochemical staining of Granzyme B- NK cells stained for CD45 (green), NKp46 (magenta) and negative for Granzyme B (cyan), together with microglia stained with iba1 (red). Scale bar = 10µm. (b) Pie chart displays the percentage of CD45+ NKp46+ Granzyme Bcells interacting with CD45+ Iba1+ and C45+ Iba1- cells in MS lesions. (c) Representative immunohistochemical staining of NK cells stained for CD45 (green), NKp46 (magenta) and negative for Granzyme B (cyan), together with astrocytes stained with GFAP (red). Scale bar = 10µm.

A major weakness of the study is that is is underpowered and thus not clear how robust or representative these findings are in MS given the heterogeneity of the disease and also potential differences in Sex, Age and lack of healthy controls. (AD samples labelled as control.)

We thank the reviewer for their comment. First we would like to comment on the presumed lack of healthy controls. In this study, we included two ‘control’ groups, one of them consisted out of non-neurological controls (“NNC”), free of any neurological disease, and the other consisted of neurological controls (“NC”), including demented and Alzheimer patients. We acknowledge that this terminology leaves the reader confused; as such, we renamed the “NC” group with patients suffering from dementia to “Dementia” and the “NNC” group of donors without neurological disease to “Controls”.

Secondly, while our sample size is rather small, it is comparable to other studies that use fresh post-mortem brain tissue (Böttcher et al, 2020).. The usage of this unique postmortem brain tissue from human donors is severely limited by the number of well-characterized samples available, their demographics and clinical background. To overcome the underpowered design and possible effects of confounders as sex and age, we validated our main finding by multiplex immunohistochemistry in a separate cohort. This included 5 controls (2 females, 3 males, f:m ratio of 0.667) and 7 MS cases (3 females and 4 males, f:m of 0.75), with a similar female/male ratio and matched age (Wilcoxon rank sum test with continuity correction, p-value = 0.41). We now included the characteristics of the validation cohort in the manuscript as well.

“Finally, to confirm that CD56bright NK cells accumulate in periventricular brain regions in MS donors, we used multiplex immunohistochemistry in an independent cohort (Table 1), wherein MS and control groups were age-matched (Wilcoxon rank sum test with continuity correction, p-value = 0.41) and had a similar female:male ratio (0.667 in controls and 0.75 in MS).”

Böttcher C, van der Poel M, Fernández-Zapata C, Schlickeiser S, Leman JKH, Hsiao CC, Mizee MR, Adelia, Vincenten MCJ, Kunkel D, Huitinga I, Hamann J, Priller J (2020) Single-cell mass cytometry reveals complex myeloid cell composition in active lesions of progressive multiple sclerosis. Acta neuropathologica communications, 8(1), 1-18

It is also important to show the NK cells are actually in the parenchyma and interacting with other cells (e.g., microglia) of the lesion. If the authors have this tissue and antibodies to do that, this would add to the study. Moreover, the details on samples and controls should be more clearly communicated in the text and legends as well as the caveats and limitations of the study in the Discussion.

The location of NK cells within the brain parenchyma is an important determinant of their function within the CNS. Thus, we included a basement membrane marker (collagen IV) in our multiplex IHC panel in order to exclude the cells within the vessel lumen. As this has not been clearly communicated, we have adjusted the sentence from the subsection Multiplex immunohistochemistry in the Methods (from “Cells within the lumen of vessels from the choroid plexus sections were excluded manually” to “Cells within the lumen of vessels were excluded manually with the aid of collagen IV staining.”). We have addressed in Essential Revisions Fig. 1 the additional IHC experiments performed to explore the interactions of NK cells with other brainresident cells. We thank the reviewer for warning us on the difficulty of our nomenclature. We have thus adjusted the labels of the three main groups throughout the manuscript as follows: Control (previously, NNC), Dementia (previously, NC) and MS (same as before). We also have expanded the limitations of this study in the Discussion.

“Our study has two main limitations, first scarcity of fresh human tissue prevented having sex and age-matched groups with large sample sizes for the CyTOF analysis. To overcome the underpowered design and possible effects of confounders, we have validated our main finding by multiplex immunohistochemistry in a separate cohort with a similar age and female/male ratio. Secondly, there is a strong contribution of blood-derived immune cells in the choroid plexus, which precluded a clear distinction between circulating and stromal immune cells. This may have prevented the detection of choroid-plexus specific changes in the stroma, such as an accumulation of CD8+ T cells in the choroid plexus from MS donors, previously described by our group using immunohistochemistry [47]. In addition, the high proportion of granulocytes in the CP as detected by our CyTOF analysis likely originates from the circulation [47,63]. Contrariwise, the scarcity of B cells, despite the high vascularisation, is in line with previous reports [47,63]; and the detection of rare ASCs in the choroid plexus but not in the blood reassures their tissue specificity [63].”

Reviewer #2 (Public Review):

The data are extensive, valuable, convincing, and entirely descriptive (as studies using human post-mortem material must be, of necessity). What emerges is a detailed account of NK cells in specific regions of the MS brain (although here the authors slightly overplay how little is known about NK cells in MS). The study provides a very comprehensive resource. The authors speculate on what their data might mean in terms of disease dynamics is a reasonable and informed way, but much of what is concluded is inference not backed up by experiment studies that would allow this to be more than a resource paper.

We thank the reviewer for his/her compliments and agree that in this manuscript we can only speculate on the role of NK cells and their way of migration or proliferation, to and within the brain. Only future research can solve these speculations. We have addressed these concerns accordingly in the discussion and have removed any concluding or far-fetched speculations which is not backed-up by our own data.

Reviewer #3 (Public Review):

The authors introduce their work in the context of the prevailing uncertainties about the pathogenesis of multiple sclerosis (MS) and, in particular, seem to reference the initiation of immune lesions in early MS. However, the work itself addresses end-stage MS situations, which is quite possibly an entirely different landscape altogether, and may not be informative about MS initiation.

We want to thank the reviewer for pointing out this misleading part of the text. We agree that our study does not provide any information on the initial stages of MS, and have therefore adjusted this part of the introduction to avoid confusion. “Brain regions around the ventricles are hotspots for MS lesions [8,21,39,52], but underlying mechanisms are poorly understood [41]. Since the majority of periventricular MS lesions occur around a central vessel [1,57], it has been suggested that vascular topography may influence MS pathology [33].”

As a textual point, the manuscript makes far too many speculations about possible cell trafficking between compartments than is justified by a cross-section study.

We appreciate this concern and we have therefore tuned down our speculations in the results and discussion sections.

That said, the work itself is a carefully done descriptive characterisation of the leucocyte landscape found in the periventricular septum, choroid plexus (and peripheral blood) post-mortem from cases of multiple sclerosis (MS), non-MS neurological disease (dementia), and non-neurological controls (8-12 each). The material is rare, the post-mortem delays are quite short, the cell lineage characterisation is fairly extensive and some of the data are well supported by immunohistochemistry.

We thank the reviewer for these compliments.

Author Response:

Reviewer #4 (Public Review):

In this work, Tee et al. study the implications of Heparan Sulfate (HS) binding mutations observed on the Enterovirus A71 (EV-A71) capsid. HS-binding mutations are observed for several virus infections and are often presumed to be a cell culture adaptation. However, in the case of EV-A71, the presence of HS-binding mutations in clinical samples and the contradictory findings in animal studies have made the clinical relevance of HS-binding a subject of debate. Therefore, to better understand the role of HS-binding in EV-A71, the authors use a mouse-adapted EV-A71 variant (MP4) and compare it to a cell-adapted strong HS-binder (MP4-97R/167G). Using these two variants, the authors show that the strong HS-binder does not require acidification for uncoating and genome release. Furthermore, it is demonstrated that the capsid stability of the HS-binding variant is compromised, resulting in pH-independent uncoating. Overall, this study provides new insights demonstrating that seemingly beneficial mutations increasing viral replication may be counterbalanced by other unintended consequences.

Strengths:

The thoroughness of the experiments performed to demonstrate that the HS-binding phenotype results in pH-independent entry and capsid destabilisation is worth highlighting. In this regard, the authors have explored viral entry using a range of approaches involving lysosomotropic drugs, viral binding assays, and neutral red-labelled viruses coupled with diverse techniques such as FISH, RNAscope, and transient expression of constitutively active molecules to inhibit parts of the viral cycle. In my opinion, this is necessary to rule out the other downstream effects of the lysomotropic drugs and to confirm the role of the HS-binding mutation in the entry phase. The use of in silico analysis coupled with negative staining electron microscopy and environmental challenge assays is notable. Finally, the demonstration of some of the work using a human-relevant strain is commendable.

We appreciate the reviewer recognition of the significance of our study and the precious advises.

Weaknesses:

A major weakness in this study is the focus on using a mouse-adapted EV-A71 strain (MP4). In the introduction, it is argued that HS-binding mutations are controversial due to their occurrence in cell culture. However, due to host limitations, mice are not the natural hosts for EV-A71 and thus, the same argument can be made for a mouse-adapted strain. It is not clear how different this strain is from circulating EV-A71 strains and the relevance of these findings to the human situation is questionable. This is particularly made evident in the discussion where it is highlighted that HS-binding variants (VP1-145G/Q mutants) have been associated with severe neurological cases while the same variants show attenuated phenotypes in mice and monkeys. This contrast between clinical data and animal studies should be highlighted in the introduction, rather than later in the discussion, as currently the in vivo animal studies are presented as the optimal situation and may lead to misconstrued conclusions from the results.

As requested by the reviewer, we included new experiments performed with a clinical strain isolated in an immunosuppressed patient (Cordey et al., 2012). We compared the sensitivity of this human strain harboring or not the VP1 L97R and E167G mutations to HCQ and confirmed that the similar differential sensitivity to HCQ was observed as with the MP4 variant. This result is presented as a new supplementary figure (Figure 6-figure supplement 1) and is described in the result section of the revised manuscript (Page 7, lines 251).

Page 7, lines 251: To determine if our observations are applicable to human strains, we examined the sensitivity of a closely related clinical strain. This strain was isolated from the respiratory tract of an immunosuppressed patient with a disseminated EV-A71 infection27. Additionally, we tested a strong HS-binding derivative that harbors the same VP1-L97R and E167G mutations as our MP4 double mutant. Notably, this human clinical strain shares 98.3% amino acid similarity with the MP4 variant used in this study and exhibits similar HS-binding phenotypes28. As shown in Figure 6-figure supplement 1, the original human strain was inhibited by HCQ, whereas the double mutant exhibited insensitivity to the drug.

We also added the comment about discrepancy between clinical data and animal studies in the introduction as requested (page 2, lines 69-76): However, epidemiological surveillance of human EV-A71 infections19-21 and experimental evidence from 2D human fetal intestinal models22, human airway organoids23 and air-liquid interface cultures24 suggest that HS binding may enhance viral replication and virulence in humans. In addition, recent research has shown that EV-A71 can be released and transmitted via cellular extrusions25 or exosomes26, potentially preventing viral trapping of HS-binding strains in the circulation. Further studies are required to evaluate the true impact of HS-binding mutations on the spread and virulence of EV-A71 in both animal models and humans.

An important consideration is that the results are based primarily on image analysis. The inclusion of RT-qPCR and/or plaque assays as supplementary data will help strengthen the findings.

We have performed RT-qPCR to confirm the immunostaining data and included them in the supplementary data (Figure 1-figure supplement 1E). Reference to these data is made in the result section [Page 4, lines 114-116: These results were confirmed by viral load quantification with real-time RT-PCR (Figure 1-figure supplement 1E).]

Moreover, there are suggestions of an intermediate binder having a different phenotype. As this intermediate binder is the clinical phenotype, data on the entry of this intermediate binder will be valuable.

While we agree with reviewer that the single mutant is an intermediate binder and exhibits a clinical phenotype, we made the decision to work with variants that display clear phenotypes, selecting MP4 and the double mutant, as the latter is fully attenuated in both immunocompetent and immunosuppressed mice (Weng et al., 2023). Additionally, we performed an experiment using HCQ, where we observed an intermediate effect with the single mutant. This further confirmed our decision to proceed with MP4 and the double mutant for all experiments. The data supporting this are shown in Author response image 1, which we are sharing exclusively with the reviewer.

Author response image 1.

Differential sensitivity of MP4, MP4-97R and MP4-97R167G to Lysosomotropic drugs

Another weakness in the study is the lack of contextualization of the results to current EV-A71 literature. For instance, SCARB2 is referred to as the internalization receptor but a recent study has shown that SCARB2 is not required for internalization (https://doi.org/10.1128%2Fjvi.02042-21). The findings from this study are consistent with the localization of SCARB2 in the lysosomal membranes. Furthermore, the same study has highlighted host sulfation as a key factor in EV-A71 entry. Post-translational sulfation introduces negatively charged residues on host proteins including HS and SCARB2. This increases the binding of HS-binding strains to these proteins. In this regard, the reduced infectivity upon soluble SCARB2 treatment may simply be due to enhanced binding rather than capsid opening as suggested in the results. Therefore, additional experiments (e.g. nSEM following soluble SCARB2 treatment) must be performed to support the conclusion of capsid opening, due to inherent instability, upon SCARB2 binding.

We apologize for not citing this relevant literature excluding the role of SCARB2 in viral attachment. We have now included these references in the revised version of the manuscript. (Page 2, lines 54-56: “Since SCARB2 is mostly localized on endosomal and lysosomal membrane and sparsely on plasma membrane3,5, it seems to play only a minor role in EV-A71 cell attachment6,7.

We thank the reviewer for mentioning the possibility that the sulfation of SCARB2 may enhance its binding to the mutated virus compared to the wild-type virus, potentially explaining the selective competitive inhibition of this variant by soluble SCARB2 produced in mammalian cells. To investigate this hypothesis, we performed nsEM imaging of the double mutant incubated with soluble SCARB2 and we observed an increase in the proportion of empty capsids in the presence of soluble SCARB2 (4% versus 0.7%), supporting our original findings that the inactivation is indeed associated with capsid opening. The results are included in the revised manuscript in Figure 5-figure supplement 4 and described on Page 7, lines 243-245: “However, the double mutant exhibited a ~5-fold increase in empty capsid percentage after treatment with sSCARB2 (Figure 5-figure supplement 4), consistent with the functional data above.”

In addition to the above, other existing literature on EV-A71 pathogenesis using organoids contradicts some of the explanations of differential phenotype in clinical observations versus mice models. In the introduction, it is suggested that reduced neurovirulence of HS-binding strains is due to binding to the vascular endothelia. However, the correlation of clinical severity to viremia (https://doi.org/10.1186/1471-2334-14-417) and the association of HS-binding mutants to clinical disease counteract this suggestion. Similarly, viral infection in human organoids with EV-A71 results in as low as 0.4% of the cells being infected (https://doi.org/10.1038/s41564-023-01339-5). In this case, if viral binding to (ubiquitously expressed) HS results in viral trapping then the HS-binding mutants should show lowered infectivity in organoid models rather than the observed higher infectivity (https://doi.org/10.3389/fmicb.2023.1045587, https://doi.org/10.1038/s41426-018-0077-2). Finally, EV-A71 release has also been shown to occur in exosomes (https://doi.org/10.1093%2Finfdis%2Fjiaa174) which effectively provides a protective lipid membrane. These recent findings must be incorporated into the article and will help better contextualize their findings.

We appreciate the reviewer thoughtful comments. We do not believe that the correlation between clinical severity and viremia contradicts the viral trapping hypothesis. For strains that do not bind to HS, the absence of viral trapping could indeed lead to higher viral concentrations in the bloodstream, potentially increasing neurovirulence. However, we agree with the reviewer that other observations in humans, along with experimental data from more relevant models such as organoids, challenge the trapping hypothesis. We are grateful for the suggested citations and have incorporated these references in the introduction, where we discuss this point in more detail

Page 2, lines 69-76: “However, epidemiological surveillance of human EV-A71 infections19-21 and experimental evidence from 2D human fetal intestinal models22, human airway organoids23 and air-liquid interface cultures24 suggest that HS binding may enhance viral replication and virulence in humans. In addition, recent research has shown that EV-A71 can be released and transmitted via cellular extrusions25 or exosomes26, potentially preventing viral trapping of HS-binding strains in the circulation. Further studies are required to evaluate the true impact of HS-binding mutations on the spread and virulence of EV-A71 in both animal models and humans.”

Overall, the authors present new findings with convincing methodology. The manuscript can be improved in the contextualization of the findings and highlighting the weakness in translating these findings to resolve the debate surrounding the relevance of HS-binding phenotype. The inclusion of additional experiments and data recommended to the authors will also help strengthen the manuscript.<br />

Author Response:

Reviewer #1 (Public Review):

This manuscript investigates the gene regulatory mechanisms that are involved in the development and evolution of motor neurons, utilizing cross-species comparison of RNA-sequencing and ATAC-sequencing data from little skate, chick and mouse. The authors suggest that both conserved and divergent mechanisms contribute to motor neuron specification in each species. They also claim that more complex regulatory mechanisms have evolved in tetrapods to accommodate sophisticated motor behaviors. While this is strongly suggested by the authors' ATAC-seq data, some additional validation would be required to thoroughly support this claim.

Strengths of the manuscript:

1) The manuscript provides a valuable resource to the field by generating an assembly of the little skate genome, containing precise gene annotations that can now be utilized to perform gene expression and epigenetic analyses. The authors take advantage of this novel resource to identify novel gene expression programs and regulatory modules in little skate motor neurons.

2) Cross-species RNA-seq and ATAC-seq data comparisons are combined in a powerful approach to identify novel mechanisms that control motor neuron development and evolution.

Weaknesses:

1) It is surprising that the analysis of RNA-seq datasets between mouse, chick, and little skate only identified 5 genes that are common between the 3 species, especially given the authors' previous work identifying highly conserved molecular programs between little skate and mouse motor neurons, including core transcription factors (Isl1, Hb9, Lhx3), Hox genes and cholinergic transmission genes. This raises some questions about the robustness of the sequencing data and whether the genes identified represent the full transcriptome of these motor neurons.

To address reviewer #1’s questions, we have generated RNA sequencing data with mouse forelimb MNs and re-analyzed the RNA-seq data using only the homologous MN populations (Figure 3) among different species. As a result, many genes (1038 genes) are commonly expressed in MNs in different species, including many known MN marker genes. In the result section, we have added the following:

“The evolution of genetic programs in MNs was investigated unbiasedly by comparing highly expressed genes in pec-MNs (percentile expression > 70) of little skate with the ones from MNs of mouse and chick, two well-studied tetrapod species. In order to compare gene expression with homologous cell types from each species, we performed RNA sequencing on forelimb MNs of mouse embryos at embryonic day 13.5 (e13.5) and wing level MNs of chick embryos at Hamburger-Hamilton (HH) stage 26–27…”

We have also compared our re-analysis with previous results in Figure 2–figure supplement 1, shown above. Most of the fin MN genes (21/24) are highly expressed in pecMNs (percentile > 70), consistent with the previous in situ experiments. In the Results we have added the following:

“Although the total number of DEGs are different from the previous data (592 vs. 135 genes in pec-MN DEGs), which might be caused by different statistical analysis with different reference genome, previous RNA-seq data based on de novo assembly and annotation using zebrafish was mostly recapitulated in our DEG analysis based on our new skate genome (21 out of 24 previous fin MN marker genes have the expression level ranked above 70th percentile in Pec-MNs; Figure 2‒figure supplement 1).”

2) The authors suggest based on analysis of binding motifs in their ATAC-seq data that the greater number of putative binding sites in the mouse MNs allows for a higher complexity of regulation and specialization of putative motor pools. This could certainly be true in theory but needs to be further validated. The authors show FoxP1 as an example, which seems to be more heavily regulated in the mouse, but there is no evidence that FoxP1 expression profile is different between mouse and skate. It is suggested in Fig.5 that FoxP1 might be differentially regulated by SnaiI in mouse and skate but the expression of SnaiI in MNs in either species is not shown.

We have added further discussion and data about differential expression of Foxp1 in mouse and little skate in Figure 5–figure supplement 16 and have discussed as follows:

“Foxp1, the major limb/fin MN determinant appears to be differentially regulated in tetrapod and little skate. Although Foxp1 is expressed in and required for the specification of all limb MNs in tetrapods, Foxp1 is downregulated in Pea3 positive MN pools during maturation in mice (Catela et al., 2016; Dasen et al., 2008). In addition, preganglionic motor column neurons (PGC MNs) in the thoracic spinal cord of mouse and chick express half the level of Foxp1 expression than limb MNs. Although PGC neurons have not yet been identified in little skate, we tested the expression level of Foxp1 using a previously characterized tetrapod PGC marker, pSmad. We observed that Foxp1 is not expressed in MNs that express pSmad (Figure 5‒figure supplement 3). Since there is currently no known marker for PGC MNs in little skate, our conclusion should be taken with caution.”

As for Snai1, in the revision we performed a motif enrichment analysis with an unbiased gene list where Snai1 didn’t show up. However, when we performed an RNA in situ hybridization experiment for Snai1 (Figure 5–figure supplement 3), we found that Snai1 is expressed in MNs of both mouse and little skate, but not in chick, which has been shown previously (Cheung et al., 2005). In order to examine the function of Snai1 in the regulation of Foxp1 expression, we ectopically expressed Snai1 in chick spinal cord by performing in ovo electroporation. However, we did not detect any changes in Foxp1. Instead we observed an increase in the number of neurons and abnormal MN exits from the spinal cord, which is the reminiscent of a previous observation (Zander et al., 2014). Although we did not detect any changes in Foxp1 expression, we cannot rule out the possibility that Snai1 regulates Foxp1 in mouse and little skate, which may require a gene knock out experiment. Because binding sites of Snai1 were not enriched in the new gene sets that we analyzed in the revision, we have not further discussed the Snai1 in the text.

3) In their discussion section the authors state that they found both conserved and divergent molecular markers across multiple species but they do not validate the expression of novel markers in either category beyond RNA-seq, for example by in situ or antibody staining.

We have added RNA in situ hybridization results in Figure 3C and Figure 3–figure supplement 1 and 2. Most of the genes were expressed in tissues in accordance with the sequencing results (6 out of 9 common MN genes; 4 out of 6 mouse specific genes; 5 out of 7 skate specific genes). Specifcally, Uchl1, Slc5a7, Alcam, and Serinc1 are expressed in MNs of all three species; Coch, Ppp1rc, Ctxn1, and Clmp are expressed in MNs of mouse but not in MNs of other species; Eya1, Etv5, Dnmbp, and Spint1 are expressed in MNs of skate but not in MNs of other species. In the result section, we have summarized the results as follow:

“These results were validated by performing RNA in situ hybridization in tissue sections on a subset of species-specific genes …”