Reviewer #3 (Public Review):

There has been a long-standing link between the biology of sulfur-containing molecules (e.g., hydrogen sulfide gas, the amino acid cysteine, and its close relative cystine, et cetera) and the biology of hypoxia, yet we have a poor understanding of how and why these two biological processes and are co-regulated. Here, the authors use C. elegans to explore the relationship between sulfur metabolism and hypoxia, examining the regulation of cysteine dioxygenase (CDO1 in humans, CDO-1 in C. elegans), which is critical to cysteine catabolism, by the hypoxia inducible factor (HIF1 alpha in humans, HIF-1 in C. elegans), which is the key terminal effector of the hypoxia response pathway that maintains oxygen homeostasis. The authors are trying to demonstrate that (1) the hypoxia response pathway is a key regulator of cysteine homeostasis, specifically through the regulation of cysteine dioxygenase, and (2) that the pathway responds to changes in cysteine homeostasis in a mechanistically distinct way from how it responds to hypoxic stress.

Briefly summarized here, the authors initiated this study by generating transgenic animals expressing a CDO-1::GFP protein chimera from the cdo-1 promoter so that they could identify regulators of CDO-1 expression through a forward genetic screen. This screen identified mutants with elevated CDO-1::GFP expression in two genes, egl-9 and rhy-1, whose wild-type products are negative regulators of HIF-1, raising the possibility that cdo-1 is a HIF-1 transcriptional target. Indeed, the authors provide data showing that cdo-1 regulation by EGL-9 and RHY-1 is dependent on HIF-1 and that regulation by RHY-1 is dependent on CYSL-1, as expected from other published findings of this pathway. The authors show that exogenous cysteine activates cdo-1 expression, reflective of what is known to occur in other systems. Moreover, they find that exogenous cysteine is toxic to worms lacking CYSL-1 or HIF-1 activity, but not CDO-1 activity, suggesting that HIF-1 mediates a survival response to toxic levels of cysteine and that this response requires more than just the regulation of CDO-1. The authors validate their expression studies using a GFP knockin at the cdo-1 locus, and they demonstrate that a key site of action for CDO-1 is the hypodermis. They present genetic epistasis analysis supporting a role for RHY-1, both as a regulator of HIF-1 and as a transcriptional target of HIF-1, in offsetting toxicity from aberrant sulfur metabolism. The authors use CRISPR/Cas9 editing to mutate a key amino acid in the prolyl hydroxylase domain of EGL-9, arguing that EGL-9 inhibits CDO-1 expression through a mechanism that is largely independent of the prolyl hydroxylase activity.

Overall, the data seem rigorous, and the conclusions drawn from the data seem appropriate. The experiments test the hypothesis using logical and clever molecular genetic tools and design. The sample size is a bit lower than is typical for C. elegans papers; however, the experiments are clearly not underpowered, so this is not an issue. The paper is likely to drive many in the field (including the authors themselves) into deeper experiments on (1) how the pathway senses hypoxia and sulfur/cysteine/H2S using these distinct mechanisms/modalities, (2) how oxygen and sulfur/cysteine/H2S homeostasis influence one another, and (3) how this single pathway evolved to sense and respond to both of these stress modalities.

My previous concerns have been addressed. The authors are commended on an excellent body of research.

Reviewer #4 (Public Review):

Summary:<br /> This is a revised manuscript that describes a role for cdo-1 in regulating cellular cysteine levels. The authors show that expression of cdo-1, predicted to encode a cysteine dioxygenase, is regulated by HIF-1, the conserved hypoxia-induced transcription factor. The expression of cdo-1 is controlled by the RHY-1/CYSL-1/EGL-9/HIF-1 pathway that has been demonstrated to be involved in the response to H2S.

Strengths:<br /> The new finding of this study is that cdo-1, predicted to encode a cysteine dioxygenase, is expressed in the hypodermis and that hypodermal expression rescues at least one phenotype of the cdo-1(mg622) mutant (ability to survive toxic sulfite accumulation in Moco-deficient conditions). Using sulfite toxicity is an interesting reporter for cellular cysteine abundance.

Weaknesses:<br /> The authors claim more than once that the H2S/Cys responsive pathway is RHY-1 - CYSL-1 - EGL-9 - HIF-1. Their data don't seem to support this claim, as they show that Pcdo-1::GFP is induced in rhy-1 mutants incubated with cysteine. It is therefore not appropriate to claim that "HIF-1-induced cysteine catabolism requires the activity of rhy-1" that they include in the description of the model in Fig 6. There is simply no evidence at all that RHY-1 has any role in modulating the activity of CDO-1 other than through transcriptional activation via HIF-1.

I don't find the arguments that this pathway is required for cysteine homeostasis per se (as claimed in the last sentence of the introduction). The authors expose worms to excess cysteine for 48 hours in liquid culture with bacteria. It is well known in these conditions that the bacteria will produce H2S from the cysteine in the culture. All of the cysteine exposure data shown can be explained by the effect of H2S exposure. This would explain why hif-1 and cysl-1 mutants die but cdo-1 mutants do not, for example. The authors don't provide any data to rule out the possibility that bacterial H2S production underlies these results. This explains why the pathway described in this work is the same as has been previously described. Similarly, there is no evidence at all to support their assertion that there are "other pathways" induced by HIF-1 to deal with sulfite produced by cysteine catabolism. However, if the main problem is H2S production (perhaps by bacteria) then cdo-1 would not be relevant and the mutants would be viable as observed.

In a couple of places, the authors seem to argue that H2S-induced expression is limited to the hypodermis and hypoxia-induced gene expression is mostly in the intestine. This is consistent with the expression of cdo-1 (this work) and nhr-57 (Budde and Roth) but it is not appropriate to generalize this. Previous work from the Ruvkun lab (Ma et al) show that the CYSL-1 regulates expression of HIF-1 targets in neurons. Moreover, HIF-1 protein accumulates in the nucleus of nearly all cells, and there is no reason to believe that there are changes in the expression of other genes in different tissues.

eLife assessment

This useful study aimed to examine the relationship of spatial frequency selectivity of single macaque inferotemporal (IT) neurons to category selectivity. There are some interesting findings in this report but some of these findings were difficult to evaluate because several critical details of the analysis are incomplete. The conclusion that single-unit spatial frequency selectivity can predict object coding needs further evidence to confirm.

Reviewer #1 (Public Review):

Summary:<br /> This study reports that IT neurons have biased representations toward low spatial frequency (SF) and faster decoding of low SFs than high SFs. High SF-preferred neurons, and low SF-preferred neurons to a lesser degree, perform better category decoding than neurons with other profiles (U and inverted U shaped). SF coding also shows more sparseness than category coding in the earlier phase of the response and less sparseness in the later phase. The results are also contrasted with predictions of various DNN models.

Strengths:<br /> The study addressed an important issue on the representations of SF information in a high-level visual area. Data are analyzed with LDA which can effectively reduce the dimensionality of neuronal responses and retain category information.

Weaknesses:<br /> The results are likely compromised by improper stimulus timing and unmatched spatial frequency spectrums of stimuli in different categories.

The authors used a very brief stimulus duration (35ms), which would degrade the visual system's contrast sensitivity to medium and high SF information disproportionately (see Nachmias, JOSAA, 1967). Therefore, IT neurons in the study could have received more degraded medium and high SF inputs compared to low SF inputs, which may be at least partially responsible for higher firing rates to low Sf R1 stimuli (Figure 1c) and poorer recall performance with median and high SF R3-R5 stimuli in LDA decoding. The issue may also to some degree explain the delayed onset of recall to higher SF stimuli (Figure 2a), preferred low SF with an earlier T1 onset (Figure 2b), lower firing rate to high SF during T1 (Figure 2c), somewhat increased firing rate to high SF during T2 (because weaker high SF inputs would lead to later onset, Figure 2d).

Figure 3b shows greater face coding than object coding by high SF and to a lesser degree by low SF neurons. Only the inverted-U-shaped neurons displayed slightly better object coding than face coding. Overall the results give an impression that IT neurons are significantly more capable of coding faces than coding objects, which is inconsistent with the general understanding of the functions of IT neurons. The problem may lie with the selection of stimulus images (Figure 1b). To study SF-related category coding, the images in two categories need to have similar SF spectrums in the Fourier domain. Such efforts are not mentioned in the manuscript, and a look at the images in Figure 1b suggests that such efforts are likely not properly made. The ResNet18 decoding results in Figure 6C, in that IT neurons of different profiles show similar face and object coding, might be closer to reality.

Reviewer #2 (Public Review):

Summary:<br /> This paper aimed to examine the spatial frequency selectivity of macaque inferotemporal (IT) neurons and its relation to category selectivity. The authors suggest in the present study that some IT neurons show a sensitivity for the spatial frequency of scrambled images. Their report suggests a shift in preferred spatial frequency during the response, from low to high spatial frequencies. This agrees with a coarse-to-fine processing strategy, which is in line with multiple studies in the early visual cortex. In addition, they report that the selectivity for faces and objects, relative to scrambled stimuli, depends on the spatial frequency tuning of the neurons.

Strengths:<br /> Previous studies using human fMRI and psychophysics studied the contribution of different spatial frequency bands to object recognition, but as pointed out by the authors little is known about the spatial frequency selectivity of single IT neurons. This study addresses this gap and they show that at least some IT neurons show a sensitivity for spatial frequency and interestingly show a tendency for coarse-to-fine processing.

Weaknesses and requested clarifications:<br /> 1. It is unclear whether the effects described in this paper reflect a sensitivity to spatial frequency, i.e. in cycles/ deg (depends on the distance from the observer and changes when rescaling the image), or is a sensitivity to cycles /image, largely independent of image scale. How is it related to the well-documented size tolerance of IT neuron selectivity?

2. The authors' band-pass filtered phase scrambled images of faces and objects. The original images likely differed in their spatial frequency amplitude spectrum and thus it is unclear whether the differing bands contained the same power for the different scrambled images. If not, this could have contributed to the frequency sensitivity of the neurons.

3. How strong were the responses to the phase-scrambled images? Phase-scrambled images are expected to be rather ineffective stimuli for IT neurons. How can one extrapolate the effect of the spatial frequency band observed for ineffective stimuli to that for more effective stimuli, like objects or (for some neurons) faces? A distribution should be provided, of the net responses (in spikes/s) to the scrambled stimuli, and this for the early and late windows.

4. The strength of the spatial frequency selectivity is unclear from the presented data. The authors provide the result of a classification analysis, but this is in normalized units so that the reader does not know the classification score in percent correct. Unnormalized data should be provided. Also, it would be informative to provide a summary plot of the spatial frequency selectivity in spikes/s, e.g. by ranking the spatial frequency bands for each neuron based on half of the trials and then plotting the average responses for the obtained ranks for the other half of the trials. Thus, the reader can appreciate the strength of the spatial frequency selectivity, considering trial-to-trial variability. Also, a plot should be provided of the mean response to the stimuli for the two analysis windows of Figure 2c and 2d in spikes/s so one can appreciate the mean response strengths and effect size (see above).

5. It is unclear why such brief stimulus durations were employed. Will the results be similar, in particular the preference for low spatial frequencies, for longer stimulus durations that are more similar to those encountered during natural vision?

6. The authors report that the spatial frequency band classification accuracy for the population of neurons is not much higher than that of the best neuron (line 151). How does this relate to the SNC analysis, which appears to suggest that many neurons contribute to the spatial frequency selectivity of the population in a non-redundant fashion? Also, the outcome of the analyses should be provided (such as SNC and decoding (e.g. Figure 1D)) in the original units instead of undefined arbitrary units.

7. To me, the results of the analyses of Figure 3c,d, and Figure 4 appear to disagree. The latter figure shows no correlation between category and spatial frequency classification accuracies while Figure 3c,d shows the opposite.

8. If I understand correctly, the "main" test included scrambled versions of each of the "responsive" images selected based on the preceding test. Each stimulus was presented 15 times (once in each of the 15 blocks). The LDA classifier was trained to predict the 5 spatial frequency band labels and they used 70% of the trials to train the classifier. Were the trained and tested trials stratified with respect to the different scrambled images? Also, LDA assumes a normal distribution. Was this the case, especially because of the mixture of repetitions of the same scrambled stimulus and different scrambled stimuli?

9. The LDA classifiers for spatial frequency band (5 labels) and category (2 labels) have different chance and performance levels. Was this taken into account when comparing the SNC between these two classifiers? Details and SNC values should be provided in the original (percent difference) instead of arbitrary units in Figure 5a. Without such details, the results are impossible to evaluate.

10. Recording locations should be described in IT, since the latter is a large region. Did their recordings include the STS? A/P and M/L coordinate ranges of recorded neurons?

11. The authors should show in Supplementary Figures the main data for each of the two animals, to ensure the reader that both monkeys showed similar trends.

12. The authors found that the deep nets encoded better the spatial frequency bands than the IT units. However, IT units have trial-to-trial response variability and CNN units do not. Did they consider this when comparing IT and CNN classification performance? Also, the number of features differs between IT and CNN units. To me, comparing IT and CNN classification performances is like comparing apples and oranges.

13. The authors should define the separability index in their paper. Since it is the main index to show a relationship between category and spatial frequency tuning, it should be described in detail. Also, results should be provided in the original units instead of undefined arbitrary units. The tuning profiles in Figure 3A should be in spikes/s. Also, it was unclear to me whether the classification of the neurons into the different tuning profiles was based on an ANOVA assessing per neuron whether the effect of the spatial frequency band was significant (as should be done).

14. As mentioned above, the separability analysis is the main one suggesting an association between category and spatial frequency tuning. However, they compute the separability of each category with respect to the scrambled images. Since faces are a rather homogeneous category I expect that IT neurons have on average a higher separability index for faces than for the more heterogeneous category of objects, at least for neurons responsive to faces and/or objects. The higher separability for faces of the two low- and high-pass spatial frequency neurons could reflect stronger overall responses for these two classes of neurons. Was this the case? This is a critical analysis since it is essential to assess whether it is category versus responsiveness that is associated with the spatial frequency tuning. Also, I do not believe that one can make a strong claim about category selectivity when only 6 faces and 3 objects (and 6 other, variable stimuli; 15 stimuli in total) are employed to assess the responses for these categories (see next main comment). This and the above control analysis can affect the main conclusion and title of the paper.

15. For the category decoding, the authors employed intact, unscrambled stimuli. Were these from the main test? If yes, then I am concerned that this represents a too small number of stimuli to assess category selectivity. Only 9 fixed + 6 variable stimuli = 15 were in the main test. How many faces/ objects on average? Was the number of stimuli per category equated for the classification? When possible use the data of the preceding selectivity test which has many more stimuli to compute the category selectivity.

eLife assessment

This fundamental study reveals the major role of calcium-binding proteins (CaBP1 and CaBP2) in sustained exocytosis from mouse inner hair cell ribbon synapses. Compelling data and analysis from CaBP1/2 double-knockout mice show enhanced calcium channel (CaV1.3) inactivation, slowed recovery from inactivation, and reduced synaptic vesicle exocytosis as assayed by membrane capacitance measurements, as well as greatly reduced in vivo spontaneous and sound-evoked spikes from the postsynaptic spiral ganglion neurons. Importantly, transgenic expression of CaBP2 led to the rescue of hearing capabilities. The continuous transmission of sound-evoked signals from auditory hair cells thus depends on the expression of both CaBP1 and CaBP2 and their suppression of CaV1.3 inactivation.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript dissects the contribution of the CaBP 1 and 2 on the calcium current in the cochlear inner hair cells. The authors measured the calcium current inactivation from the double knock-out CaBP1 and 2 and showed that both proteins contribute to voltage-dependent and calcium-dependent inactivation. Synaptic release was reduced in the double KO. As a consequence, the authors observed a depressed activity within the auditory nerve. Taken together, this study identifies a new player that regulates the stimulation-secretion coupling in the auditory sensory cells.

Strengths:<br /> In this study, the authors bring compelling evidence that CaBP 1 and 2 are both involved in the inactivation of the calcium current, from cellular up to system level, and by taking care to probe different experimental conditions such as different holding potentials and by rescuing the phenotype with the re-expression of CaBP2. Indeed, while changing the holding potential worsens the secretion, it completely changes the kinetics of the inactivation recovery. It alerts the reader that probing different experimental conditions that may be closer to physiology is better suited to uncovering any deleterious phenotype. This gave pretty solid results.

Weaknesses:<br /> Although this study clearly points out that CaBP1 is involved in the calcium current inactivation, it is not clear how CaBP1 and CaBP2 act together (but this is probably beyond the scope of the study). Another point is that the authors re-express CaBP2 to largely rescue the phenotype in the double KO but no data are available to know whether the re-expression of both CaBP1 and CaBP2 would achieve a full recovery and what would be the effect of the sole re-expression of CaBP1 in the double KO.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Oestreicher et al, the authors use patch-clamp electrophysiology, immunofluorescent imaging of the cochlea, auditory function tests, and single-unit recordings of auditory afferent neurons to probe the unique properties of calcium signaling in cochlear hair cells that allow rapid and sustained neurotransmitter release. The calcium-binding proteins (CaBPs) are thought to modify the inactivation of the Cav1.3 calcium channels in IHCs that initiate vesicle fusion, reducing the calcium-dependent inactivation (CDI) of the channels to allow sustained calcium influx to support neurotransmitter release. The authors use knockout mice of Cabp1 and Cabp2 in a double knockout (Cabp1/2 DKO) to show that these molecules are required for enabling sustained calcium currents by reducing CDI and enabling proper IHC neurotransmitter release. They further support their evidence by re-introducing Cabp2 using an injection of AAV containing the Cabp2 sequence into the cochlea, which restores some of the auditory function and reduces CDI in patch-clamp recordings.

Strengths:<br /> Overall the data is convincing that Cabp1/2 is required for reducing CDI in cochlear hair cells, allowing their sustained neurotransmitter release and sound encoding. Figures are well-prepared, recordings are careful and stats are appropriate, and the manuscript is well-written. The discussion appropriately considers aspects of the data that are not yet explained and await further experimentation.

Weaknesses:<br /> There are some sections of the manuscript that pool data from different experiments with slightly different conditions (wt data from a previous paper, different calcium concentrations, different holding voltages, tones vs clicks, etc). This makes the work harder to follow and more complicated to explain. However, the major conclusion, that cabp1 and 2 work together to reduce calcium-dependent inactivation of L-type calcium channels in cochlear inner hair cells, still holds.

Another weakness is that the authors used injections of AAV-containing sequences for Cabp2, but do not present data from sham surgeries. In most cases, the improvement of hearing function with AAV injection is believable and should be attributed to the cabp2 function. However, in at least one instance (Figure 4B), the results of the AAV injection experiments may be overinterpreted - the authors show that upon AAV injection, the hair cells have a much longer calcium current recovery following a large, long depolarization to inactivate the calcium channels. Without comparison to sham surgery, it is not known if this result could be a subtle result of the surgery or indeed due to the Cabp2 expression.<br /> It would be great to see the auditory nerve recordings in AAV-injected animals that have a recovery of ABRs. However, this is a challenging experiment that requires considerable time and resources, so is not required.

Reviewer #3 (Public Review):

Summary:<br /> The authors attempted to unravel the role of the Ca2+-binding proteins CaBP1 and CaBP2 for the hitherto enigmatic lack of Ca2+-dependent inactivation of Ca2+ currents in sensory inner hair cells (IHCs). As Ca2+ currents through Cav1.3 channels are crucial for exocytosis, the lack of inactivation of those Ca2+ currents is essential for the indefatigable sound encoding by IHCs. Using a deaf mouse model lacking both CaBP1 and CaBP2, the authors convincingly demonstrate that both CaBP1 and CaBP2 together confer a lack of inactivation, with CaBP2 being far more effective. This is surprising given the mild phenotype of the single knockouts, which has been published by the authors before. Re-admission of CaBP2 through viral gene transfer into the inner ear of double-knockout mice largely restored hearing function, normal Ca2+ current properties, and exocytosis.

Strengths:<br /> 1. In vitro electrophysiology: perforated patch-clamp recordings of Ca2+/Ba2+ currents of inner hair cells (IHCs) from 3-4 week-old mice - very difficult recordings - necessary to not interfere with intracellular Ca2+ buffers, including CaBP1 and CaBP2.<br /> 2. Capacitance (exocytosis) recordings from IHCs in perforated patch mode.<br /> 3. The insight that a negative holding potential might underestimate the impact of lack of CaBP1/2 on the inactivation of ICa in IHCs. As the physiological holding potential is much more positive than a preferred holding potential in patch clamp experiments it has a strong impact on inactivation in the pauses between depolarization mimicking receptor potentials.<br /> This truly advances our thinking about the stimulation of IHCs and accumulating inactivation of the Cav1.3 channels.<br /> 4. Insight that the voltage sine method with usual voltage excursions (35 mV) to determine the membrane capacitance (for exocytosis measurements) also favors the inactivated state of Cav1.3 channels<br /> 5. Use of double ko mice (for both CaBP1 and CaBP2, DKO) and use of DKO with virally injected CaBP2-eGFP into the inner ear.<br /> 6. Use of DKO animals/IHCs/SGNs after virus-mediated CaBP2 gene transfer shows a great amount of rescue of the normal ICa inactivation phenotype.<br /> 7. In vivo measurements of SGN AP responses to sound, which is highly demanding.<br /> 8. In vivo measurements of hearing thresholds, DPOAE characteristics, and ABR wave I amplitudes/latencies of DKO mice and DKO+injected mice compared to WT mice.

Very thorough analysis and presentation of the data, excellent statistical analysis.

The authors achieved their aims. Their results fully support their conclusions. The methods used by the authors are state-of-the-art.

The impacts on the field are the following:<br /> Regulation of inactivation of Cav1.3 currents is crucial for the persistent functioning of Cav1.3 channels in sensory transduction.<br /> The findings of the authors better explain the phenotype of the human autosomal recessive DFNB93, which is based on the malfunction of CaBP2.<br /> Future work - by the authors or others - should address the molecular mechanisms of the interaction of CaBP1 and 2 in regulating Cav1.3 inactivation.

Weaknesses:<br /> I do not see weaknesses.<br /> What is not explained (but was not the aim of the authors) is how the CaBPs 1 and 2 interact with the Cav1.3 channels and with each other to reduce CDI. Also, why DFNB93, which is based on mutation of the CaBP2 gene, lead to a severe phenotype in humans in contrast to the phenotype of the CaBP2 ko mouse.

eLife assessment

Given a great need for novel human model systems to study small cell lung cancer (SCLC), the authors describe an important pre-clinical model with broad potential for the study of how genetic perturbations or drug treatments alter SCLC tumor growth, metastasis, and response to therapy. For the major finding, the authors provide convincing evidence that RB/TP53 suppression coupled with MYC overexpression in an ES cell-derived model system results in aggressive and metastatic SCLC. However, comparisons of the RB/TP53-suppressed, MYC overexpressing model with RB/TP53-suppressed cells in supporting the minor conclusion that MYC overexpression increases the neuroendocrine compartment are incomplete, and the impact of the work would have been increased with the inclusion of a broader set of genetic perturbations, such as over-expression of MYCL, to better model major SCLC phenotypes. The new model described will be of significant interest to researchers studying lung cancer.

Reviewer #1 (Public Review):

Summary:<br /> The authors introduced their previous paper with the concise statement that "the relationships between lineage-specific attributes and genotypic differences of tumors are not understood" (Chen et al., JEM 2019, PMID: 30737256). For example, it is not clear why combined loss of RB1 and TP53 is required for tumorigenesis in SCLC or other aggressive neuroendocrine (NE) cancers, or why the oncogenic mutations in KRAS or EGFR that drive NSCLC tumorigenesis are found so infrequently in SCLC. This is the main question addressed by the previous and current papers.

One approach to this question is to identify a discrete set of genetic/biochemical manipulations that are sufficient to transform non-malignant human cells into SCLC-like tumors. One group reported the transformation of primary human bronchial epithelial cells into NE tumors through a complex lentiviral cocktail involving the inactivation of pRB and p53 and activation of AKT, cMYC, and BCL2 (PARCB) (Park et al., Science 2018, PMID: 30287662). The cocktail previously reported by Chen and colleagues to transform human pluripotent stem-cell (hPSC)-derived lung progenitors (LPs) into NE xenografts was more concise: DAPT to inactivate NOTCH signaling combined with shRNAs against RB1 and TP53. However, the resulting RP xenografts lacked important characteristics of SCLC. Unlike SCLC, these tumors proliferated slowly and did not metastasize, and although small subpopulations expressed MYC or MYCL, none expressed NEUROD1.

MYC is frequently amplified or expressed at high levels in SCLC, and here, the authors have tested whether inducible expression of MYC could increase the resemblance of their hPSC-derived NE tumors to SCLC. These RPM cells (or RPM T58A with stabilized cMYC) engrafted more consistently and grew more rapidly than RP cells, and unlike RP cells, formed liver metastases when injected into the renal capsule. Gene expression analyses revealed that RPM tumor subpopulations expressed NEUROD1, ASCL1, and/or YAP1.

The hPSC-derived RPM model is a major advance over the previous RP model. This may become a powerful tool for understanding SCLC tumorigenesis and progression and for discovering gene dependencies and molecular targets for novel therapies. However, the specific role of cMYC in this model needs to be clarified.

cMYC can drive proliferation, tumorigenesis, or apoptosis in a variety of lineages depending on concurrent mutations. For example, in the Park et al., study, normal human prostate cells could be reprogrammed to form adenocarcinoma-like tumors by activation of cMYC and AKT alone, without manipulation of TP53 or RB1. In their previous manuscript, the authors carefully showed the role of each molecular manipulation in NE tumorigenesis. DAPT was required for NE differentiation of LPs to PNECs, shRB1 was required for expansion of the PNECs, and shTP53 was required for xenograft formation. cMYC expression could influence each of these steps, and importantly, could render some steps dispensable. For example, shRB1 was previously necessary to expand the DAPT-induced PNECs, as neither shTP53 nor activation of KRAS or EGFR had no effect on this population, but perhaps cMYC overexpression could expand PNECs even in the presence of pRB, or even induce LPs to become PNECs without DAPT. Similarly, both shRB1 and shTP53 were necessary for xenograft formation, but maybe not if cMYC is overexpressed. If a molecular hallmark of SCLC, such as loss of RB1 or TP53, has become dispensable with the addition of cMYC, this information is critically important in interpreting this as a model of SCLC tumorigenesis.

To interpret the role of cMYC expression in hPSC-derived RPM tumors, we need to know what this manipulation does without manipulation of pRB, p53, or NOTCH, alone or in combination. Seven relevant combinations should be presented in this manuscript: (1) cMYC alone in LPs, (2) cMYC + DAPT, (3) cMYC + shRB1, (4) cMYC + DAPT + shRB1, (5) cMYC + shTP53, (6) cMYC + DAPT + shTP53, and (7) cMYC + shRB1 + shTP53. Wild-type cMYC is sufficient; further exploration with the T58A mutant would not be necessary.

This reviewer considers that there should be a presentation of the effects of these combinations on LP differentiation to PNECs, expansion of PNECs as well as other lung cells, xenograft formation and histology, and xenograft growth rate and capacity for metastasis. If this could be clarified experimentally, and the results discussed in the context of other similar approaches such as the Park et al., paper, this study would be a major addition to the field.

Reviewer #2 (Public Review):

Summary:<br /> Chen et al use human embryonic stem cells (ESCs) to determine the impact of wildtype MYC and a point mutant stable form of MYC (MYC-T58A) in the transformation of induced pulmonary neuroendocrine cells (PNEC) in the context of RB1/P53 (RP) loss (tumor suppressors that are nearly universally lost in small cell lung cancer (SCLC)). Upon transplant into immune-deficient mice, they find that RP-MYC and RP-MYC-T58A cells grow more rapidly, and are more likely to be metastatic when transplanted into the kidney capsule, than RP controls. Through single-cell RNA sequencing and immunostaining approaches, they find that these RPM tumors and their metastases express NEUROD1, which is a transcription factor whose expression marks a distinct molecular state of SCLC. While MYC is already known to promote aggressive NEUROD1+ SCLC in other models, these data demonstrate its capacity in a human setting that provides a rationale for further use of the ESC-based model going forward. Overall, these findings provide a minor advance over the previous characterization of this ESC-based model of SCLC published in Chen et al, J Exp Med, 2019.

The major conclusion of the paper is generally well supported, but some minor conclusions are inadequate and require important controls and more careful analysis.

Strengths:<br /> 1. Both MYC and MYC-T58A yield similar results when RP-MYC and RP-MYCT58A PNEC ESCs are injected subcutaneously, or into the renal capsule, of immune-deficient mice, leading to the conclusion that MYC promotes faster growth and more metastases than RP controls.

2. Consistent with numerous prior studies in mice with a neuroendocrine (NE) cell of origin (Mollaoglu et al, Cancer Cell, 2017; Ireland et al, Cancer Cell, 2020; Olsen et al, Genes Dev, 2021), MYC appears sufficient in the context of RB/P53 loss to induce the NEUROD1 state. Prior studies also show that MYC can convert human ASCL1+ neuroendocrine SCLC cell lines to a NEUROD1 state (Patel et al, Sci Advances, 2021); this study for the first time demonstrates that RB/P53/MYC from a human neuroendocrine cell of origin is sufficient to transform a NE state to aggressive NEUROD1+ SCLC. This finding provides a solid rationale for using the human ESC system to better understand the function of human oncogenes and tumor suppressors from a neuroendocrine origin.

Weaknesses:<br /> 1. There is a major concern about the conclusion that MYC "yields a larger neuroendocrine compartment" related to Figures 4C and 4G, which is inadequately supported and likely inaccurate. There is overwhelming published data that while MYC can promote NEUROD1, it also tends to correlate with reduced ASCL1 and reduced NE fate (Mollaoglu et al, Cancer Cell, 2017; Zhang et al, TLCR, 2018; Ireland et al, Cancer Cell, 2020; Patel et al, Sci Advances, 2021). Most importantly, there is a lack of in vivo RP tumor controls to make the proper comparison to judge MYC's impact on neuroendocrine identity. RPM tumors are largely neuroendocrine compared to in vitro conditions, but since RP control tumors (in vivo) are missing, it is impossible to determine whether MYC promotes more or less neuroendocrine fate than RP controls. It is not appropriate to compare RPM tumors to in vitro RP cells when it comes to cell fate. Upon inspection of the sample identity in S1B, the fibroblast and basal-like cells appear to only grow in vitro and are not well represented in vivo; it is, therefore, unclear whether these are transformed or even lack RB/P53 or express MYC. Indeed, a close inspection of Figure S1B shows that RPM tumor cells have little ASCL1 expression, consistent with lower NE fate than expected in control RP tumors.

In addition, since MYC appears to require Notch signaling to induce NE fate (Ireland et al), the presence of DAPT in culture could enrich for NE fate despite MYC's presence. It's important to clarify in the legend of Fig 4A which samples are used in the scRNA-seq data and whether they were derived from in vitro or in vivo conditions (as such, Supplementary Figure S1B should be provided in the main figure). Given their conclusion is confusing and challenges robustly supported data in other models, it is critical to resolve this issue properly. I suspect when properly resolved, MYC actually consistently does reduce NE fate compared to RP controls, even though tumors are still relatively NE compared to completely distinct cellular identities such as fibroblasts.

2. The rigor of the conclusions in Figure 1 would be strengthened by comparing an equivalent number of RP animals in the renal capsule assay, which is n = 6 compared to n = 11-14 in the MYC conditions.

3. Statistical analysis is not provided for Figures 2A-2B, and while the results are compelling, may be strengthened by additional samples due to the variability observed.

4a. Related to Figure 3, primary tumors and liver metastases from RPM or RPM-T58A-expressing cells express NEUROD1 by immunohistochemistry (IHC) but the putative negative controls (RP) are not shown, and there is no assessment of variability from tumor to tumor, ie, this is not quantified across multiple animals.

4b. Relatedly, MYC has been shown to be able to push cells beyond NEUROD1 to a double-negative or YAP1+ state (Mollaoglu et al, Cancer Cell, 2017; Ireland et al, Cancer Cell, 2020), but the authors do not assess subtype markers by IHC. They do show subtype markers by mRNA levels in Fig 4B, and since there is expression of ASCL1, and potentially expression of YAP1 and POU2F3, it would be valuable to examine the protein levels by IHC in control RP vs. RPM samples.

5. Given that MYC has been shown to function distinctly from MYCL in SCLC models, it would have raised the impact and value of the study if MYC was compared to MYCL or MYCL fusions in this context since generally, SCLC expresses a MYC family member. However, it is quite possible that the control RP cells do express MYCL, and as such, it would be useful to show.

Reviewer #3 (Public Review):

Summary:<br /> The authors continue their study of the experimental model of small cell lung cancer (SCLC) they created from human embryonic stem cells (hESCs) using a protocol for differentiating the hESCs into pulmonary lineages followed by NOTCH signaling inactivation with DAPT, and then knockdown of TP53 and RB1 (RP models) with DOX inducible shRNAs. To this published model, they now add DOX-controlled activation of expression of a MYC or T58A MYC transgenes (RPM and RPMT58A models) and study the impact of this on xenograft tumor growth and metastases. Their major findings are that the addition of MYC increased dramatically subcutaneous tumor growth and also the growth of tumors implanted into the renal capsule. In addition, they only found liver and occasional lung metastases with renal capsule implantation. Molecular studies including scRNAseq showed that tumor lines with MYC or T58A MYC led surprisingly to more neuroendocrine differentiation, and (not surprisingly) that MYC expression was most highly correlated with NEUROD1 expression. Of interest, many of the hESCs with RPM/RPMT58A expressed ASCL1. Of note, even in the renal capsule RPM/RPMT58A models only 6/12 and 4/9 mice developed metastases (mainly liver with one lung metastasis) and a few mice of each type did not even develop a renal sub capsule tumor. The authors start their Discussion by concluding: " In this report, we show that the addition of an efficiently expressed transgene encoding normal or mutant human cMYC can convert weakly tumorigenic human PNEC cells, derived from a human ESC line and depleted of tumor suppressors RB1 and TP53, into highly malignant, metastatic SCLC-like cancers after implantation into the renal capsule of immunodeficient mice.".

Strengths:<br /> The in vivo study of a human preclinical model of SCLC demonstrates the important role of c-Myc in the development of a malignant phenotype and metastases. Also the role of c-Myc in selecting for expression of NEUROD1 lineage oncogene expression.

Weaknesses:<br /> There are no data on results from an orthotopic (pulmonary) implantation on generation of metastases; no comparative study of other myc family members (MYCL, MYCN); no indication of analyses of other common metastatic sites found in SCLC (e.g. brain, adrenal gland, lymph nodes, bone marrow); no studies of response to standard platin-etoposide doublet chemotherapy; no data on the status of NEUROD1 and ASCL1 expression in the individual metastatic lesions they identified.

Reviewer #1 (Public Review):

Summary:<br /> The authors were trying to achieve that Tgif1 expression is regulated by EAK1/2 and PTH in a time-dependent manner, and its roles in suppressing Pak3 for facilitating osteoblast adhesion. The authors further tried to show that the Tgif1-Pak3 signaling plays a significant role in osteoblast migration to the site of bone repair and bone remodeling.

Strengths:<br /> - In a previous study, it was demonstrated that Tgif1 is a target gene of PTH, and the absence of Tgif1 failed to increase bone mass by PTH treatment (Saito et al., Nat Commun., 2019). In this study, the authors found that Tgif1-Pak3 signaling prompts osteoblast migration through osteoblast adhesion to prompt bone regeneration. This novel finding provides a better understanding of how Tgif1 expression in osteoblasts regulates adherence, spreading, and migration during bone healing and bone remodeling.

- The authors demonstrated that ERK1/2 and PTH regulate Tgif1 expression in a time-dependent manner and its role in suppressing Pak3 through various experimental approaches such as luciferase assay, ChIP assay, and gene silencing. These results contribute to the overall strength of the article.

Weaknesses:<br /> -The authors need to further justify why they focused on Pak3 in the introduction by mentioning its known function for cell adhesion.

-Some results indicated statistically significant but small changes. The authors need to explain in the discussion part why they believe this is the major mechanism or why there may be some other possible mechanisms.

-The study does not include enough in vivo data to claim that this mechanism is crucial for bone healing and bone remodeling in vivo.

Reviewer #2 (Public Review):

Summary:<br /> Bolamperti S. et al. 2023 investigate whether the expression of TG-interacting factor (Tgif1) is essential for osteoblastic cellular activity regarding morphology, adherence, migration/recruitment, and repair. Towards this end, germ-line Tgif1 deletion (Tgif1-/-) mice or male mice lacking expression of Tgif1 in mature osteoblastic and osteocytic cells (Dmp1-Cre+; Tgif1fl/fl) and corresponding controls were studied in physiological, bone anabolic, and bone fracture-repair conditions. Both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl exhibited decreased osteoblasts on cancellous bone surfaces and adherent to collagen I-coated plates. Tgif1-/- mice exhibit impaired healing in the tibial midshaft fracture model, as indicated by decreased bone volume (BV/Cal.V), osteoid (OS/BS), and low osteoblasts (number and surface). Likewise, both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl show impaired PTH 1-34, (100 µg/kg, 5x/wk for 3 wks) osteoblast activation in vivo, as detected by increases in quiescent bone surfaces. Mechanistic in vitro studies then utilized primary osteoblasts isolated from Tgif1-/- mice and siRNA Tgif1 knockdown OCY454 cells to further investigate and identify the downstream Tgif1 target driving these osteoblastic impairments. In vitro, Tgif1-/- osteoblastic and Tgif1 knockdown OCY454 cells exhibit decreased migration, abnormal morphology, and decreased focal adhesions/cells. Unexpectantly though, localization assays revealed Tgif1 to primarily concentrate in the nucleus and not to co-localize with focal adhesions (paxillin, talin). Also, the expression of major focal adhesion components (paxillin, talin, FAK, Src, etc.) or the Cdc42 family was not altered by loss of Tgif1 expression. In contrast, PAK3 expression is markedly upregulated by loss of Tgif1. In silico analysis followed by mechanistic molecular assays involving ChIP, siRNA (Tgif1, PAK3), and transfection (rat PAK3 promoter) techniques show that Tgif1 physically binds to a specific site in the PAK3 promoter region. Further, the knockdown of PAK3 rescues the Tgif1-deficient abnormal morphology in OCY454 cells. This is the first study to identify the novel transcriptional repression of PAK3 by Tgif1 as well as the specific Tgif1 binding site within the PAK3 promoter.

Strengths:<br /> This work has a plethora of strengths. The co-authors achieved their aim of eliciting the role of Tgif1 expression in osteoblastic cellular functions (morphology, spreading/attachment, migration). Further, this work is the first to depict the novel mechanism of Tgif1 transcriptional repression of PAK3 by a thorough usage of mechanistic molecular assays (in silico analysis, ChIP, siRNA, transfection etc.). The conclusions are well supported and justified by these findings, as the appropriate controls, sample sizes (statistical power), statistics, and assays were fully utilized.

The claims and conclusions are justified by the data.

Weaknesses:<br /> The discussion section could be expanded with a few sentences regarding limitations to the current study and potential future directions.

eLife assessment

This important work substantially advances our understanding of osteoblast migration to the sites of bone formation and regeneration. The evidence supporting the conclusion is convincing, with rigorous in vitro assays for cellular and biochemical aspects and with appropriate in vivo models. The work will be of broad interest to developmental biologists and bone biologists.

eLife assessment

This study presents valuable insights into the potential role of a general transcription factor in MYCN-dependent regulation of transcription. The study presents solid evidence that TFIIIC and MYCN interact to control transcription. The methods, data, and analyses broadly support the claims with minor weaknesses, yet the logic can be improved, and several specific issues should be addressed. The paper would be of interest to molecular biologists working on MYCN-dependent regulation of gene expression.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript entitled "Association with TFIIIC limits MYCN accumulation in hubs of active promoters and chromatin accumulation of non-phosphorylated RNA polymerase II" the authors examine how the cohesin complex component (and RNA pol III associated factor) TFIIIC interacts with MYCN and controls transcription. They confirm that TFIIIC co-purifies with MYCN, dependent on its amino terminus, as shown in previous work. The authors also find that TFIIIC and MYCN are both found in promoter hubs and suggest that TFIIIC inhibits MYCN's association with these hubs. Finally, the authors indicate that TFIIIC/MYCN alters exosome function, and BRCA1-dependent effects, at MYCN-regulated loci.

Strengths:<br /> The authors utilize multiple experimental approaches to investigate the potential biological and genomic impacts of MYCN association with TFIIIC - the findings are interesting in suggesting that this interaction may limit or otherwise regulate MYC activity.

Weaknesses:<br /> (1) In Figure 1, the authors show that TF3C binds to the amino terminus of MYCN (Myc box I region), as shown previously. The data in Figure 1 B-D support, but do not rigorously confirm a 'direct' interaction because it has not been ruled out that accessory proteins mediating the association may be present in the mixture.

(2) The authors indicate in Figure 2 that TF3C has essentially no effect on MYCN-dependent gene expression and/or transcription elongation. Yet a previous study (PMID: 29262328) associated with several of the same authors concluded that TF3C positively affects transcription elongation. The authors make no attempt to reconcile these disparate results and need to clarify this point.

(3) Figures 2B and C show that unphosphorylated pol2 is TSS-centered, and Ser2-P pol2 occupation is centered beyond the TES. From this data, however, the reader can't tell how much of the phospho-Ser2- pol2 is centered on the TSS. The authors should include overall plots over TSS and TES, and also perhaps the gene-body to allow a better comparison for TSS and TES plotted for both antibodies over the collected gene sets.

(4) The authors see more TF3C at promoters in cells with MYCN (Figure 2F). What are the levels of TF3C in the absence and presence of MYCN?

(5) The finding that TF3C is increased at TSS (Figure 2F) doesn't necessarily indicate that 1) MYCN is recruiting TF3C there, and 2) that this is due to the phosphorylation status of pol2. It could mean many other things. The logic of conflating these 3 points based on the data shown is questionable.

(6) Figure 3A doesn't add much to the paper, as it is overplotted and no relationship is clear, except that Pol2 and MYCN occupy many of the same sites. Perhaps a less complex or different type of plot would allow the interactions to be better visible.

(7) That depletion of TF3C leads to increased promoter hubs may or may not have anything to do with its association with MYCN (Figure 4E). This could be a direct consequence of its known structural function in cohesin complexes, and the MYCN changes as a secondary consequence of this (also see point 4, above).

(8) Depletion of TF3C5 results in a loss of EXOSC5 (exosome) at TSS in the presence and absence of MYCN (Figure 5B). As TF3C5 is a cohesin, could this simply be a consequence of genomic structure changes?

(9) The authors suggest that RNA dynamics are affected by changes in exosome function (RNA degradation, etc). What effect, if any does TF3C depletion have on the overall gene expression profile?

Reviewer #2 (Public Review):

This manuscript reports several interesting observations that invite follow-up. The notion that hubs, and perhaps condensates that may (or may not embrace them) are functionally and physiologically important is an open issue at this time. The authors note that TFIIIC helps to prune extraneous connections from hubs, but do not comment that the connections that are maintained are also reinforced. At the same time only modest changes in gene expression are associated with expanded or decreased connections and changes in bound proteins. One interesting possibility might be that standard methods for assessing expression miss changes in global or background transcription. It seems that the TFIIIC-MYCN-ER connection has features that would help to suppress such background. The results invite a more global consideration of TFIIIC than as primarily RNAPIII/small RNA transcription factor and of MYCN as an E-box dependent transcription factor. The results use state-of-the-art methods to develop interesting new ideas that have the potential to instruct further studies that may reveal new mechanisms of action for TFIIIC and MYCN

Strengths:<br /> Use of a variety of methods to assess the genomic response to increased MYCN in the presence or absence of TFIIIC. Establishes in vitro and in vivo the TFIIIC-MYCN complex.

Weaknesses:<br /> Dynamic inferences are made without kinetic experiments.

Reviewer #3 (Public Review):

Summary:<br /> Vidal et al. investigated how TFIIIC may mediate MYCN effects on transcription. The work builds upon previous reports from the same group where they describe MYCN interactors in neuroblastoma cells (Buchel et al, 2017), which include TFIIIC, and their different roles in MYCN-dependent control of RNA polymerase II function (Herold et al, 2019) (Roeschert et al, 2021) (Papadopoulus et al, 2022). Using baculovirus expression systems, they confirm that MYCN-TFIIIC interaction is direct and likely relevant for neuroblastoma cell proliferation. However, transcriptomics analyses led them to conclude that TFIIC is largely dispensable for MYCN-dependent gene expression. Instead, they propose that TFIIC limits MYCN-mediated promoter-promoter 3D chromatin contacts, which would in turn facilitate the recruitment of the nascent RNA degradation machinery and restrict the accumulation of non-phosphorylated RNA polymerase II at promoters. How this mechanism may impact on MYCN-driven neuroblastoma cell biology remains to be elucidated.

Strengths:<br /> This study presents a nice variety of genomic datasets addressing the specific role of TFIIIC in MYCN-dependent functions. In particular, the technically challenging HiChIP sequencing experiments performed under various conditions provide very useful information about the interplay between MYCN and TFIIIC in the regulation of 3D chromatin contacts. The authors show that MYCN and TFIIIC participate both in unique and overlapping long-range chromatin contacts and that the expression of each of these proteins limits the function of the other. Together, their results suggest a dynamic and interconnected relationship between MYCN and TFIIIC in regulating 3D chromatin contacts.

Weaknesses:<br /> The connection between the three major findings presented in this study regarding the role of TFIIIC in the regulation of MYCN function remains unclear. Specifically, how the TFIIIC-dependent restriction of MYCN localization to promoter hubs enhances the association of factors involved in nascent RNA degradation to prevent the accumulation of inactive RNA polymerase II at promoters is not apparent. As they are currently presented, these findings appear as independent observations. Cross-comparison of the different datasets obtained may provide some insight into addressing this question.

Another concern involves the disparities in RNA polymerase II ChIP-seq results between this study and earlier ones conducted by the same group. In Figure 2, the authors demonstrate that activation of MYCN results in a reduction of non-phosphorylated RNA polymerase II across all expressed genes. This discovery contradicts prior findings obtained using the same methodology, where it was concluded that the expression of MYCN had no significant effect on the chromatin association of hypo-phosphorylated RNA polymerase II (Buchel et al, 2017). In this regard, the choice of the 8WG16 antibody raises concern, as fluctuations in the signal may be attributed to changes in the phosphorylation levels of the C-terminal domain. It remains unclear why the authors decided against using antibodies targeting the N-terminal domain of RNA polymerase II, which are unaffected by phosphorylation and consistently demonstrated a significant signal reduction upon MYCN activation in their previous studies (Buchel et al, 2017) (Herold et al, 2019). Similarly, the authors previously proposed that depletion of TFIIIC5 abrogates the MYCN-dependent increase of Ser2-phosphorylated RNA polymerase II (Buchel et al, 2017), whereas they now show that it has no obvious impact. These aspects need clarification.

Finally, the varied techniques employed to explore the role of TFIIIC in MYCN-dependent recruitment of nascent RNA degradation factors make it challenging to draw definitive conclusions about which factor is affected and which one is not. While conducting ChIPseq experiments for all factors may be beyond the scope of this manuscript, incorporating proximity ligation assays (PLA) or ChIP-qPCR assays with each factor would have enabled a more direct and comprehensive comparison.

Reviewer #1 (Public Review):

Interactions known to be important for melanosome transport include exon F and the globular tail domain (GTD) of MyoVa with Mlph. Motivated by a discrepancy between in vitro and cell culture results regarding necessary interactions for MyoVa to be recruited to the melanosome, the authors used a series of pull-down and pelleting assays experiments to identify an additional interaction that occurs between exon G of MyoVa and Mlph. This interaction is independent of and synergistic with the interaction of Mlph with exon F. However, the interaction of the actin-binding domain of Mlph can occur either with exon G or with the actin filament, but not both simultaneously. These data lead to a modified recruitment model where both exon F and exon G enhance the binding of Mlph to auto-inhibited MyoVa, and then via an unidentified switch (PKA?) the actin-binding domain of Mlph dissociates from MyoVa and interacts with the actin filament to enhance MyoVa processivity.

The only weakness noted is that the authors could have had a more complete story if they pursued whether PKA phosphorylation/dephosphorylation of Mlph is indeed the switch for the actin-binding domain of Mlph to interact with exon G versus the actin filament.

Reviewer #2 (Public Review):

The authors identify a third component in the interaction between myosin Va and melanophilin- an interaction between a 32-residue sequence encoded by exon-g in myosin Va and melanophilin's actin-binding domain. This interaction has implications for how melanosome motility may be regulated.

While this work is largely well done, I believe that additional work would be required to make a more compelling case (e.g. some affinity measurements, necessary controls for the dominant negative experiments).  First, the study provides just one more piece to a well-developed story (the role of exon-F and the GTD in myosin Va: melanophilin (Mlph) interaction), much of which was published 20 years ago by several labs. Second, the study does not demonstrate a physiological significance for their findings other than that exon-G plays an auxiliary role in the binding of myosin Va to Mlph. For example, what dictates the choice between Mlph's actin binding domain (ABD) binding to actin or to exon-G. Is it a PTM or local actin concentration? It is unlikely to be alternative splicing as exon-G is present in all spliced isoforms of myosin Va. And what changes re melanosome dynamics in cells between these two alternatives? Similarly, the paper does not provide any in vitro evidence that binding to exon-G instead of actin effects the processivity of a Rab27a/Myosin Va/Mlph transport complex. For example, if the ABD sticks to exon-G instead of actin, does that block Mlph's ability to promote processivity through its interaction with the actin filament during transport? In summary, given that the authors did not directly test their model either in vitro or in cells, I do not think this story represent a significant conceptual advance.

eLife assessment

To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer-1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The results described include rigorous kinetic analysis of the enzymatic activity of the complex and a moderate resolution cryo-EM structure. The results are convincing and valuable to the broader understanding of the evolution of antiviral defense.

Reviewer #1 (Public Review):

Summary:<br /> The authors establish a recombinant insect cell expression and purification scheme for the antiviral Dicer complex of C. elegans. In addition to Dicer-1, the complex harbors two additional proteins, the RIG-I-like helicase DRH-1, and the dsRNA-binding protein RDE-4. The authors show that the complex prefers blunt-end dsRNA over dsRNAs that contain overhangs. Furthermore, whereas ATP-dependent dsRNA cleavage only exacerbates regular dsRNA cleavage activity, the presence of RDE-4 is essential to ATP-dependent and ATP-independent dsRNA cleavage. Single-particle cryo-EM studies of the ternary C. elegans Dicer complex reveal that the N-terminal domain of DRH-1 interacts with the helicase domain of DCR-1, thereby relieving its autoinhibitory state. Lastly, the authors show that the ternary complex is able to processively cleave long dsRNA, an activity primarily relying on the helicase activity of DRH-1.

Strengths:<br /> • First thorough biochemical characterization of the antiviral activity of C. elegans Dicer in complex with the RIG-I-like helicase DRH-1 and the dsRNA-binding protein RDE-4.<br /> • Discovery that RDE-4 is essential to dsRNA processing, whereas ATP hydrolysis is not.<br /> • Discovery of an autoinhibitory role of DRH-1's N-terminal domain (in analogy to the CARD domains of RIG-I).<br /> • First structural insights into the ternary complex DCR-1:DRH-1:RDE-4 by cryo-EM to medium resolution.<br /> • Trap experiments reveal that the ternary DCR-1 complex cleaves blunt-ended dsRNA processively. Likely, the helicase domain of DRH-1 is responsible for this processive cleavage.

Weaknesses:<br /> • Cryo-EM Structure of the ternary Dicer-1:DRH-1:RED-4 complex to only medium resolution.<br /> • High-resolution structure of the C-terminal domain of DRH-1 bound to dsRNA does not reveal the mechanism of how blunt-end dsRNA and overhang-containing one are being discriminated.<br /> • The cryo-EM structure of DCR1:DRH-1:RDE-4 in the presence of ATP only reveals the helicase and CTD domains of DRH-1 bound to dsRNA. No information on dsRNA termini recognition is presented. The paragraph seems detached from the general flow of the manuscript.<br /> • The antiviral DCR-1:DRH-1:RDE-4 complex shows largely homologous activities and regulation than Drosophila Dicer-2.

Reviewer #2 (Public Review):

Summary:<br /> To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer-1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The three subunits were co-expressed to promote stable purification of the complex. This complex promoted ATP-dependent cleavage of blunt-ended dsRNAs. A detailed kinetic analysis was also carried out to determine the role of each subunit of the trimeric complex in both the specificity and efficiency of cleavage. These studies indicate that RDE-4 is critical for cleavage while DRC-1 is primarily involved in the specificity of the reaction, and DRH-1 promotes ATP hydrolysis. Finally, a moderate density (6-7 angstrom) cryo-EM structure is presented with attempts to position each of the components.

Strengths:<br /> 1. Newly described methods for studying the C. elegans DICER complex.<br /> 2. New structure, albeit only moderate resolution.<br /> 3. Kinetic study of the complex in the presence and absence of individual subunits and mutations, provides detailed insight into the contribution of each subunit.

Weaknesses:<br /> 1. Limited insight due to limited structural resolution.<br /> 2. No attempts to extend findings to other Dicer or RLR systems.

eLife assessment

This valuable study shows that eliminating a large portion of the principal neurons in the mammalian olfactory bulb does not affect the initial establishment of the circuit but has an impact on its maintenance. The strength of the paper is that the anatomical changes induced by genetic ablation of neurons are clear-cut. There is a solid description of the structural and behavioral effects of ablating the majority of M/T neurons; however, the conclusions are incompletely supported by the findings.

Reviewer #1 (Public Review):

This paper aims to address the establishment and maintenance of neural circuitry in the case of a massive loss of neurons. The authors used genetic manipulations to ablate the principal projection neurons, the mitral/tufted cells, in the mouse olfactory bulb. Using diphtheria toxin (Tbx21-Cre:: loxP-DTA line) the authors ablated progressively large numbers of M/T cells postnatally. By injecting diphtheria toxin (DT) into the Tbx21-Cre:: loxP-iDTR line, the authors were able to control the timing of the ablation in the adult stage. Both methods led to the successful elimination of a majority of M/TCs by 4 months of age. The authors made a few interesting observations. First, they found that the initial pruning of the remaining M/T cell primary dendrite was unaffected. However, in adulthood, a significant portion of these cells extended primary dendrites to innervate multiple glomeruli. Moreover, the incoming olfactory sensory neuron (OSN) axons, as examined for those expressing the M72 receptor, showed a divergent innervation pattern as well. The authors conclude that M/T cell density is required to maintain the dendritic structures and the olfactory map. To address the functional consequences of eliminating a large portion of principal neurons, the authors conducted a series of behavioral assays. They found that learned odor discrimination was largely intact. On the other hand, mating and aggression were reduced. The authors concluded that learned behaviors are more resilient than innate ones.

The study is technically sound, and the results are clear-cut. The most striking result is the contrast between the normal dendritic pruning during early development and the expanded dendritic innervation in adulthood. It is a novel discovery that can lead to further investigation of how the single-glomerulus dendritic innervation is maintained. The authors conducted a few experiments to address potential mechanisms, but it is inconclusive, as detailed below. It is also interesting to see that the massive neuronal loss did not severely impact learned odor discrimination. This result, together with previous studies showing nearly normal odor discrimination in the absence of large portions of the olfactory bulb or scrambled innervation patterns, attests to the redundancy and robustness of the sensory system. The discussion should take into account these other studies in a historical context.

Main comments:

1. In previous studies, it has been concluded that dendritic pruning unfolds independently, regardless of the innervation pattern or activity of the OSNs. The new observation bolsters this conclusion by showing that a loss of neighboring M/T cells does not affect the developmental process. A more nuanced discussion comparing the results of these studies would strengthen the paper.

2. The authors propose that a certain density of M/T is required to prevent the divergent innervation of primary dendrites, but the evidence is not sufficient to support this proposal. The experiment with low-dose DT injection to ablate a smaller portion of M/T cells did not change the percentage of cells innervating two or more glomeruli. The authors suggest that a threshold must be met, but this threshold is not determined. It would be possible to adjust the DT injection dose to find this threshold.

3. The authors suggest that neural activity is not required for this plasticity. The evidence was derived primarily from naris occlusion and neuronal silencing using Kir2.1. While the results are consistent with the notion, it is a rather narrow interpretation of how neural activity affects circuit configuration. Perturbation of neural activity also entails an increase in firing. Inducing the activity of the neurons may alter this plasticity. Silencing per se may induce a homeostatic response that expands the neurite innervation pattern to increase synaptic input to compensate for the loss of activity. Thus, further silencing the cells may not reduce multi-glomerular innervation, but an increased activity may.

4. There is a discrepancy between this study and the one by Fujimoto et al. (Developmental Cell; 2023), which shows that not only glutamatergic inputs to the primary dendrite can facilitate pruning of remaining dendrites but also Kir2.1 overexpression can significantly perturb dendritic pruning. This discrepancy is not discussed by the authors.

5. An alternative interpretation of the discrepancy between the apparent normal pruning by p10 and expanded dendritic innervation in adulthood is that there are more cells before P10, when ~25% of M/T cells are present, but at a later date only 1-3% are present. The relationship between the number of M/T cells and single glomerulus innervation has not been explored during postnatal development. It would be important to test this hypothesis.

6. The authors attribute the change in the olfactory map to the loss of M/T cells. Another obvious possibility is that the diffused projection is a response to the change in the olfactory bulb size. With less space to occupy, the axons may be forced to innervate neighboring glomeruli. It is not known how the total number of glomeruli is affected. This question could be addressed by tracking developmental changes in bulb volume and glomerular numbers.

7. The retained ability to discriminate odors upon reinforced training is not surprising in light of a number of earlier studies. For example, Slotnick and colleagues have shown that rats losing ~90% of the OB can retain odor discrimination. Weiss et al have shown that humans without an olfactory bulb can perform normal olfactory tasks. Gronowitz et al have used theoretical prediction and experimental results to demonstrate that perturbing the olfactory map does not have a major impact on olfactory discrimination.<br /> Fleischmann et al have shown that mice with a monoclonal nose can discriminate odors. The authors should discuss their results in these contexts.

8. It should be noted that odor discrimination resulting from reinforcement training does not mean normal olfactory function. It is a highly artificial situation as the animals are overtrained. It should not be used as a measure of the robustness of the olfactory sense. Natural odor discrimination (without training), detection threshold, and innate appetitive/aversive response to certain odors may be affected. These experiments were not conducted.

9. The social behaviors were conducted using relatively coarse measures (vaginal plug and display of aggression). Moreover, these behaviors are most likely affected by the disruption of the AOB mitral cells and have little to do with the dendritic pruning process described in the paper. It is misleading to lump social behaviors with innate responses to odors.

Reviewer #2 (Public Review):

The authors make the interesting observation that the developmental refinement of apical M/T cell dendrites into individual glomeruli proceeds normally even when the majority of neighboring M/T cells are ablated. At later stages, the remaining neurons develop additional dendrites that invade multiple glomeruli ectopically, and similarly, OSN inputs to glomeruli lose projection specificity as well. The authors conclude that the normal density of M/T neurons is not required for developmental refinement, but rather for maintaining specific connectivity in adults.

The observations are indeed quite striking; however, the authors' conclusions are not entirely supported by the data.

1. It is unclear whether the expression of diphtheria toxin that eventually leads to the ablation of the large majority of M/T neurons compromises the cell biology of the remaining ones.

2. The authors interpret the growth of ectopic dendrites later in life as a lack of maintenance of dendrite structure; however, maybe the observed changes reflect actually adaptations that optimize wiring for extremely low numbers of M/T neurons. The finding that olfactory behavior was less affected than predicted supports this interpretation.

3. The number of remaining M/T neurons is much higher at P10 than later. Can the relatively large number of remaining neurons (or their better health status) be the reason that dendrites refine normally at the early developmental stages rather than a (currently unknown) developmental capacity that preserves refinement?

4. While the effect of reduced M/T neuron density on both M/T dendrites and OSN axons is described well, the relationship between both needs to be characterized better: Is one effect preceding the other or do they occur simultaneously? Can one be the consequence of the other?

5. Page 7: the observation that not all neurons develop additional dendrites is not a sign of differences between cell types, it may be purely stochastic.

6. Page 8: the fact that activity blockade did not affect the formation of ectopic dendrites does not suggest that the process is not activity-dependent: both manipulations have the same effect and may just mask each other.

7. It remains unclear how the observed structural changes can explain the behavioral effects.

eLife assessment

This paper describes valuable results from studies investigating circuits in the brain that underlie behavioral responses in fearful situations. The authors identified a role for a class of neurons that are sufficient to cause these stereotyped behaviors including freezing behaviors. These solid studies increase our understanding of brain pathways regulating these types of behaviors.

Reviewer #1 (Public Review):

Summary:

The authors have developed and optimized a footprinting assay to monitor the recruitment of mRNAs to a reconstituted translation initiation system. This assay is named Recruitment-Sequencing (Rec-Seq) and enables the analysis of many purified mRNAs in the reconstituted system.

This system possesses the ability to determine how competition occurs between mRNAs for the initiation machinery. This is the first approach using a reconstituted system that enables this important feature, and this is an important advance for the field.

Strengths:

Using purified mRNAs in a fully reconstituted system and being able to monitor start site selection is an important advance. The method enables one to observe changes in mRNA recruitment and start site selection in response to the absence or presence of different initiation components or accessory proteins.

Weaknesses:

Start site fidelity in purified reconstituted systems can be dramatically altered in different buffer conditions. Interpretation of the observed changes to start site selection in mRNAs in the absence or presence of Ded1 using only the one buffer condition used is therefore limited.

eLife assessment

This important work provides a robust yet simple protocol to isolate small extracellular vesicles from small volumes of plasma. The evidence supporting the conclusions is convincing, although a more thorough statistical comparison of the different techniques and technique combinations explored in the study would have been appreciated. The work will be of broad interest to cell biologists and biochemists.

Reviewer #1 (Public Review):

Summary:<br /> In their manuscript, Kong Fang et al describe a robust pipeline for the isolation of small extracellular vesicles through a combination of size exclusion chromatography and miniaturized density gradient separation. Subsequently, they prove that the method is reproducible and suitable for small-volume operations while at the same time not compromising the quality of vesicles.

Strengths:<br /> The paper narrates a robust method for purifying high-quality sEVs from small amounts of blood plasma. They also demonstrate that through this approach, they can derive sEVs without compromising the protein composition, integrity of the vesicles, or contamination with other proteins or lipids.

Weaknesses:<br /> The paper is a nice summary of how to enrich sEVs from blood samples. Although well performed and substantiated with data, the paper primarily deals with method development and optimisation.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors manage to optimize a simple and rapid protocol using SEC followed by DGCU to isolate sEVs with adequate purity and yield from small volumes of plasma. Isolated fractions containing sEVs using SEC, DGCU, SEC-DGCU, and DGCU-SEC are compared in terms of their yield, purity surface protein profile, and RNA content. Although the combined use of these methodologies has already been evaluated in previous works, the authors manage to adapt them for the use of small volumes of plasma, which allows working in 1.5 mL tubes and reducing the centrifugation time to 2 hours.

The authors finally find that although both the SEC-DGCU and DGCU-SEC combinations achieve isolates with high purity, the SEC-DGCU combination results in higher yields.

This work provides an interesting tool for the rapid obtention of sEVs with sufficient yield and purity for detailed characterization which could be very useful in research and clinical therapy.

Strengths:<br /> -The work is well-written and organized.<br /> -The authors clearly state the problem they want to address, that is, optimizing a method that allows sEV to be isolated from small volumes of plasma.<br /> -Although these methodologies have been tested in previous works, the authors manage to isolate sEVs of high purity and good performance through a simple and fast methodology.<br /> -The characteristics of all isolated fractions are exhaustively analyzed through various state-of-the-art methodologies.<br /> -They present a good interpretation of the results obtained through the methodologies used.

Weaknesses:<br /> -Lack of references that support some of the results obtained.<br /> -Although this work focuses on comparing different techniques and their combinations to find an optimal option, the authors do not use any statistical method that reliably shows the differences between these techniques, except when repeatability is measured.

eLife assessment

This useful study compares gene expression patterns among different autonomic ganglia and will be of interest to developmental neuroscientists and neurophysiologists. The study expands the database of genes expressed by subpopulations of autonomic neurons in ganglia, a key step in decoding their developmental origins and physiological functions. The evidence supporting the alternative view that the pelvic ganglionic neurons are actually modified sympathetic neurons is incomplete and may cause confusion, given the enrichment of cholinergic neurons, as well as the large number of molecular and functional differences known to be present between cranial and sacral neurons.

Reviewer #1 (Public Review):

In recent years, these investigators have been engaged in a debate regarding the classification of the sacral parasympathetic system as "sympathetic" rather than "parasympathetic," based on shared developmental ontogeny of spinal preganglionic neurons. In this current study, these investigators conducted single-cell RNAseq analyses of four groups of autonomic neurons: paravertebral sympathetic neurons (stellate and lumbar train ganglia), prevertebral sympathetic neurons (coeliac-mesenteric ganglia), rostral parasympathetic ganglia (sphenopalatine ganglia), and the caudal pelvic ganglia (containing traditionally recognized sacral "parasympathetic cholinergic neurons," which the investigators sought to challenge in terms of nomenclature). The authors argued that the pelvic ganglionic neurons shared the expression of more genes with sympathetic ganglia, as opposed to parasympathetic ganglia. Additionally, the pelvic neurons did not express a set of genes observed in the rostral parasympathetic sphenopalatine ganglia. Based on these findings, they claimed that the sacral autonomic system should be considered sympathetic rather than parasympathetic.

However, noradrenergic sympathetic neurons and cholinergic neurons, by the virtue of expressing different neurotransmitters, could have distinct roles. It is true that some cholinergic neurons reside in the sympathetic train ganglia as well, such as those innervating the sweat gland and some vascular systems; in this sense, the pelvic ganglia share some features with sympathetic ganglia, except that the pelvic ganglia contain a much higher percentage of cholinergic neurons compared with sympathetic ganglia. It is much simpler and easier to divide the autonomic nervous system into sympathetic neurons that relieve noradrenaline versus parasympathetic neurons that relieve acetylcholine, and these two systems often act in antagonistic manners, even though in some cases, these two systems can work synergistically. As such, it is not justified to claim that "pelvic organs receive no parasympathetic innervation".

Reviewer #2 (Public Review):

Summary:<br /> Recent advances in single cell profiling of gene expression (RNA) permit the analysis of specialized cell types, an approach that has great value in the nervous system which is characterized by prodigious neuronal diversity. The novel data in this study focus primarily on genetic profiling to compare autonomic neurons from ganglia associated with the cranial parasympathetic outflow (sphenopalatine (also known as pteropalatine), the thoraco-lumbar sympathetic outflow (stellate, coeliac) and the sacral parasympathetic outflow (pelvic). Using statistical methods to reduce the dimensionality of the data and map gene expression, the authors provide interesting evidence that cranial parasympathetic and sacral sympathetic ganglia differ from each other and from sympathetic ganglia (Figures 1, S1 - S4). The authors interpret the mapping analysis as evidence that the cranial and sacral outflows differ so that calling them both parasympathetic is unjustified. Based on anatomical localization of markers (Figure 2 ) (mainly transcription factors) the authors show a similarity between the sympathetic and pelvic ganglion. In Figure 3 they present evidence that some pelvic ganglionic neurons are dually innervated by sympathetic preganglionic neurons and sacral preganglionic neurons. These observations are interpreted to mean that the pelvic ganglion is not parasympathetic, but rather a modified sympathetic ganglion - hence the title of the manuscript.

Strengths:<br /> The extensive use of single cell profiling in this work is both interesting and exciting. Although still in its early stages, it holds promise for a deepened understanding of autonomic development and function. As noted in the introduction, this study extends previous work by Professor Brunet and his associates.

Weaknesses:<br /> This work further documents differences between the cranial and sacral parasympathetic outflows that have been known since the time of Langley - 100 years ago. The approach taken by Brunet et al. has focused on late neonatal and early postnatal development, a time when autonomic function is still maturing. In addition, the sphenopalatine and other cranial ganglia develop from placodes and the neural crest, while sympathetic and sacral ganglia develop from the neural crest alone. How then do genetic programs specifying brainstem and spinal development differ and how can this account for kinship that Brunet documents between spinal and sacral ganglia? One feature that seems to set the pelvic ganglion apart is the mixture of 'sympathetic' and 'parasympthetic' ganglion cells and the convergence of preganglionic sympathetic and parasympathetic synapses on individual ganglion cells (Figure 3). This unusual organization has been reported before using microelectrode recordings (see Crowcroft and Szurszewski, J Physiol (1971) and Janig and McLachlan, Physiol Rev (1987)). Anatomical evidence of convergence in the pelvic ganglion has been reported by Keast, Neuroscience (1995). It should also be noted that the anatomy of the pelvic ganglion in male rodents is unique. Unlike other species where the ganglion forms a distributed plexus of mini-ganglia, in male rodents the ganglion coalesces into one structure that is easier to find and study. Interestingly the image in Figure 3A appears to show a clustering of Chat-positive and Th-positive neurons. Does this result from the developmental fusion of mini ganglia having distinct sympathetic and parasympathetic origins. In addition, Brunet et al dismiss the cholinergic and noradrenergic phenotypes as a basis for defining parasympathetic and parasympathetic neurons. However, see the bottom of Figure S4 and further counterarguments in Horn (Clin Auton Res (2018)). What then about neuropeptides, whose expression pattern is incompatible with the revised nomenclature proposed by Brunet et al.? Figure 1B indicates that VIP is expressed by sacral and cranial ganglion cells, but not thoracolumbar ganglion cells. The authors do not mention neuropeptide Y (NPY). The immunocytochemistry literature indicates that NPY is expressed by a large subpopulation of sympathetic neurons but never by sacral or cranial parasympathetic neurons.

The title of this paper is misleading because it implies a conclusion that is not adequately supported by the data and that is difficult for a general reader to parse. Independent assessments by two referees both agreed on title's problematic message. If one can get beyond the title, then the paper does contain data that is of interest. The authors compared single cell gene expression in neurons from the cranial sphenopalatine ganglion, paravertebral chain ganglia (stellate and lumbar), the prevertebral coeliac ganglion and the bladder ganglion. The cranial and pelvic ganglia are parasympathetic, while the paravertebral and prevertebral ganglia are sympathetic. The gene expression data identified differences between the cranial, sympathetic, and pelvic ganglia. Based primarily on this finding the authors concluded that the sacral bladder ganglion is not parasympathetic. Since some genes suggest a kinship between the pelvic and sympathetic neurons, the authors conclude that the pelvic neurons are pelvo-sympathetic - hence the title. This nomenclature does little to improve understanding of the autonomic motor system and it ignores important anatomical and functional properties that underlie existing definitions of the sympathetic and parasympathetic systems. The idea that the cranial and sacral autonomic outflows have some differences is not new (see for example Nilsson, 1983 and Janig, 2022). Since many of the genes identified in the present study are HOX genes and other transcription factors that specify the rostro-caudal axis during development, it is also not surprising that these genes suggest a kinship between sacral parasympathetic neurons and sympathetic neurons, all of which derive from the neural crest and are supplied by the spinal cord. The different profile of cranial parasympathetic neurons is also not surprising given that they derive from a mixture of placodal and neural crest progenitors and are supplied by the brainstem. (see my previous comments for anatomical and functional criteria that further support the existing nomenclature for the sympathetic and parasympathetic motor systems.

eLife assessment

This useful study compares gene expression patterns among different autonomic ganglia and will be of interest to developmental neuroscientists and neurophysiologists. The study expands the database of genes expressed by subpopulations of autonomic neurons in ganglia, a key step in decoding their developmental origins and physiological functions. The alternative view that the pelvic ganglionic neurons are actually modified sympathetic neurons is incompletely demonstrated given the large number of molecular and functional differences known to be present between cranial and sacral neurons.

eLife assessment

This study explores how Ebola virus evades human immune responses. The study reports a potential new mechanism wherein Ebola virus traps human IRF3, a key transcription factor involved in immune signaling, into virus-produced "inclusion bodies". The topic is important, the paper has many merits, and the biochemical assays are solid. However, the current data do not clearly explain the relationship between the VP35 protein and IRF3.

eLife assessment

This study presents a potentially valuable discovery which indicates that activation of the P2RX7 pathway by the small molecule HEI3090 can reduce lung fibrosis after its establishment by inflammatory damage. If confirmed, the study could clarify the role of specific immune networks in the establishment and progression of lung fibrosis. The presented data and analyses showing the efficacy of HEI3090 small molecule acting via the P2RX7 pathway in reducing lung fibrosis are solid. The studies also show that genetic deletion of P2RX7 itself can reduce the extent of fibrosis. P2RX7 can thus have distinct effects in various phases of the development of lung fibrosis. There is a need for additional definitive studies that specifically identify the discrete phases of when inflammasome activation via P2RX7 signaling can worsen fibrosis versus when the same signaling can be beneficial. It also needs to be established whether distinct immune cell populations mediate the detrimental and beneficial effects of P2RX7 activation in lung fibrosis.

Author Response

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

Point to point response for the editors

We are deeply grateful for the time you have devoted to reviewing this manuscript, and we sincerely thank you. Your insightful feedback has been instrumental in enhancing the quality of our work.

In the revised version of the manuscript, we have carefully addressed each of the concerns you raised. Below, you will find a detailed summary of how your feedback has been incorporated to improve the overall content and clarity of the document.

1. P2RX7 effects: In Figure 2, the vehicle treated P2RX7 knockout (panel M) shows an Ashcroft score of about 1.5 after BLM. Comparing this to the Ashcroft score of 3 after BLM in the wildtype (panel C) suggests that P2RX7 deletion is an effective way to reduce fibrosis by half!.

The argument that HEI3090 also reduces fibrosis by activating P2RX7 is of course very difficult to convey and it seems contradictory that P2RX7 deletion and P2RX7 activation can be both anti-fibrotic. This is an unusual claim and confuses the reviewers as well as the future readers.

This has many important health implications because activating an inflammatory pathway via P2RX7 and IL-18 could be risky in terms of a fibrosis treatment as inflammatory activation can also worsen fibrosis. The authors' own P2RX7 KO data (untreated vehicle groups) indeed confirms that P2RX7 can be pro-fibrotic.

We thank the editors for their comment highlighting the lack of clarity in our message. Indeed, we verified whether the antifibrotic action of HEI3090 depends on the expression of P2RX7 by inducing lung fibrosis in P2RX7 KO mice. In doing so, we initially observed that P2RX7 plays a role in the development of BLM-induced lung fibrosis. This is illustrated by a decrease of 50% in the Ashcroft score, as shown in Figure 2M and Supplemental Figure 2C of the revised manuscript.

To increase the clarity of your message, we added in the text the following paragraph:

"We further verified whether the antifibrotic action of HEI3090 depends on the expression of P2RX7 by inducing lung fibrosis in p2rx7 knockout (KO) mice. In doing so, we initially observed that P2RX7 plays a role in the development of BLM-induced lung fibrosis. This is illustrated by a decrease of 50% in the Ashcroft score, with a mean value of 1.7 in P2RX7 knockout mice compared to 3 in wild-type mice (Figure 2M and Supplemental Figure 2C). It is important to note that p2rx7 -/- mice still exhibit signs of lung fibrosis, such as thickening of the alveolar wall and a reduction in free air space, in comparison to naïve mice that received PBS instead of BLM (see Supplemental Figure 2A). This result confirms a previous report indicating that BLM-induced lung fibrosis partially depends on the activation of the P2RX7/pannexin-1 axis, leading to the production of IL-1β in the lung. Additionally, in contrast to the observations in WT mice, HEI3090 failed to attenuate the remaining lung fibrosis in p2rx7 -/- mice, as measured by the Ashcroft score (Figure 2M), the percentage of lung tissue with fibrotic lesions, or the intensity of collagen fibers (Supplemental Figure 2D). These results show that P2RX7 alone participates in fibrosis and that HEI3090 exerts a specific antifibrotic effect through this receptor (see Supplemental Figure 2C)."

Since we used the HEI3090 compound in this study and to be closer to the results, we have replaced the title of 2 chapters in the results section as followed:

“HEI3090 inhibits the onset of pulmonary fibrosis in the bleomycin mouse model” instead of P2RX7 activation inhibits the onset of pulmonary fibrosis in the bleomycin mouse model and “HEI3090 shapes immune cell infiltration in the lungs" instead of P2RX7 activation shapes immune cell infiltration in the lungs

We concur that the observation of both anti-fibrotic effects following P2RX7 deletion and P2RX7 activation appears contradictory. This specific aspect has been thoroughly addressed and extensively discussed in the revised manuscript.

“A major unmet need in the field of IPF is new treatment to fight this uncurable disease. In this preclinical study, we demonstrate the ability of immune cells to limit lung fibrosis progression. Based on the hypothesis that a local activation of a T cell immune response and upregulation of IFN-γ production has antifibrotic proprieties, we used the HEI3090 positive modulator of the purinergic receptor P2RX7, previously developed in our laboratory (Douguet et al., 2021), to demonstrate that activation of the P2RX7/IL-18 pathway attenuates lung fibrosis in the bleomycin mouse model. We have demonstrated that lung fibrosis progression is inhibited by HEI3090 in the fibrotic phase but also in the acute phase of the BLM fibrosis mouse model, i.e. during the period of inflammation. This lung fibrosis mouse model commonly employed in preclinical investigations, has recently been recognized as the optimal model for studying IPF (Jenkins et al., 2017). In this model, the intrapulmonary administration of BLM induces DNA damage in alveolar epithelial type 1 cells, triggering cellular demise and the release of ATP. The extracellular release of ATP from injured cells activates the P2RX7/pannexin 1 axis, initiating the maturation of IL1β and subsequent induction of inflammation and fibrosis. In line with this, mice lacking P2RX7 exhibited reduced neutrophil counts in their bronchoalveolar fluids and decreased levels of IL1β in their lungs compared to WT mice (Riteau et al., 2010). Based on these findings, Riteau and colleagues postulated that the inhibition of P2RX7 activity may offer a potential strategy for the therapeutic control of fibrosis in lung injury. In the present study we provided strong evidence showing that selective activation of P2RX7 on immune cells, through the use of HEI3090, can dampen inflammation and fibrosis by releasing IL-18. The efficacy of HEI3090 to inhibit lung fibrosis was evaluated histologically on the whole lung’s surface by evaluating the severity of fibrosis using three independent approaches applied to the whole lung, the Ashcroft score, quantification of fibroblasts/myofibroblasts (CD140a) and polarized-light microscopy of Sirius Red staining to quantify collagen fibers. All these methods of fibrosis assessment revealed that HEI3090 exerts an inhibitory effect on lung fibrosis, underscoring the necessity for a thorough pre-clinical assessment of HEI3090's mode of action. Notably, HEI3090 functions as an activator, rather than an inhibitor, of P2RX7, further emphasizing the importance of elucidating its intricate mechanisms.”

We trust that the detailed explanation provided therein will adequately persuade both the reviewers and future readers.

1. The statistical concerns are based on the phrasing of "the experiment was stopped when significantly statistical results were observed". This is different from the power analysis approach that the authors describe in their latest rebuttal. However, it raises the question why the power analysis was performed using "on a one-way ANOVA analysis comparing in each experiment the vehicle and the treated group". The analyses in the manuscript use the Mann-Whitney test for several comparisons which ahs the assumption that the samples do NOT have a normal distribution. An ANOVA and t-tests have the assumption that samples are normally distributed. If the power analysis and "statistical forecasting" assumed a normal distribution and used an ANOVA, then shouldn't all the analyses also use a statistical test appropriate for normally distributed samples such as ANOVA and t-tests?

Several of the data points in the figures seem to be normally distributed and therefore t-test for two group comparisons would be more appropriate. The most rigorous approach would be to check for normal distribution before choosing the correct statistical test and using the t-test/ANOVA in normally distributed data as well as Mann-Whitney for non-normally distributed data.

We described in the Material and Method section of the revised manuscript our approach to determine the size of experimental group.

“The determination of experimental group sizes involved conducting a pilot experiment with four mice in each group. Subsequently, a power analysis, based on the pilot experiment's findings (which revealed a 40% difference with a standard error of 0.9, α risk of 0.05, and power of 0.8), was performed to ascertain the appropriate group size for studying the effects of HEI3090 on BLM-induced lung fibrosis. The results of the pilot experiment and power analysis indicated that a group size of four mice was sufficient to characterize the observed effects. For each full-scale experiment, we initiated the study with 6 to 8 mice per group, ensuring a minimum of 5 mice in each group for robust statistical analysis. Additionally, we systematically employed the ROULT method to identify and subsequently exclude any outliers present in each experiment before conducting statistical analyses”.

We now described in the Material and Method section how we carried out the statistical analyses.

“Quantitative data were described and presented graphically as medians and interquartiles or means and standard deviations. The distribution normality was tested with the Shapiro's test and homoscedasticity with a Bartlett's test. For two categories, statistical comparisons were performed using the Student's t-test or the Mann–Whitney's test. For three and more categories, analysis of variance (ANOVA) or non-parametric data with Kruskal–Wallis was performed to test variables expressed as categories versus continuous variables. If this test was significant, we used the Tukey's test to compare these categories and the Bonferroni’s test to adjust the significant threshold. For the Gene Set Enrichment Analyses (GSEA), bilateral Kolmogorov–Smirnov test, and false discovery rate (FDR) were used. All statistical analyses were performed by biostatistician using Prism8 program from GraphPad software. Tests of significance was two-tailed and considered significant with an alpha level of P < 0.05. (graphically: * for P < 0.05, ** for P < 0.01, *** for P < 0.001).”

We also added in the legend of each figure, the statistical analysis used to determine each p-values.

1. Adoptive transfer: The concerns of the reviewers include an unclear analysis of the effects of adoptive transfer itself and the approaches used to analyze the data independent of the HEI3090 effect. For example, in Figure 4, the adoptive transfer IL18-/- cells (vehicle group) leads to an Ashcroft score of about 1 and among the lowest of the BLM exposed mice. Does that mean that IL18 is pro-fibrotic and that its absence is beneficial? If yes, it would go against the core premise of the study that IL18 is beneficial. Statistical comparisons of the all the vehicle conditions in the adoptive transfer would help clarify whether adoptive transfer of NLRP3-/-, IL18-/- in wild-type and P2RX7-/- mice reduces or increases fibrosis. Such multiple comparisons are necessary to fully understand the adoptive transfer studies and would also require the appropriate statistical test with corrections for multiple comparisons such as Kruskal-Wallis for data without normal distribution and ANOVA with post hoc correction for normal distribution.

We added a new paragraph in the revised version of the manuscript to explain the adoptive transfer approach.

“We wanted to further investigate the mechanism of action of HEI3090 by identifying the cellular compartment and signaling pathway required for its activity. Since the expression of P2RX7 and the P2RX7-dependent release of IL-18 are mostly associated with immune cells (Ferrari et al., 2006), and since HEI3090 shapes the lung immune landscape (Figure 3), we investigated whether immune cells were required for the antifibrotic effect of HEI3090. To do so, we conducted adoptive transfer experiments wherein immune cells from a donor mouse were intravenously injected one day before BLM administration into an acceptor mouse. The intravenous injection route was chosen as it is a standard method for targeting the lungs, as previously documented (Wei and Zhao, 2014). This approach was previously used with success in our laboratory (Douguet et al., 2021). It is noteworthy that this adoptive transfer approach did not influence the response to HEI3090. This was observed consistently in both p2rx7 -/- mice and p2rx7 -/- mice that received splenocytes of the same genetic background. In both cases, HEI3090 failed to mitigate lung fibrosis, as depicted in Figure 2M and Supplemental Figures 2D and 6A and B.”

We added the Supplemental Figure 7 showing that the genetic background does not impact lung fibrosis at steady step levels where p-values were analyzed by one-way ANOVA, with Kruskal-Wallis test for multiple comparisons.

Author response image 1.

Supplemental Figure 7 : The genetic background does not impact lung fibrosis at steady step levels. p2rx7-/- mice were given 3.106 WT, nlrp3-/ , i118-/ or illb -l- splenocytes i_v_ one day prior to BLM delivery (i_n_ 2.5 LJ/kg) p2rx7-/- mice or p2rx7-/- mice adoptively transferred with splenocytes from indicated genetic background were treated daily i.p with mg/kg HE13090 or vehicle for 14 days. Fibrosis score assessed by the Ashcroft method. P-values were analyzed on all treated and non treated groups by one-way ANOVA, with Kruskal-Wallis test for multiple comparisons. The violin plot illustrates the distribution of Ashcroft scores across indicated experimental groups. The width of the violin at each point represents the density of data, and the central line indicates the median expression level. Each point represents one biological replicate. ns, not significant

Reviewer #1 (Public Review):

In this revised preprint the authors investigate whether a presumably allosteric P2RX7 activating compound that they previously discovered reduces fibrosis in a bleomycin mouse model. They chose this particular model as publicly available mRNA data indicate that the P2RX7 pathway is downregulated in idiopathic pulmonary fibrosis patients compared to control individuals. In their revised manuscript, the authors use three proxies of lung damage, Ashcroft score, collagen fibers, and CD140a+ cells, to assess lung damage following the administration of bleomycin. These metrics are significantly reduced on HEI3090 treatment. Additional data implicate specific immune cell infiltrates and cytokines, namely inflammatory macrophages and damped release of IL-17A, as potential mechanistic links between their compound and reduced fibrosis. Finally, the researchers transplant splenocytes from WT, NLRP3-KO, and IL-18-KO mice into animals lacking the P2RX7 receptor to specifically ascertain how the transplanted splenocytes, which are WT for P2RX7 receptor, respond to HEI3090 (a P2RX7 agonist). Based on these results, the authors conclude that HEI3090 enhanced IL-18 production through the P2RX7-NLRP3 inflammasome axis to dampen fibrosis.

These findings could be interesting to the field, as there are conflicting results as to whether NLRP3 activation contributes to fibrosis and if so, at what stage(s) (e.g., acute damage phase versus progression). The revised manuscript is more convincing in that three orthogonal metrics for lung damage were quantified.

However, deletion of the P2RX7 receptor itself reduces the extent of fibrosis, suggesting that P2RX7 signaling can be pro-fibrotic. In the absence of P2RX7, the effects of HEI3900 are also abolished, suggesting that HEI3900 acts in part via P2RX7 signaling. This suggests a paradox that P2RX7 signaling can be both detrimental and beneficial in fibrosis and there is need for a better understanding of when P2RX7 signaling is beneficial and when it is detrimental in lung fibrosis. HEI3900-induced activation of P2RX7 seems to be beneficial but this primarily is shown for when fibrosis is already established. As the P2RX7 genetic deletion mouse model has less fibrosis, P2RX7 signaling and inflammasome activation may be deleterious during the formation of disease but it is also possible that HEI3900 has other beneficial effects that are not directly related to P2RX7.

Molecularly, additional evidence on specificity, such as thermal proteome profiling and direct biophysical binding experiments, would also enhance the authors' argument that the compound indeed binds P2RX7 directly and specifically. Since all small molecules have some degree of promiscuity, the absence of an additional P2RX7 modulator, or direct recombinant IL-18 administration, is needed to orthogonally validate the functional importance of this pathway. Another way the authors could probe pathway specificity would involve co-administering α-IL-18 with HEI3090 in several key experiments (similar to Figure 4L).

Reviewer #2 (Public Review):

In the study by Hreich et al, the potency of P2RX7-specific positive modulator HEI3090, developed by the authors, for the treatment of Idiopathic pulmonary fibrosis (IPF) was investigated. Recently, the authors have shown that HEI3090 can protect against lung cancer by stimulating dendritic cell P2RX7, resulting in IL-18 production that stimulates IFN-γ production by T and NK cells (DOI: 10.1038/s41467-021-20912-2). Interestingly, HEI3090 increases IL-18 levels only in the presence of high eATP. Since the treatment options for IPF are limited, new therapeutic strategies and targets are needed. The authors first show that P2RX7/IL-18/IFNG axis is downregulated in patients with IPF. Next, they used a bleomycin-induced lung fibrosis mouse model to show that the use of a positive modulator of P2RX7 leads to the activation of the P2RX7/IL-18 axis in immune cells that limits lung fibrosis onset or progression. Mechanistically, treatment with HEI3090 enhanced IL-18-dependent IFN-γ production by lung T cells leading to a decreased production of IL-17 and TGFβ, major drivers of IPF. The major novelty is the use of the small molecule HEI3090 to stimulate the immune system to limit lung fibrosis progression by targeting the P2RX7, which could be potentially combined with current therapies available. Overall, the study was well performed and the manuscript is clear.

eLife assessment

The authors develop a novel genetic strategy for specific and comprehensive labeling of axo-axonic cells, also referred to as Chandelier cells, in the mouse brain. The approach and analysis are rigorous such that the data convincingly support the key conclusions, including the expanded distribution of axo-axonic cells throughout the brain. This study provides valuable new information about the distribution of this neuronal cell type, as well as new tools for future studies. This work will be of broad interest to neuroscientists who work on the anatomical and functional organization of neural circuits.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, the authors set out to develop genetic tools that can specifically and comprehensively label Axo-Axonic Cells (AACs), also known as Chandelier cells. These AACs possess unique morphological and connectivity features, making them an ideal subject for studying various aspects of cell types across different experimental methods. To achieve both specificity and comprehensiveness in AAC labeling, the authors employ an intersectional strategy that combines lineage origin and molecular markers. This approach successfully targets AACs across the mouse brain and reveals their widespread distribution in various brain structures beyond the previously known regions. Additionally, the authors utilize rabies transneuronal labeling to provide a comprehensive overview of AACs, their variations, and input sources throughout the brain. This experimental approach offers a powerful model system for investigating the role of AACs in circuit development and function across diverse brain regions.

Strengths:<br /> Genetic Tools and Specificity: The authors' genetic tools show qualitative evidence of specificity for AACs, opening new avenues for targeted research on these cells. The use of intersectional strategies enhances the precision of AAC labeling.

Widespread Distribution: The study significantly broadens our understanding of AAC distribution, revealing their presence in brain regions beyond what was previously documented. This expanded knowledge is a valuable contribution to the field.

Transneuronal Labeling: The inclusion of rabies transneuronal labeling provides a comprehensive view of AACs, their variations, and input sources, allowing for a more holistic understanding of their role in neural circuits.

Weaknesses:<br /> Quantitative Analysis: While the claim of specificity appears qualitatively convincing, the manuscript could be improved with more quantitative analysis.

Comprehensiveness Claim: The assertion of comprehensiveness, implying labeling "almost all" AACs in all brain regions, is challenging to substantiate conclusively. Acknowledging the limitations of proving complete comprehensiveness and discussing them in the discussion section would be more appropriate than asserting it in the results section.

Local Inputs: While the manuscript focuses on inter-areal inputs to AACs, it would benefit from exploring local inputs as well. Identifying the local neurons that target AACs and analyzing their patterns could provide valuable insights into AAC function within specific brain regions.

Discussion Focus: The discussion section should delve deeper into the biological implications of the findings, moving beyond technical significance. Exploring similarities and differences in input patterns between AACs and other cell types, and linking them to the locations of starter cells or specific connectivity patterns in the brain, would enrich the discussion. For instance, investigating whether input patterns can be predicted based on the locations of starter cells or connectivity specificity could provide valuable insights.

Reviewer #2 (Public Review):

Summary:<br /> The goals of this study were to develop a genetic approach that would specifically and comprehensively target axo-axonic cells (AACs) throughout the brain and then to describe the patterns and characteristics of the targeted AACs in multiple, selected brain regions. The investigators have been successful in providing the most complete description of the regional distribution of putative (pAACs) throughout the brain to date. The supporting evidence is convincing, even though incomplete in some brain regions. The findings should serve as a guide for more detailed studies of AACs within each brain region and lead to new insights into the connectivity and functional organization of this important group of GABAergic interneurons.

Strengths:<br /> The study has numerous strengths. A major strength is the development of a unique intersectional genetic strategy that uses cell lineage (Nkx2.1) and molecular (Unc5b or Pthlh) markers to identify axo-axonic AACs specifically and, apparently, nearly completely throughout the mouse brain. While AACs have been described previously in the cerebral cortex, hippocampus, and amygdala, there has been no specific genetic marker that selectively identifies all AACs in these regions.

The current genetic strategy has labeled pAACs in a large number of additional brain regions, including the claustrum-insular complex, extended amygdala, and several olfactory centers. In general, the findings provide support for the specificity of the methods for targeting AACs, and include some examples of labeling near markers of axon initial segments. However, the Investigators are careful to refer to labeled neurons as "putative AACs" as they have not been fully characterized and their identity verified.

The descriptions and numerous low-magnification images of the brain provide a roadmap for subsequent, detailed studies of AACs in numerous brain regions. The overview and summaries of the findings in the Abstract, Introduction, and Discussion are particularly clear and helpful in placing the extensive regional descriptions of AACs in context.

Weaknesses:<br /> One weakness of the study is the lack of an illustration of the high-resolution cell labeling that can be achieved with the methods, including labeling of numerous rows of axon terminals in contact with axon initial segments. The initial images of the brain-wide distribution of putative AACs are necessarily presented at low magnification. Although the authors indicate that the cells have "highly characteristic AAC labeling patterns throughout the neocortex, hippocampus and BLA", these morphological details cannot be visualized by the reader at the current magnification, even when the images are enlarged on the computer screen. Some of the details become evident in later Figures, but an initial illustration of single cell labeling with confocal microscopy, or tracing of their characteristic axonal arbors, would support the specificity of the labeling in the low magnification images.

Table 1 indicates that the AAC identity of the cells has been validated in many brain regions but not in all. The methods used for validation have not been described and should be included for completeness. The authors are careful to acknowledge that labeled cells in some regions have not been validated and refer to such cells as pAACs.

The intersectional genetic methods included the use of the lineage marker Nkx2.1 with either Unc5b or Pthlh as the molecular marker. As described, the mice with intersectional targeting of Nkx2.1 and Unc5b appear to show the most specific brain-wide labeling for AACs, and the majority of the descriptions are from these mice. The targeting with Nkx2.1 and Pthlh is less convincing. The title for Figure 1 Supplemental Figure 3 suggests a similar AAC distribution in the Pthlh;Nkx2.1 mouse compared to the Unc5b;Nkx2.1 mouse. However, the descriptions of the individual panels suggest a number of inconsistencies and non-AAC labeling. The heavy labeling in the caudate and cells in layer 4 is particularly problematic. Based on the data presented, it appears that heavy labeling achieved in these mice could not be relied on for specific labeling of all AACs, although specific labeling could be achieved under some conditions, such as following tamoxifen administration at select ages.

The methods described for dense labeling and single-cell labeling are described briefly in the methods. Some discussion of the development of the methods would be useful, including how it was determined that methods for heavy labeling identified AACs specifically and completely.

Reviewer #3 (Public Review):

Summary:<br /> Raudales et al. aimed at providing an insight into the brain-wide distribution and synaptic connectivity of bona fide GABAergic inhibitory interneuron subtypes focusing on the axo-axonic cell (AAC), one of the most distinctive interneuron subtypes, which innervates the axon initial segments of glutamatergic projection neurons. They establish intersectional genetic strategies that enable them to specifically and comprehensively capture AACs based on their lineage (Nkx2.1) and marker expression (Unc5b, Pthlh). They find that AACs are deployed across essentially all the pallium-derived brain structures as well as the anterior olfactory nucleus, taenia tecta, and lateral septum. They show that AACs in distinct areas and layers of the neocortex as well as different subregions of the hippocampal formation display unique soma and synaptic density and morphological variations. Rabies virus-based retrograde monosynaptic input tracing reveals that AACs in the neocortex, the hippocampus, and the basolateral amygdala receive synaptic inputs from common as well as specific brain regions and supports the utility of this novel genetic approach. This study elucidates brain-wide neuroanatomical features and morphological variations of AACs with solid techniques and analysis. Their novel AAC-targeting strategies will facilitate the study of their development and function in different brain regions. The conclusions in this paper are well supported by the data. However, there are a few comments to strengthen this study.

1) The definition of putative AAC (pAAC) is unclear and Table 1 may not be accurate. Although the authors find synaptic cartridges of RFP-labeled cells in the claustro-insular complex and the dorsal endopiriform nuclei, they still consider these cells as pAACs (not validated). The authors claim that without examining the presence of synaptic cartridges, RFP-labeled cells in the hypothalamus and the bed nuclei of the stria terminalis (BNST) are pAACs while those in the L4 of the somatosensory cortex in Pthlh;Nkx2.1;Ai65 mice are non-AACs. In Table 1, the BNST is supposed to contain AACs (validated), but in the text, the authors claim that RFP-labeled cells in the BNST are pAACs. Could the authors clarify how AACs, pAACs, and non-AACs are defined?

2) The intersectional strategies presented in this study could also specifically capture developing AACs. If so, how early are AACs labeled in the brain? It would also be nice if the authors could add a simple schematic like Fig. 1a showing the time course of Pthlh expression.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Some suggestions:

1) It's obviously concerning that your GWAS results are not at all robust to the approach used (Fig S3). Did you try something non-parametric, like a Kruskal-Wallis test?

We used both GWAS and crosses (F2) to validate the presence of the QTL. So ,evidence is not only brought by GWAS. We did not use non parametric tests as we will have difficulty to account for population structure/relatedness with such approaches. Our GWAS approach is certainly a little underpowered associated with the number of individuals we used and certainly the polygenic nature of the root growth traits. But F2 crosses allow us to put more evidence weight on some region we identified with GWAS.

2) You don't explain what you do with heterozygotes, nor discuss the level of inbreeding in general.

We are dealing with inbred lines, but indeed there are not completely fixed inbred lines. For the remaining heterozygotes, they were randomly fixed in one or the other alleles. The median heterozygosity value was low at 5.6%. We clarified this point in the material and methods.

3) The finding that over 30% of RNA-seq reads don't seem to have an annotated home should give you pause. Do they map anywhere? At least discuss what is going on. Also, note that you likely have enormous errors in SNP-calling due to cryptic structural variation - think about what this might do?

We agree with reviewer #1. We added a few sentences in the result section to clarify this point: “When further analyzed, 15.15% of the unmapped reads (with no correspondence to predicted CDS) were found not to match the reference genome. These might correspond either to unsequenced regions or to genotype-specific genomic regions that are not present in the reference line. The remaining unmapped reads corresponded to either rRNA and tRNA genes (40.28% of the unmapped reads) or to non-annotated genes or non-coding RNAs (44.57% of the unmapped reads).” As we used the same reference genome for mapping the RNAseq reads, some genes might not being present in our analysis for the two lines we studied.

4) Did you consider moving PgGRXC9 into Arabidopsis?

This is a great suggestion. In fact, we plan to explore more how some GRXs regulate root growth and how this is conserved in plants in a follow up project. This is however beyond the scope of this manuscript.

Minor suggestions:

1) Why not calculate H^2 simply as line variance divided by total?

Heritability estimated on single individuals in population, approaches generally used for human and animal breeding led directly to line variance divided by total phenotypic variance.

But in plant breeding (or plant science), we generally work on replicated genotypes in different blocks/experimental repetition. So we estimate the heritability of the mean phenotype of genotypes. There is ample literature (Nyquist, 1991; Holland et al. 2003; for a very nice and smartly written explanation, on the introduction of this PhD: http://opus.uni-hohenheim.de/volltexte/2020/1720/pdf/20200221_PhD_Thesis_Publikationsversion.pdf). Calculation of heritability (of the mean phenotype) should take into account for the calculation of the phenotypic variance (denominator) the number of replicate genotypes (we do not have a single plant, but several clones when using inbred lines: n). The meaning of the formula is that the error in the model is inflated because we have n replicate plants per genotype. And so to estimate the heritability of the average genotype, we have to take into account this inflated variance in the errors.

2) While the paper overall is well-written, the captions need further proof-reading.

We corrected all the captions.

Reviewer #2 (Recommendations For The Authors):

Major suggestions:

1) The experimental support for the mutant phenotype of roxy19 needs to be further substantiated. Current methods available for CRISPR mutagenesis make it relatively easy to generate additional alleles. Alternatively, the authors could complement the mutant with a wild-type copy of the gene. These approaches represent the standard of the field and should be used here as well.

We agree with rev #2. We added some sentences in the discussion to stress out the limitations of our study to link the QTL to PgGRXC9.

As stated above we’d like to explore more how some GRXs regulate root growth and how this is conserved in plants. We plan to generate new single and multiple mutants in ROXY19 and its closest homologues (using CRISPR). This is, however, beyond this manuscript.

2) The authors may want to state more clearly what the hypothesis is for how redox levels might contribute to root length differences and more clearly state what the limits of their current study are.

We modified the discussion to try to clearly indicate the limitations of our study.

3) Differences in root growth can be the consequence of a number of different parameters that contribute to root elongation and the authors need to more clearly define which of these are likely affected in their different genotypes.

We agree with Reviewer #2. However, as stated before, we plan to further explore the molecular and cellular mechanisms responsible for the phenotype we observe in Arabidopsis. This will need extra work and is beyond the scope of this manuscript.

4) Page 13, first paragraph. The authors provide an overly strong statement that suggests they have determined the molecular basis for the difference in PgGRXC9: " Altogether, our results suggest that PgGRXC9 is a positive regulator of root growth and that a polymorphism in the promoter region of PgGRXC9 associated with changes in its expression level appeared responsible for a quantitative difference in root growth between the two lines."

While their results suggest the PgGRXC9 locus is associated with root growth variation, they have not directly tested the effect of the polymorphisms in the promoter on gene expression and this statement needs to be weakened.

We changed the text to: “Altogether, our results suggest that PgGRXC9 is a positive regulator of root growth and that a polymorphism in the promoter region of PgGRXC9 might led to changes in its expression level and ultimately to a quantitative difference in root growth between the two lines. However, the effect of the polymorphisms in the promoter on gene expression need to be tested to validate this hypothesis.”

We also changed the title of the manuscript to better reflect our results.

Minor suggestions:

1) Page 4: "FTSW below 0.3 was considered a stressful condition." It was not specified how this threshold was determined.

This value corresponds to the measured FTSW value at which pearl millet genotypes subjected to a dry down generally start to reduce their transpiration rate (see Fig. 1 of Kholová et al, 2010; https://doi.org/10.1093/jxb/erp314). At FTSW values above 0.3, transpiration is not affected. At FTSW values around 0.3, the water supply from pearl millet roots cannot fully support transpiration. The plant enters a drought stress responsive phase and progressively closes its stomata to reduce water losses and decrease plant productive functions to match water supply. We have clarified this in the manuscript.

2) Page 6: Figure 1; footnote: at the end of the description of panel A, a comma is missing between "red" and "blue."

Thanks for pointing that out. This was corrected.

3) The root growth data determined by X-ray imaging is not significant (Fig S4B), yet the authors describe the result in the main text without qualification. The authors should clarify this in the text.

We added some text to clarify this.

4) Page 9: Figure 2C; It would be better to enlarge these images and annotate them to indicate what specific anatomical features have been measured. Currently, only an expert in the field would be able to interpret these images.

While we understand the point made by Reviewer #2, Fig2C was meant to illustrate differences in the root tip of the two lines.

5) Page 9: Figures 2D and E; the number of biological samples measured is not indicated (what is "n"?).

Thanks again for pointing this out. This was added to the figure legend.

6) Page 14: Figure 4B; scale bar needs to be included.

Scale bars were added to the pictures.

7) Page 14: Figure 4; I recommend adding confocal images or DIC of cleared root apex tissues to easily compare the RAM size and cell lengths in both WT and roxy19 mutant.

Once again, we plan to have a follow up study on the molecular and cellular mechanisms of action of ROXY19 and its closest homologues on root development. We believe a thorough analysis of differences in phenotype could be illustrated in a future manuscript.

8) Page 18: main text; "we propose that redox regulation in the root meristem is responsible for a root growth QTL in pearl millet." This statement is ambiguous in the description of the mechanism. The authors do not clarify if the role they propose for PgGRXC9 is in the meristematic or elongation zone. Likely the authors are not able to know precisely where the gene is acting at this point, and so the presented hypothesis needs to more clearly state what limitations there are in assigning a mode of action for the PgGRXC9 and ROXY19 genes in root growth.

We rewrote this paragraph to clarify the current gap in our understanding of the putative PgGRXC9 function.

eLife assessment

This is an important paper that combines methods ranging from agronomy and plant breeding to Arabidopsis functional genetics, to argue that polymorphism in a single gene affects crop yield in pearl millet by affecting root cell elongation and drought stress resilience in a poorly studied crop. The overall argument is plausible but whether the solid evidence generated with Arabidopsis experiments can be extended to pearl millet itself is unclear.

Reviewer #1 (Public Review):

The authors use a combination of crop modeling and field experiments to argue that drought during seedling establishment likely severely impacts the yield of pearl millet, an important but understudied cereal crop and that rapid seedling root elongation could play a major role in mitigating this. They further argue that this trait has a strong genetic basis and that major polymorphisms in candidate genes can be identified using standard methods from modern genetics and genomics. Finally, they use homology with the model plant Arabidopsis thaliana to argue that the function of one putatively causal gene is to regulate root cell elongation.

The major strength of this paper is that it convincingly demonstrates how modern methods from plant breeding and model organisms can be combined to address questions of great practical importance in important but poorly understood crops. The notion that it is possible to connect single-locus polymorphism and cellular biology to drought tolerance and crop yield in pearl millet is not a trivial one.

The weakness is obvious: while the argument made is convincing, it must be recognized that the strength of the evidence is by no means of the level expected in a model organism. Conclusions could easily be wrong, and there is no direct evidence that regulatory variation in PgGRXC9 leads to higher crop yield via cell elongation and seedling drought tolerance. However, generating such evidence in a poorly studied crop would be a monumental undertaking, and should probably not be the priority of people working on pearl millet!

The utility of this work is that it suggests that it is practicable to gain valuable insight into crop adaptation by clever use of modern methods from a variety of sources.

Reviewer #2 (Public Review):

Carla de la Fuente et al., utilize a diversity of approaches to understand which plant traits contribute to the stress resilience of pearl millet in the Sahelian desert environment. By comparing data resulting from crop modeling of pearl millet growth and meteorological data from a span of 20 years, the authors clearly determined that early season drought resilience is contributed by accelerated growth of the seedling primary root, which confirms a hypothesis generated in a previous study, Passot et al., 2016. To determine the genetic basis for this trait, they performed a combination of GWAS, QTL analysis, and RNA sequencing and identified a previously unannotated coding sequence of a glutaredoxin C9-like protein, PgGRXC9, as the strongest candidate. Phenotypic analysis using a mutant of the closest Arabidopsis homolog AtROXY19 suggests the broad conservation of this pathway. Comparisons between the transcript of PgGRXC9 by in situ hybridization (this work) and AtROXY19 pattern expression (Belin et al., 2014) support the hypothesis that this pathway acts in the elongation zone of the root. Additional analysis of cell production and elongation rates in root apex in both pearl millet and A. thaliana suggests that PgGRXC9 specifically regulates primary root through the promotion of cell elongation. While several studies have established the connection between redox status of cells and root growth, the current study represents an important contribution to the field because of the agricultural importance of the plant studied, and the connection made between this developmental trait and stress resilience in a specific and stressful environmental context of the Sahelian desert.

eLife assessment

The manuscript addresses a fundamental question: are IDRs responsible for subnuclear clustering of transcription factors? A screen of 75 IDRs yielded convincing evidence that IDRs are rarely sufficient for subnuclear clustering, while the experimental design and data analysis provided limited evidence for the authors' claims regarding transcription factor clustering.

Author Response

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

eLife assessment

The study is an important advancement to the consideration of antimalarial drug resistance: the authors make use of both modelling results and supporting empirical evidence to demonstrate the role of malaria strain diversity in explaining biogeographic patterns of drug resistance. The theoretical methods and the corresponding results are convincing, with the novel model presented moving beyond existing models to incorporate malaria strain diversity and antigen-specific immunity. This work is likely to be interesting to malaria researchers and others working with antigenically diverse infectious diseases.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper is an attempt to explain a geographic paradox between infection prevalence and antimalarial resistance emergence. The authors developed a compartmental model that importantly contains antigenic strain diversity and in turn antigen-specific immunity. They find a negative correlation between parasite prevalence and the frequency of resistance emergence and validate this result using empirical data on chloroquine-resistance. Overall, the authors conclude that strain diversity is a key player in explaining observed patterns of resistance evolution across different geographic regions.

The authors pose and address the following specific questions:

1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities?

2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal?

3. Does the model explain biogeographic patterns of drug resistance evolution?

Strengths:

The model built by the authors is novel. As emphasized in the manuscript, many factors (e.g., drug usage, vectorial capacity, population immunity) have been explored in models attempting to explain resistance emergence, but strain diversity (and strain-specific immunity) has not been explicitly included and thus explored. This is an interesting oversight in previous models, given the vast antigenic diversity of Plasmodium falciparum (the most common human malaria parasite) and its potential to "drive key differences in epidemiological features".

The model also accounts for multiple infections, which is a key feature of malarial infections, with individuals often infected with either multiple Plasmodium species or multiple strains of the same species. Accounting for multiple infections is critical when considering resistance emergence, as with multiple infections there is within-host competition which will mediate the fitness of resistant genotypes. Overall, the model is an interesting combination of a classic epidemiological model (e.g., SIR) and a population genetics model.

In terms of major model innovations, the model also directly links selection pressure via drug administration with local transmission dynamics. This is accomplished by the interaction between strain-specific immunity, generalized immunity, and host immune response.

R: We thank the reviewer for his/her appreciation of the work.

Weaknesses:

In several places, the explanation of the results (i.e., why are we seeing this result?) is underdeveloped. For example, under the section "Response to drug policy change", it is stated that (according to the model) low diversity scenarios show the least decline in resistant genotype frequency after drug withdrawal; however, this result emerges mechanistically. Without an explicit connection to the workings of the model, it can be difficult to gauge whether the result(s) seen are specific to the model itself or likely to be more generalizable.

R: We acknowledge that the explanation of certain results needs to be improved. We have now added the explanation of why low diversity scenarios show the least decline in resistance frequency after drug withdrawal: “Two processes are responsible for the observed trend: first, resistant genotypes have a much higher fitness advantage in low diversity regions even with reduced drug usage because infected hosts are still highly symptomatic; second, due to low transmission potential in low diversity scenarios (i.e., longer generation intervals between transmissions), the rate of change in parasite populations is slower.” (L243-247). We also compared the drug withdrawal response to that of the generalized-immunity-only model (L268-271). The medium transmission region has the fastest reduction in resistance frequency, followed by the high and low transmission regions, which differs from the full model that incorporates strain-specific diversity.

In addition, to provide the context of different biogeographic transmission zones, we now include a new figure (now Fig. 3) that presents the parameter space of transmission potential and strain diversity of different continents, which demonstrates that PNG and South America have less strain diversity than expected by transmission potential (L179-184 and L198-202). Therefore, these two regions have low disease prevalence and high resistance frequency.

The authors emphasize several model limitations, including the specification of resistance by a single locus (thus not addressing the importance of recombination should resistance be specified by more than one locus); the assumption that parasites are independently and randomly distributed among hosts (contrary to empirical evidence); and the assumption of a random association between the resistant genotype and antigenic diversity. However, each of these limitations is addressed in the discussion.

R: As pointed out by the referee, our model presents several limitations that have all been addressed in the discussion and considered for future extensions.

Did the authors achieve their goals? Did the results support their conclusion?

Returning to the questions posed by the authors:

1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities? Yes. The authors demonstrate a negative relationship between prevalence/strain diversity and resistance frequency (Figure 2).

2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal? Yes. The authors find that, under resistance invasion and some level of drug treatment, resistance frequency decreased with the number of strains (Figure 4). The authors also find that lower strain diversity results in a slower decline in resistant genotypes after drug withdrawal and higher equilibrium resistance frequency (Figure 6).

3. Does the model explain biogeographic patterns of drug resistance evolution? Yes. The authors find that their full model (which includes strain-specific immunity) produces the empirically observed negative relationship between resistance and prevalence/strain diversity, while a model only incorporating generalised immunity does not (Figure 8).

Utility of work to others and relevance within and beyond the field?

This work is important because antimalarial drug resistance has been an ongoing issue of concern for much of the 20th century and now 21st century. Further, this resistance emergence is not equitably distributed across biogeographic regions, with South America and Southeast Asia experiencing much of the burden of this resistance emergence. Not only can widespread resistant strains be traced back to these two relatively low-transmission regions, but these strains remain at high frequency even after drug treatment ceases.

Reviewer #2 (Public Review):

Summary:

The evolution of resistance to antimalarial drugs follows a seemingly counterintuitive pattern, in which resistant strains typically originate in regions where malaria prevalence is relatively low. Previous investigations have suggested that frequent exposures in high-prevalence regions produce high levels of partial immunity in the host population, leading to subclinical infections that go untreated. These subclinical infections serve as refuges for sensitive strains, maintaining them in the population. Prior investigations have supported this hypothesis; however, many of them excluded important dynamics, and the results cannot be generalized. The authors have taken a novel approach using a deterministic model that includes both general and adaptive immunity. They find that high levels of population immunity produce refuges, maintaining the sensitive strains and allowing them to outcompete resistant strains. While general population immunity contributed, adaptive immunity is key to reproducing empirical patterns. These results are robust across a range of fitness costs, treatment rates, and resistance efficacies. They demonstrate that future investigations cannot overlook adaptive immunity and antigenic diversity.

R: We thank the reviewer for his/her appreciation of the work.

Strengths:

Overall, this is a very nice paper that makes a significant contribution to the field. It is well-framed within the body of literature and achieves its goal of providing a generalizable, unifying explanation for otherwise disparate investigations. As such, this work will likely serve as a foundation for future investigations. The approach is elegant and rigorous, with results that are supported across a broad range of parameters.

Weaknesses:

Although the title states that the authors describe resistance invasion, they do not support or even explore this claim. As they state in the discussion (line 351), this work predicts the equilibrium state and doesn't address temporal patterns. While refuges in partially immune hosts may maintain resistance in a population, they do not account for the patterns of resistance spread, such as the rapid spread of chloroquine resistance in Africa once it was introduced from Asia.

R: We do agree that resistance invasion is not the focus of our manuscript. Rather we mainly investigate the maintenance and decline after drug withdrawal. Therefore, we changed the title to “Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of anti-malarial drug resistance” (L1-4).

We did, however, present a fast initial invasion phase for the introduction of resistant genotypes regardless of transmission scenarios in Fig. 5 (now Fig. 6). Even though the focus of the manuscript is to investigate long term persistence of resistant genotypes, we did emphasize that the initial invasion phase and how that changes the host immunity profile are key to the coexistence of resistant and wild-type genotypes (L228-239).

As the authors state in the discussion, the evolution of compensatory mutations that negate the cost of resistance is possible, and in vitro experiments have found evidence of such. It appears that their results are dependent on there being a cost, but the lower range of the cost parameter space was not explored.

R: It is true that compensatory mutations might mitigate the negative fitness consequences. We didn’t add a no-cost scenario because in general if there is no cost but only benefit (survival through drug usage), then resistant haplotypes will likely be fixed in the population. This is contingent on the assumption that these compensatory mutations are in perfect linkage with resistant alleles, which is unlikely in high-transmission scenarios. Our model does not incorporate recombination, but earlier models (Dye & Williams 1997, Hastings & D’Alessandro 2000) have demonstrated that recombination will delay the fixation of resistant alleles in high-transmission.

As suggested, we ran our model with costs equal 0 and 0.01 (Fig. 2C and L189-191). We found that resistant alleles almost always fix except for when diversity is extremely high, treatment/resistance efficacy is low. In these cases, additional benefits brought by more transmission from resistant alleles do not bring many benefits (as lower GI classes have a very small number of hosts). This finding does not contradict a wider range of coexistence between wild-type and resistant alleles when the cost is higher. We therefore added these scenarios to our updated results.

Author response image 1.

The use of a deterministic, compartmental model may be a structural weakness. This means that selection alone guides the fixation of new mutations on a semi-homogenous adaptive landscape. In reality, there are two severe bottlenecks in the transmission cycle of Plasmodium spp., introducing a substantial force of stochasticity via genetic drift. The well-mixed nature of this type of model is also likely to have affected the results. In reality, within-host selection is highly heterogeneous, strains are not found with equal frequency either in the population or within hosts, and there will be some linkage between the strain and a resistance mutation, at least at first. Of course, there is no recourse for that at this stage, but it is something that should be considered in future investigations.

R: We thank the reviewer for their insightful comments on the constraints of the deterministic modeling approach. We’ve added these points to discussion in the paragraph discussing the second limitation of the model (L359-364).

The authors mention the observation that patterns of resistance in high-prevalence Papua New Guinea seem to be more similar to Southeast Asia, perhaps because of the low strain diversity in Papua New Guinea. However, they do not investigate that parameter space here. If they did and were able to replicate that observation, not only would that strengthen this work, it could profoundly shape research to come.

R: We appreciate the suggestion to investigate the parameter space of Papua New Guinea. We now include a new figure (now Fig. 3) that presents the parameter space of transmission potential and strain diversity of different continents, which demonstrates that PNG and South America have less strain diversity than expected by transmission potential (L179-184 and L198-202). This translates to low infectivity for most mosquito bites, and most infections only occur in hosts with lower generalized immunity. Therefore resistant genotypes will help ensure disease transmission in these symptomatic hosts and be strongly selected to be maintained.

Reviewer #1 (Recommendations For The Authors):

1. I found lines 41-49 difficult to follow. Please rephrase (particularly punctuation) for clarity.

R: We have edited the lines to improve the writing (L41-50)):

“Various relationships between transmission intensity and stable frequencies of resistance were discovered, each of which has some empirical support: 1) transmission intensity does not influence the fate of resistant genotypes [Models: Koella and Antia (2003); Masserey et al. (2022); Empirical: Diallo et al. (2007); Shah et al. (2011, 2015)]; 2) resistance first increases in frequency and slowly decreases with increasing transmission rates [Models: Klein et al. (2008, 2012)]; and 3) Valley phenomenon: resistance can be fixed at both high and low end of transmission intensity [Model: Artzy-Randrup et al. (2010); Empirical: Talisuna et al. (2002)]. Other stochastic models predict that it is harder for resistance to spread in high transmission regions, but patterns are not systematically inspected across the parameter ranges [Model: Whitlock et al. (2021); Model and examples in Ariey and Robert (2003)].”

1. Line 65: There should be a space after "recombination" and before the citation.

R: Thank you for catching the error. We’ve added the space (L64).

1. I'm interested in the dependency of the results on the assumption that there is a cost to resistance via lowered transmissibility (lines 142-145). I appreciate that variation in the cost(s) of resistance in single and mixed infections is explored; however, from what I can tell the case of zero cost is not explored.

R: As suggested, we have now added the no-cost scenario. Please see the response to the Reviewer2 weaknesses paragraph 2.

1. I felt the commentary/explanation of the response to drug policy change was a bit underdeveloped. I would have liked a walk-through of why in your model low diversity scenarios show the slowest decline in resistant genotypes after switching to different drugs.

R: We acknowledge that the explanation of the response to drug policy change needs to be improved. We have now added the explanation of why we observe low diversity scenarios show the least decline in resistance frequency after drug withdrawal: “Two processes are responsible for the seen trend: first, resistant genotypes have a much higher fitness advantage in low diversity regions even with reduced drug usage because infected hosts are still highly symptomatic; second, due to low transmission potential in low diversity scenarios (i.e., longer generation intervals between transmissions), the rate of change in parasite populations is slower.” (L243-247). We also compared the drug withdrawal response to that of the generalized-immunity-only model. The medium transmission region has the fastest reduction in resistance frequency, followed by the high and low transmission regions, which differs from the full model that incorporates strain-specific diversity.

1. Line 352: persistent drug usage?

R: Yes, we meant persistent drug usage. We’ve clarified the writing (L389-391).

1. The organisation of the manuscript would benefit from structuring around the focal questions so that the reader can easily find the answers to the focal questions within the results and discussion sections.

R: This is a great suggestion. We modified the subheadings of results to provide answers to focal questions (L151, L179, L203-204, and L240).

1. Line 353: Please remove either "shown" or "demonstrated".

R: Thank you for catching the grammatical error, we’ve retained “shown” only for the sentence (L391-392).

Reviewer #2 (Recommendations For The Authors):

Overall, this was very nice work and a pleasure to read.

Major:

1. Please provide a much more thorough explanation of how resistance invasions are modeled. It is not clear from the text and could not be replicated.

R: We have now added a section “drug treatment and resistance invasion” in Methods and Materials to explain how resistance invasions are modeled (L488-496):

“Given each parameter set, we ran the ODE model six times until equilibrium with the following genotypic compositions: 1) wild-type only scenario with no drug treatment; 2) wild-type only scenario with 63.2% drug treatment (0.05 daily treatment rate); 3) wild-type only scenario with 98.2% drug treatment (0.2 daily treatment rate); 4) resistant-only scenario with no drug treatment; 5) resistance invasion with 63.2% drug treatment; 6) resistance invasion with 98.2% drug treatment. Runs 1-4 start with all hosts in G0,U compartment and ten parasites. Runs 5 and 6 (resistance invasion) start from the equilibrium state of 2 and 3, with ten resistant parasites introduced. We then followed the ODE dynamics till the next equilibrium.”

1. Please make your raw data, code, and replicable examples that produce the figures in the manuscript available.

R: We have added the data availability session, which provides the GitHub site with all the code for the model, data processing, and figures: All the ODE codes, numerically-simulated data, empirical data, and analyzing scripts are publicly available at https://github.itap.purdue.edu/HeLab/MalariaResistance.

1. Regarding the limitations described in the paragraph about the model in the public response, these results would be strengthened if there were separate compartments for strains which could be further divided into sensitive and resistant. Could you explore this for at least a subset of the parameter space?

R: In our model, sensitive and resistant pathogens are always modeled as separate compartments (Fig. S1B and Appendix 1). In Results/Model structure, L135-136, we stated the setup:

“The population sizes of resistant (PR) or sensitive (wild-type; PW) parasites are tracked separately in host compartments of different G and drug status.”

1. To what extent do these results rely on a cost to resistance? Were lower costs explored? This would be worth demonstrating. If this cannot be maintained without cost, do you think this is because there is no linkage between strain and resistance?

R: As suggested, we have now added the no-cost scenario (Fig. 2C and L189-191). Please see the response to the Reviewer1 weaknesses paragraph 2. In sum, under a no-cost scenario, if treatment rate is low, then wild-type alleles will still be maintained in high transmission scenarios; when treatment rate is high, resistant alleles will always be fixed.

Minor:

1. "Plasmodium" should be italicized throughout. Ironically, italics aren't permitted in this form.

R: We did italicize “Plasmodium” or “P. falciparum” throughout the text. If the reviewer is referring to “falciparum malaria”, the convention is not to italicize falciparum in this case.

1. Fig 1A: the image is reversed for the non-infected host with prior exposure to strain A. Additionally, the difference between colors for WT and resistant is not visible in monochrome.

R: Thank you for pointing out the problem of color choice in monochrome. We have modified the figure. The image in Fig 1A is not reversed for non-infected hosts with prior exposure to strain A. We now spell out “S” to be “specific immunity”, and explain it better in the figure legend.

1. Fig 2B: add "compare to the pattern of prevalence shown in Fig 2A" or something similar to make the comparison immediately clear.

R: We thank the reviewer’s suggestion. We’ve added a sentence to contrast Fig 2A and B in the Figure legend: “A comparison between the prevalence pattern in (A) and resistance frequency in (B) reveals that high prevalence regions usually correspond to low resistance frequency at the end of resistance invasion dynamics.”

1. Figs 2B & C: Please thoroughly explain how you produced this data in the methods section and briefly describe it in the results sections.

R: We agree that the modeling strategies need to be explained better. Since we explained the rationale for the parameter ranges and the prevalence patterns we observe in the results section “Appropriate pairing of strain diversity and vectorial capacity” (now “Impact of strain diversity and transmission potential on disease prevalence”), we added sentences in this section to explain how we run models until equilibrium for wild-only infections with or without drug treatment (L152-178). Then in the following section “Drug-resistance and disease prevalence” section, we explain how we obtained the resistance invasion data:

“To investigate resistance invasion, we introduce ten resistant infections to the equilibrium states of drug treatment with wild-type only infections, and follow the ODE dynamics till the next equilibrium” (L180-181).

1. Fig 3: The axis labels are not particularly clear. For the Y axis, please state in the label what it is the frequency of (either the mutation or the phenotype). In the X axis, it is better to spell that out in words, like "P. falciparum prevalence in children".

R: Thank you for pointing this out. We’ve modified the axes labels of Fig. 3 (now Fig. 4): X-axis: “P. falciparum prevalence in children aged 2-10”; Y-axis: “Frequency of resistant genotypes (pfcrt 76T)”.

1. Fig 4 and the rest of the figures of this nature: Showing an equilibrium-state timestep before treatment was introduced would improve the readers' understanding of the dynamics.

R: We agree that the equilibrium state before treatment is important. In fact, we have those states in our figure 4 (now figure 5): the left panel- “Daily treatment rate 0” indicates the equilibrium-state timestep before treatment. We clarified this point in the caption.

1. Fig 5 is very compelling, but the relationships in Fig 5 would be clearer if the Y axes were not all different. Consider using the same scale for the hosts, and the same scale for resistant parasites (both conditions) and WT parasites, 113 strains. It may be clearer to reference them if they are given as A-F instead of three figures each for A and B.

R: We agree with the suggested changes and have modified figure 5 (now Fig. 6): we used one Y-axis scale for the hosts, and one Y-axis scale for the parasites. The wild-type one is very low for the low diversity scenario, thus we included one inset plot for that case.

1. Fig 5 caption: High immune protection doesn't select against resistance. The higher relative fitness of the sensitive strain selects against resistance in a high-immunity environment.

R: Thank you for pointing this out. Here we meant that a reduction in resistant population after the initial overshoot occurs in both diversity levels. We are not comparing resistant strains to sensitive ones. We’ve modified the sentence to: “The higher specific immunity reduces the infectivity of new strains, leading to a reduction of the resistant parasite population regardless of the diversity level”.

1. Line 242: "keep" should be plural.

R: We’ve corrected “keep” to “keeps” (L267).

1. Line 360 and elsewhere: The strength of the results is somewhat overstated at times. This absolutely supports the importance of strain-specific immunity, but these results do not explain patterns of the origin of resistance and there are a number of factors that are not incorporated (a necessary evil of modeling to be sure).

R: Thank you for pointing this out. We’ve modified discussion to remove the overstated strength of results:

1) Original: “The inclusion of strain diversity in the model provides a new mechanistic explanation as to why Southeast Asia has been the original source of resistance to certain antimalarial drugs, including chloroquine.”

Modified: “The inclusion of strain diversity in the model provides a new mechanistic explanation as to why Southeast Asia has persisting resistance to certain antimalarial drugs, including chloroquine, despite a lower transmission intensity than Africa. “ (L328-330)

2) In sum, we show that strain diversity and associated strain-specific host immunity, dynamically tracked through the macroparasitic structure, can explainpredict the complex relationship between transmission intensity and drug-resistance frequencies.

1. The color palettes are not discernible in grayscale, especially the orange/blue/gray in Fig 2. The heatmaps appear to be in turbo, the only viridis palette that isn't grayscale-friendly. Just something to keep in mind for the accessibility of individuals with achromatopsia and most people who print out papers.

R: Thank you for the visualization suggestions. We updated all the figures with the “viridis:magma” palette. As for the orange/blue/gray scale used in Fig 2C, it is difficult to pick nine colors that are discernable in brightness in grayscale. Currently, the four colors correspond to clonal genotype cost (i.e. green, red, grey, and blue), and the three-level brightness maps to mixed genotype cost.

eLife assessment

The study is an important advancement to the consideration of antimalarial drug resistance: the authors make use of both modelling results and supporting empirical evidence to demonstrate the role of malaria strain diversity in explaining biogeographic patterns of drug resistance. The theoretical methods and the corresponding results are compelling, with the novel model presented moving beyond existing models to incorporate malaria strain diversity and antigen-specific immunity. This work is likely to be interesting to malaria researchers and others working with antigenically diverse infectious diseases.

Reviewer #1 (Public Review):

Summary:<br /> The paper is an attempt to explain a geographic paradox between infection prevalence and antimalarial resistance emergence. The authors developed a compartmental model that importantly contains antigenic strain diversity and in turn antigen-specific immunity. They find a negative correlation between parasite prevalence and the frequency of resistance emergence and validate this result using empirical data of chloroquine-resistance. Overall, the authors conclude that strain diversity is a key player in explaining observed patterns of resistance evolution across different geographic regions.

The authors pose and address the following specific questions:<br /> 1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities?<br /> 2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal?<br /> 3. Does the model explain biogeographic patterns of drug resistance evolution?

Strengths:<br /> The model built by the authors is novel. As emphasized in the manuscript, many factors (e.g., drug usage, vectorial capacity, population immunity) have been explored in models attempting to explain resistance emergence, but strain diversity (and strain specific immunity) has not been explicitly included and thus explored. This is an interesting oversight in previous models, given the vast antigenic diversity of Plasmodium falciparum (the most common human malaria parasite) and its potential to "drive key differences in epidemiological features".

The model also accounts for multiple infections, which is a key feature of malarial infections, with individuals often infected with either multiple Plasmodium species or multiple strains of the same species. Accounting for multiple infections is critical when considering resistance emergence, as with multiple infections there is within-host competition which will mediate the fitness of resistant genotypes. Overall, the model is an interesting combination of a classic epidemiological model (e.g., SIR) and a population genetics model.

In terms of major model innovations, the model also directly links selection pressure via drug administration with local transmission dynamics. This is accomplished by the interaction between strain-specific immunity, generalized immunity and host immune response.

Weaknesses:<br /> The authors emphasize several model limitations, including the specification of resistance by a single locus (thus not addressing the importance of recombination should resistance be specified by more than one locus); the assumption that parasites are independently and randomly distributed among hosts (contrary to empirical evidence); and the assumption of a random association between the resistant genotype and antigenic diversity. However, each of these limitations are addressed in the discussion.

Did the authors achieve their goals? Did the results support their conclusion?<br /> Returning to the questions posed by the authors:<br /> 1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities? Yes. The authors demonstrate a negative relationship between prevalence/strain diversity and resistance frequency (Figure 2).

2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal? Yes. The authors find that, under resistance invasion and some level of drug treatment, resistance frequency decreased with the number of strains (Figure 4). The authors also find that lower strain diversity results in a slower decline in resistant genotypes after drug withdrawal and higher equilibrium resistance frequency (Figure 6).

3. Does the model explain biogeographic patterns of drug resistance evolution? Yes. The authors find that their full model (which includes strain-specific immunity) produces the empirically observed negative relationship between resistance and prevalence/strain diversity, while a model only incorporating generalised immunity does not (Figure 8).

Utility of work to others and relevance within and beyond the field?<br /> This work is important because antimalarial drug resistance has been an ongoing issue of concern for much of the 20th century and now 21st century. Further, this resistance emergence is not equitably distributed across biogeographic regions, with South America and Southeast Asia experiencing much of the burden of this resistance emergence. Not only can widespread resistant strains be traced back to these two relatively low-transmission regions, but these strains remain at high frequency even after drug treatment ceases.

Reviewer #2 (Public Review):

Summary:<br /> The evolution of resistance to antimalarial drugs follows a seemingly counterintuitive pattern, in which resistant strains typically originate in regions where malaria prevalence is relatively low. Previous investigations have suggested that frequent exposures in high-prevalence regions produce high levels of partial immunity in the host population, leading to subclinical infections that go untreated. These subclinical infections serve as refuges for sensitive strains, maintaining them in the population. Prior investigations have supported this hypothesis; however, many of them excluded important dynamics, and the results cannot be generalized. The authors have taken a novel approach using a deterministic model that includes both general and adaptive immunity. They find that high levels of population immunity produce refuges, maintaining the sensitive strains and allowing them to outcompete resistant strains. While general population immunity contributed, adaptive immunity is key to reproducing empirical patterns. These results are robust across a range of fitness costs, treatment rates, and resistance efficacies. Given sufficient antigenic diversity and high transmission, sensitive parasites remain in circulation even when there is no cost to resistance. This work demonstrates that future investigations cannot overlook adaptive immunity and antigenic diversity.

Strengths:<br /> Overall, this is a very nice paper that makes a significant contribution to the field. It is well-framed within the body of literature and achieves its goal of providing a generalizable, unifying explanation for otherwise disparate investigations. The model is innovative. The approach is elegant and rigorous, with results that are supported across a broad range of parameters when considered within an equilibrium setting. Their exploration of geographical patterns of resistance makes the results of their simulations even more compelling. As such, this work will likely serve as a foundation for many future investigations.

Weaknesses:

Although the authors model resistance invasion, it does not align with empirical observations of the spread of resistance. For example, Plasmodium's mutation rate and population size mean that mutations providing chloroquine resistance should arise repeatedly even within a single infection. Nevertheless, Africa remained free of chloroquine resistant strains until a lineage was introduced from Asia. Upon introduction, it spread across the continent within ten years. The difference between the fate of chloroquine resistance originating in Africa versus chloroquine resistance originating in Asia cannot be attributed to changes in population immunity and treatment.

The source of this disparity may be in part attributable to the use of a deterministic, compartmental model, as the authors mention in the discussion. Strains are not explicitly modeled. This means that in terms of the distribution of strain diversity, the resistant and the sensitive compartments are identical, and the locus determining resistance is equally distributed across all strain backgrounds. However, substantial rates of linkage disequilibrium and clonal reproduction are found even in high transmission settings. The model assumptions may be met at equilibrium, but are not appropriate for most scenarios involving the invasion of a rare mutation.

Author Response

eLife assessment

This study presents a valuable method to visualize the location of the cell types discovered through single-cell RNA sequencing. The evidence supporting the claims is solid, but the inclusion of a larger number of samples would strengthen the study. It would also be helpful to have the methods explained in more detail. The work will be of interest to those seeking to identify new cell types from scRNA-seq and snRNA-seq data.

Response: We are surprised about the editor’s assessment of our paper as a “valuable” method. This is the first Drosophila adult spatial transcriptomics paper. Hence, we would at least consider this being an “important” method. Spatial transcriptomics has thus far only been done in embryos, which are easy to process for FISH for many decades. Integration with single-cell data is also new. We are further surprised that this assessment does not mention the identification of subcellular mRNA patterns in adult muscles as an “important” biological finding of this paper. We are not aware that any localized mRNAs in Drosophila muscles were known prior to our study. This shows the advantage of spatial transcriptomics over single-cell techniques.

The work indeed does not represent a full spatial fly adult atlas – however, a proof of principle study covering both the head and body that we consider at least “important”.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing the identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify unknown clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomic datasets generated by others.

Strengths:

The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas.

Response: We thank the reviewer for their comment, but are surprised that this assessment does not mention the identification of subcellular mRNA patterns in adult muscles as an important biological finding of this paper. This might be due to the visualization problem that this reviewer was facing with a greyscale version of the PDF as mentioned in the comments below. We do not know what caused the technical problem for this reviewer (the PDF figures are in color on the eLife website and on bioRxiv). We are surprised that the eLife discussion session did not resolve this issue.

Weaknesses:

Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as in neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells.

Response: The size of the fly is one of the most challenging aspects of performing spatial transcriptomics. The small size of the samples led to detachment from the slides, which we solved by coating the slides with gelatin. While the resolution of Molecular Cartography is high (<200nm), in the brain challenges remain as noted by the reviewer. Drosophila neuronal nuclei are notoriously small and cannot be easily resolved with current techniques. We agree that for a full atlas either expansion microscopy, 3D techniques or even higher resolution will be required.

Reviewer #2 (Public Review):

Summary:

The landmark publication of the "Fly Atlas" in 2022 provided a single cell/nuclear transcriptomic dataset from 15 individually dissected tissues, the entire head, and the body of male and female flies. These data led to the annotation of more than 250 cell types. While certainly a powerful and data-rich approach, a significant step forward relies on mapping these data back to the organism in time and space. The goal of this manuscript is to map 150 transcripts defined by the Fly Atlas by FISH and in doing so, provide, for the first time, a spatial transcriptomic dataset of the adult fly. Using this approach (Molecular Cartography with Resolve Biosciences), the authors, furthermore, distinguish different RNA localizations within a cell type. In addition, they seek to use this approach to define previously unannotated clusters found in the Fly Atlas. As a resource for the community at large interested in the computational aspects of their pipeline, the authors compare the strengths and weaknesses of their approach to others currently being performed in the field.

Strengths:

1. The authors use Resolve Biosciences and a novel bioinformatics approach to generate a FISH-based spatial transcriptomics map. To achieve this map, they selected 150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset and were used in the 2022 paper to annotate specific cell types; moreover, the authors chose several highly expressed genes characteristic of unannotated cell types. Together, the approach and generated data are important next steps in translating the transcriptomic data to spatial data in the organism.

Response: We thank the reviewer for this comment but would like to add that the statement that we selected “150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset” is not correct. We have chosen genes with widely differing expression levels (log-scale range of 3.95 in body, 5.76 in head). Many of the chosen genes are also transcription factors. In fact, the here introduced method is more sensitive than the single cell atlas: the tinman positive cells were readily located (even non-heart cells were found to express tinman), whereas in the single cell FCA data tinman expression is often not detected in the cardiomyocytes (Tinman is detected in 273 cells in the entire FCA (mean expression of 1.44 UMI in positive cells), and in 71 cells out of 273 cardial cells (26%)).

Author response image 1.

Density plots for body (left) and head (right) showing levels of gene expression detected in scRNA-seq (body: Fly Cell Atlas, Li et al. 2022, head: Pech et al. (2023)). Blue: all genes, red: genes used in the spatial study.

1. Working with Resolve, the authors developed a relatively high throughput approach to analyze the location of transcripts in Drosophila adults. This approach confirmed the identification of particular cell types suggested by the FlyAtlas as well as revealed interesting subcellular locations of the transcripts within the cell/tissue type. In addition, the authors used co-expression of different RNAs to unbiasedly identify "new cell types". This pipeline and data provide a roadmap for additional analyses of other time points, female flies, specific mutants, etc.

2. The authors show that their approach reveals interesting patterns of mRNA distribution (e.g alpha- and beta-Trypsin in apical and basal regions of gut enterocytes or striped patterns of different sarcomeric proteins in body muscle). These observations are novel and reveal unexpected patterns. Likewise, the authors use their more extensive head database to identify the location of cells in the brain. They report the resolution of 23 clusters suggested by the single-cell sequencing data, given their unsupervised clustering approach. This identification supports the use of spatial cell transcriptomics to characterize cell types (or cell states).

3. Lastly, the authors compare three different approaches --- their own described in this manuscript, Tangram, and SpaGE - which allow integration of single cell/nuclear RNA-seq data with spatial localization FISH. This was a very helpful section as the authors compared the advantages and disadvantages (including practical issues, like computational time).

Weaknesses:

1. Experimental setup. It is not clear how many and, for some of the data, the sex of the flies that were analyzed. It appears that for the body data, only one male was analyzed. For the heads, methods say male and female heads, but nothing is annotated in the figures. As such, it remains unclear how robust these data are, given such a limited sample from one sex. As such, the claims of a spatial atlas of the entire fly body and its head ("a rosetta stone") are overstated. Also, the authors should clearly state in the main text and figure legends the sex, the age, how many flies, and how many replicates contributed to the data presented (not just the methods). What also adds to the confusion is the use of "n" in para 2 of the results. " ... we performed coronal sections at different depths in the head (n=13)..." 13 sections in total from 1 head or sections from 13 heads? Based on the body and what is shown in the figure, one assumes 13 sections from one head. Please clarify.

Response: While we agree that sex differences present indeed an interesting opportunity to study with spatial transcriptomics, our goal was not to define male/female differences but rather to establish the technology to go into this detail if wanted in the future. In the revised version, we will provide a more detailed description of the sections, including their sex/genotype/age. We would like to point out that we verified the specificity of our FISH method on all the body sections (Figure 2A, TpnC4 & Act88F) and not only on one. Furthermore, we also would like to state that the idea of “a rosetta stone” was mentioned as a future prospect. We will rewrite the discussion to make this more clear.

1. Probes selected: Information from the methods section should be put into the main text so that it is clear what and why the gene lists were selected. The current main text is confusing. If the authors want others to use their approach, then some testing or, at the very least, some discussion of lower expressed genes should be added. How useful will this approach be if only highly expressed genes can be resolved? In addition, while it is understood that the company has a propriety design algorithm for the probes, the authors should comment on whether the probes for individual genes detect all isoforms or subsets (exons and introns?), given the high level of splicing in tissues such as muscle.

Response: As stated above, while there is a slight bias to higher expressed genes (as expected for marker genes), we have also used very low expressed genes like tinman (body) or sens (head). This shows that our method is more sensitive than single-cell data, as ALL cardiomyocytes can be identified by tinman expression and not only some are positive, as is the case in the FCA data. In fact, the method can’t resolve too highly expressed genes due to optical crowding of the signal leading to a worse quantification. For this reason, ninaE was removed from the analysis (as mentioned in Spatial transcriptomics allows the localization of cell types in the head and brain and in Methods).

As mentioned in the Methods, the probes are designed on gene level targeting all isoforms, but favoring principal isoforms (weighted by APPRIS level). The high level of splicing is indeed interesting and we expect that in the future spatial transcriptomics can help to generate more insight in this.

1. Imaging: it isn't clear from the text whether the repeated rounds of imaging impacted data collection. In many of what appear to be "stitched" images, there are gradients of signal (eg, figure 2F); please comment. Also, since this a new technique, could a before and after comparison of the original images and the segmented images be shown in the supplemental data so that the reader can better appreciate how the authors assessed/chose/thresholded their data? More discussion of the accuracy of spot detection would be helpful.

Response: Any high-resolution imaging (pixel size = 138 nm) of a large field of view (>1mm) uses a stitching method to combine several individual images to reconstruct a large field of view. This does not generate signal gradients, apart from lower signal at the extreme edges of each of the individual images. The spot detection algorithm was written and used by Resolve Biosciences and benchmarked for human (Hela) and mouse (NIH-3T3) cell lines in Groiss et al. 2021 (Highly resolved spatial transcriptomics for detection of rare events in cells, biorxiv). The specificity of the decoded probes was found to lie between 99.45 and 99.9% here, matching the results we found for TpnC4 and Act88F (99.4 and 99.8%). We will add their analysis to our discussion.

1. The authors comment on how many RNAs they detected (first paragraph of results). How do these numbers compare to the total mRNA present as detected by single-cell or single-nuclear sequencing?

Response: The total number of mRNAs detected per spatial transcriptomics experiment is much higher for the body samples compared to single-cell experiments (FCA data). In the head it is slightly lower, but here it is important to note that not all cell types are present in each slice in the head (while they are all present in the head scRNA experiments). A comparison on the cell-type level would be more meaningful, and we will investigate this for the revision.

Author response image 2.

Barplots showing total number of mRNA molecules detected in Molecular Cartography (Resolve, spatial spots) and in snRNA-seq data from the Fly Cell Atlas (10x Genomics, UMIs). Individual black dots show individual experiments, counts are only shown for the chosen gene panel for each sample. Bar shows the mean, with error bars representing the standard error.

1. Using this higher throughput method of spatial transcriptomics, the authors discern different cell types and different localization patterns within a tissue/cell type.

a. The authors should comment on the resolution provided by this approach, in terms of the detection of populations of mRNAs detected by low throughput methods, for example, in glia, motor neuron axons, and trachea that populate muscle tissue. Are these found in the images? Please show.

Response: We did not add any markers for trachea in our gene panel, but we do detect sparse spots of repo (glia) and elav/VGlut in the muscle tissues (Gad1/VAChT are hardly detected in the muscle tissue). This is consistent with the glutamatergic nature of motor neurons in Drosophila as described previously (Schuster CM (2006) Glutamatergic synapses of Drosophila neuromuscular junctions: a high-resolution model for the analysis of experience-dependent potentiation. Cell Tissue Res 326: 287–299.)

Author response image 3.

Molecular Cartography zoomed in on indirect flight muscle. Segmented nuclei are shown in white (based on DAPI), scalebars represent 100 μm).

b. The authors show interesting localization patterns in muscle tissue for different sarcomere protein-coding mRNAs, including enrichment of sls in muscle nuclei located near the muscle-tendon attachment sites. As this high throughput approach is newly being applied to the adult fly, it would increase confidence in these data, if the authors would confirm these data using a low throughput FISH technique. For example, do the authors detect such alternating "stripes" ( Act 88F, TpnC4, and Mhc) or enriched localization (sls) using FISH that doesn't rely on the repeated colorization, imaging, decolorization of the probes?

Response: We thank the reviewer for their interest in the localization patterns in muscle tissue. We could confirm localized mRNA in all the sections analyzed, in flight muscles as well as in leg muscles. We furthermore show that Act 88F, TpnC4 are not detected outside of flight muscle cells (99.4% and 99.8% of the single molecular signal in flight muscles only). Hence, we already show the specificity test in a much more quantitative way compared to traditional FISH, which often includes amplification.

1. The authors developed an unbiased method to identify "new cell types" which relies on co-expression of different transcripts. Are these new cell types or a cell state? While expression is a helpful first step, without any functional data, the significance of what the authors found is diminished. The authors need to soften their statements.

Response: The term “new cell types” only appears in the title. We agree that with the current spatial map we cannot be sure to have found “new cell types”, instead we have shown where unannotated clusters from scRNA-seq map, based on gene expression. Therefore, we will tone down the title in the revised version and thank the reviewer for this valuable suggestion.

Appraisal:

The authors' goal is to map single cell/nuclear RNAseq data described in the 2022 Fly Atlas paper spatially within an organism to achieve a spatial transcriptomic map of the adult fly; no doubt, this is a critical next step in our use of 'omics approaches. While this manuscript does the hard work of trying to take this next step, including developing and testing a new pipeline for high throughput FISH and its analysis, it falls short, in its present form, in achieving this goal. The authors discuss creating a robust spatial map, based on one male fly. Moreover, they do not reveal principles of mRNA localization, as stated in the abstract; they show us patterns, but nothing about the logic or function of these patterns. This same criticism can be said of the identification of "new cell types, just based on RNA colocalization. In both cases (mRNA subcellular localization or cell type identification), further data in the form of validation with traditional low throughput FISH and genetic manipulations to assess the relation to cell function are required for the authors to make such claims.

Response: We have indeed used one male fly for the adult male body data. This is mainly due to the cost of the sample processing. We used 12 individuals for the head samples (from 1 individual we acquired 2 sections, a total of 13 sections). We show that the body samples show a high correlation with each other, while the head samples cover multiple depths of the head. Still, even in the head, we find that sections at similar depths show a high similarity to each other in terms of gene-gene co-expression and expression patterns. Although obtaining more sections would be valuable, we don’t believe it to be necessary for the current goals. Additional replicates beyond the ones we already provide would require significant amounts of extra time and budget, while they would produce similar results as we already show. We are therefore reluctant to repeat the effort again.

The usage of the term “new cell types” is indeed ambiguous and we will tone this down in the revised version. Instead, we meant that unannotated clusters could be mapped to their location. In the text, we further specify that this means that now we only have inferred the location of the nuclei and that for neurons their function/processes are still unknown. As such, our data provides a starting point to identify new cell types since their marker genes and nuclear location are inferred. The next step to identify “new cell types” would indeed be to acquire genetic access to the cell types and characterize them in more detail. This is currently beyond our goals, and therefore we will tone down the title in the revised version and thank the reviewer for this valuable suggestion.

Discussion of likely impact:

If revised, these data, and importantly the approach, would impact those working on Drosophila adults as well as those working in other model systems where single cell/nuclear sequencing is being translated to the spatial localization within the organism. The subcellular localization data - for example, the size of transcripts and how that relates to localization or the patterns of sarcomeric protein localization in muscle - are intriguing, and would likely impact our thinking on RNA localization, transport, etc if confirmed. Lastly, the authors compare their computational approaches to those available in the field; this is valuable as this is a rapidly evolving field and such considerations are critical for those wishing to use this type of approach.

Response: We believe that our manuscript as it stands now is already an “important” paper that will strongly impact the Drosophila community (and beyond the spatial transcriptomics community). As it stands, it provides the groundwork for a full Drosophila adult spatial atlas, similar to how early scRNA-seq datasets provided a framework for the Fly Cell Atlas. In the manuscript we provide both experimental information on how to successfully perform spatial transcriptomics (treating slides for optimal attachment) and the data serves as a benchmark for future experiments to improve upon (similar to how early Drop-seq datasets were compared to later 10x datasets in single-cell transcriptomics). In addition, it also provides proof of principle methods on how to integrate the FCA data with these spatial data and it identifies localized mRNA species in large adult muscle cells, showing the complementarity of spatial techniques with single-cell RNA-seq. To conclude, this is the first spatial adult Drosophila transcriptomics paper, locating 150 mRNA species with easy data access in our user portal (https://spatialfly.aertslab.org/).

eLife assessment

This study presents a valuable method to visualize the location of the cell types discovered through single-cell RNA sequencing. The evidence supporting the claims is solid, but the inclusion of a larger number of samples would strengthen the study. It would also be helpful to have the methods explained in more detail. The work will be of interest to those seeking to identify new cell types from scRNA-seq and snRNA-seq data.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing the identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify unknown clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomic datasets generated by others.

Strengths:<br /> The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas.

Weaknesses:<br /> Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as in neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells.

Reviewer #2 (Public Review):

Summary:<br /> The landmark publication of the "Fly Atlas" in 2022 provided a single cell/nuclear transcriptomic dataset from 15 individually dissected tissues, the entire head, and the body of male and female flies. These data led to the annotation of more than 250 cell types. While certainly a powerful and data-rich approach, a significant step forward relies on mapping these data back to the organism in time and space. The goal of this manuscript is to map 150 transcripts defined by the Fly Atlas by FISH and in doing so, provide, for the first time, a spatial transcriptomic dataset of the adult fly. Using this approach (Molecular Cartography with Resolve Biosciences), the authors, furthermore, distinguish different RNA localizations within a cell type. In addition, they seek to use this approach to define previously unannotated clusters found in the Fly Atlas. As a resource for the community at large interested in the computational aspects of their pipeline, the authors compare the strengths and weaknesses of their approach to others currently being performed in the field.

Strengths:<br /> 1. The authors use Resolve Biosciences and a novel bioinformatics approach to generate a FISH-based spatial transcriptomics map. To achieve this map, they selected 150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset and were used in the 2022 paper to annotate specific cell types; moreover, the authors chose several highly expressed genes characteristic of unannotated cell types. Together, the approach and generated data are important next steps in translating the transcriptomic data to spatial data in the organism.<br /> 2. Working with Resolve, the authors developed a relatively high throughput approach to analyze the location of transcripts in Drosophila adults. This approach confirmed the identification of particular cell types suggested by the FlyAtlas as well as revealed interesting subcellular locations of the transcripts within the cell/tissue type. In addition, the authors used co-expression of different RNAs to unbiasedly identify "new cell types". This pipeline and data provide a roadmap for additional analyses of other time points, female flies, specific mutants, etc.<br /> 3. The authors show that their approach reveals interesting patterns of mRNA distribution (e.g alpha- and beta-Trypsin in apical and basal regions of gut enterocytes or striped patterns of different sarcomeric proteins in body muscle). These observations are novel and reveal unexpected patterns. Likewise, the authors use their more extensive head database to identify the location of cells in the brain. They report the resolution of 23 clusters suggested by the single-cell sequencing data, given their unsupervised clustering approach. This identification supports the use of spatial cell transcriptomics to characterize cell types (or cell states).<br /> 4. Lastly, the authors compare three different approaches --- their own described in this manuscript, Tangram, and SpaGE - which allow integration of single cell/nuclear RNA-seq data with spatial localization FISH. This was a very helpful section as the authors compared the advantages and disadvantages (including practical issues, like computational time).

Weaknesses:<br /> 1. Experimental setup. It is not clear how many and, for some of the data, the sex of the flies that were analyzed. It appears that for the body data, only one male was analyzed. For the heads, methods say male and female heads, but nothing is annotated in the figures. As such, it remains unclear how robust these data are, given such a limited sample from one sex. As such, the claims of a spatial atlas of the entire fly body and its head ("a rosetta stone") are overstated. Also, the authors should clearly state in the main text and figure legends the sex, the age, how many flies, and how many replicates contributed to the data presented (not just the methods). What also adds to the confusion is the use of "n" in para 2 of the results. " ... we performed coronal sections at different depths in the head (n=13)..." 13 sections in total from 1 head or sections from 13 heads? Based on the body and what is shown in the figure, one assumes 13 sections from one head. Please clarify.<br /> 2. Probes selected: Information from the methods section should be put into the main text so that it is clear what and why the gene lists were selected. The current main text is confusing. If the authors want others to use their approach, then some testing or, at the very least, some discussion of lower expressed genes should be added. How useful will this approach be if only highly expressed genes can be resolved? In addition, while it is understood that the company has a propriety design algorithm for the probes, the authors should comment on whether the probes for individual genes detect all isoforms or subsets (exons and introns?), given the high level of splicing in tissues such as muscle.<br /> 3. Imaging: it isn't clear from the text whether the repeated rounds of imaging impacted data collection. In many of what appear to be "stitched" images, there are gradients of signal (eg, figure 2F); please comment. Also, since this a new technique, could a before and after comparison of the original images and the segmented images be shown in the supplemental data so that the reader can better appreciate how the authors assessed/chose/thresholded their data? More discussion of the accuracy of spot detection would be helpful.<br /> 4. The authors comment on how many RNAs they detected (first paragraph of results). How do these numbers compare to the total mRNA present as detected by single-cell or single-nuclear sequencing?<br /> 5. Using this higher throughput method of spatial transcriptomics, the authors discern different cell types and different localization patterns within a tissue/cell type.<br /> a. The authors should comment on the resolution provided by this approach, in terms of the detection of populations of mRNAs detected by low throughput methods, for example, in glia, motor neuron axons, and trachea that populate muscle tissue. Are these found in the images? Please show.<br /> b. The authors show interesting localization patterns in muscle tissue for different sarcomere protein-coding mRNAs, including enrichment of sls in muscle nuclei located near the muscle-tendon attachment sites. As this high throughput approach is newly being applied to the adult fly, it would increase confidence in these data, if the authors would confirm these data using a low throughput FISH technique. For example, do the authors detect such alternating "stripes" ( Act 88F, TpnC4, and Mhc) or enriched localization (sls) using FISH that doesn't rely on the repeated colorization, imaging, decolorization of the probes?<br /> 6. The authors developed an unbiased method to identify "new cell types" which relies on co-expression of different transcripts. Are these new cell types or a cell state? While expression is a helpful first step, without any functional data, the significance of what the authors found is diminished. The authors need to soften their statements.

Appraisal:<br /> The authors' goal is to map single cell/nuclear RNAseq data described in the 2022 Fly Atlas paper spatially within an organism to achieve a spatial transcriptomic map of the adult fly; no doubt, this is a critical next step in our use of 'omics approaches. While this manuscript does the hard work of trying to take this next step, including developing and testing a new pipeline for high throughput FISH and its analysis, it falls short, in its present form, in achieving this goal. The authors discuss creating a robust spatial map, based on one male fly. Moreover, they do not reveal principles of mRNA localization, as stated in the abstract; they show us patterns, but nothing about the logic or function of these patterns. This same criticism can be said of the identification of "new cell types, just based on RNA colocalization. In both cases (mRNA subcellular localization or cell type identification), further data in the form of validation with traditional low throughput FISH and genetic manipulations to assess the relation to cell function are required for the authors to make such claims.

Discussion of likely impact:<br /> If revised, these data, and importantly the approach, would impact those working on Drosophila adults as well as those working in other model systems where single cell/nuclear sequencing is being translated to the spatial localization within the organism. The subcellular localization data - for example, the size of transcripts and how that relates to localization or the patterns of sarcomeric protein localization in muscle - are intriguing, and would likely impact our thinking on RNA localization, transport, etc if confirmed. Lastly, the authors compare their computational approaches to those available in the field; this is valuable as this is a rapidly evolving field and such considerations are critical for those wishing to use this type of approach.

eLife assessment

In this valuable contribution, the authors demonstrate that the infusion of NAD+ may prevent death and reduce disease severity from lethal experimental bacterial sepsis, possibly through inflammasome inhibition, without reducing bacterial load. They provide solid evidence for these protective effects of NAD+, though the precise mechanisms involved remain unclear and need further support and elucidation. The core findings may well have clinical implications but, in addition to mechanistic clarifications, contextualised interpretation as metabolic adaptation to sepsis would create wider interest.

eLife assessment

This detailed and well powered manuscript explores auditory perception of modulated noise in the presence of transcranial alternating-current stimulation (tACS) and shows valuable results suggesting that there are subject-specific effects when the phase of 2-Hz tACS varies relative to the phase of the noise modulation. The strength of the evidence is mixed. There is convincing evidence that tACS alters perception significantly in individuals; however, the effects are inconsistent across subjects and even across sessions, frustrating attempts to draw conclusions about the underlying mechanisms of the idiosyncratic effects. Despite these limitations, the paper will be of great interest to researchers interested in determining when and how tACS influences neural processes, especially those interested in neural entrainment and its relationship to perception.

Reviewer #1 (Public Review):

Trenker et al. report cryo-EM structures of HER4/HER2 heterodimers and HER4 homodimers bound to Neuregulin-1β (Nrg1β) and Betacellulin (BTC). As observed for prior cryo-EM structures of full-length or near full-length HER-family receptors only the extracellular regions are visualized, presumably owing to flexibility in the relative orientation of extra- and intra-cellular regions. The authors observe no appreciable differences between Nrg1β and BTC bound heterodimers, both ligands, in this case being high-affinity ligands, and modest "scissor-like" differences in the subunit relationships in HER4 homodimers with Nrg1β and BTC bound.

The authors also show that, as they showed for HER3, the HER4 dimerization arm is not indispensable for forming heterodimers with HER2 despite the HER4 dimerization arm forming a more canonical interaction with HER2. Perhaps most interestingly, the authors observe glycan interactions that appear to stabilize intra- and inter-subunit interactions in HER4 homodimers but that inter-subunit glycans are not present in HER2/HER4 heterodimers. The authors speculate that these glycan interactions may contribute to the apparent propensity of HER4 to homodimerize vs. heterodimerize with HER2.

I realize that an important role of reviewers is to provide authors with informed and critical comments, but I found this manuscript a well-written, thoughtful, and important contribution. My only note is that I am not an electron microscopist so have assumed the microscopy has been carried out expertly and rely on other reviewers to vet structure determinations.

Author Response

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

We thank the reviewers for truly valuable advice and comments. We have made multiple corrections and revisions to the original pre-print accordingly per the following comments:

1. Pro1153Leu is extremely common in the general population (allele frequency in gnomAD is 0.5). Further discussion is warranted to justify the possibility that this variant contributes to a phenotype documented in 1.5-3% of the population. Is it possible that this variant is tagging other rare SNPs in the COL11A1 locus, and could any of the existing exome sequencing data be mined for rare nonsynonymous variants?

One possible avenue for future work is to return to any existing exome sequencing data to query for rare variants at the COL11A1 locus. This should be possible for the USA MO case-control cohort. Any rare nonsynonymous variants identified should then be subjected to mutational burden testing, ideally after functional testing to diminish any noise introduced by rare benign variants in both cases and controls. If there is a significant association of rare variation in AIS cases, then they should consider returning to the other cohorts for targeted COL11A1 gene sequencing or whole exome sequencing (whichever approach is easier/less expensive) to demonstrate replication of the association.

Response: Regarding the genetic association of the common COL11A1 variant rs3753841 (p.(Pro1335Leu)), we do not propose that it is the sole risk variant contributing to the association signal we detected and have clarified this in the manuscript. We concluded that it was worthy of functional testing for reasons described here. Although there were several common variants in the discovery GWAS within and around COL11A1, none were significantly associated with AIS and none were in linkage disequilibrium (R2>0.6) with the top SNP rs3753841. We next reviewed rare (MAF<=0.01) coding variants within the COL11A1 LD region of the associated SNP (rs3753841) in 625 available exomes representing 46% of the 1,358 cases from the discovery cohort. The LD block was defined using Haploview based on the 1KG_CEU population. Within the ~41 KB LD region (chr1:103365089- 103406616, GRCh37) we found three rare missense mutations in 6 unrelated individuals, Table below. Two of them (NM_080629.2: c.G4093A:p.A1365T; NM_080629.2:c.G3394A:p.G1132S), from two individuals, are predicted to be deleterious based on CADD and GERP scores and are plausible AIS risk candidates. At this rate we could expect to find only 4-5 individuals with linked rare coding variants in the total cohort of 1,358 which collectively are unlikely to explain the overall association signal we detected. Of course, there also could be deep intronic variants contributing to the association that we would not detect by our methods. However, given this scenario, the relatively high predicted deleteriousness of rs3753841 (CADD= 25.7; GERP=5.75), and its occurrence in a GlyX-Y triplet repeat, we hypothesized that this variant itself could be a risk allele worthy of further investigation.

Author response table 1.

We also appreciate the reviewer’s suggestion to perform a rare variant burden analysis of COL11A1. We did conduct pilot gene-based analysis in 4534 European ancestry exomes including 797 of our own AIS cases and 3737 controls and tested the burden of rare variants in COL11A1. SKATO P value was not significant (COL11A1_P=0.18), but this could due to lack of power and/or background from rare benign variants that could be screened out using the functional testing we have developed.

1. COL11A1 p.Pro1335Leu is pursued as a direct candidate susceptibility locus, but the functional validation involves both: (a) a complementation assay in mouse GPCs, Figure 5; and (b) cultured rib cartilage cells from Col11a1-Ad5 Cre mice (Figure 4). Please address the following:

2A. Is Pro1335Leu a loss of function, gain of function, or dominant negative variant? Further rationale for modeling this change in a Col11a1 loss of function cell line would be helpful.

Response: Regarding functional testing, by knockdown/knockout cell culture experiments, we showed for the first time that Col11a1 negatively regulates Mmp3 expression in cartilage chondrocytes, an AIS-relevant tissue. We then tested the effect of overexpressing the human wt or variant COL11A1 by lentiviral transduction in SV40-transformed chondrocyte cultures. We deleted endogenous mouse Col11a1 by Cre recombination to remove the background of its strong suppressive effects on Mmp3 expression. We acknowledge that Col11a1 missense mutations could confer gain of function or dominant negative effects that would not be revealed in this assay. However as indicated in our original manuscript we have noted that spinal deformity is described in the cho/cho mouse, a Col11a1 loss of function mutant. We also note the recent publication by Rebello et al. showing that missense mutations in Col11a2 associated with congenital scoliosis fail to rescue a vertebral malformation phenotype in a zebrafish col11a2 KO line. Although the connection between AIS and vertebral malformations is not altogether clear, we surmise that loss of the components of collagen type XI disrupt spinal development. in vivo experiments in vertebrate model systems are needed to fully establish the consequences and genetic mechanisms by which COL11A1 variants contribute to an AIS phenotype.

2B. Expression appears to be augmented compared WT in Fig 5B, but there is no direct comparison of WT with variant.

Response: Expression of the mutant (from the lentiviral expression vector) is increased compared to mutant. We observed this effect in repeated experiments. Sequencing confirmed that the mutant and wildtype constructs differed only at the position of the rs3753841 SNP. At this time, we cannot explain the difference in expression levels. Nonetheless, even when the variant COL11A1 is relatively overexpressed it fails to suppress MMP3 expression as observed for the wildtype form.

2C. How do the authors know that their complementation data in Figure 5 are specific? Repetition of this experiment with an alternative common nonsynonymous variant in COL11A1 (such as rs1676486) would be helpful as a comparison with the expectation that it would be similar to WT.

Response: We agree that testing an allelic series throughout COL11A1 could be informative, but we have shifted our resources toward in vivo experiments that we believe will ultimately be more informative for deciphering the mechanistic role of COL11A1 in MMP3 regulation and spine deformity.

2D. The y-axes of histograms in panel A need attention and clarification. What is meant by power? Do you mean fold change?

Response: Power is directly comparable to fold change but allows comparison of absolute expression levels between different genes.

2E. Figure 5: how many technical and biological replicates? Confirm that these are stated throughout the figures.

Response: Thank you for pointing out this oversight. This information has been added throughout.

1. Figure 2: What does the gross anatomy of the IVD look like? Could the authors address this by showing an H&E of an adjacent section of the Fig. 2 A panels?

Response: Panel 2 shows H&E staining. Perhaps the reviewer is referring to the WT and Pax1 KO images in Figure 3? We have now added H&E staining of WT and Pax1 KO IVD as supplemental Figure 3E to clarify the IVD anatomy.

1. Page 9: "Cells within the IVD were negative for Pax1 staining ..." There seems to be specific PAX1 expression in many cells within the IVD, which is concerning if this is indeed a supposed null allele of Pax1. This data seems to support that the allele is not null.

Response: We have now added updated images for the COL11A1 and PAX1 staining to include negative controls in which we omitted primary antibodies. As can be seen, there is faint autofluorescence in the PAX1 negative control that appears to explain the “specific staining” referred to by the reviewer. These images confirm that the allele is truly a null.

1. There is currently a lack of evidence supporting the claim that "Col11a1 is positively regulated by Pax1 in mouse spine and tail". Therefore, it is necessary to conduct further research to determine the direct regulatory role of Pax1 on Col11a1.

Response: We agree with the reviewer and have clarified that Pax1 may have either a direct or indirect role in Col11a1 regulation.

1. There is no data linking loss of COL11A1 function and spine defects in the mouse model. Furthermore, due to the absence of P1335L point mutant mice, it cannot be confirmed whether P1335L can actually cause AIS, and the pathogenicity of this mutation cannot be directly verified. These limitations need to be clearly stated and discussed. A Col11a1 mouse mutant called chondroysplasia (cho), was shown to be perinatal lethal with severe endochondral defects (https://pubmed.ncbi.nlm.nih.gov/4100752/). This information may help contextualize this study.

Response: We partially agree with the reviewer. Spine defects are reported in the cho mouse (for example, please see reference 36 Hafez et al). We appreciate the suggestion to cite the original Seegmiller et al 1971 reference and have added it to the manuscript.

1. A recent article (PMID37462524) reported mutations in COL11A2 associated with AIS and functionally tested in zebrafish. That study should be cited and discussed as it is directly relevant for this manuscript.

Response: We agree with the reviewer that this study provides important information supporting loss of function I type XI collagen in spinal deformity. Language to this effect has been added to the manuscript and this study is now cited in the paper.

1. Please reconcile the following result on page 10 of the results: "Interestingly, the AISassociated gene Adgrg6 was amongst the most significantly dysregulated genes in the RNA-seq analysis (Figure 3c). By qRT-PCR analysis, expression of Col11a1, Adgrg6, and Sox6 were significantly reduced in female and male Pax1-/- mice compared to wild-type mice (Figure 3d-g)." In Figure 3f, the downregulation of Adgrg6 appears to be modest so how can it possibly be highlighted as one of the most significantly downregulated transcripts in the RNAseq data?

Response: By “significant” we were referring to the P-value significance in RNAseq analysis, not in absolute change in expression. This language was clearly confusing, and we have removed it from the manuscript.

1. It is incorrect to refer to the primary cell culture work as growth plate chondrocytes (GPCs), instead, these are primary costal chondrocyte cultures. These primary cultures have a mixture of chondrocytes at differing levels of differentiation, which may change differentiation status during the culturing on plastic. In sum, these cells are at best chondrocytes, and not specifically growth plate chondrocytes. This needs to be corrected in the abstract and throughout the manuscript. Moreover, on page 11 these cells are referred to as costal cartilage, which is confusing to the reader.

Response: Thank you for pointing out these inconsistencies. We have changed the manuscript to say “costal chondrocytes” throughout.

Minor points

• On 10 of the Results: "These data support a mechanistic link between Pax1 and Col11a1, and the AIS-associated genes Gpr126 and Sox6, in affected tissue of the developing tail." qRT-PCR validation of Sox6, although significant, appears to be very modestly downregulated in KO. Please soften this statement in the text.

Response: We have softened this statement.

• Have you got any information about how the immortalized (SV40) costal cartilage affected chondrogenic differentiation? The expression of SV40 seemed to stimulate Mmp13 expression. Do these cells still make cartilage nodules? Some feedback on this process and how it affects the nature of the culture what be appreciated.

Response: The “+ or –“ in Figure 5 refers to Ad5-cre. Each experiment was performed in SV40-immortalized costal chondrocytes. We have removed SV40 from the figure and have clarified the legend to say “qRT-PCR of human COL11A1 and endogenous mouse Mmp3 in SV40 immortalized mouse costal chondrocytes transduced with the lentiviral vector only (lanes 1,2), human WT COL11A1 (lane 3), or COL11A1P1335L. Otherwise we absolutely agree that understanding Mmp13 regulation during chondrocyte differentiation is important. We plan to study this using in vivo systems.

• Figure 1: is the average Odds ratio, can this be stated in the figure legend?

Response: We are not sure what is being asked here. The “combined odds ratio” is calculated as a weighted average of the log of the odds.

• A more consistent use of established nomenclature for mouse versus human genes and proteins is needed.

Human:GENE/PROTEIN Mouse: Gene/PROTEIN

Response: Thank you for pointing this out. The nomenclature has been corrected throughtout the manuscript.

• There is no Figure 5c, but a reference to results in the main text. Please reconcile. -There is no Figure 5-figure supplement 5a, but there is a reference to it in the main text. Please reconcile.

Response: Figure references have been corrected.

• Please indicate dilutions of all antibodies used when listed in the methods.

Response: Antibody dilutions have been added where missing.

• On page 25, there is a partial sentence missing information in the Histologic methods; "#S36964 Invitrogen, CA, USA)). All images were taken..."

Response: We apologize for the error. It has been removed.

• Table 1: please define all acronyms, including cohort names.

Response: We apologize for the oversight. The legend to the Table has been updated with definitions of all acronyms.

• Figure 2: Indicate that blue staining is DAPI in panel B. Clarify that "-ab" as an abbreviation is primary antibody negative.

Response: A color code for DAPI and COL11A! staining has been added and “-ab” is now defined.

• Page 4: ADGRG6 (also known as GPR126)...the authors set this up for ADGRG6 but then use GPR126 in the manuscript, which is confusing. For clarity, please use the gene name Adgrg6 consistently, rather than alternating with Gpr126.

Response: Thank you for pointing this out. GPR126 has now been changed to ADGRG6 thoughout the manuscript.

• REF 4: Richards, B.S., Sucato, D.J., Johnston C.E. Scoliosis, (Elsevier, 2020). Is this a book, can you provide more clarity in the Reference listing?

Response: Thank you for pointing this out. This reference has been corrected.

• While isolation was addressed, the methods for culturing Rat cartilage endplate and costal chondrocytes are poorly described and should be given more text.

Response: Details about the cartilage endplate and costal chondrocyte isolation and culture have been added to the Methods.

• Page 11: 1st paragraph, last sentence "These results suggest that Mmp3 expression"... this sentence needs attention. As written, I am not clear what the authors are trying to say.

Response: This sentence has been clarified and now reads “These results suggest that Mmp3 expression is negatively regulated by Col11a1 in mouse costal chondrocytes.”

• Page 13: line 4 from the bottom, "ECM-clearing"? This is confusing do you mean ECM degrading?

Response: Yes and thank you. We have changed to “ECM-degrading”.

• Please use version numbers for RefSeq IDs: e.g. NM_080629.3 instead of NM_080629

Response: This change has been made in the revised manuscript.

• It would be helpful for readers if the ethnicity of the discovery case cohort was clearly stated as European ancestry in the Results main text.

Response: “European ancestry” has been added at first description of the discovery cohort in the manuscript.

• Avoid using the term "mutation" and use "variant" instead.

Response: Thank you for pointing this out. “Variant” is now used throughout the manuscript.

• Define error bars for all bar charts throughout and include individual data points overlaid onto bars.

Response: Thank you. Error bars are now clarified in the Figure legends.

Author Response

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

Reviewer #1:

1. The most important concern that I have refers to the FDTD simulations to characterize the ZMW, as shown in Appendix 2, Figure 4. So far, the explanations given in the caption of Figure 4 are confusing and misleading: the authors should provide more detailed explanations on how the simulations were performed and the actual definition of the parameters used. In particular:

a. lines 1330-1332: it is not clear to me how the fluorescence lifetime can be calculated from the detected signal S (z), and why they are horizontal, i.e., no z dependence? Which lifetimes are the authors referring to?

b. lines 1333-1335: Where do these values come from? And how do they relate to panels D & E? From what I can see in these panels the lifetimes are highly dependent on z and show the expected reduction of lifetime inside the nanostructures.

c. lines 1336-1337: Why the quantum yield of the dyes outside the ZMW differs from those reported in the literature? In particular the changes of quantum yield and lifetime for Alexa 488 are very large (also mentioned in the corresponding part of Materials & Methods but not explained in any detail).

We thank the Reviewer for his detailed questions on the FDTD simulations. We have now added the missing equation related to the computation of signal-averaged fluorescence lifetimes from the FDTD simulations. Specifically to the three points raised:

a) The fluorescence lifetime is indeed not calculated from the detected signal S(z), but from the radiative and non-radiative rates in the presence of the ZMW as given in eq. 9-10. However, we use the detected signal S(z) to compute the average fluorescence lifetime over the whole z-profile of the simulation box, which we relate to the experimentally measured fluorescence lifetimes as given in Appendix 7, Figure 1. We have now added the equation to compute the signal-weighted fluorescence lifetimes, which we denote as <𝜏>S , in eq. 13 in the methods. To clarify this point, we have added the symbol <𝜏>S to the plots in Appendix 2, Figure 4 D-E and Appendix 7, Figure 1 C-D.

b) The estimated lifetimes were obtained as the signal-weighted average over the lifetime profiles, (<𝜏>S) as given in the new eq. 13. All plotted quantities, i.e., the detection efficiency η, quantum yield ϕ, detected signal S(z), and fluorescence lifetime, are computed from the radiative and loss rates obtained from the FDTD simulation according to eqs. 8-11. To make this clearer, we have now added the new Appendix 2 – Figure 5 which shows the z-profiles of the quantities (radiative and loss rates) used to derive the experimental observables.

c) There are multiple reasons for the differences of the quantum yields of the two analytes used in this study compared to the literature values. For cyanine dyes such as Alexa647, it is well known that steric restriction (as e.g. caused by conjugation to a biomolecule) can lead to an increase of the quantum yield and fluorescence lifetime. We observe a minor increase of the fluorescence lifetime for Alexa647 from the literature value of 1.17 ns to a value of 1.37 ns when attached to Kap95, which is indicative of this effect. In the submitted manuscript, this was discussed in the methods in lines 936-938 (lines 938-945 in the revised manuscript). For the dye Alexa488, which is used to label the BSA protein, this effect is absent. Instead, we observe (as the Reviewer correctly notes) a quite drastic reduction of the fluorescence lifetime compared to the unconjugated dye from 4 ns to 2.3 ns. In cases where a single cysteine is labeled on a protein, such a drastic reduction of the quantum yield usually indicates the presence of a quenching moiety in proximity of the labeling site, such as tryptophane, which acts via the photo-induced electron transfer mechanism. Indeed, BSA contains two tryptophanes that could be responsible for the low quantum yield of the conjugated dyes. The situation is complicated by the fact that BSA contains 35 cysteines that can potentially be labeled (although 34 are involved in disulfide bridges). The labeled BSA was obtained commercially and the manufacturer lists the degree of labeling as ~6 dye molecules per protein, with a relative quantum yield of 0.2 compared to the standard fluorescein. This corresponds to an absolute quantum yield of ~0.16, which is low compared to the literature value for Alexa488 of ~0.8.

Based on the measured fluorescence lifetime, we estimate a quantum yield of 0.46, which is higher than the photometrically obtained value of 0.16 reported by the manufacturer. Fully quenched, nonfluorescent dyes will not contribute to the lifetime measurement but are detected in the photometric quantum yield estimates. The difference between the lifetime and photometric based quantum yield estimates thus suggest that part of the fluorophores are almost fully quenched. While it is unknown where the dyes are attached to the protein, the low quantum yield could be indicative of dye-dye interactions via pi-pi stacking, which can often lead to non-fluorescent dimers. This is supported by the fact that the manufacturer reports color differences between batches of labeled protein, which indicate spectral shifts of the absorption spectrum when dye-dye adducts are formed by π-π stacking. We have now added a short discussion of this effect in lines 938-941. We note that the conclusions drawn on the quenching effect of the metal nanostructure remain valid despite the drastic reduction of the quantum yield for Alexa488, which leads to a further quantum yield reduction of the partly quenched reference state.

2) A second important concern refers to Figure 3: Why is there so much variability on the burst intensities reported on panels C, D? They should correspond to single molecule translocation events and thus all having comparable intensity values. In particular, the data shown for BSA in panel D is highly puzzling, since it not only reflects a reduced number of bursts (which is the main finding) but also very low intensity values, suggesting a high degree of quenching of the fluorophore being proximal to the metal on the exit side of the pore. In fact, the count rates for BSA on the uncoated pore range form 50-100kcounts/s, while on the coated pores thy barely reach 30 kcounts/s, a clear indication of quenching. Importantly, and in direct relation to this, could the authors exclude the possibility that the low event rates measured on BSA are largely due to quenching of the dye by getting entangled in the Nsp mesh just underneath the pore but in close contact to the metal?

The Reviewer raises a valid concern, but further analysis shows that this is unproblematic. Notably, the burst intensities are in fact not reduced, in contrast to the visual impression obtained from the time traces shown in the figure. The time trace of the BSA intensity is visually dominated by high-intensity bursts which mask the low-intensity bursts in the plot. In contrast, in Figure 3 the reduced number of BSA events results in a sparser distribution of the intensity spikes, which allows low-intensity events to be seen. Different to the visual inspection, the spike-detection algorithm does not exhibit any bias in terms of the duration or the number of photons of the detected events between the different conditions for both BSA and Kap95, as shown in the new Appendix 7 – Figure 1. Using FCS analysis it can be tested whether the event duration varies between the different conditions shown in Figure 3 C-D. This did not show a significant difference in the estimated diffusion time for BSA (Appendix 7 – Figure 1 C,D). Contrary to the suggestion of the Reviewer, we also do not observe any indication of quenching by the metal between uncoated and Nsp1-coated pores for BSA. Such quenching should result in differences of the fluorescence lifetimes, which however is not evident in our experimental data (Appendix 7 – Figure 1 F).

3) Line 91: I suggest the authors remove the word "multiplexed" detection since it is misleading. Essentially the authors report on a two-color excitation/detection scheme which is far from being really multiplexing.

We have changed the word to “simultaneous” now and hope this avoids further confusion.

4) Line 121: why are the ZMW fabricated with palladium? Aluminum is the gold-standard to reduce light transmissivity. An explanation for the choice of this material would be appreciated by the community.

In a previous study (Klughammer and Dekker, Nanotechnology, 2021), we established that palladium can have distinct advantages compared to other ZMW metals such as aluminum and gold, most prominently, an increased chemical stability and reduced photoluminescence. For this study, we chose palladium over aluminum as it allowed the use of simple thiol chemistry for surface modification. In the beginning of the project, we experimented with aluminum pores as well. We consistently found that the pores got closed after measuring their ionic conductance in chlorine-containing solutions such as KCl or PBS. This problem was avoided by choosing palladium.

5) Lines 281-282: This statement is somewhat misleading, since it reads such that the molecules stay longer inside the pore. However, if I understand correctly, these results suggest that Kap95 stays closer to the metal on the exit side. This is because measurements are being performed on the exit side of the pore as the excitation field inside the pore is quite negligible.

We thank the Reviewer for this comment and have clarified the text in lines 290-292 as suggested to: “(…) this indicates that, on the exit side, Kap95 diffuses closer to the pore walls compared to BSA due to interactions with the Nsp1 mesh”

6) Lines 319-320: Although the MD simulations agree with the statement being written here, the variability could be also due to the fact that the proteins could interact in a rather heterogenous manner with the Nsp mesh on the exit side of the pore, transiently trapping molecules that then would stay longer and/or closer to the metal altering the emission rate of the fluorophores. Could the authors comment on this?

The variation mentioned in the text refers to a pore-to-pore variation and thus needs to be due to a structural difference between individual pores. This effect would also need to be stable for the full course of an experiment, typically hours. We did not find any structural changes in the fluorescence lifetimes measured on individual pores such as suggested by the Reviewer. We think that the suggested mechanism would show up as distinct clusters in Appendix 7 – Figure 1 E,F where we found no trace of such a change to happen. If we understand correctly, the Reviewer suggests a mechanism, not based on changes in the Nup layer density, that would lead to a varying amount of trapping of proteins close to the surface. Such a behavior should show up in the diffusion time of each pore ( Appendix 7 – figure 1 C,D), where we however find no trace of such an effect.

7) Lines 493-498: These claims are actually not supported by the experimental data shown in this contribution: a) No direct comparison in terms of signal-to-noise ratio between fluorescence-based and conductance-based readouts has been provided in the ms. b) I would change the word multiplexed by simultaneous since it is highly misleading. c) The results shown are performed sequentially and thus low throughput. d) Finally, the use of unlabeled components is dubious since the detection schemes relies on fluorescence and thus requiring labeling.

We thank the Reviewer for pointing this out.

a) We have now added a section in appendix 3 that discusses the signal-to-noise ratios. In brief, there are three observations that led us to conclude that ZMWs provide beneficial capabilities to resolve individual events from the background:

1. The signal-to-background ratio was determined to be 67±53 for our ZMW data of Kap95 which is an order of magnitude higher compared to the ~5.6 value for a conductance-based readout.

2. The detection efficiency for ZMWs is independent of the Kap95 occupancy within the pore. This is different from conductance based approaches that have reduced capability to resolve individual Kap95 translocations at high concentrations.

3. The fraction of detected translocations is much higher for ZMWs than for conductance-based data (where lots of translocations occur undetected) and matches closer to the theoretical predictions.

b) We have changed the wording accordingly.

c) We agree with the Reviewer that our method is still low throughput. However, the throughput is markedly increased compared to previous conductance-based nanopore measurements. This is because we can test many (here up to 8, but potentially many more) pores per chip in one experiment, whereas conductance-based readouts are limited to a single pore. We have now changed the wording to “increased throughput” in line 507 to avoid confusion.

d) We agree that only labeled components can be studied directly with our methods. However, the effect of unlabeled analytes can be assessed indirectly without any perturbation of the detection scheme due to the specificity of the fluorescent labeling. This is distinct from previous nanopore approaches using a conductance-based readout that lack specificity. In our study, we have for example used this advantage of our approach to access event rates at high concentrations (1000nM Kap95, 500nM BSA) and large pore diameters by reducing the fraction of labeled analyte in the sample. Finally, the dependence of the BSA leakage rate as a function of the concentration of Kap95 (Figure 6) relies on a specific readout of BSA events in the presence of large amounts of Kap95, which would be impossible in conductance-based experiments.

8) Line 769: specify the NA of the objective. Using a very long working distance would also affect the detection efficiency. Have the authors considered the NA of the objective on the simulations of the detection efficiency? This information should be included and it is important as the authors are detecting single molecule events.

We used an NA of 1.1 for the simulation of the Gaussian excitation field in the FDTD simulations, corresponding to the NA of the objective lens used in the experiments and as specified in the methods. The Reviewer is correct that the NA also affects the absolute detection efficiency of the fluorescence signal due to the finite opening angle of the collection cone of ~56˚. In our evaluation of the simulations, we have neglected this effect for simplicity, because the finite collection efficiency of the objective lens represents only an additional constant factor that does not depend on the parameters of the simulated system, such as the pore diameter. Instead, we focused solely the effect of the ZMW and defined the detection efficiency purely based on the fraction of the signal that is emitted towards the detection side and can potentially be detected in the experiment, which also provides the benefit that the discussed numbers are independent of the experimental setup used.

To clarify this, we have now made this clearer in the method text on lines 917-920.

9) Line 831: I guess that 1160ps is a mistake, right?

This is not a mistake. We performed a tail fit of the fluorescence decay curves, meaning that the initial rise of the decay was excluded from the fit. The initial part of the fluorescence decay is dominated by the instrument response function (IRF) of the system, with an approximate width of ~500 ps. To minimize the influence of the IRF on the tail fit, we excluded the first ~1 ns of the fluorescence decay.

10) Lines 913-917: Why are the quantum yield of Alexa 488 and lifetime so much reduced as compared to the published values in literature?

See answer to point 1. We have added a short discussion at lines 938-941 where we speculate that the reduced quantum yield is most likely caused by dye-dye interactions due to the high degree of labeling of ~6 dyes per protein.

11) Lines 1503-1509: The predicted lifetimes with the Nsp-1 coating have not been shown in Appendix 2 - Figure 4. How have they been estimated?

We have not performed predictions of fluorescence lifetimes in the presence of an Nsp1 coating. Predictions of the fluorescence lifetime in the absence of the Nsp1 coating were obtained by assuming a uniform occupancy of the molecules over the simulation box. A prediction of the fluorescence lifetimes in the presence of the Nsp1 coating would require a precise knowledge of the spatial distribution of analytes, which depends, among other factors, on the extension of the Nsp1 brushes and the interaction strengths with the FG repeats. While simulations provide some insights on this, we consider a quantitative comparison of predicted and measured fluorescence lifetimes in the presence of the Nsp1 coating beyond the scope of the present study.

12) Lines 1534-1539: I disagree with this comment, since the measurements reported here have been performed outside the nano-holes, and thus the argument of Kap95 translocating along the edges of the pore and being responsible for the reduced lifetime does not make sense to me.

In accordance with our answer to point 5 above, we have now changed the interpretation to the proximity of Kap95 to the metal surface on the exit side, rather than speculating on the path that the protein takes through the pore (lines 1662-1664), as follows:

“This indicates that, in the presence of Nsp1, Kap95 molecules diffuse closer to or spend more time in proximity of the metal nanoaperture on the exit side.”

Reviewer #2:

(Numbers indicate the line number.)

48: should cite more recent work: Timney et al. 2016 Popken et al 2015

59: should cite Zilman et al 2007, Zilman et al 2010

62: should cite Zilman et al 2010

We thank the Reviewer for the suggestions and have added them to the manuscript now.

65: one should be careful in making statements that the "slow" phase is immobile, as it likely rapidly exchanging NTRs with the "fast" phase.

We have removed this description and replaced it by “This 'slow phase' exhibits a reduced mobility due to the high affinity of NTRs to the FG-Nup mesh.” to avoid misunderstanding.

67: Schleicher 2014 does not provide evidence of dedicated channels

We agree with the Reviewer and therefore moved the reference to an earlier position in the sentence.

74-75: must cite work by Lusk & Lin et al on origami nanochannels

We thank the Reviewer for this suggestion. We have now added a reference to the nanotraps of Shen et al. 2021, JACS, in line 75. In addition, we now also refer to Shen et al. 2023, NSMB, in the discussion where viral transport is discussed.

77: Probably Jovanovic- Talisman (2009)?

We thank the Reviewer for pointing out this typo.

93; should cite Auger&Montel et al, PRL 2014

We thank the Reviewer for pointing out this reference. To give proper credit to previous ZMW, we have now incorporated a sentence in lines 100-102 citing this reference.

111-112: there appears to be some internal inconsistency between this interpretation and the BSA transport mostly taking place through the "central hole" (as seems to be implied by Equation (3). Probably it should be specified explicitly that the "central hole" in large channels is a "void".

We thank the Reviewer for this suggestion and have added a clarifying sentence.

115-177: This competition was studied in Jovanovic-Talisman 2009 and theoretically analysed in Zilman et al Plos Comp Biol 2010. The differences in the results and the interpretation should be discussed.

We agree, therefore it is discussed in the discussion section (around line 594) and now added the reference to Zilman et al.

Figure 2 Caption: "A constant flow..." - is it clear that is flow does not generate hydrodynamic flow through the pore?

The Reviewer raises an important point. Indeed, the pressure difference over the membrane generates a hydrodynamic flow through the pore that leads to a reduction of the event rate compared to when no pressure is applied. However, as all experiments were performed under identical pressures, one can expect a proportional reduction of the absolute event rates due to the hydrodynamic flow against the concentration gradient. In other words, this will not affect the conclusions drawn on the selectivity, as it is defined as a ratio of event rates.

We have now added additional data on the influence of the hydrodynamic flow on the translocation rate in Appendix 3 – Figure 2, where we have measured the signal of free fluorophores at high concentration on the exit side of the pore as a function of the applied pressure. The data show a linear dependence of the signal reduction on the applied pressure. At the pressure values used for the experiments of 50 mbar, we see a ~5% reduction compared to the absence of pressure, implying that the reported absolute event rates are underestimated only by ~5%. Additionally we have added such data for Kap95 translocations that shows a similar effect (however less consistent). Measuring the event rate at zero flow is difficult, since this leads to an accumulation of fluorophores on the detection side.

Figure 3: it would help to add how long is each translocation, and what is the lower detection limit. A short explanation of why the method detects actual translocations would be good

With our method, unfortunately, we can not assess the duration of a translocation event since we only see the particle as it exists the pore. Instead, the measured event duration is determined by the time it takes for the particle to diffuse out of the laser focus. This is confirmed by FCS analysis of translocation events that show the same order of magnitude of diffusion times as for free diffusion (Appendix 7 – Figure 1 C,D) in contrast to a massively reduced diffusion time within a nanopore. In Figure 2D we show the detection efficiency at different locations around the ZMW as obtained from FDTD simulations and discuss the light blocking. This clearly shows that the big majority of the fluorescence signal comes from the laser illuminated side and therefore only particles that translocated through the ZMW are detected as presented between lines 170-190. In Yang et al. 2023, bioRxiv (https://doi.org/10.1101/2023.06.26.546504) a more detailed discussion about the optical properties of Pd nanopores is given.

This point also explains why we see actual translocations: since the light is blocked by the ZMW, fluorophores can only be detected after they have translocated. On parts of the membrane without pores and upstream the amount of spikes found in a timetrace was found to be negligibly small. Additionally, if a significant part of the signal would be contributed by leaking fluorescence from the dark top side, there should no difference in BSA event rate found between small open and Nsp1 pores which we did not observe.

With respect to the lower detection limit for events: In the burst search algorithm we require a false positive level rate of lower than 1 event in 100. Additionally, as described in Klughammer and Dekker, Nanotechnology (2021), we apply an empirical filtering to remove low signal to noise ratio events that contain less than 5 detected photons per event or a too low event rate. From the event detection algorithm there is no lower limit set on the duration of an event. Such a limit is then set by the instrument and the maximum frequency it which it can detect photons. This time is below 1μs. Practically we don’t find events shorter than 10μs as can be seen in the distribution of events where also the detection limits can be estimated (Appendix 7 – figure 1 A and B.)

Equation (1): this is true only for passive diffusion without interactions (see eg Hoogenboom et al Physics Reports 2021 for review). Using it for pores with interactions would predict, for instance, that the inhibition of the BSA translocation comes from the decrease in D which is not correct.

We agree with the Reviewer that this equation would not reproduce the measured data in a numerically correct way. We included it to justify why we subsequently fit a quadratic function to the data. As we write in line 260 we only used the quadratic equation “as a guide to the eye and for numerical comparison” and specifically don’t claim that this fully describes the translocation process. In this quadratic function, we introduced a scaling factor α that can be fitted to the data and thus incorporates deviations from the model. In appendix 5 we added a more elaborate way to fit the data including a confinement-based reduction of the diffusion coefficient (although not incorporating interactions). Given the variations of the measured translocation rates, the data is equally well described by both the simple and the more complex model function.

Equation (1): This is not entirely exact, because the concentration at the entrance to the pore is lower than the bulk concentration, which might introduce corrections

We agree with the Reviewer and have added that the concentration difference Δc is measured at the pore entrance and exit, and this may be lower than the bulk concentration. As described in our reaction to the Reviewer’s previous comment, equation (1) only serves as a justification to use the quadratic dependence and any deviations in Δc are absorbed into the prefactor α in equation (2).

Equation (3): I don't understand how this is consistent with the further discussion of BSA translocation. Clearly BSA can translocate through the pore even if the crossection is covered by the FG nups (through the "voids" presumably?).

The Reviewer raises an important point here. Equation 3 can only be used for a pore radius r > rprot + b. b was determined to be 11.5 nm and rprot is 3.4 nm for BSA, thus it needs to be that r > 15 nm. We would like to stress, however, that b does not directly give a height of a rigid Nsp1 ring but is related to the configuration of the Nsp1 inside the pore. Equation (3) (and equation (2)) were chosen because even these simple equations could fit the experimentally measured translocation rates well, and not because they would accurately model the setup in the pore. As we found from the simulations, the BSA translocations at low pore diameters presumably happen through transient openings of the mesh. The dynamics leading to the stochastic opening of voids on average leads to the observed translocation rate.

296-297: is it also consistent with the simulations?

We compare the experimentally and simulated b values in lines 387-388 and obtained b=9.9 ± 0.1 nm from the simulations (as obtained from fitting the translocation rates and not from measuring the extension of the Nsp1 molecules) and 11.5 ± 0.4 nm from the experiments – which we find in good agreement.

331: has it been established that the FG nups equilibrate on the microsecond scale?

As an example, we have analyzed the simulation trajectory of the most dense nanopore (diameter = 40 nm, grafting = 1/200 nm2). In Author response image 1 we show for each of the Nsp1-proteins how the radius of gyration (Rg) changes in time over the full trajectory (2 μs + 5 μs). As expected, the Rg values reached the average equilibrium values very well within 2 μs simulation time, showing that the FG-Nups indeed equilibrate on the (sub)microsecond scale.

Author response image 1.

334-347: the details of the method should be explained explicitly in the supplementary (how exactly voids distributions are estimated and the PMF are calculated etc)

The void analysis was performed with the software obtained from the paper of Winogradoff et al. In our Methods we provide an overview of how this software calculates the void probability maps and how these are converted into PMFs. For a more detailed description of how exactly the analysis algorithm is implemented in the software, we refer the reader to the original work. The analysis codes with the input files that were used in this manuscript have been made public ( https://doi.org/10.4121/22059227.v1 ) along with the manuscript.

Equation (4) is only an approximation (which works fine for high barriers but not the low ones). Please provide citations/derivation.

To our knowledge, the Arrhenius relation is a valid approximation for our nanopore simulations. We are unaware of the fact that it should not work for low barriers and cannot find mention of this in the literature. It would be helpful if the Reviewer can point us to relevant literature.

Figure 4: how was transport rate for Kaps calculated?

As mentioned in lines 388-391, we assumed that the Kap95 translocation rate through Nsp1-coated pores is equal to that for open pores, as we did not observe any significant hindrance of Kap95 translocation by the Nsp1 mesh in the experiment (Figure 4 A,C).

378: It's a bit strange to present the selectivity ratio as prediction of the model when only BSA translocation rate was simulated (indirectly).

We agree with the Reviewer that ideally we should also simulate the Kap95 translocation rate to obtain an accurate selectivity measure of the simulated nanopores. However, as the experiments showed very similar Kap95 translocation rates for open pores and Nsp1-coated pores, we believe it is reasonable to take the Kap95 rates for open and Nsp1-pores to be equal.

Figure 5C and lines 397: I am a bit confused how is this consistent with Figure 4D?

Figure 5C and figure 4D both display the same experimental data, where 4D only focuses on a low diameter regime. In relation to line 397 (now 407), the Nsp1 mesh within the 60-nm pore dynamically switches between closed configurations and configurations with an open channel. When taking the temporal average of these configurations, we find that the translocation rate is higher than for a closed pore but lower than for a fully open pore. The stochastic opening and closing of the Nup mesh results in the continuous increase of the translocation rates with increasing diameter, which is in contrast to a step-wise increase that would be expected from an instantaneous collapse of the Nsp1 mesh at a certain pore diameter.

428-439: Please discuss the differences from Jovanovic-Talisman 2009.

How our results for a Kap95 induced change of the BSA translocation rate are related to previous literature is discussed extensively in the lines 598-620.

440: How many Kaps are in the pore at different concentrations?

This is a very interesting question that we were, unfortunately, not able to answer within the scope of this project. With our fluorescent based methods we could not determine this number because the excitation light does not reach well into the nanopore.

In our previous work on Nsp1-coated SiN nanopores using conductance measurements, we quantified the drop in conductance at increasing concentrations of Kap95 (Fragasso et al., 2023, NanoResearch, http://dx.doi.org/10.1007/s12274-022-4647-1). From this, we estimated that on average ~20 Kap95 molecules are present in a pore with a diameter of 55 nm at a bulk concentration of 2 µM. In these experiments, however, the height of the pore was only ~20 nm, which is much lower compared to 100 nm long channel used here, and the grafting density of 1 per 21 nm2 was high compared to the grafting density here of 1 per 300 nm2. Assuming that the Kap95 occupancy scales linearly with the number of binding sites (FG repeats) in the vicinity of the pore, and hence the amount of Nsp1 molecules bound to the pore, we would expect approximately ~7 Kap95 molecules in a pore of similar diameter under saturating (> 1 µM) concentrations.

On the other hand, the simulations showed that the density of Nsp1 within the pore is equal to the density within the 20-nm thick SiN pores (line 380). For the longer channel and lower grafting density used here, Nsp1 was also more constrained to the pore compared to thinner pores used in previous studies (Fragasso et al., 2023, NanoResearch), where the grafted protein spilled out from the nanopores. Thus assuming that the Kap95 occupancy depends on the protein density in the pore volume rather than the total protein amount grafted to the pore walls, we would estimate a number of 100 Kap95 molecules per pore.

These varying numbers already show that we cannot accurately provide an estimate of the Kap95 occupancy within the pore from our data due to limitations of the ZMW approach.

445: how is this related to the BSA translocation increase?

For the calculation of the selectivity ratio, we assumed the normalized Kap95 translocation rate to be independent of the Kap95 concentration. Hence, the observed trends of the selectivity ratios at different concentrations of Kap95, as shown in Figure 6 D, are solely due to a change in the BSA translocation rate at different concentrations of Kap95, as given in Figure 6 B,C.

462-481: it's a bit confusing how this interfaces with the "void" analysis ( see my previous comments)

We agree that the phenomenological descriptions in terms of transient openings (small, dynamic voids) that for larger pores become a constantly opened channel (a single large, static void) might cause some confusion to the reader. In the last part of the results, we aimed to relate the loss of the BSA rate to a change of the Nsp1 mesh. We acknowledge that the model of a rim of Nsp1 and an open center described in Figure 5F is highly simplifying . We now explain this in the revised paper at lines 483-486 by referring to an effective layer thickness which holds true under the simplifying assumption of a central transport channel.

Figure 6D: I think the illustration of the effect of kaps on the brush is somewhat misleading: at low pore diameters, it is possible that the opposite happens: the kaps concentrate the polymers towards the center of the pore. It should be also made clear that there are no kaps in simulations (if I understand correctly?)

Indeed, at small pore diameters we think it would be possible to observe what the Reviewer describes. The illustration should only indicate what we think is happening for large pore diameters where we observed the opening of a central channel. To avoid confusion, we now shifted the sketches to panel G where the effective layer thickness is discussed.

Indeed, as stated in lines 331-340 no Kap95 or BSA molecules were present in the simulations. We have now clarified this point in lines 872-876.

518: Please provide more explanation on the role of hydrodynamics pressure.

We have now performed additional experiments and quantified the effect of the pressure to be a ~5% reduction of the event rates, as described in the answer to a previous question above.  

Reviewer #3 (Recommendations For The Authors):

No experiments have been performed with the Ran-Mix regeneration system. It would be beneficial to add Ran-Mix to the trans compartment and see how this would affect Kap95 translocation events frequency and passive cargo diffusion. As the authors note in their outlook, this setup offers an advantage in using Ran-Mix and thus could also be considered here or in a future follow-up study.

We thank the Reviewer for this suggestion. We think, however, that it is beyond the scope of this paper and an interesting subject for a follow-up study.

Author Response

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

eLife assessment:

This important study represents a comprehensive computational analysis of Plasmodium falciparum gene expression, with a focus on var gene expression, in parasites isolated from patients; it assesses changes that occur as the parasites adapt to short-term in vitro culture conditions. The work provides technical advances to update a previously developed computational pipeline. Although the findings of the shifts in the expression of particular var genes have theoretical or practical implications beyond a single subfield, the results are incomplete and the main claims are only partially supported.

The authors would like to thank the reviewers and editors for their insightful and constructive assessment. We particularly appreciate the statement that our work provides a technical advance of our computational pipeline given that this was one of our main aims. To address the editorial criticisms, we have rephrased and restructured the manuscript to ensure clarity of results and to support our main claims. For the same reason, we removed the var transcript differential expression analysis, as this led to confusion.

Public Reviews:

Reviewer #1:

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching..." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites...". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Thank you for this comment. We were happy to modify the wording in the abstract to have consistency with the results presented by highlighting that modest but unpredictable var gene switching was observed while substantial changes were found in the core transcriptome. Moreover, any differences observed in core transcriptome between ex vivo samples from naïve and pre-exposed patients are diminished after one cycle of cultivation making inferences about parasite biology in vivo impossible.

Therefore, – to our opinion – the statement in the last sentence is well supported by the data presented.

Line 43–47: “Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.” Nevertheless, we would like to note that this study was in a unique position to assess changes at the individual patient level as we had successive parasite generations. This comparison is not done in most cross-sectional studies and therefore these small, unpredictable changes in the var transcriptome are missed.

Reviewer #2:

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

We are grateful for this suggestion and agree that a table comparing the pros and cons of all existing methods would be helpful for the general reader and also highlight the key advantages of our new approach. A table comparing previous methods for var gene and transcript characterisation has been added to the manuscript and is referenced in the introduction (line 107).

Author response table 1.

Comparison of previous var assembly approaches based on DNA- and RNA-sequencing.

Reviewer #3:

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately, there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

We agree with the reviewer that the different methods and results of the var transcriptome analysis can be difficult to reconcile. To address this, we have included a summary table with a brief description of the rationale and results of each approach in our analysis pipeline.

Author response table 2.

Summary of the different levels of analysis performed to assess the effect of short-term parasite culturing on var and core gene expression, their rational, method, results, and interpretation.

Additionally, the var transcript differential expression analysis was removed from the manuscript, because this study was in a unique position to perform a more focused analysis of var transcriptional changes across paired samples, meaning the per-patient approach was more suitable. This allowed for changes in the var transcriptome to be identified that would have gone unnoticed in the traditional differential expression analysis.

We thank the reviewer for his highly important comment about adjusting for life cycle stage. Var gene expression is highly stage-dependent, so any quantitative comparison between samples does need adjustment for developmental stage. All life cycle stage adjustments were done using the mixture model proportions to be consistent with the original paper, described in the results and methods sections:

• Line 219–221: “Due to the potential confounding effect of differences in stage distribution on gene expression, we adjusted for developmental stage determined by the mixture model in all subsequent analyses.”

• Line 722–725: “Var gene expression is highly stage dependent, so any quantitative comparison between samples needs adjustment for developmental stage. The life cycle stage proportions determined from the mixture model approach were used for adjustment.“

The rank-expression analysis did not have adjustment for life cycle stage as the values were determined as a percentage contribution to the total var transcriptome. The var group level and the global var gene expression analyses were adjusted for life cycle stages, by including them as an independent variable, as described in the results and methods sections.

Var group expression:

• Line 321–326: “Due to these results, the expression of group A var genes vs. group B and C var genes was investigated using a paired analysis on all the DBLα (DBLα1 vs DBLα0 and DBLα2) and NTS (NTSA vs NTSB) sequences assembled from ex vivo samples and across multiple generations in culture. A linear model was created with group A expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. The same was performed using group B and C expression levels.“

• Line 784–787: “DESeq2 normalisation was performed, with patient identity and life cycle stage proportions included as covariates and differences in the amounts of var transcripts of group A compared with groups B and C assessed (Love et al., 2014). A similar approach was repeated for NTS domains.”

Gobal var gene expression:

• Line 342–347: “A linear model was created (using only paired samples from ex vivo and generation 1) (Supplementary file 1) with proportion of total gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. This model showed no significant differences between generations, suggesting that differences observed in the raw data may be a consequence of small changes in developmental stage distribution in culture.”

• Line 804–806: “Significant differences in total var gene expression were tested by constructing a linear model with the proportion of gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as an independent variables and the patient identity included as a random effect.“

The analysis of the conserved var gene expression was adjusted for life cycle stage:

• Line 766–768: “For each conserved gene, Salmon normalised read counts (adjusted for life cycle stage) were summed and expression compared across the generations using a pairwise Wilcoxon rank test.”

And life cycle stage estimates were included as covariates in the design matrix for the domain differential expression analysis:

• Line 771–773: “DESeq2 was used to test for differential domain expression, with five expected read counts in at least three patient isolates required, with life cycle stage and patient identity used as covariates.”

Reviewer #1:

1. In the legend to Figure 1, the authors cite "Deitsch and Hviid, 2004" for the classification of different var gene types. This is not the best reference for this work. Better citations would be Kraemer and Smith, Mol Micro, 2003 and Lavstsen et al, Malaria J, 2003.

We agree and have updated the legend in Figure 1 with these references, consistent with the references cited in the introduction.

1. In Figures 2 and 3, each of the boxes in the flow charts are largely filled with empty space while the text is nearly too small to read. Adjusting the size of the text would improve legibility.

We have increased the size of the text in these figures.

1. My understanding of the computational method for assessing global var gene expression indicates an initial step of identifying reads containing the amino acid sequence LARSFADIG. It is worth noting that VAR2CSA does not contain this motif. Will the pipeline therefore miss expression of this gene, and if so, how does this affect the assessment of global var gene assessment? This seems relevant given that the authors detect increased expression of var2csa during adaptation to culture.

To address this question, we have added an explanation in the methods section to better explain our analysis. Var2csa was not captured in the global var gene expression analysis, but was analyzed separately because of its unique properties (conservation, proposed role in regulating var gene switching, slightly divergent timing of expression, translational repression).

• Line 802/3: “Var2csa does not contain the LARSFADIG motif, hence this quantitative analysis of global var gene expression excluded var2csa (which was analysed separately).”

1. In Figures 4 and 7, panels a and b display virtually identical PCA plots, with the exception that panel A displays more generations. Why are both panels included? There doesn't appear to be any additional information provided by panel B.

We agree and have removed Figure 7b for the core transcriptome PCA as it did not provide any new information. The var transcript differential analysis (displayed in Figure 4) has been removed from the manuscript.

1. On line 560-567, the authors state "However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at higher levels." What are the high levels being referred to here?

We have replaced this sentence to make it clearer what the different levels are (global var gene expression, var domain and var type).

• Line 526/7: “However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at the var domain, var type and global var gene expression level.”

Reviewer #2:

The authors make no mention or assessment of previously published var gene assembly methods from clinical samples that focus on genomic or transcriptomic approaches. These include:

https://pubmed.ncbi.nlm.nih.gov/28351419/

https://pubmed.ncbi.nlm.nih.gov/34846163/

These methods should be compared to the method for var gene assembly outlined by the co-authors, especially as the authors say that their method "overcomes previous limitations and outperforms current methods" (128-129). The second reference above appears to be a method to measure var expression in clinical samples and so should be particularly compared to the approach outlined by the authors.

Thank you for pointing this out. We have included the second reference in the introduction of our revised manuscript, where we refer to var assembly and quantification from RNA-sequencing data. We abstained from including the first paper in this paragraph (Dara et al., 2017) as it describes a var gene assembly pipeline and not a var transcript assembly pipeline.

• Line 101–105: “While approaches for var assembly and quantification based on RNA-sequencing have recently been proposed (Wichers et al., 2021; Stucke et al., 2021; Andrade et al., 2020; TonkinHill et al., 2018, Duffy et al., 2016), these still produce inadequate assembly of the biologically important N-terminal domain region, have a relatively high number of misassemblies and do not provide an adequate solution for handling the conserved var variants (Table S1).”

Additionally, we have updated the manuscript with a table (Table S1) comparing these two methods plus other previously used var transcript/gene assembly approaches (see comment to the public reviews).

But to address this particular comment in more detail, the first paper (Dara et al., 2017) is a var gene assembly pipeline and not a var transcript assembly pipeline. It is based on assembling var exon 1 from unfished whole genome assemblies of clinical samples and requires a prior step for filtering out human DNA. The authors used two different assemblers, Celera for short reads (which is no longer maintained) and Sprai for long reads (>2000bp), but found that Celera performed worse than Sprai, and subsequently used Sprai assemblies. Therefore, this method does not appear to be suitable for assembling short reads from RNA-seq.

The second paper (Stucke et al. 2021) focusses more on enriching for parasite RNA, which precedes assembly. The capture method they describe would complement downstream analysis of var transcript assembly with our pipeline. Their assembly pipeline is similar to our pipeline as they also performed de novo assembly on all P. falciparum mapping and non-human mapping reads and used the same assembler (but with different parameters). They clustered sequences using the same approach but at 90% sequence identity as opposed to 99% sequence identity using our approach. Then, Stucke et al. use 500nt as a cut-off as opposed to the more stringent filtering approach used in our approach. They annotated their de novo assembled transcripts with the known amino acid sequences used in their design of the capture array; our approach does not assume prior information on the var transcripts. Finally, their approach was validated only for its ability to recover the most highly expressed var transcript in 6 uncomplicated malaria samples, and they did not assess mis-assemblies in their approach.

For the methods (619–621), were erythrocytes isolated by Ficoll gradient centrifugation at the time of collection or later?

We have updated the methods section to clarify this.

• Line 586–588: “Blood was drawn and either immediately processed (#1, #2, #3, #4, #11, #12, #14, #17, #21, #23, #28, #29, #30, #31, #32) or stored overnight at 4oC until processing (#5, #6, #7, #9, #10, #13, #15, #16, #18, #19, #20, #22, #24, #25, #26, #27, #33).”

Was the current pipeline and assembly method assessed for var chimeras? This should be described.

Yes, this was quantified in the Pf 3D7 dataset and also assessed in the German traveler dataset. For the 3D7 dataset it is described in the result section and Figure S1.

• Line 168–174: “However, we found high accuracies (> 0.95) across all approaches, meaning the sequences we assembled were correct (Figure 2 – Figure supplement 1b). The whole transcript approach also performed the best when assembling the lower expressed var genes (Figure 2 – Figure supplement 1e) and produced the fewest var chimeras compared to the original approach on P. falciparum 3D7. Fourteen misassemblies were observed with the whole transcript approach compared to 19 with the original approach (Table S2). This reduction in misassemblies was particularly apparent in the ring-stage samples.” - Figure S1:

Author response image 1.

Performance of novel computational pipelines for var assembly on Plasmodium falciparum 3D7: The three approaches (whole transcript: blue, domain approach: orange, original approach: green) were applied to a public RNA-seq dataset (ENA: PRJEB31535) of the intra-erythrocytic life cycle stages of 3 biological replicates of cultured P. falciparum 3D7, sampled at 8-hour intervals up until 40hrs post infection (bpi) and then at 4-hour intervals up until 48 (Wichers al., 2019). Boxplots show the data from the 3 biological replicates for each time point in the intra-erythrocytic life cycle: a) alignment scores for the dominantly expressed var gene (PF3D7_07126m), b) accuracy scores for the dominantly var gene (PF3D7_0712600), c) number of contigs to assemble the dominant var gene (PF3D7_0712600), d) alignment scores for a middle ranking expressed vargene (PF3D7_0937800), e) alignment scores for the lowest expressed var gene (PF3D7_0200100). The first best blast hit (significance threshold = le-10) was chosen for each contig. The alignment score was used to evaluate the each method. The alignment score represents √accuracy* recovery. The accuracy is the proportion of bases that are correct in the assembled transcript and the recovery reflects what proportion of the true transcript was assembled. Assembly completeness of the dominant vargene (PF3D7 071200, length = 6648nt) for the three approaches was assessed for each biological f) biological replicate 1, g) biological replicate 2, h) biological replicate 3. Dotted lines represent the start and end of the contigs required to assemble the vargene. Red bars represent assembled sequences relative to the dominantly whole vargene sequence, where we know the true sequence (termed “reference transcript”).

For the ex vivo samples, this has been discussed in the result section and now we also added this information to Table 1.

• Line 182/3: “Remarkably, with the new whole transcript method, we observed a significant decrease (2 vs 336) in clearly misassembled transcripts with, for example, an N-terminal domain at an internal position.”

• Table 1:

Author response table 3.

Statistics for the different approaches used to assemble the var transcripts. Var assembly approaches were applied to malaria patient ex vivo samples (n=32) from (Wichers et al., 2021) and statistics determined. Given are the total number of assembled var transcripts longer than 500 nt containing at least one significantly annotated var domain, the maximum length of the longest assembled var transcript in nucleotides and the N50 value, respectively. The N50 is defined as the sequence length of the shortest var contig, with all var contigs greater than or equal to this length together accounting for 50% of the total length of concatenated var transcript assemblies. Misassemblies represents the number of misassemblies for each approach. **Number of misassemblies were not determined for the domain approach due to its poor performance in other metrics.

Line 432: "the core gene transcriptome underwent a greater change relative to the var transcriptome upon transition to culture." Can this be shown statistically? It's unclear whether the difference in the sizes of the respective pools of the core genome and the var genes may account for this observation.

We found 19% of the core transcriptome to be differentially expressed. The per patient var transcript analysis revealed individually highly variable but generally rather subtle changes in the var transcriptome. The different methods for assessing this make it difficult to statistically compare these two different results.

The feasibility of this approach for field samples should be discussed in the Discussion.

In the original manuscript we reflected on this already several times in the discussion (e.g., line 465/6; line 471–475; line 555–568). We now have added another two sentences at the end of the paragraph starting in line 449 to address this point. It reads now:

• Line 442–451: “Our new approach used the most geographically diverse reference of var gene sequences to date, which improved the identification of reads derived from var transcripts. This is crucial when analysing patient samples with low parasitaemia where var transcripts are hard to assemble due to their low abundancy (Guillochon et al., 2022). Our approach has wide utility due to stable performance on both laboratory-adapted and clinical samples. Concordance in the different var expression profiling approaches (RNA-sequencing and DBLα-tag) on ex vivo samples increased using the new approach by 13%, when compared to the original approach (96% in the whole transcript approach compared to 83% in Wichers et al., 2021. This suggests the new approach provides a more accurate method for characterising var genes, especially in samples collected directly from patients. Ultimately, this will allow a deeper understanding of relationships between var gene expression and clinical manifestations of malaria.”

MINOR

The plural form of PfEMP1 (PfEMP1s) is inconsistently used throughout the text.

Corrected.

404-405: statistical test for significance?

Thank you for this suggestion. We have done two comparisons between the original analysis from Wichers et al., 2021 and our new whole transcript approach to test concordance of the RNAseq approaches with the DBLα-tag approach using paired Wilcoxon tests. These comparisons suggest that our new approach has significantly increased concordance with DBLα-tag data and might be better at capturing all expressed DBLα domains than the original analysis (and the DBLα-approach), although not statistically significant. We describe this now in the result section.

• Line 352–361: “Overall, we found a high agreement between the detected DBLα-tag sequences and the de novo assembled var transcripts. A median of 96% (IQR: 93–100%) of all unique DBLα-tag sequences detected with >10 reads were found in the RNA-sequencing approach. This is a significant improvement on the original approach (p= 0.0077, paired Wilcoxon test), in which a median of 83% (IQR: 79–96%) was found (Wichers et al., 2021). To allow for a fair comparison of the >10 reads threshold used in the DBLα-tag approach, the upper 75th percentile of the RNA-sequencingassembled DBLα domains were analysed. A median of 77.4% (IQR: 61–88%) of the upper 75th percentile of the assembled DBLα domains were found in the DBLα-tag approach. This is a lower median percentage than the median of 81.3% (IQR: 73–98%) found in the original analysis (p= 0.28, paired Wilcoxon test) and suggests the new assembly approach is better at capturing all expressed DBLα domains.”

Figure 4: The letters for the figure panels need to be added.

The figure has been removed from the manuscript.

Reviewer #3:

It is difficult from Table S2 to determine how many unique var transcripts would have enough coverage to be potentially assembled from each sample. It seems unlikely that 455 distinct vars (~14 per sample) would be expressed at a detectable level for assembly. Why not DNA-sequence these samples to get the full repertoire for comparison to RNA? Why would so many distinct transcripts be yielded from fairly synchronous samples?

We know from controlled human malaria infections of malaria-naive volunteers, that most var genes present in the genomic repertoire of the parasite strain are expressed at the onset of the human blood phase (heterogenous var gene expression) (Wang et al., 2009; Bachmann et al, 2016; Wichers-Misterek et al., 2023). This pattern shifts to a more restricted, homogeneous var expression pattern in semi-immune individuals (expression of few variants) depending on the degree of immunity (Bachmann et al., 2019).

Author response image 2.

In this cohort, 15 first-time infections are included, which should also possess a more heterogenous var gene expression in comparison to the pre-exposed individuals, and indeed such a trend is already seen in the number of different DBLa-tag clusters found in both patient groups (see figure panel from Wichers et al. 2021: blue-first-time infections; grey–pre-exposed). Moreover, Warimwe et al. 2013 have shown that asymptomatic infections have a more homogeneous var expression in comparison to symptomatic infections. Therefore, we expect that parasites from symptomatic infections have a heterogenous var expression pattern with multiple var gene variants expressed, which we could assemble due to our high read depth and our improved var assembly pipeline for even low expressed variants.

Moreover, the distinct transcripts found in the RNA-seq approach were confirmed with the DBLα tag data. To our opinion, previous approaches may have underestimated the complexity of the var transcriptome in less immune individuals.

Mapping reads to these 455 putative transcripts and using this count matrix for differential expression analysis seems very unlikely to produce reliable results. As acknowledged on line 327, many reads will be mis-mapped, and perhaps most challenging is that most vars will not be represented in most samples. In other words, even if mapping were somehow perfect, one would expect a sparse matrix that would not be suitable for statistical comparisons between groups. This is likely why the per-patient transcript analysis doesn't appear to be consistent. I would recommend the authors remove the DE sections utilizing this approach, or add convincing evidence that the count matrix is useable.

We agree that this is a general issue of var differential expression analysis. Therefore, we have removed the var differential expression analysis from this manuscript as the per patient approach was more appropriate for the paired samples. We validated different mapping strategies (new Figure S6) and included a paragraph discussing the problem in the result section:

• Line 237–255: “In the original approach of Wichers et al., 2021, the non-core reads of each sample used for var assembly were mapped against a pooled reference of assembled var transcripts from all samples, as a preliminary step towards differential var transcript expression analysis. This approach returned a small number of var transcripts which were expressed across multiple patient samples (Figure 3 – Figure supplement 2a). As genome sequencing was not available, it was not possible to know whether there was truly overlap in var genomic repertoires of the different patient samples, but substantial overlap was not expected. Stricter mapping approaches (for example, excluding transcripts shorter than 1500nt) changed the resulting var expression profiles and produced more realistic scenarios where similar var expression profiles were generated across paired samples, whilst there was decreasing overlap across different patient samples (Figure 3 – Figure supplement 2b,c). Given this limitation, we used the paired samples to analyse var gene expression at an individual subject level, where we confirmed the MSP1 genotypes and alleles were still present after short-term in vitro cultivation. The per patient approach showed consistent expression of var transcripts within samples from each patient but no overlap of var expression profiles across different patients (Figure 3 – Figure supplement 2d). Taken together, the per patient approach was better suited for assessing var transcriptional changes in longitudinal samples. It has been hypothesised that more conserved var genes in field isolates increase parasite fitness during chronic infections, necessitating the need to correctly identify them (Dimonte et al., 2020, Otto et al., 2019). Accordingly, further work is needed to optimise the pooled sample approach to identify truly conserved var transcripts across different parasite isolates in cross-sectional studies.” - Figure S6:

Author response image 3.

Var expression profiles across different mapping. Different mapping approaches Were used to quantify the Var expression profiles of each sample (ex Vivo (n=13), generation I (n=13), generation 2 (n=10) and generation 3 (n=l). The pooled sample approach in Which all significantly assembled van transcripts (1500nt and containing3 significantly annotated var domains) across samples were combined into a reference and redundancy was removed using cd-hit (at sequence identity = 99%) (a—c). The non-core reads of each sample were mapped to this pooled reference using a) Salmon, b) bowtie2 filtering for uniquely mapping paired reads with MAPQ and c) bowtie2 filtering for uniquely mapping paired reads with a MAPQ > 20. d) The per patient approach was applied. For each patient, the paired ex vivo and in vitro samples were analysed. The assembled var transcripts (at least 1500nt and containing3 significantly annotated var domains) across all the generations for a patient were combined into a reference, redundancy was removed using cd-hit (at sequence identity: 99%), and expression was quantified using Salmon. Pie charts show the var expression profile With the relative size of each slice representing the relative percentage of total var gene expression of each var transcript. Different colours represent different assembled var transcripts with the same colour code used across a-d.

For future cross-sectional studies a per patient analysis that attempts to group per patient assemblies on some unifying structure (e.g., domain, homology blocks, domain cassettes etc) should be performed.

Line 304. I don't understand the rationale for comparing naïve vs. prior-exposed individuals at ex-vivo and gen 1 timepoints to provide insights into how reliable cultured parasites are as a surrogate for var expression in vivo. Further, the next section (per patient) appears to confirm the significant limitation of the 'all sample analysis' approach. The conclusion on line 319 is not supported by the results reported in figures S9a and S9b, nor is the bold conclusion in the abstract about "casting doubt" on experiments utilizing culture adapted

We have removed this comparison from the manuscript due to the inconsistencies with the var per patient approach. However, the conclusion in the abstract has been rephrased to reflect the fact we observed 19% of the core transcript differentially expressed within one cycle of cultivation.

Line 372/391 (and for the other LMM descriptions). I believe you mean to say response variable, rather than explanatory variable. Explanatory variables are on the right hand side of the equation.

Thank you for spotting this inaccuracy, we changed it to “response variable” (line 324, line 343, line 805).

Line 467. Similar to line 304, why would comparisons of naïve vs. prior-exposed be informative about surrogates for in vivo studies? Without a gold-standard for what should be differentially expressed between naïve and prior-exposed in vivo, it doesn't seem prudent to interpret a drop in the number of DE genes for this comparison in generation 1 as evidence that biological signal for this comparison is lost. What if the generation 1 result is actually more reflective of the true difference in vivo, but the ex vivo samples are just noisy? How do we know? Why not just compare ex vivo vs generation 1/2 directly (as done in the first DE analysis), and then you can comment on the large number of changes as samples are less and less proximal to in vivo?

In the original paper (Wichers et al., 2021), there were differences between the core transcriptome of naïve vs previously exposed patients. However, these differences appeared to diminish in vitro, suggesting the in vivo core transcriptome is not fully maintained in vitro.

We have added a sentence explaining the reasoning behind this analysis in the results section:

• Lines 414–423: “In the original analysis of ex vivo samples, hundreds of core genes were identified as significantly differentially expressed between pre-exposed and naïve malaria patients. We investigated whether these differences persisted after in vitro cultivation. We performed differential expression analysis comparing parasite isolates from naïve (n=6) vs pre-exposed (n=7) patients, first between their ex vivo samples, and then between the corresponding generation 1 samples. Interestingly, when using the ex vivo samples, we observed 206 core genes significantly upregulated in naïve patients compared to pre-exposed patients (Figure 7 – Figure supplement 3a). Conversely, we observed no differentially expressed genes in the naïve vs pre-exposed analysis of the paired generation 1 samples (Figure 7 – Figure supplement 3b). Taken together with the preceding findings, this suggests one cycle of cultivation shifts the core transcriptomes of parasites to be more alike each other, diminishing inferences about parasite biology in vivo.”

Overall, I found the many DE approaches very frustrating to interpret coherently. If not dropped in revision, the reader would benefit from a substantial effort to clarify the rationale for each approach, and how each result fits together with the other approaches and builds to a concise conclusion.

We agree that the manuscript contains many different complex layers of analysis and that it is therefore important to explain the rationale for each approach. Therefore, we now included the summary Table 3 (see comment to public review). Additionally, we have removed the var transcript differential expression due to its limitations, which we hope has already streamlined our manuscript.

Author Response

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

We sincerely thank the reviewers for their in-depth consideration of our manuscript and their helpful reviews. Their efforts have made the paper much better. We have responded to each point. The previously provided public responses have been updated they are included after the private response for convenience.

Reviewer #1 (Recommendations For The Authors):

1. In general, the manuscript will benefit from copy editing and proof reading. Some obvious edits;

2. Page 6 line 140. Do the authors mean Cholera toxin B?

Response: We corrected this error and went through the entire paper carefully correcting for grammar and increased clarity.

• Page 8 line 173. Methylbetacyclodextrin is misspelled.

Response: Yes, corrected.

• Figure 4c is missing representative traces for electrophysiology data.

• Figure 4. Please check labeling ordering in figure legend as it does not match the panels in the figure.

Thank you for the correction and we apologize for the confusion in figure 4. We uploaded an incomplete figure legend, and the old panel ‘e’ was not from an experiment that was still in the figure. It was removed and the figure legends are now corrected.

• Please mention the statistical analysis used in all figure legends.

Response: Thank you for pointing out this omission, statistics have been added.

• Although the schematics in each figure helps guide readers, they are very inconsistent and sometimes confusing. For example, in Figure 5 the gating model is far-reaching without conclusive evidence, whereas in Figure 6 it is over simplified and unclear what the image is truly representing (granted that the downstream signaling mechanism and channel is not known).

Response: Figure 5d is the summary figure for the entire paper. We have made this clearer in the figure legend and we deleted the title above the figure that gave the appearance that the panel relates to swell only. It is the proposed model based on what we show in the paper and what is known about the activation mechanism of TREK-1.

Figure 6 is supposed to be simple. It is to help the reader understand that when PA is low mechanical sensitivity is high. Without the graphic, previous reviewers got confused about threshold going down and mechanosensitivity going up and how the levels of PA relate. Low PA= high sensitivity. We’ve added a downstream effector to the right side of the panel to avoid any biased to a putative downstream channel effector. The purpose of the experiment is to show PLD has a mechanosensitive phenotype in vivo.

Reviewer #2 (Recommendations For The Authors):

This manuscript outlines some really interesting findings demonstrating a mechanism by which mechanically driven alterations in molecular distributions can influence a) the activity of the PLD2 molecule and subsequently b) the activation of TREK-1 when mechanical inputs are applied to a cell or cell membrane.

The results presented here suggest that this redistribution of molecules represents a modulatory mechanism that alters either the amplitude or the sensitivity of TREK-1 mediated currents evoked by membrane stretch. While the authors do present values for the pressure required to activate 50% of channels (P50), the data presented provides incomplete evidence to conclude a shift in threshold of the currents, given that many of the current traces provided in the supplemental material do not saturate within the stimulus range, thus limiting the application of a Boltzmann fit to determine the P50. I suggest adding additional context to enable readers to better assess the limitations of this use of the Boltzmann fit to generate a P50, or alternately repeating the experiments to apply stimuli up to lytic pressures to saturate the mechanically evoked currents, enabling use of the Boltzmann function to fit the data.

Response: We thank the reviewer for pointing this out. We agree the currents did not reach saturation. Hence the term P50 could be misleading, so we have removed it from the paper. We now say “half maximal” current measured from non-saturating pressures of 0-60 mmHg. We also deleted the xPLD data in supplemental figure 3C since there is insufficient current to realistically estimate a half maximal response.

In my opinion, the conclusions presented in this manuscript would be strengthened by an assessment of the amount of TREK-1 in the plasma membrane pre and post application of shear. While the authors do present imaging data in the supplementary materials, these data are insufficiently precise to comment on expression levels in the membrane. To strengthen this conclusion the authors could conduct cell surface biotinylation assays, as a more sensitive and quantitative measure of membrane localisation of the proteins of interest.

1. Response: as mentioned previously, we do not have an antibody to the extracellular domain. Nonetheless to better address this concern we directly compared the levels of TREK-1, PIP2, and GM1; in xPLD2, mPLD2, enPLD2 with and without shear. The results are in supplemental figure 2. PLD2 is known to increase endocytosis1 and xPLD2 is known to block both agonist induced and constitutive endocytosis of µ-opioid receptor2. The receptor is trapped on the surface. This is true of many proteins including Rho3, ARF4, and ACE21 among others. In agreement with this mechanism, in Figure S2C,G we show that TREK increases with xPLD and the localization can clearly be seen at the plasma membrane just like in all of the other publications with xPLD overexpression. xPLD2 would be expected to inhibit the basal current but we presume the increased expression likely has compensated and there is sufficient PA and PG from other sources to allow for the basal current. It is in this state that we then conduct our ephys and monitor with a millisecond time resolution and see no activation. We are deriving conclusion from a very clear response—Figure 1b shows almost no current, even at 1-10 ms after applying pressure. There is little pressure current when we know the channel is present and capable of conducting ion (Figure 1d red bar). After shear there is a strong decrease in TREK-1 currents on the membrane in the presence of xPLD2. But it is not less than TREK-1 expression with mPLD2. And since mouse PLD2 has the highest basal current and pressure activation current. The amount of TREK-1 present is sufficient to conduct large current. To have almost no detective current would require at least a 10 fold reduction compared to mPLD2 levels before we would lack the sensitivity to see a channel open. Lasty endocytosis typically in on the order of seconds to minutes, no milliseconds.

2. We have shown an addition 2 independent ways that TREK-1 is on the membrane during our stretch experiments. Figure 1d shows the current immediately prior to applying pressure for wt TREK-1. When catalytically dead PLD is present (xPLD2) there is almost normal basal current. The channel is clearly present. And then in figure 1a we show within a millisecond there is no pressure current. As a control we added a functionally dead TREK-1 truncation (xTREK). Compared to xPLD2 there is clearly normal basal current. If this is not strong evidence the channel was available on the surface for mechanical activation please help us understand why. And if you think within 2.1 ms 100% of the channel is gone by endocytosis please provide some evidence that this is possible so we can reconsider.

3. We have TIRF super resolution imaging with ~20 nm x-y resolution and ~ 100nm z resolution and Figure 2b clearly shows the channel on the membrane. When we apply pressure in 1b, the channel is present.

4. Lastly, In our previous studies we showed activation of PLD2 by anesthetics was responsible for all of TREK-1’s anesthetic sensitivity and this was through PLD2 binding to the C-terminus of TREK-15. We showed this was the case by transferring anesthetic sensitivity to an anesthetic insensitive homolog TRAAK. This established conclusively the basic premise of our mechanism. Here we show the same C-terminal region and PLD2 are responsible for the mechanical current observed by TREK-1. TRAAK is already mechanosensitive so the same chimera will not work for our purposes here. But anesthetic activation and mechanical activation are dramatically different stimuli, and the fact that the role of PLD is robustly observed in both should be considered.

The authors discuss that the endogenous levels of TREK-1 and PLD2 are "well correlated: in C2C12 cells, that TREK-1 displayed little pair correlation with GM1 and that a "small amount of TREK-1 trafficked to PIP2". As such, these data suggest that the data outlined for HEK293T cells may be hampered by artefacts arising from overexpression. Can TREK-1 currents be activated by membrane stretch in these cells C2C12 cells and are they negatively impacted by the presence of xPLD2? Answering this question would provide more insight into the proposed mechanism of action of PLD2 outlined by the authors in this manuscript. If no differences are noted, the model would be called into question. It could be that there are additional cell-specific factors that further regulate this process.

Response: The low pair correlation of TREK-1 and GM1 in C2C12 cells was due to insufficient levels of cholesterol in the cell membrane to allow for robust domain formation. In Figure 4b we loaded C2C12 cells with cholesterol using the endogenous cholesterol transport protein apoE and serum (an endogenous source of cholesterol). As can be seen in Fig. 4b, the pair correlation dramatically increased (purple line). This was also true in neuronal cells (N2a) (Fig 4d, purple bar). And shear (3 dynes/cm2) caused the TREK-1 that was in the GM1 domains to leave (red bar) reversing the effect of high cholesterol. This demonstrates our proposed mechanism is working as we expect with endogenously expressed proteins.

There are many channels in C2C12 cells, it would be difficult to isolate TREK-1 currents, which is why we replicated the entire system (ephys and dSTORM) in HEK cells. Note, in figure 4c we also show that adding cholesterol inhibits TREK-1 whole cell currents in HEK293cells.

As mentioned in the public review, the behavioural experiments in D. melanogaster can not solely be attributed to a change in threshold. While there may be a change in the threshold to drive a different behaviour, the writing is insufficiently precise to make clear that conclusions cannot be drawn from these experiments regarding the functional underpinnings of this outcome. Are there changes in resting membrane potential in the mutant flys? Alterations in Nav activity? Without controlling for these alternate explanations it is difficult to see what this last piece of data adds to the manuscript, particularly given the lack of TREK-1 in this organism. At the very least, some editing of the text to more clearly indicate that these data can only be used to draw conclusions on the change in threshold for driving the behaviour not the change in threshold of the actual mechanotransduction event (i.e. conversion of the mechanical stimulus into an electrochemical signal).

Response: We agree; features other than PLDs direct mechanosensitivity are likely contributing. This was shown in figure 6g left side. We have an arrow going to ion channel and to other downstream effectors. We’ve added the putative alteration to downstream effectors to the right side of the panel. This should make it clear that we no more speculate the involvement of a channel than any of the other many potential downstream effectors. As mentioned above, the figure helps the reader coordinate low PA with increased mechanosensitivity. Without the graphic reviewers got confused that PA increased the threshold which corresponds to a decreased sensitivity to pain. Nonetheless we removed our conclusion about fly thresholds from the abstract and made clearer in the main text the lack of mechanism downstream of PLD in flies including endocytosis. Supplemental Figure S2H also helps emphasize this. .

Nav channels are interesting, and since PLD contribute to endocytosis and Nav channels are also regulated by endocytosis there is likely a PLD specific effect using Nav channels. There are many ways PA likely regulates mechanosensitive thresholds, but we feel Nav is beyond the scope of our paper. Someone else will need to do those studies. We have amended a paragraph in the conclusion which clearly states we do not know the specific mechanism at work here with the suggestions for future research to discover the role of lipid and lipid-modifying enzymes in mechanosensitive neurons.

There may be fundamental flaws in how the statistics have been conducted. The methods section indicates that all statistical testing was performed with a Student's t-test. A visual scan of many of the data sets in the figures suggests that they are not normally distributed, thus a parametric test such as a Student's t-test is not valid. The authors should assess if each data set is normally distributed, and if not, a non-parametric statistical test should be applied. I recommend assessing the robustness of the statistical analyses and adjusting as necessary.

Response: We thank the reviewer for pointing this out, indeed there is some asymmetry in Figure 6C-d. The p values with Mann Whitney were slightly improved p=0.016 and p=0.0022 for 6c and 6d respectively. For reference, the students t-test had slightly worse statistics p=0.040 and p=0.0023. The score remained the same 1 and 2 stars respectively.

The references provided for the statement regarding cascade activation of the TRPs are incredibly out of date. While it is clear that TRPV4 can be activated by a second messenger cascade downstream of osmotic swelling of cells, TRPV4 has also been shown to be activated by mechanical inputs at the cell-substrate interface, even when the second messenger cascade is inhibited. Recommend updating the references to reflect more current understanding of channel activation.

Response: We thank the reviewer for pointing this out. We have updated the references and changed the comment to “can be” instead of “are”. The reference is more general to multiple ion channel types including KCNQ4. This should avoid any perceived conflict with the cellsubstrate interface mechanism which we very much agree is a correct mechanism for TRP channels.

Minor comments re text editing etc:

The central messages of the manuscript would benefit from extensive work to increase the precision of the writing of the manuscript and the presentation of data in the figures, such textual changes alone would help address a number of the concerns outlined in this review, by clarifying some ambiguities. There are numerous errors throughout, ranging from grammatical issues, ambiguities with definitions, lack of scale bars in images, lack of labels on graph axes, lack of clarity due to the mode of presentation of sample numbers (it would be far more precise to indicate specific numbers for each sample rather than a range, which is ambiguous and confusing), unnecessary and repeat information in the methods section. Below are some examples but this list is not exhaustive.

Response: Thank you, reviewer # 1 also had many of these concerns. We have gone through the entire paper and improved the precision of the writing of the manuscript. We have also added the missing error bar to Figure 6. And axis labels have been added to the inset images. The redundancy in cell culture methods has been removed. Where a range is small and there are lots of values, the exact number of ‘n’ are graphically displayed in the dot plot for each condition.

Text:

I recommend considering how to discuss the various aspects of channel activation. A convention in the field is to use mechanical activation or mechanical gating to describe that process where the mechanical stimulus is directly coupled to the channel gating mechanism. This would be the case for the activation of TREK-1 by membrane stretch alone. The increase in activation by PLD2 activity then reflects a modulation of the mechanical activation of the channel, because the relevant gating stimulus is PA, rather than force/stretch. The sum of these events could be described as shear-evoked or mechanically-evoked, TREK-1 mediated currents (thus making it clear that the mechanical stimulus initiates the relevant cascade, but the gating stimulus may be other than direct mechanical input.) Given the interesting and compelling data offered in this manuscript regarding the sensitisation of TREK-1 dependent mechanicallyevoked currents by PLD2, an increase in the precision of the language would help convey the central message of this work.

Response; We agree there needs to be convention. We have taken the suggestion of mechanically evoked and we suggest the following definitions:

1. Mechanical activation of PLD2: direct force on the lipids releasing PLD2 from nonactivating lipids.

2. Mechanical activation/gating of TREK1: direct force from lipids from either tension or hydrophobic mismatch that opens the channel.

3. Mechanically evoked: a mechanical event that leads to a downstream effect. The effect is mechanically “evoked”.

4. Spatial patterning/biochemistry: nanoscopic changes in the association of a protein with a nanoscopic lipid cluster or compartment.

An example of where discussion of mechanical activation is ambiguous in the text is found at line 109: "channel could be mechanically activated by a movement from GM1 to PIP2 lipids." In this case, the sentence could be suggesting that the movement between lipids provides the mechanical input that activates the channel, which is not what the data suggest.

Response: Were possible we have replaced “movement” with “spatial patterning” and “association” and “dissociation” from specific lipid compartment. This better reflects the data we have in this paper. However, we do think that a movement mechanically activates the channel, GM1 lipids are thick and PIP2 lipids are thin, so movement between the lipids could activate the channel through direct lipid interaction. We will address this aspect in a future paper.

Inconsistencies with usage:

• TREK1 versus TREK-1

Response: corrected to TREK-1

• mPLD2 versus PLD2

Response: where PLD2 represents mouse this has been corrected.

• K758R versus xPLD2

Response: we replaced K758R in the methods with xPLD2.

• HEK293T versus HEK293t Response: we have changed all instances to read HEK293T.

• Drosophila melanogaster and D. melanogaster used inconsistently and in many places incorrectly

Response: we have read all to read the common name Drosophila.

Line 173: misspelled methylbetacyclodextrin

Response corrected

Line 174: degree symbol missing

Response corrected

Line 287: "the decrease in cholesterol likely evolved to further decrease the palmate order in the palmitate binding site"... no evidence, no support for this statement, falsely attributes intention to evolutionary processes .

Response: we have removed the reference to evolution at the request of the reviewer, it is not necessary. But we do wish to note that to our knowledge, all biological function is scientifically attributed to evolution. The fact that cholesterol decreases in response to shear is evidence alone that the cell evolved to do it.

Line 307: grammatical error

Response: the redundant Lipid removed.

Line 319: overinterpreted - how is the mechanosensitivy of GPCRs explained by this translocation?

Response: all G-alpha subunits of the GPCR complex are palmitoylated. We showed PLD (which has the same lipidation) is mechanically activated. If the palmitate site is disrupted for PLD2, then it is likely disrupted for every G-alpha subunit as well.

Line 582: what is the wild type referred to here?

Response: human full length with a GFP tag.

Methods:

• Sincere apologies if I missed something but I do not recall seeing any experiments using purified TREK-1 or flux assays. These details should be removed from the methods section

Response: Removed.

• There is significant duplication of detail across the methods (three separate instances of electrophysiology details) these could definitely be consolidated.

Response: Duplicates removed.

Figures:

• Figure 2- b box doesn't correspond to inset. Bottom panel should provide overview image for the cell that was assessed with shear. In bottom panel, circle outlines an empty space.

Response: We have widened the box slightly to correspond so the non shear box corresponds to the middle panel. We have also added the picture for the whole cell to Fig S2g and outlined the zoom shown in the bottom panel of Fig 2b as requested. The figure is of the top of a cell. We also added the whole cell image of a second sheared cell.

Author response image 1.

• Figure 3 b+c: inset graph lacking axis labels

Response; the inset y axis is the same as the main axis. We added “pair corr. (5nM)” and a description in the figure legend to make this clearer. The purpose of the inset is to show statistical significance at a single point. The contrast has been maximized but without zooming in points can be difficult to see.

• Figure 5: replicate numbers missing and individual data points lacking in panels b + c, no labels of curve in b + c, insets, unclear what (5 nm) refers to in insets.

Response: Thank you for pointing out these errors. The N values have been added. Similar to figure 3, the inset is a bar graph of the pair correlation data at 5 nm. A better explanation of the data has been added to the figure legend.

• Figure 6: no scale bar, no clear membrane localization evident from images presented, panel g offers virtually nothing in terms of insight

Response: We have added scale bars to figure 6b. Figure 6g is intentionally simplistic, we found that correlating decreased threshold with increased pain was confusing. A previous reviewer claimed our data was inconsistent. The graphic avoids this confusion. We also added negative effects of low PA on downstream effects to the right panel. This helps graphically show we don’t know the downstream effects.

Reviewer #3 (Recommendations For The Authors):

Minor suggestions:

1. line 162, change 'heat' to 'temperature'.

Response: changed.

1. in figure 1, it would be helpful to keep the unit for current density consistent among different panels. 1e is a bit confusing: isn't the point of Figure 1 that most of TREK1 activation is not caused by direct force-sensing?

Response: Yes, the point of figure 1 is to show that in a biological membrane over expressed TREK-1 is a downstream effector of PLD2 mechanosensation which is indirect. We agree the figure legend in the previous version of the paper is very confusing.

There is almost no PLD2 independent current in our over expressed system, which is represented by no ions in the conduction pathway of the channel despite there being tension on the membrane.

Purified TREK-1 is only mechanosensitive in a few select lipids, primarily crude Soy PC. It was always assumed that HEK293 and Cos cells had the correct lipids since over expressed TREK-1 responded to mechanical force in these lipids. But that does not appear to be correct, or at least only a small amount of TREK-1 is in the mechanosensitive lipids. Figure 1e graphically shows this. The arrows indicate tension, but the channel isn’t open with xPLD2 present. We added a few sentences to the discussion to further clarify.

Panels c has different units because the area of the tip was measured whereas in d the resistance of the tip was measured. They are different ways for normalizing for small differences in tip size.

1. line 178, ~45 of what?

Response: Cells were fixed for ~30 sec.

1. line 219 should be Figure 4f?

Response: thank you, yes Figure 4f.

Previous public reviews with minor updates.

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

Response: The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

Response: The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We have added this observation to the paper.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Response: Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. We have added the error bars to make this more obvious. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Response: Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Response: Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microticopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensi>zed by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

Response: We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force. We have removed the speculative conclusions about fly thresholds from the abstract.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensi>zing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microticopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microticopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from sta>c measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

Response: We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 2 below). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1 prior to stretch. The opposite was true, xPLD2 increased TREK-1 expression (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 2.

TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

Response: We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype. We tried to make it clear in the figure and in the text that we have no evidence of a particular mechanism downstream of PLD mechanosensation.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing.

1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Response: yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. We added the following sentence and reference : “The xPLD2 expression is >10x the endogenous PLD2 (enPLD2) and out computes the TREK-1 binding site for PLD25.”

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

Response: If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Author response image 2 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Author response image 2) and coexpression with xPLD increases TREK expression (Figure S2f). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2. A few sentences was added to the discussion. We also performed dSTORM on the TREKtruncated using EGFP. TREK-truncated goes to PIP2 (see figure 2 of 6)

Author response image 3.

To better compare the levels of TREK-1 before and after shear, we added a supplemental figure S2f where the protein was compared simultaneously in all conditions. 15 min of shear significantly decreased TREK-1 except with mPLD2 where the levels before shear were already lowest of all the expression levels tested.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the >me gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Response: Shear was maintained during the fixing. xPLD2 blocks endocytosis, presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREK trunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Par>cularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

Response: The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

“This is a provocative report that identi0ies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which 0its nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the 0irst such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disrup0on to test the predic0ons of their model.”

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

Response: We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Ouen, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Response: Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

References cited:

1 Du G, Huang P, Liang BT, Frohman MA. Phospholipase D2 localizes to the plasma membrane and regulates angiotensin II receptor endocytosis. Mol Biol Cell 2004;15:1024–30. htps://doi.org/10.1091/mbc.E03-09-0673.

2 Koch T, Wu DF, Yang LQ, Brandenburg LO, Höllt V. Role of phospholipase D2 in the agonist-induced and constistutive endocytosis of G-protein coupled receptors. J Neurochem 2006;97:365–72. htps://doi.org/10.1111/j.1471-4159.2006.03736.x.

3 Wheeler DS, Underhill SM, Stolz DB, Murdoch GH, Thiels E, Romero G, et al. Amphetamine activates Rho GTPase signaling to mediate dopamine transporter internalization and acute behavioral effects of amphetamine. Proc Natl Acad Sci U S A 2015;112:E7138–47. htps://doi.org/10.1073/pnas.1511670112.

4 Rankovic M, Jacob L, Rankovic V, Brandenburg L-OO, Schröder H, Höllt V, et al. ADP-ribosylation factor 6 regulates mu-opioid receptor trafficking and signaling via activation of phospholipase D2. Cell Signal 2009;21:1784–93. htps://doi.org/10.1016/j.cellsig.2009.07.014.

5 Pavel MA, Petersen EN, Wang H, Lerner RA, Hansen SB. Studies on the mechanism of general anesthesia. Proc Natl Acad Sci U S A 2020;117:13757–66. htps://doi.org/10.1073/pnas.2004259117.

6 Call IM, Bois JL, Hansen SB. Super-resolution imaging of potassium channels with genetically encoded EGFP. BioRxiv 2023. htps://doi.org/10.1101/2023.10.13.561998.

Author Response:

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We should have made this clearer.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. This should have been made clear in the figure legend. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a. cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microscopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensitized by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensitizing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microscopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microscopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from static measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 1). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1. The opposite was true, xPLD2 increased TREK-1 expression increased (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 1.<br /> TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

>

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing. 1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. This should have been described more carefully in this paper and the studies pointed out that establish this conclusion.

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Figure R1 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Figure R1) and co-expression with xPLD increases TREK expression (Figure S2). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the time gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Shear was maintained during the fixing. We do not know why shear changes PIP2 and TREK-1 levels. Presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREKtrunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Particularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

"This is a provocative report that identifies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which fits nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the first such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disruption to test the predictions of their model."

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Often, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

Author Response

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

Reviewer #1 (Public Review):

The present work establishes 14-3-3 proteins as binding partners of spastin and suggests that this binding is positively regulated by phosphorylation of spastin. The authors show evidence that 14-3-3 >- spastin binding prevents spastin ubiquitination and final proteasomal degradation, thus increasing the availability of spastin. The authors measured microtubule severing activity in cell lines and axon regeneration and outgrowth as a prompt to spastin activity. By using drugs and peptides that separately inhibit 14-3-3 binding or spastin activity, they show that both proteins are necessary for axon regeneration in cell culture and in vivo models in rats.

The following is an account of the major strengths and weaknesses of the methods and results.

Major strengths

-The authors performed pulldown assays on spinal cord lysates using GST-spastin, then analyzed pulldowns via mass spectrometry and found 3 peptides common to various forms of 14-3-3 proteins. In co-expression experiments in cell lines, recombinant spastin co-precipitated with all 6 forms of 14-3-3 tested.

-By protein truncation experiments they found that the Microtubule Binding Domain of spastin contained the binding capability to 14-3-3. This domain contained a putative phosphorylation site, and substitutions that cannot be phosphorylated cannot bind to spastin.

-spastin overexpression increased neurite growth and branching, and so did the phospho null spastin. On the other hand, the phospho mimetic prevents all kinds of neurite development.

-Overexpression of GFP-spastin shows a turn-over of about 12 hours when protein synthesis is inhibited by cycloheximide. When 14-3-3 is co-overexpressed, GFP-spastin does not show a decrease by 12 hours. When S233A is expressed, a turn-over of 9 hours is observed, indicating that the ability to be phosphorylated increases the stability of the protein.

-In support of that notion, the phospho-mimetic S233D makes it more stable, lasting as much as the over-expression of 14-3-3.

-Authors show that spastin can be ubiquitinated, and that in the presence of ubiquitin, spastin-MT severing activity is inhibited.

-By combining FCA with Spastazoline, the authors claim that FCA increased regeneration is due to increased spastin Activity in various models of neurite outgrowth and regeneration in cell culture and in vivo, the authors show impressive results on the positive effect of FCA in regeneration, and that this is abolished when spastin is inhibited.

Major weaknesses

-However convincing the pull-downs of the expressed proteins, the evidence would be stronger if a co-immunoprecipitation of the endogenous proteins were included.

We thank the reviewer for their succinct summary of the main results and strengths of our study. We acknowledge the reviewers' valuable suggestions and agree that performing endogenous co-immunoprecipitation (co-IP) experiments in neurons is crucial for supporting our conclusions. To address this question, cortical neurons were cultured in vitro for endogenous IP experiment. The cortical neurons were cultured using a neurobasal medium supplemented with 2% B27, and using cytarabine to inhibit the proliferation of glial cells. The proteins were then extracted and subjected to the immunoprecipitation experiments using antibodies against spastin. The results, as shown in Fig.1C in the revised manuscript, clearly demonstrate that 14-3-3 protein indeed interacts with spastin within neurons.

-To better establish the impact of spastin phosphorylation in the interaction, there is no indication that the phosphomimetic (S233D) can better bind spastin, and this result is contradicting to the conclusion of the authors that spastin-14-3-3 interaction is necessary for (or increases) spastin function.

Thank you for your valuable and constructive comments. We agree with your consideration. To reinforce the importance of phosphorylated spastin in this binding model, we conducted additional experiments by transfecting S233D into 293T cells and performed immunoprecipitation experiments (Fig.2H). The results clearly demonstrate that spastin (S233D) exhibits enhanced binding to spastin, indicating that phosphorylation at the S233 site is critical for this interaction. Additionally, we observed that spastin (S233D) maintains its binding to 14-3-3 even in the presence of staurosporine. This data further supports and strengthens our conclusions.

-To fully support the authors' suggestion that 14-3-3 and spastin work in the same pathway to promote regeneration, I believe that some key observations are missing.

1-There is no evidence showing that 14-3-3 overexpression increases the total levels of spastin, not only its turnover.

Thank you for your consideration and valuable input. We have previously demonstrated that overexpression of 14-3-3 leads to an increase in the protein levels of spastin in the absence of CHX (Fig.3E&F). Furthermore, we also observed an upregulated protein levels of spastin S233D compared to the wild-type (Fig.3G). We have now included these results in the revised manuscript.

2- There is no indication that increasing the ubiquitination of spastin decreases its levels. To suggest that proteasomal activity is affecting the levels of a protein, one would expect that proteasomal inhibition (with bortezomib or epoxomycin), would increase its levels.

Thanks for your concern. We believe that this evidence is critical. Indeed, another study by our team is working to elucidate the ubiquitination degradation pathway of spastin. In addition, a previous study has shown that phosphorylation of the S233 site of spastin can affect its protein stability (Spastin recovery in hereditary spastic paraplegia by preventing neddylation-dependent degradation, doi:10.26508/lsa.202000799.). To better support our conclusions, we have supplemented the results in Fig.3L&M. The results showed that the proteasome inhibitor MG132 could significantly increase the protein level of spastin, whereas CHX could significantly decrease the protein level of spastin, and the degradation of spastin is significantly hindered in the presence of both CHX and MG132. This experiment also further showed that ubiquitination of spastin reduced its protein level.

3- Authors show that S233D increases MT severing activity, and explain that it is related to increased binding to 14-3-3. An alternative explanation is that phosphorylation at S233 by itself could increase MT severing activity. The authors could test if purified spastin S233D alone could have more potent enzymatic activity.）

We appreciate the reviewer’s consideration. After investigating the interaction between 14-3-3 and spastin, we first aimed to determine whether the S233 phosphorylation mutation of spastin influenced its microtubule-severing activity. We found that overexpression of both S233A and S233D mutants resulted in significant microtubule severing (as indicated by a significant decrease in microtubule fluorescence intensity) (Fig.S2). Furthermore, it is noteworthy that S233 is located outside the microtubule-binding domain (MTBD, 270-328 amino acids) and the AAA region (microtubule-severing region, 342-599 amino acids) of spastin. Based on our initial observations, we believe that the phosphorylation of the S233 residue in spastin does not impact its microtubule-severing function. Additionally, under the same experimental conditions, we observed that the green fluorescence intensity of GFP-spastin S233D was significantly higher than that of GFP-spastin S233A. Based on these phenomena, we speculated that phosphorylation of the S233 residue of spastin might affect its protein stability, leading us to conduct further experiments. Furthermore, we fully acknowledge the reviewer's concern; however, due to technical limitations, we were unable to perform an in vitro assay to test the microtubule-severing activity of spastin. We have provided an explanation for this consideration in the revised version.

-Finally, I consider that there are simpler explanations for the combined effect of FC-A and spastazoline. FC-A mechanism of action can be very broad, since it will increase the binding of all 14-3-3 proteins with presumably all their substrates, hence the pathways affected can rise to the hundreds. The fact that spastazoline abolishes FC-A effect, may not be because of their direct interaction, but because spastin is a necessary component of the execution of the regeneration machinery further downstream, in line with the fact that spastizoline alone prevented outgrowth and regeneration, and in agreement with previous work showing that normal spastin activity is necessary for regeneration.

We appreciate the considerations raised by the reviewer. It is evident that spastin is not the exclusive substrate protein for 14-3-3, and it is challenging to demonstrate that 14-3-3 promotes nerve regeneration and recovery of spinal cord injury directly through spastin in vivo. However, we have identified the importance of 14-3-3 and spastin in the process of nerve regeneration. Importantly, we have conducted supplementary experiments to support the stabalization of spastin by FC-A treatment within neurons (Fig.4M), as well as the repair process of spinal cord injury in vivo (Fig.5D). The results showed that FC-A treatment in cortical neurons could enhance the stability of spastin protein levels, and we also demonstrated a consistent trend of upregulated protein levels of spastin and 14-3-3 following spinal cord injury. Moreover, the protein levels were significantly elevated in the the FC-A group of mice. These results also support that 14-3-3 enhances spastin protein stability to promote spinal cord injury repair. The manuscript was revised accordingly.

Reviewer #2 (Public Review):

Summary:

The idea of harnessing small molecules that may affect protein-protein interactions to promote axon regeneration is interesting and worthy of study. In this manuscript, Liu et al. explore a 14-3-3-spastin complex and its role in axon regeneration.

Strengths:

Some of the effects of FC-A on locomotor recovery after spinal cord contusion look interesting.

Weaknesses:

The manuscript falls short of establishing that a 14-3-3-spastin complex is important for any FC-A-dependent effects and there are several issues with data quality that make it difficult to interpret the results. Importantly, the effects of the spastin inhibitor have a major impact on neurite outgrowth suggesting that cells simply cannot grow in the presence of the inhibitor and raising serious questions about any selectivity for FC-A - dependent growth. Aspects of the histology following spinal cord injury were not convincing.

We sincerely appreciate the reviewer for evaluating our manuscript. Given the multitude of substrates that interact with 14-3-3, and considering spastin's indispensable role in neuroregeneration, it is indeed challenging to experimentally establish that FC-A's neuroregenerative effect is directly mediated through spastin in vivo. Therefore, we have provided additional crucial evidence regarding the changes in spastin protein levels following spinal cord injury, as well as the application of FC-A after spinal cord injury. Furthermore, we have made relevant adjustments to the uploaded images to enhance the resolution of the presented figures, as detailed in the subsequent response.

Reviewer #3 (Public Review):

Summary: The current manuscript c laims that 14-3-3 interacts with spastin and that the 14-3-3/spastin interaction is important to regulate axon regeneration after spinal cord injury.

Strengths:

In its present form, this reviewer identified no clear strengths for this manuscript.

Weaknesses:

In general, most of the figures lack sufficient quality to allow analyses and support the author's claims (detailed below). The legends also fail to provide enough information on the figures which makes it hard to interpret some of them. Most of the quantifications were done based on pseudo-replication. The number of independent experiments (that should be defined as n) is not shown. The overall quality of the written text is also low and typos are too many to list. The original nature of the spinal cord injury-related experiments is unclear as the role of 14-3-3 (and spastin) in axon regeneration has been extensively explored in the past.

We sincerely appreciate the careful consideration and rigorous evaluation provided by the reviewer. In the revised version, we have made effort to present high-resolution figures and provide more detailed figure legends. Furthermore, we have made relevant adjustments to the statistical methods in accordance with the reviewer's suggestions. The manuscript has also undergone a thorough review and correction process to eliminate any writing-related errors. Please refer to the following response.

To the best of our knowledge, there has been no clear reports on the efficacy of 14-3-3 in the repair of spinal cord injury. Kaplan A et al. (doi: 10.1016/j.neuron.2017.02.018) reported a reduction in die-back of the corticospinal tract following spinal cord injury using FC-A as a filler in situ in the lesion site. However, the specific effects of FC-A on spinal cord injury, such as motor function and neural reactivity, as well as the expression characteristic of 14-3-3 after spinal cord injury, have not been extensively elucidated. Additionally, prior research on spastin's role in axon regeneration primarily focused on the effects in Drosophila, and its regenerative effects in the central nervous system of adult mammals after injury have not been reported. Therefore, our study provides crucial insights into the importance of 14-3-3 and spastin in the process of spinal cord injury repair in mammals.

Reviewer #1 (Recommendations For The Authors):

There are many spelling and grammar errors, please revise. Examples:

-approach revealed14-3-3

-We have detected different many 14-3-3 peptides

-Line 1057 (D) 14-3-3 agnoist FC-A

-There is a discrepancy between panel names and figure legend in Figure 4.

-There is another discrepancy between the color coding of treatments in Figure 7. All panels show "injury" in red and FC-A in orange, but in panel E, these are swapped. This is confusing to readers.

Thank you for the thorough and rigorous review. We have re-colored the relevant chart. The manuscript has also undergone a thorough review to eliminate any writing-related errors.

Most images from confocal microscopy are blurred or low resolution. They should be sharper for the type of microscopy used.

We have adjusted and re-uploaded the images with higher resolution. Additionally, we have enlarged the relevant images.

The list of all peptides retrieved in the Mass-Spec analyses of the GST-spastin pulldown must be publicly available, according to eLife rules.

Thank you for your suggestion. We have now uploaded the mass spectrometry data.

To determine where the 14-3-3/spastin protein142 complex functions in neurons, we double stained hippocampal neurons with spastin143 and 14-3-3 antibody, and found that 14-3-3 was colocalized with spastin in the entire144 cell compartment (Figure 1C).

Colocalization by confocal fluorescence microscopy is not evidence for protein complexes.

While co-localization experiments may not directly demonstrate protein-protein interactions, they can still provide valuable insights into the cellular localization of the proteins and suggest potential interactions between them. Therefore, we adjusted the statement.

Fig1F- Co-immunoprecipitation assay results confirmed that all 14-3-3 isoforms could form direct complexes with spastin.

CoIP in cells overexpressing the proteins is not evidence that it is direct. That they can interact directly with each other can be extracted from the evidence in vitro with purified proteins.

We agree with this and we have changed our statement accordingly.

For a broad audience to have a better understanding, the authors have to explain their a.a. subtitucions of Serine233, one being mimicking phosphorylation (S233D) and the other rendering the protein not being able to be phosphorylated in that position (S233A).

We appreciate the suggestion. We have provided a more detailed explanation in revised manuscript.

The panel of neuronas in Fig2G is mislabeled, because it is twice spastin S233A, instead of S233D.

We apologize for this mistake and we have corrected it in the panel.

FCA may increase the interaction of 14-3-3 with any of its substrates, including spastin. One would appreciate evidence that FCA increases the MT-severing activity of spastin, as assumed by authors

We appreciate the reviewer’s suggestion. In this study, we overexpressed spastin to investigate its microtubule severing activity. It is important to note that overexpressing spastin significantly exceeds the normal physiological concentration of the protein. Using excessive amounts of FC-A to enhance the interaction between 14-3-3 and spastin in cells can lead to cell toxicity. Therefore, we chose to overexpress 14-3-3 instead of employing excessive FC-A.

In Fig2F, the interaction of 14-3-3 with Spas-S233D would have been very informative.

Thank you for the constructive suggestions from the reviewer. We have supplemented the corresponding co-immunoprecipitation experiments (Fig.).

The functional effect of S233A and S233D does not correlate with a function of 14-3-3 in neurite outgrowth. This is because S233A does not interact with 14-3-3, however, it is as good as WT spastin... meaning that binding of 14-3-3 with spastin is not necessary...

We appreciate the reviewer's consideration. The observed phenomenon of spastin WT and S233A promoting axon growth do not align with the physiological state within neurons. This may mask the true effects of S233A or S233D on neuronal axon growth. It is documented that the proper dosage of spastin is essential for neuronal growth and regeneration, as excessive or insufficient amounts can hinder axon growth. Excessive spastin levels can disrupt the overall cellular MTs. Therefore, spastin were moderately expressed by adjusting the transfection dosage and duration. Nevertheless, we were unable to precisely control the expression levels of spastin for both WT and S233A, also resulting in an overexpression state compared to the physiological state. As a result, the crucial role of spastin S233 in neural growth under physiological conditions may be masked. We have addressed this issue in the revised version of our manuscript.

In panels 3C and D it is not clear if it does contain 14-3-3.... it seems it does not... but clarify.

We apologize for any confusion. Since there is endogenous 14-3-3 present in the cells, we utilized spastin S233A and S233D to mimic the binding pattern with 14-3-3 according to the established interaction model. This information has been clarified in the original manuscript.

Line 217 should indicate Figure 3, not Figure 5

We have made the corresponding corrections.

In F3G, it is intriguing that the input blot shows a decrease in Ubiquitin proteins when there is expression of flag ubiquitin...

We apologize for the error in our presentation. In the control group, we actually overexpressed Flag-ubiquitin and GFP instead of Flag and GFP-spastin. Additionally, to further elucidate the impact of different phosphorylation states on spastin ubiquitination and degradation, we have conducted additional ubiquitination experiments (Fig.3N), which are now included in the revised version of our manuscript.

S233 mutations seem to affect the effective turnover of spastin, but does not seem to change the levels of the spastin protein...hence, the conclusion that 14-3-3 protects from degradation is overstated.

We thank the reviewers for the careful review and we have revised the statement accordingly.

The mode of action of R18 FCA should be introduced earlier in the text.

Thank you for the reviewer's correction. We have provided a corresponding description of the effects of FC-A and R18 on the interaction between 14-3-3 and spastin in the ubiquitination experiments section of the manuscript.

Line 296 reads: Our results revealed that levels of 14-3-3 protein remained high even at 30 DPI, indicating that 14-3-3 plays an important role in the recovery of spinal cord injury.

This is overstated since it can well be that an upregulated protein is inhibitory. We thank the reviewers for their consideration and we have made adjustments accordingly.

It is not clear if 14-3-3 prevents ubiquitination of spastin, then its levels should be higher... it is noteworthy that they did not measure its levels in nerve tissue after injury. For example, in experiments shown in Figure 5A, it would have been very useful the observation of the levels of spastin.

We appreciate the reviewer's consideration. We have now included the assessment of spastin protein levels following spinal cord injury. Additionally, we have collected the injured spinal cord lysates in mice treated with FC-A for western blot analysis. The results revealed that the expression trend of 14-3-3 protein is largely consistent with spastin after spinal cord injury. Furthermore, the treatment with FC-A was found to enhance the expression of spastin after spinal cord injury (Fig. 5C&D)."

Panel 5G reads "nerve regeneration across the lesion site", but it actually measured NF levels, according to the legend.

Thanks to the reviewers for the critical review. We have revised the chart accordingly.

361 "BMS" should be explained in the results section for a better understanding of the results by non-experts.

Thank you to the reviewers for their suggestions. We have explained this in the results section accordingly.

Reviewer #2 (Recommendations For The Authors):

1. The results of the mass spec and co-IP in Figure 1 are unclear.

a) Are all of the peptides in Fig. 1A from 14-3-3 and were there only 3 14-3-3 peptides that were identified?

The mass spectrum results did identify only three 14-3-3 peptides, and these three peptides were highly conserved across all isoforms.

b) The blot in panel B needs to show the input band for spastin and 14-3-3 from the same gel and not spliced so that the level of enrichment can be evaluated in the co-IP.

Thanks to the reviewer's comments, we have presented the whole gel (Fig.1B)

c) Further, does an IP for 14-3-3 co-precipitate spastin?

Thank you for your concern. We appreciate your feedback. Our 14-3-3 antibody is capable of Western blot experiments and recognizes all subtypes (Pan 14-3-3, Cell Signaling Technology, Cat #8312). Unfortunately, it is not suitable for immunoprecipitation (IP) experiments. Therefore, we have employed additional approaches, namely immunoprecipitation and pull-down assays, to further investigate the interaction between 14-3-3 and spastin.

1. It is difficult to say anything about 14-3-3 - spastin co-localization in hippocampal neurons (1c) since 14-3-3 labels the entire hippocampal neuron so any protein will co-localize.

We appreciate the comments. The co-localization experiments have provided evidence of the relative expression of both 14-3-3 and spastin in neurons, suggesting their potential interaction within neuronal cells. We have made the necessary revisions to accurately describe the results of the co-localization experiments in the manuscript.

To further investigate the interaction between 14-3-3 and spastin within neurons, we have conducted additional co-immunoprecipitation (Co-IP) experiments using cortical neuron lysates (Fig.1C).

1. The molecular weight of 14-3-3 is 25-28 kDa but the band in panel 1B and in subsequent figures it is below 15 kDa. Fig. 1F - the spastin band also seems to be low compared to predicted molecular weight and other W. Blot reports in the literature so some indication of how the antibody was validated would be important.

Apologies for the mistakes. We have carefully re-evaluated the western blot images (See Author response image 1). We have confirmed that the molecular weight of the 14-3-3 protein is approximately 33 kDa. In the case of spastin, its molecular weight is around 55-70 kDa. Additionally, the GFP-spastin fusion protein has an estimated molecular weight of approximately 90 kDa. We have conducted a thorough verification and made appropriate adjustments to the molecular weight labels in all western blot images.

Author response image 1.

1. Fig 1G is a co-immunoprecipitation and it is not clear what the authors mean by "direct complexes" as claimed in line 150 of the results since this does not show direct binding between 14-3-3 and spastin. None of the assays in Fig. 1 assess "direct" binding between the two proteins and the authors should be clear in their interpretation.

We agree with the reviewer's comments and have removed the word "direct" from the text.

1. Fig. 1D - there is no validation that staurosporine (protein kinase inhibitor, not protein kinase as per typo in Line 167) affects the phosphorylation levels of spastin.

Thank you for your valuable comments. In our group, we have conducted another study that has confirmed the involvement of CAMKII in mediating spastin phosphorylation. Furthermore, we have found that the addition of staurosporine significantly reduces the phosphorylation levels of spastin (unpublished results). In response to the reviewer's comment, we are pleased to provide western blot experiments demonstrating the effect of staurosporine on reducing spastin phosphorylation. The phosphorylation levels of spastin were assessed using a Pan Phospho antibody (Fig.2D).

1. Fig. 2F - it would be important to test if spastin S233D interacts more robustly with 14-3-3 and if this is insensitive to staurosporine.

Thank you for your comments. The suggestion provided by the reviewer is highly significant for supporting our conclusion that "phosphorylation of spastin is a prerequisite for its interaction with 14-3-3." Therefore, we have conducted additional immunoprecipitation experiments to further supplement our findings (Fig.2H). The experimental results demonstrate that the binding affinity between spastin S233D and 14-3-3 is stronger compared to spastin WT.

1. Line 179 "Next, we transfected Ser233 mutation of spastin (spastin S233A or spastin S233D) with flag tagged 14-3-3 and generated Pearson's correlation coefficients. Results revealed that spastin 181 S233D was markedly colocalized with 14-3-3, with minimal colocalization with spastin S233A (Figure 2A-B)." Assuming the authors are referring to supplemental Figure 2, the 14-3-3 covers the entire cell thus I think measures of co-localization are uninterpretable.

We agree with the reviewer's comment. We realize that 14-3-3θ exhibits a ubiquitous cellular distribution, which renders the measurement of its co-localization coefficients inconclusive. Therefore, we have decided to remove Supplementary Figure 2 from the manuscript.

1. Line 189 "Consistent with earlier results, spastin promoted neurite outgrowth, as evidenced by both the length and total branches of neurite." - It is unclear what earlier results the authors are referring to. The authors should clarify how they determined the "moderate" expression level.

We thank the review’s suggestions. The "earlier results" mentioned here refers to previously published articles, we now have added relevant references. Existing literature indicates that an appropriate dosage of spastin is necessary for neuronal growth and regeneration. However, both excessive and insufficient amounts of spastin are detrimental to axonal growth. Excessive spastin disrupts the overall microtubule network within cells. We controlled plasmid transfection dosage and transfection durations to achieve moderate expression. We have provided an explanation of these details in the revised version.

1. The effects of WT spastin and spastin S233A were similar in spite of the fact that S233A does not bind to 14-3-3, which is inconsistent with the author's model that spastin-14-3-3 binding promotes growth. Line 191 - the authors mention that spastin S233D was toxic but I do not see any cell death measurements. I assume the bottom right panel in Fig. 2G labelled as spastin S233A is mislabeled and should be S233D.

In response to comment 8, the transfection of both wild-type (WT) spastin and S233A mutant failed to precisely control the expression levels around the physiological concentration. Consequently, we observed an overexpression of spastin in both cases, which obscured the critical role of S233 phosphorylation in neurite outgrowth. We have addressed this issue in the revised version of the manuscript.

1. Fig. 3. Does spastin(S233D) bind constitutively to 14-3-3? Why is spastin S233A not less stable than WT spastin based on the author's model?

We propose that 14-3-3 is more likely to interact with spastin S233D in a non-constitutive manner. The instability of the S233A protein is attributed to the disruption of its ubiquitination degradation process due to the absence of 14-3-3 binding.

1. The ubiquitin blot in Fig. 3G is not convincing and not quantified.

We acknowledge the mislabeling in our figures. In the control group, Flag-Ubiquitin was also overexpressed, and we transfected GFP as a control instead of GFP-spastin. To further enhance the reliability, we conducted additional ubiquitination experiments (Fig.3N), which revealed a significant increase in spastin (S233A) ubiquitination levels compared to the WT group, consistent with previous research findings (Spastin recovery in hereditary spastic paraplegia by preventing neddylation-dependent degradation, doi:10.26508/lsa.202000799). Additionally, we observed that the addition of R18 could partially enhance spastin ubiquitination levels, as quantitatively illustrated in the figure (Fig.3O). This result further underscores the inhibitory role of 14-3-3 in the ubiquitination degradation pathway of spastin.

1. I do not understand how the glutamate injury fits with the narrative (Fig. 4C).

Excessive glutamate exposure can induce severe intracellular oxidative stress reactions, leading to the disruption of physiological processes such as mitochondrial energy production. This, in turn, results in the swelling and lysis of neuronal processes, a phenomenon known as neuronal necrosis. During this state, neurite maintenance is obstructed, and neurites exhibit swelling and breakage (Glutamate-induced neuronal death: a succession of necrosis or apoptosis depending on mitochondrial function. Neuron. 1995 Oct;15(4):961-73). We have provided a more comprehensive explanation of this phenomenon in the revised version of our manuscript.

1. Some commentary about the selectivity of spastazoline to inhibit spastin should be included - it would be helpful if the authors could explain that this is a spastin inhibitor in the manuscript. FC-A still seems to promote growth in the presence of spastazoline suggesting that the FC-A effects are not dependent on spastin (Fig. 4E). The statistical analysis section of the materials and methods indicates that multiple groups were analyzed by one-way ANOVA. This seems unusual since the controls for cellular transfection are different than for small molecules (FC-A) and for peptides such as R18. As such, there is no vehicle control for the FC-A condition and it is difficult to assess the FC-A vs Spastazoline vs FA-A + Spastoazoline. The authors should clarify (Fig. 4E-J)

Thank you for the reviewer’s suggestions. In the revised version, we have provided a more detailed explanation of the specific inhibition of spastin's severing function by spastazoline.

We observed that FC-A, in combination with spastazoline, still exhibited a certain degree of promotion in neurite growth compared to the injury group under the glutamate circumstances. Evidently, spastin is not the exclusive substrate for 14-3-3, and FC-A might delay cellular oxidative stress reactions by facilitating the interaction of 14-3-3 with other substrates, such as the FOXO transcription factors as mentioned in the introduction. Nevertheless, our results still demonstrate that the addition of spastazoline significantly diminishes the promoting effect of FC-A on neurite growth, indicating that FC-A affects neuronal growth by impacting spastin.

Furthermore, in the drug-treated groups, we overexpressed GFP to trace the morphology of neurons. Culture media were exchanged following transfection, and during media exchange, drugs were added. And an equivalent amount of DMSO or ethanol were added as controls to rule out the influence of solvents on neurons.

1. There is a good possibility that spastin is required for all axon regeneration and that there is no selectivity for the FC-A pathway and this is a major issue with the interpretation of the manuscript (Fig 4K-L).

We acknowledge this point. Clearly, spastin is not the exclusive substrate for 14-3-3, and our experimental evidence does not establish that 14-3-3 solely promotes neuronal regeneration through spastin. Nevertheless, we have identified the significance of 14-3-3 and spastin in the process of neural regeneration. Furthermore, we conducted complementary experiments to support the stability of spastin by FC-A treatment both in vitro and in vivo. We found an enhanced protein expression in cortical neurons after FC-A treatment (Fig.4M). Also, the results indicate a consistent elevation trend in the protein levels of spastin and 14-3-3 following spinal cord injury (Fig.5C&H). Moreover, in the FC-A group of mice, there was a significant increase in spastin protein levels (Fig.5D&I). These results also support that 14-3-3 promotes spinal cord injury repair by enhancing spastin protein stability.

1. Fig. 5C- it is unclear where the photomicrographs were taken relative to the lesion.

We obtained tissue sections from the lesion core and the above segments for histological analysis. Given the scarcity of neural compartment at the injury center, we select tissue slices as close as possible to lesion core to illustrate the relationship between 14-3-3 and the injured neurons. We have provided an explanation of this in the revised version of the manuscript.

1. The authors need to provide some evidence that the FC-A and spastazoline compounds are accessing the CNS following IP injection.

We thank the review’s suggestion. Although direct visualization evidence of FC-A and spastazoline entering the CNS is challenging to obtain, several indicators suggest drug penetration into spinal cord tissue. Firstly, behavioral and electrophysiological experiments in vivo demonstrate that drug injections indeed affect the neural activity of mice. Secondly, following spinal cord injury, the blood-spinal cord barrier was disrupted at the injury site, combined with the fact that both FC-A (molecular weight: 680.82 Da) and spastazoline (molecular weight: 382.51 Da) are small molecule drugs, these increases the likelihood of these small molecules entering the injured spinal cord tissue. Furthermore, our microtubule staining results indicated that FC-A and spastazoline did influence the acetylation ratio of microtubules. These findings support the drug penetration into spinal cord tissue.

1. Some quantification of Fig. 5D would be important to support the contention that the lesion site is impacted by FC-A treatment.

Thank you for the suggestion. We have included quantitative analysis for Figure 5D (Figure) as recommended.

1. The NF and 5-HT staining in Fig. 5D and in Fig. 7A and B does not clearly define fibers and is not convincing.

We appreciate the concerns. While we did not present whole nerve fibers, we therefore employed NF and 5-HT immunoreactive fluorescence intensity as an indicator to assess the regeneration of nerve fibers as previously described, but not axons per square millimeter (Baltan S, et, al. J Neurosci. 2011 Mar 16;31(11):3990-9; Iwai M, et, al. Stroke. 2010 May;41(5):1032-7; Wang Y, et, al. Elife. 2018 Sep 12;7:e39016; Altmann C, et, al. Mol Neurodegeneration. 2016 Oct 22;11(1):69).

Our results showed that in the spinal cord injury group, there was strongly decreased NF-positive stainning (with a slight increase in 5-HT). In contrast, the FC-A treatment group exhibited a significant higher abundance of NF-positive signals (or an increased 5-HT signal) in the lesion site, which also suggests the reparative effect of FC-A on nerves. We also intend to refine our immunohistochemical methods in future experiments.

Minor Comments: 1. Line 80 -84. To my knowledge the only manuscripts examining the effects of spastin in axon regeneration models includes the analysis in drosophila (i.e. ref 15 and 16) and a study in sciatic nerve that reported an index of functional recovery but did not perform any histology to assess axon regeneration phenotypes. The literature should be more accurately reflected in the introduction.

We appreciate the suggestions from the reviewer. In the revised version, we have provided further clarification on the novelty of spastin in the spinal cord injury repair process.

1. Line 73: The meaning of the following statement needs to be clarified: "spastin has two major isoforms, namely M1 and M87, coded form different initial sites."

We have provided additional elaboration for this statement in the revised version.

1. Line 216: Results indicated that GFP-spastin could be ubiquitinated, while inhibiting the 217 binding of 14-3-3/spastin promoted spastin ubiquitination (Figure 5G)." - Should be Fig 3G

Sorry about the mistake. We have made the corresponding changes in the revised version.

1. Line 255: "Briefly, we established a neural injury model as previously described(31)" - the basics of the injury model need to be described in this manuscript.

In the revised version, we have provided further elaboration on the glutamate-induced neuronal injury model.

Reviewer #3 (Recommendations For The Authors):

Figure 1: A- Both legend and text fail to provide detail on this specific panel.

We have provided a more detailed and comprehensive description of the legend and results in this section.

B- Is the contribution of non-neuronal cells for co-IPs relevant? Co-IP with isolated neuronal extracts (instead of spinal cord tissue) should be performed.

We thank the review’s suggestion. To further elucidate their interaction within neurons, cortical neurons were cultured (Cultured in Neurobasal medium supplemented with 2%B27 and cytarabine was used to inhibit glial cell growth) and cells were lysed for co-IP experiments (Fig.1C), and the results demonstrated the interaction between 14-3-3 and spastin within neurons.

C- Both spastin and 14-3-3 appear to label the entire neuron with similar intensities throughout the entire cell which is rather unusual. Conditions of immunofluorescence should be improved and z-projections should be provided to support co-localization.

Thanks for the comment. Our dual-labeling experiments indicated that 14-3-3 exhibits a characteristic pattern of whole-cell distribution. Therefore, this result cannot confirm the interaction between 14-3-3 and spastin within neurons, but it does provide evidence regarding the intracellular distribution patterns of 14-3-3 and spastin. Consequently, we supplemented neuronal endogenous co-IP experiments to further demonstrate the direct interaction between 14-3-3 and spastin within neurons, and we have modified the wording in the revised version accordingly.

D- xx and yy axis information is either lacking or incomplete.

We have made the corrections to the figures.

E- It would be useful to show the conservation between the different 14-3-3 isoforms.

We appreciate the suggestions. We have included a conservation analysis of 14-3-3 to assist readers in better understanding these results (Fig.1F).

Figure 2:

D- The experiment using a general protein kinase inhibitor does not allow concluding that the specific phosphorylation of spastin is sufficient for binding to 14-3-3. An alternative phosphorylated protein might be involved in the process.

We appreciate the reviewer's consideration. We believe this serves as a prerequisite condition to demonstrate that "14-3-3 binding to spastin requires spastin phosphorylation." In fact, another project in our group has confirmed that CAMK II can mediate spastin phosphorylation, and the addition of staurosporine significantly reduces spastin phosphorylation levels (unpublished results). Here, we provide the western blot experiment showing the decrease in spastin phosphorylation under staurosporine treatment, with phosphorylation levels detected using the Pan Phospho antibody (Fig.2D).

H and I- Pseudo-replication. Only independent experiments should be plotted and not data on multiple cells obtained in the same experiment. Please indicate the number of independent experiments.

We appreciate the reviewer's correction. We now have included the mean value of three independent experiments and we have made relevant revisions to the statistical charts.

Figure 3:

The rationale for the hypothesis that spastin S233D transfection might upregulate the expression of spastin relative to WT and spastin S233A is unclear.

We appreciate the reviewer's consideration. We have supplemented the relevant results, as depicted in the Fig.3G, which demonstrates that 14-3-3 can enhance the protein levels of spastin, and phosphorylated spastin (S233D) exhibits a significantly increased protein level compared to wild-type spastin. These findings indicate that 14-3-3 not only inhibits the degradation of spastin but also increases its protein levels.

I- pseudo-replication. Please plot and do statistical analysis of independent experiments.

Thank you for the reviewer's corrections. We have made the necessary revisions.

Figure 4: E-J: I- pseudo-replication. Please plot and do statistical analysis of independent experiments.

Thank you for the reviewer's corrections. We have made the necessary revisions.

Figure 5:

B- Please show individual data points.

Thank you for the reviewer's corrections. We have made the necessary revisions.

D- Longitudinal images of spinal cords where spastazoline was used cannot correspond to contusion as there is a very sharp discontinuity between the rostral and caudal spinal cord tissue. A full transection seems to have occurred. Alternatively, technical problems with tissue collection/preservation might have occurred.

Thank you for the reviewer's consideration. The sharp discontinuity observed in the spastazoline group is not due to modeling issues but rather a result of the drug's effects on the injury site. This is primarily because spastin plays a crucial role not only in neuronal development but also in mitosis. Since the highly active proliferation of stromal cells at the injury site, . spastazoline may inhibit the proliferation of injury site-related stormal cells, thereby impeding the wound healing process following spinal cord injury, resulting in the observed discontinuous injury gap. We have made the corresponding revision accordingly.

E- Images do not have the quality to allow analysis. 5HT staining should not be considered as a clear axonal labeling is not seen. This is also the case for neurofilament staining.

We appreciate the concerns. While we did not present whole nerve fibers, we therefore employed NF and 5-HT immunoreactive fluorescence intensity as an indicator to assess the regeneration of nerve fibers as previously described, but not axons per square millimeter (Baltan S, et, al. J Neurosci. 2011 Mar 16;31(11):3990-9; Iwai M, et, al. Stroke. 2010 May;41(5):1032-7; Wang Y, et, al. Elife. 2018 Sep 12;7:e39016; Altmann C, et, al. Mol Neurodegeneration. 2016 Oct 22;11(1):69).

Our results showed that in the spinal cord injury group, there was strongly decreased NF-positive stainning (with a slight increase in 5-HT). In contrast, our FC-A treatment group exhibited a significant higher abundance of NF-positive signals (or an increased 5-HT signal) in the lesion site, which also suggests the reparative effect of FC-A on nerves. We also intend to refine our immunohistochemical methods in future experiments.

F- Images do not allow analysis. Higher magnifications are needed.

Thank you for the reviewer's consideration. We have now included higher-magnification images (Fig.5M) to address this concern.

Figure 7:

Same issues as in Figure 5.

A- Images do not have the quality to allow analysis. 5HT staining should not be considered as a clear axonal labeling is not seen.

B- Images do not have the quality to allow analysis. Neurofilament staining should not be considered as clear axonal labeling is not seen. MBP staining does not have a pattern consistent with myelin staining

We appreciate the concerns. While we did not present whole nerve fibers, we therefore employed NF and 5-HT immunoreactive fluorescence intensity as an indicator to assess the regeneration of nerve fibers as previously described, but not axons per square millimeter (Baltan S, et, al. J Neurosci. 2011 Mar 16;31(11):3990-9; Iwai M, et, al. Stroke. 2010 May;41(5):1032-7; Wang Y, et, al. Elife. 2018 Sep 12;7:e39016; Altmann C, et, al. Mol Neurodegeneration. 2016 Oct 22;11(1):69). In this study, sagittal slices were used. MBP covers the axonal surface, indicating its co-localization with the axons. However, as we did not present intact nerve fibers, so we were unable to show the typical myelin staining of MBP.

Author Response

Reviewer 1 (Public Review):

1. With respect to the predictions, the authors propose that the subjects, depending on their linguistic background and the length of the tone in a trial, can put forward one or two predictions. The first is a short-term prediction based on the statistics of the previous stimuli and identical for both groups (i.e. short tones are expected after long tones and vice versa). The second is a long-term prediction based on their linguistic background. According to the authors, after a short tone, Basque speakers will predict the beginning of a new phrasal chunk, and Spanish speakers will predict it after a long tone.

In this way, when a short tone is omitted, Basque speakers would experience the violation of only one prediction (i.e. the short-term prediction), but Spanish speakers will experience the violation of two predictions (i.e. the short-term and long-term predictions), resulting in a higher amplitude MMN. The opposite would occur when a long tone is omitted. So, to recap, the authors propose that subjects will predict the alternation of tone durations (short-term predictions) and the beginning of new phrasal chunks (long-term predictions).

The problem with this is that subjects are also likely to predict the completion of the current phrasal chunk. In speech, phrases are seldom left incomplete. In Spanish is very unlikely to hear a function-word that is not followed by a content-word (and the opposite happens in Basque). On the contrary, after the completion of a phrasal chunk, a speaker might stop talking and a silence might follow, instead of the beginning of a new phrasal chunk.

Considering that the completion of a phrasal chunk is more likely than the beginning of a new one, the prior endowed to the participants by their linguistic background should make us expect a pattern of results actually opposite to the one reported here.

Response: We acknowledge the plausibility of the hypothesis advanced by Reviewer #1. We would like to further clarify the rationale that led us to predict that the hypothesized long-term predictions should manifest at the onset of (and not within) a “phrasal chunk”. The hypothesis does not directly concern the probability of a short event to follow a long one (or the other way around), which to our knowledge has not been systematically quantified in previous cross-linguistic studies. Rather, it concerns how the auditory system forms higher-level auditory chunks based on the rhythmic properties of the native language, which is what the previous behavioral studies on perceptual grouping have addressed (e.g., Iversen 2008; Molnar et al. 2014; Molnar et al. 2016). When presented with sequences of two tones alternating in duration, Spanish speakers typically report perceiving the auditory stream as a repetition of short-long chunks separated by a pause, while speakers of Basque usually report the opposite long-short grouping bias. These results suggest that the auditory system performs a chunking operation by grouping pairs of tones into compressed, higher-level auditory units (often perceived as a single event). The way two constituent tones are combined depends on linguistic experience. Based on this background, we hypothesized the presence of (i) a short-term system that merely encodes a repetition of alternations rule and predicts transitions from one constituent tone to the other (a → b → a → b, etc.); (ii) a long-term system that encodes a repetition of concatenated alternations rule and predicts transitions from one high-level unit to the other (ab → ab, etc.). Under this view, we expect predictions based on the long-term system to be stronger at the onset of (rather than within) high-level units and therefore omissions of the first constituent tone to elicit larger responses than omissions of the second constituent tone.

In other words, the omission of the onset tone would reflect the omission of the whole chunk. On the other hand, the omission of the internal tone would be better handled by the short-term system, involved in processing the low-level structure of our sequences.

A similar concern was also raised by Reviewer #2. We will include the view proposed by Reviewer #1 and Reviewer #2 in the updated version of the manuscript.

1. The authors report an interaction effect that modulates the amplitude of the omission response, but caveats make the interpretation of this effect somewhat uncertain. The authors report a widespread omission response, which resembles the classical mismatch response (in MEG) with strong activations in sensors over temporal regions. Instead, the interaction found is circumscribed to four sensors that do not overlap with the peaks of activation of the omission response.

Response: We appreciate that all three reviewers agreed on the robustness of the data analysis pipeline. The approach employed to identify the presence of an interaction effect was indeed conservative, using a non-parametric test on combined gradiometers data, no a priori assumptions regarding the location of the effect, and small cluster thresholds (cfg.clusteralpha = 0.05) to enhance the likelihood of detecting highly localized clusters with large effect sizes. This approach led to the identification of the cluster illustrated in Figure 2c, where the interaction effect is evident. The fact that this interaction effect arises in a relatively small cluster of sensors does not alter its statistical robustness. The only partial overlap of the cluster with the activation peaks might simply reflect the fact that distinct sources contribute to the generation of the omission-MMN, which has been demonstrated in numerous prior studies (e.g., Zhang et al., 2018; Ross & Hamm, 2020).

Furthermore, the boxplot in Figure 2E suggests that part of the interaction effect might be due to the presence of two outliers (if removed, the effect is no longer significant). Overall, it is possible that the reported interaction is driven by a main effect of omission type which the authors report, and find consistently only in the Basque group (showing a higher amplitude omission response for long tones than for short tones). Because of these points, it is difficult to interpret this interaction as a modulation of the omission response.

Response: The two participants mentioned by Reviewer #1, despite being somewhat distant from the rest of the group, are not outliers according to the standard Tukey’s rule. As shown in Author response image 1 below, no participant fell outside the upper (Q3+1.5xIQR) and lower whiskers (Q1-1.5xIQR) of the boxplot.

Author response image 1.

The presence of a main effect of omission type does not impact the interpretation of the interaction, especially considering that these effects emerge over distinct clusters of channels.

The code to generate Author response image 1 and the corresponding statistics have been added to the script “analysis_interaction_data.R” in the OSF folder (https://osf.io/6jep8/).

It should also be noted that in the source analysis, the interaction only showed a trend in the left auditory cortex, but in its current version the manuscript does not report the statistics of such a trend.

Response: Our interpretation of the results for the present study is mainly driven by the effect observed on sensor-level data, which is statistically robust. The source modeling analyses (in non-invasive electrophysiology) provide a possible model of the candidate brain sources driving the effect observed at the sensor level. The source showing the interactive effect in our study is the left auditory cortex. More details and statistics will be provided in the reviewed version of the manuscript.

Reviewer #2 (Public Review):

1. Despite the evidence provided on neural responses, the main conclusion of the study reflects a known behavioral effect on rhythmic sequence perceptual organization driven by linguistic background (Molnar et al. 2016, particularly). Also, the authors themselves provide a good review of the literature that evidences the influence of long-term priors in neural responses related to predictive activity. Thus, in my opinion, the strength of the statements the authors make on the novelty of the findings may be a bit far-fetched in some instances.

Response: We will consider the suggestion of reviewer #2 for the new version of the manuscript. Overall, we believe that the novelty of the current study lies in bridging together findings from two research fields - basic auditory neuroscience and cross-linguistic research - to provide evidence for a predictive coding model in the auditory that uses long-term priors to make perceptual inferences.

1. Albeit the paradigm is well designed, I fail to see the grounding of the hypotheses laid by the authors as framed under the predictive coding perspective. The study assumes that responses to an omission at the beginning of a perceptual rhythmic pattern will be stronger than at the end. I feel this is unjustified. If anything, omission responses should be larger when the gap occurs at the end of the pattern, as that would be where stronger expectations are placed: if in my language a short sound occurs after a long one, and I perceptually group tone sequences of alternating tone duration accordingly, when I hear a short sound I will expect a long one following; but after a long one, I don't necessarily need to expect a short one, as something else might occur.

Response: A similar point was advanced by Reviewer #1. We tried to clarify our hypothesis (see above). We will consider including this interpretation in the updated version of the manuscript.

1. In this regard, it is my opinion that what is reflected in the data may be better accounted for (or at least, additionally) by a different neural response to an omission depending on the phase of an underlying attentional rhythm (in terms of Large and Jones rhythmic attention theory, for instance) and putative underlying entrained oscillatory neural activity (in terms of Lakatos' studies, for instance). Certainly, the fact that the aligned phase may differ depending on linguistic background is very interesting and would reflect the known behavioral effect.

Response: We thank the reviewer for this comment, which is indeed very pertinent. Below are some comments highlighting our thoughts on this.

1) We will explore in more detail the possibility that the aligned phase may differ depending on linguistic background, which is indeed very interesting. However, we believe that even if a phase modulation by language experience is found, it would not negate the possibility that the group differences in the MMN are driven by different long-term predictions. Rather, since the hypothesized phase differences would be driven by long-term linguistic experience, phase entrainment may reflect a mechanism through which long-term predictions are carried. On this point, we agree with the Reviewer when says that “this view would not change the impact of the results but add depth to their interpretation”.

2) Related to the point above: Despite evoked responses and oscillations are often considered distinct electrophysiological phenomena, current evidence suggests that these phenomena are interconnected (e.g., Studenova et al., 2023). In our view, the hypotheses that the MMN reflects differences in phase alignment and long-term prediction errors are not mutually exclusive.

3) Despite the plausibility of the view proposed by reviewer #2, many studies in the auditory neuroscience literature putatively consider the MMN as an index of prediction error (e.g., Bendixen et al., 2012; Heilbron and Chait, 2018). There are good reasons to believe that also in our study the MMN reflects, at least in part, an error response.

In the updated version of the manuscript, we will include a paragraph discussing the possibility that the reported group differences in the omission MMN might be partially accounted for by differences in neural entrainment to the rhythmic sound sequences.

Reviewer #3 (Public Review):

The main weaknesses are the strength of the effects and generalisability. The sample size is also relatively small by today's standards, with N=20 in each group. Furthermore, the crucial effects are all mostly in the .01>P<.05 range, such as the crucial interaction P=.03. It would be nice to see it replicated in the future, with more participants and other languages. It would also have been nice to see behavioural data that could be correlated with neural data to better understand the real-world consequences of the effect.

Response: We appreciate the positive feedback from Reviewer #3. Concerning this weakness highlighted: we agree with Reviewer #3 that it would be nice to see this study replicated in the future with larger sample sizes and a behavioral counterpart. Overall, we hope this work will lead to more studies using cross-linguistic/cultural comparisons to assess the effect of experience on neural processing. In the context of the present study, we believe that the lack of behavioral data does not undermine the main findings of this study, given the careful selection of the participants and the well-known robustness of the perceptual grouping effect (e.g., Iversen 2008; Yoshida et al., 2010; Molnar et al. 2014; Molnar et al. 2016). As highlighted by Reviewer #2, having Spanish and Basque dominant “speakers as a sample equates that in Molnar et al. (2016), and thus overcomes the lack of direct behavioral evidence for a difference in rhythmic grouping across linguistic groups. Molnar et al. (2016)'s evidence on the behavioral effect is compelling, and the evidence on neural signatures provided by the present study aligns with it.”

References

1. Bendixen, A., SanMiguel, I., & Schröger, E. (2012). Early electrophysiological indicators for predictive processing in audition: a review. International Journal of Psychophysiology, 83(2), 120-131.

2. Heilbron, M., & Chait, M. (2018). Great expectations: is there evidence for predictive coding in auditory cortex?. Neuroscience, 389, 54-73.

3. Iversen, J. R., Patel, A. D., & Ohgushi, K. (2008). Perception of rhythmic grouping depends on auditory experience. The Journal of the Acoustical Society of America, 124(4), 2263-2271.

4. Molnar, M., Lallier, M., & Carreiras, M. (2014). The amount of language exposure determines nonlinguistic tone grouping biases in infants from a bilingual environment. Language Learning, 64(s2), 45-64.

5. Molnar, M., Carreiras, M., & Gervain, J. (2016). Language dominance shapes non-linguistic rhythmic grouping in bilinguals. Cognition, 152, 150-159.

6. Ross, J. M., & Hamm, J. P. (2020). Cortical microcircuit mechanisms of mismatch negativity and its underlying subcomponents. Frontiers in Neural Circuits, 14, 13.

7. Simon, J., Balla, V., & Winkler, I. (2019). Temporal boundary of auditory event formation: An electrophysiological marker. International Journal of Psychophysiology, 140, 53-61.

8. Studenova, A. A., Forster, C., Engemann, D. A., Hensch, T., Sander, C., Mauche, N., ... & Nikulin, V. V. (2023). Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG. bioRxiv, 2023-02.

9. Yoshida, K. A., Iversen, J. R., Patel, A. D., Mazuka, R., Nito, H., Gervain, J., & Werker, J. F. (2010). The development of perceptual grouping biases in infancy: A Japanese-English cross-linguistic study. Cognition, 115(2), 356-361.

10. Zhang, Y., Yan, F., Wang, L., Wang, Y., Wang, C., Wang, Q., & Huang, L. (2018). Cortical areas associated with mismatch negativity: A connectivity study using propofol anesthesia. Frontiers in Human Neuroscience, 12, 392.

Author Response

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

Reviewer #1:

Watanuki et al used metabolomic tracing strategies of U-13C6-labeled glucose and 13C-MFA to quantitatively identify the metabolic programs of HSCs during steady-state, cell-cycling, and OXPHOS inhibition. They found that 5-FU administration in mice increased anaerobic glycolytic flux and decreased ATP concentration in HSCs, suggesting that HSC differentiation and cell cycle progression are closely related to intracellular metabolism and can be monitored by measuring ATP concentration. Using the GO-ATeam2 system to analyze ATP levels in single hematopoietic cells, they found that PFKFB3 can accelerate glycolytic ATP production during HSC cell cycling by activating the rate-limiting enzyme PFK of glycolysis. Additionally, by using Pfkfb3 knockout or overexpressing strategies and conducting experiments with cytokine stimulation or transplantation stress, they found that PFKFB3 governs cell cycle progression and promotes the production of differentiated cells from HSCs in proliferative environments by activating glycolysis. Overall, in their study, Watanuki et al combined metabolomic tracing to quantitatively identify metabolic programs of HSCs and found that PFKFB3 confers glycolytic dependence onto HSCs to help coordinate their response to stress. Even so, several important questions need to be addressed as below:

We sincerely appreciate the constructive feedback from the reviewer. Additional experiments and textual improvements have been made to the manuscript based on your valuable suggestions. In particular, the major revisions are as follows: First, we investigated the extent to which other metabolites, not limited to the glycolytic system, affect metabolism in HSCs after 5-FU treatment. Second, the extent to which PFKFB3 contributes to the expansion of the HSPC pool in the bone marrow was adjusted to make the description more accurate based on the data. Finally, we overexpressed PFKFB3 in HSCs derived from GO-ATeam2 mice and confirmed that PRMT1 inhibition did not reduce the ATP concentration. We believe that the reviewer's valuable comments have further deepened our knowledge of the significance of glycolytic activation by PFKFB3 that we have demonstrated. Our response to the "Recommendations for Authors" is listed first, followed by our responses to all "Public Review" comments as follows:

(Recommendations For The Authors):

1. The methods used in key experiments should be described in more detail. For example, in the section on ‘Conversion of GO-ATeam2 fluorescence to ATP concentration’, the knock-in strategy for GO-ATeam2 should be described, as well as U-13C6 -glucose tracer assays.

As per your recommendation, we have described the key experimental method in more detail in the revised manuscript: the GO-ATeam2 knock-in method was reported by Yamamoto et al. 1. Briefly, they used a CAG promoter-based knock-in strategy targeting the Rosa26 locus to generate GO-ATeam2 knock-in mice. A description of the method has been added to Methods and the reference has been added to the citation.

For the U-13C6-glucose tracer analysis, the following points were added to describe the details of the analysis: First, a note was added that the number of cells used for the in vitro tracer analysis was the number of cells used for each sample. Second, we added the solution from which the cells were collected by sorting. We added that the incubation was performed under 1% O2 and 5% CO2.

1. Confusing image label of Supplemental Figure 1H should be corrected in line 253.

We have corrected the incorrect figure caption on line 217 in the revised manuscript to "Supplemental Figure 1N" as you suggested.

1. The percentage of the indicated cell population should also be shown in Figure S1B.

As you indicated, we have included the percentages for each population in Supplemental Figure 1B.

Author response image 1.

1. Please pay attention to the small size of the marks in the graph, such as in Figure S1F and so on.

As you indicated, we have corrected the very small text contained in Figure S1F. Similar corrections have been made to Figures S1B and S5A.

1. Please pay attention to the label of line in Figure S6A-D.

Thank you very much for the advice. We have added line labels to the graph in the original Figures S6A–D.

(Specific comments)

1. Based on previous reports, the authors expanded the LSK gate to include as many HSCs as possible (Supplemental Figure 1B). However, while they showed the gating strategy on Day 6 after 5-FU treatment, results from other time-points should also be displayed to ensure the strict selection of time-points.

Thank you for pointing this out. First, we did not enlarge the Sca-1 gating in this study. We apologize for any confusion caused by the incomplete description. The gating of c-Kit is based on that shown by Umemoto et al (Figure EV1A) 2, who used 250 mg/kg 5-FU, so their c-Kit reduction is more pronounced than ours.

We followed this study and compared c-Kit expression in Lin-Sca-1+CD150+CD48-EPCR+ gates to BMMNCs on day 6 after 5-FU administration (150 mg/kg). The results are shown below.

Author response image 2.

>

Since the MFI of c-Kit was downregulated, we used gating that extended the c-Kit gate to lower-expression regions on day 6 after 5-FU administration (revised Figure S1C). At other time points, LSK gating was the same as in the PBS-treated group, as noted in the Methods.

1. In Figure 1, the authors examined the metabolite changes on Day 6 after 5-FU treatment. However, it is important to consider whether there are any dynamic adjustments to metabolism during the early and late stages of 5-FU treatment in HSCs compared to PBS treatment, in order to coordinate cell homeostasis despite no significant changes in cell cycle progression at other time-points.

Thank you for pointing this out. Below are the results of the GO-ATeam2 analysis during the very early phase (day 3) and late phase (day 15) after 5-FU administration (revised Figures S7A–H).

Author response image 3.

In the very early phase, such as day 3 after 5-FU administration, cell cycle progression had not started (Figure S1C) and was not preceded by metabolic changes. Meanwhile, in the late phase, such as day 15 after 5-FU administration, the cell cycle and metabolism returned to a steady state. In summary, the timing of the metabolic changes coincided with that of cell cycle progression. This point is essential for discussing the cell cycle-dependent metabolic system of HSCs and has been newly included in the Results (page 11, lines 321-323).

1. As is well known, ATP can be produced through various pathways, including glycolysis, the TCA cycle, the PPP, NAS, lipid metabolism, amino acid metabolism and so on. Therefore, it is important to investigate whether treatment with 5-FU or oligomycin affects these other metabolic pathways in HSCs.

As the reviewer pointed out, ATP production by systems other than the glycolytic system of HSCs is also essential. In this revised manuscript, we examined the effects of the FAO inhibitor (Etomoxir, 100 µM) and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON, 2mM) alone or in combination on the ATP concentration of HSCs after PBS or 5-FU treatment. As shown below, there was no apparent decrease in ATP concentration (revised Figures S7J–M).

Author response image 4.

Fatty acid β-oxidation activity was also measured in 5-FU-treated HSCs using the fluorescent probe FAOBlue and was unchanged compared to PBS-treated HSCs (revised Figure S7N).

Author response image 5.

Notably, the addition of 100 µM etomoxir plus glucose and Pfkfb3 inhibitors resulted in a rapid decrease in ATP concentration in HSCs (revised Figures S7O–P). This indicates that etomoxir partially mimics the effect of oligomycin, suggesting that at a steady state, OXPHOS is driven by FAO, but can be compensated by the acceleration of the glycolytic system by Pfkfb3. Meanwhile, the exposure of HSCs to Pfkfb3 inhibitors in addition to 2 mM DON, which is an extremely high dose considering that the Ki value of DON for glutaminase is 6 µM, did not reduce ATP (revised Figures S7O–P). This suggests that ATP production from glutaminolysis is limited in HSCs at a steady state.

Author response image 6.

These points suggest that OXPHOS is driven by fatty acids at a steady state, but unlike the glycolytic system, FAO is not further activated by HSCs after 5-FU treatment. The results of these analyses and related descriptions are included in the revised manuscript (page 11, lines 332-344).

1. In part 2, they showed that oligomycin treatment of HSCs exhibited activation of the glycolytic system, but what about the changes in ATP concentration under oligomycin treatment? Are other metabolic systems affected by oligomycin treatment?

Thank you for your thoughtful comments. The relevant results we have obtained so far with the GO-ATeam2 system are as follows: First, OXPHOS inhibition in the absence of glucose significantly decreases the ATP concentration of HSCs (Figure 4C). Meanwhile, OXPHOS inhibition in the presence of glucose maintains the ATP concentration of HSCs (Figure 5B). Since it is difficult to imagine a completely glucose-free environment in vivo, it is thought that ATP concentration is maintained by the acceleration of the glycolytic system even under hypoxic or other conditions that inhibit OXPHOS.

Meanwhile, glucose tracer analysis shows that OXPHOS inhibition suppresses nucleic acid synthesis (NAS) except for the activation of the glycolytic system (Figures 2C–F). This is because phosphate groups derived from ATP are transferred to nucleotide mono-/di-phosphate in NAS, but OXPHOS, the main source of ATP production, is impaired, along with the enzyme conjugated with OXPHOS in the process of NAS (dihydroorotate dehydrogenase, DHODH). We have added a new paragraph in the Discussion section (page 17, lines 511-515) to provide more insight to the reader by summarizing and discussing these points.

1. In Figure 5M, it would be helpful to include a control group that was not treated with 2-DG. Additionally, if Figure 5L is used as the control, it is unclear why the level of ATP does not show significant downregulation after 2-DG treatment. Similarly, in Figure 5O, a control group with no glucose addition should be included.

Thank you for your advice. The experiments corresponding to the control groups in Figures 5M and O were in Figures 5L and N, respectively, but we have combined them into one graph (revised Figures 5L–M). The results more clearly show that PFKFB3 overexpression enhances sensitivity to 2-DG, but also enhances glycolytic activation upon oligomycin administration.

Author response image 7.

1. In this study, their findings suggest that PFKFB3 is required for glycolysis of HSCs under stress, including transplantation. In Figure 7B, the results showed that donor-derived chimerism in PB cells decreased relative to that in the WT control group during the early phase (1 month post-transplant) but recovered thereafter. Although the transplantation cell number is equal in two groups of donor cells, it is unclear why the donor-derived cell count decreased in the 2-week post-transplantation period and recovered thereafter in the Pfkgb3 KO group. Therefore, they should provide an explanation for this. Additionally, they only detected the percentage of donor-derived cells in PB but not from BM, which makes it difficult to support the argument for Increasing the HSPC pool.

As pointed out by the reviewer, it is interesting to note that the decrease in peripheral blood chimerism in the PFKFB3 knockout is limited to immediately after transplantation and then catches up with the control group (Figure 7B). We attribute this to the fact that HSPC proliferation is delayed immediately after transplantation in PFKFB3 deficiency, but after a certain time, PB cells produced by the delayed proliferating HSPCs are supplied. In support of this, the PFKFB3 knockout HSPCs did not exhibit increased cell death after transplantation (Figure 7K), while a delayed cell cycle was observed (Figures 7G–J). A description of this point has been added to the Discussion (page 19, lines 573-579).

In addition, the knockout efficiency in bone marrow cells could not be verified because the number of cells required for KO efficiency analysis was not available. Therefore, we have added a statement on this point and have toned down our overall claim regarding the extent to which PFKFB3 is involved in the expansion of the HSPC pool (page 15, lines 474-476).

1. In Figure 7E, they collected the BM reconstructed with Pfkfb3- or Rosa-KO HSPCs two months after transplantation, and then tested their resistance to 5-FU. However, the short duration of the reconstruction period makes it difficult to draw conclusions about the effects on steady-state blood cell production.

We agree that we cannot conclude from this experiment alone that PFKFB3 is completely unnecessary in steady state because, as you pointed out, the observation period of the experiment in Figure 7E is not long. We have toned down the claim by stating that PFKFB3 is only less necessary in steady-state HSCs compared to proliferative HSCs (page 15, lines 460-461).

1. PFK is allosterically activated by PFKFB, and other members of the PFKFB family could also participate in the glycolytic program. Therefore, they should investigate their function in contributing to glycolytic plasticity in HSCs during proliferation. Additionally, they should also analyze the protein expression and modification levels of other members. Although PFKFB3 is the most favorable for PFK activation, the role of other members should also be explored in HSC cell cycling to provide sufficient reasoning for choosing PFKFB3.

To further justify why we chose PFKFB3 among the PFKFB family members, we reviewed our data and the publicly available Gene Expression Commons (GEXC) 3. PFKFB3 is the most highly expressed member of the PFKFB family in HSCs (revised Figure 4F), and its expression increases with proliferation (Author response image 9). In addition to this, we have also cited the literature 4 indicating that AZ PFKFB3 26 is a Pfkfb3-specific inhibitor that we used in this paper, and added a note to this point (that it is specific) (page 11, lines 327-329). Through these revisions, we sought to strengthen the rationale for Pfkfb3 as the primary target of the analysis.

Author response image 8.

Author response image 9.

1. In this study, the authors identified PRMT1 as the upstream regulator of PFKFB3 that is involved in the glycolysis activation of HSCs. However, PRMT1 is also known to participate in various transcriptional activations. Thus, it is important to determine whether PRMT1 affects glycolysis through transcriptional regulation or through its direct regulation of PFKFB3? Additionally, the authors should investigate whether PRMT1i inhibits ATP production in normal HSCs. Moreover, could we combine Figure 6I and 6J for analysis. Finally, the authors could conduct additional rescue experiments to demonstrate that the effect of PRMT1 inhibitors on ATP production can be rescued by overexpression of PFKFB3.

Although PRMT1 inhibition reduced m-PFKFB3 levels in HSCs, 5-FU treatment also reduced or did not alter Pfkfb3 transcript levels (Figures 6B, G) and the expression of genes such as Hoxa7/9/10, Itga2b, and Nqo1, which are representative transcriptional targets of PRMT1, in proliferating HSCs after 5-FU treatment (revised Figure S9).

Author response image 10.

These results suggest that PRMT1 promotes PFKFB3 methylation, which increases independently of transcription in HSCs after 5-FU treatment.

A summary analysis of the original Figures 6I and 6J is shown below (revised Figure 6I).

Author response image 11.

Finally, we tested whether the inhibition of the glycolytic system and the decrease in ATP concentration due to PRMT1 inhibition could be rescued by the retroviral overexpression of PFKFB3. We found that PFKFB3 overexpression did not decrease the ATP concentration in HSCs due to PRMT1 inhibition (revised Figure 6J). Therefore, PFKFB3 overexpression mitigated the decrease in ATP concentration caused by PRMT1 inhibition. These data and related statements have been added to the revised manuscript (page 14, lines 427-428).

Author response image 12.

Reviewer #2:

In the manuscript Watanuki et al. want to define the metabolic profile of HSCs in stress/proliferative (myelosuppression with 5-FU), and mitochondrial inhibition and homeostatic conditions. Their conclusions are that during proliferation HSCs rely more on glycolysis (as other cell types) while HSCs in homeostatic conditions are mostly dependent on mitochondrial metabolism. Mitochondrial inhibition is used to demonstrate that blocking mitochondrial metabolism results in similar features of proliferative conditions.

The authors used state-of-the-art technologies that allow metabolic readout in a limited number of cells like rare HSCs. These applications could be of help in the field since one of the major issues in studying HSCs metabolism is the limited sensitivity of the“"standard”" assays, which make them not suitable for HSC studies.

However, the observations do not fully support the claims. There are no direct evidence/experiments tackling cell cycle state and metabolism in HSCs. Often the observations for their claims are indirect, while key points on cell cycle state-metabolism, OCR analysis should be addressed directly.

We sincerely appreciate the reviewer's constructive comments. Thank you for highlighting the importance of the highly sensitive metabolic assay developed in this study and the findings based on it. Meanwhile, the reviewer's comments have made us aware of areas where we can further improve this manuscript. In particular, in the revised manuscript, we have performed further studies to demonstrate the link between the cell cycle and metabolic state. Specifically, we further subdivided HSCs by the uptake of in vivo-administered 2-NBDG and performed cell cycle analysis. Next, HSCs after PBS or 5-FU treatment were analyzed by a Mito Stress test using the Seahorse flux analyzer, including ECAR and OCR, and a more direct relationship between the cell cycle state and the metabolic system was found. We believe that the reviewer's valuable suggestions have helped us clarify more directly the importance of the metabolic state of HSCs in response to cell cycle and stress that we wanted to show and emphasize the usefulness of the GO-ATeam2 system. Our response to "Recommendations For The Authors" is listed first, followed by our responses to all comments in "Public Review" as follows:

(Recommendations For The Authors):

In general, I believe it would be important:

1. to directly associate cell cycle state with metabolic state. For example, by sorting HSC (+/- 5FU) based on their cell cycle state (exploiting the mouse model presented in the manuscript or by defining G0/G1/G2-S-M via Pyronin/Hoechst staining which allow to sort live cells) and follow the fate of radiolabeled glucose.

Thank you for raising these crucial points. Unfortunately, it was difficult to perform the glucose tracer analysis by preparing HSCs with different cell cycle states as you suggested due to the amount of work involved. In particular, in the 5-FU group, more than 60 mice per group were originally required for an experiment, and further cell cycle-based purification would require many times that number of mice, which we felt was unrealistic under current technical standards. As an alternative, we administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than those with low 2-NBDG uptake and are comparable to HSCs after 5-FU treatment, although the overall population of HSCs exiting the G0 phase and entering the G1 phase increased after 5-FU treatment. In both PBS/5-FU-treated groups, these large differences in cell cycle glucose utilization suggest a direct link between HSC proliferation and glycolysis activation. If a more sensitive type of glucose tracer analysis becomes available in the future, it may be possible to directly address the reviewer's comments. We see this as a topic for the future. The descriptions of the above findings and perspectives have been added to the Results and Discussion section (page 7, lines 208-214, page 20, lines 607-610).

Author response image 13.

1. Use other radio labeled substrates (fatty acid, glutamate)

Thank you very much for your suggestion. While this is an essential point for future studies, we believe it is not the primary focus of the paper. We are planning another research project on tracer analysis using labeled fatty acids and glutamates, which we will report on in the near future. We have clearly stated in the Abstract and Introduction of the revised manuscript, that the focus of this study is on changes in glucose metabolism when HSCs are stressed (page 3, line 75 and 87, page 5, lines 135).

Instead, we added the following analyses of metabolic changes in fatty acids and glutamate using the GO-ATeam2 system. HSCs derived from GO-ATeam2 mice treated with PBS or 5-FU were used to measure changes in ATP concentrations after exposure to the fatty acid beta-oxidation (FAO) inhibitor etomoxir and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON). Etomoxir was used at 100 µM, a concentration that inhibits FAO without inhibiting mitochondrial electron transfer complex I, as previously reported 5. DON was used at 2 mM, a concentration that sufficiently inhibits the enzyme as the Ki for glutaminase is 6 µM. In this experiment, etomoxir alone, DON alone, or etomoxir and DON in combination did not decrease the ATP concentration of HSCs in the PBS and 5-FU groups (revised Figures S7J–M), suggesting that FAO and glutaminolysis were not essential for ATP production in HSCs in the short term. Thus, according to the analysis using the GO-Ateam2 system, HSCs exposed to acute stresses change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. Since there are reports that FAO and glutaminolysis are required for HSC maintenance in the long term 5,6, compensatory pathways may be able to maintain ATP levels in the short term. A description of these points has been added to the Discussion (page 11, lines 332-344).

Author response image 14.

1. Include OCR analyses.

In addition to the ECAR data of the Mito Stress test (original Figures 2G–H), OCR data were added to the revised manuscript (revised Figures 2H, S3D). Compared to c-Kit+ myeloid progenitors (LKS- cells), HSC showed a similar increase in ECAR, while the decrease in OCR was relatively limited. A possible explanation for this is that glycolytic and mitochondrial metabolism are coupled in c-Kit+ myeloid progenitors, whereas they are decoupled in HSCs. This is also suggested by the glucose plus oligomycin experiment in Figures 5B, C, and S6A–D (orange lines). In summary, in HSCs, glycolytic and mitochondrial ATP production are decoupled and can maintain ATP levels by glycolytic ATP production alone, whereas in progenitors including GMPs, the two ATP production systems are constantly coupled, and glycolysis alone cannot maintain ATP concentration. We have added descriptions of these points in the Results and Discussion section (page 8, lines 240-243, page 18, lines 558-561).

Author response image 15.

Next, a Mito Stress test was performed using HSCs derived from PBS- or 5-FU-treated mice in the presence or absence of oligomycin (revised Figures 1G–H, S3A–B). Without oligomycin treatment, ECAR in 5-FU-treated HSCs was higher than in PBS-treated HSCs, and OCR was unchanged. Oligomycin treatment increased ECAR in both PBS- and 5-FU-treated HSCs, whereas OCR was unchanged in PBS-treated HSCs, but significantly decreased in 5-FU-treated HSCs. Changes in ECAR in response to oligomycin differed between HSC proliferation or differentiation: ECAR increased in 5-FU-treated HSCs but not in LKS- progenitors (original Figures 2G–H). This suggests a metabolic feature of HSCs in which the coupling of OXPHOS with glycolysis seen in LKS- cells is not essential in HSCs even after cell cycle entry. The results and discussion of this experiment have been added to page 7, lines 194-201 and page 18, lines 558-561).

Author response image 16.

1. Correlate proliferation-mitochondrial inhibition-metabolic state

We agree that it is important to clarify this point. First, OXPHOS inhibition and proliferation similarly accelerate glycolytic ATP production with PFKFB3 (Figures 4G, I, and 5F–I). Meanwhile, oligomycin treatment rapidly decreases ATP in HSCs with or without 5-FU administration (Figure 4C). These results suggest that OXPHOS is a major source of ATP production both at a steady state and during proliferation, even though the analysis medium is pre-saturated with hypoxia similar to that in vivo. This has been added to the Discussion section (page 17, lines 520-523).

1. Tune down the claim on HSCs in homeostatic conditions since from the data it seems that HSCs rely more on anaerobic glycolysis.

Thanks for the advice. The original Figures S2C, D, F, and G show that HSC is dependent on the anaerobic glycolytic system even at a steady state, so we have toned down our claims (page 7, lines 192-194).

1. For proliferative HSCs mitochondrial are key. When you block mitochondria with oligomycin there's the biggest drop in ATP.

In the revised manuscript, we have tried to highlight the key findings that you have pointed out. First, we mentioned in the Discussion (page 17, lines 523-525) that previous studies suggested the importance of mitochondria in proliferating HSCs. Meanwhile, the GO-ATeam2 and glucose tracer analyses in this study newly revealed that the glycolytic system activated by PFKFB3 is activated during the proliferative phase, as shown in Figure 4C. We also confirmed that mitochondrial ATP production is vital in proliferating HSCs, and we hope to clarify the balance between ATP-producing pathways and nutrient sources in future studies.

1. To better clarify this point authors, authors should do experiments in hypoxic conditions and compare it to oligomycin treatment and showing that mito-inhibition acts differently on HSCs (considering that all these drugs are toxic for mitochondria and induce rapidly stress responses ex: mitophagy).

We apologize for any confusion caused by not clearly describing the experimental conditions. As pointed out by the reviewer, we also recognize the importance of experiments in a hypoxic environment. All GO-ATeam2 analyses were performed in a medium saturated sufficiently under hypoxic conditions and analyzed within minutes, so we believe that the medium did not become oxygenated (page S5-S6, lines 160-163 in the Methods). Despite being conducted under such hypoxic conditions, the substantial decrease in ATP after oligomycin treatment is intriguing (original Figures 4C, 5B, 5C). The p50 value of mitochondria (the partial pressure of oxygen at which respiration is half maximal) is 0.1 kPa, which is less than 0.1% of the oxygen concentration at atmospheric pressure 7. Thus, biochemically, it is consistent that OXPHOS can maintain sufficient activity even in a hypoxic environment like the bone marrow. We are currently embarking on a study to determine ATP concentration in physiological hypoxic conditions using in vivo imaging within the bone marrow, which we hope to report in a separate project. We have discussed these points, technical limitations, and perspectives in the Discussion section (page 20, lines 610-612).

• In Figure 1 C, D, E and F, the comparison should be done as unpaired t test and the control group should not be 1 as the cells comes from different individuals.

Thank you very much for pointing this out. We have reanalyzed and revised the figures (revised Figures 1C–F)

Author response image 17.

• In Figure S2A, the post-sorting bar of 6PG, R5P and S7P are missing.

Metabolites below the detection threshold (post-sorting samples of 6PG, R5P, and S7P) are now indicated as N.D. (not detected) (revised Figure S2A).

Author response image 18.

• In the 2NBDG experiments, authors should add the appropriate controls, since it has been shown that 2NBDG cellular uptake do not correctly reflect glucose uptake (Sinclair LV, Immunometabolism 2020) (a cell type dependent variations) thus inhibitors of glucose transporters should be added as controls (cytochalasin B; 4,6-O-ethylidene-a-D-glucose) it would be quite challenging to test it in vivo but it would be sufficient to show that in vitro in the different HSPCs analyzed.

We appreciate the essential technical point raised by the reviewer. In the revised manuscript, we performed a 2-NBDG assay with cytochalasin B and phloretin as negative controls. After PBS treatment, 2-NBDG uptake was higher in 5-FU-treated HSCs compared to untreated HSCs. This increase was inhibited by both cytochalasin B and phloretin. In PBS-treated HSCs, cytochalasin B did not downregulate 2-NBDG uptake, whereas phloretin did. Although cytochalasin B inhibits glucose transporters (GLUTs), it is also an inhibitor of actin polymerization. Therefore, its inhibitory effect on GLUTs may be weaker than that of phloretin. We have revised the figure (revised Figure S1L) and added the corresponding description (page 7, lines 207-208).

Author response image 19.

• S5C: authors should show the cell number for each population. If there's a decreased in % in Lin- that will be reflected in all HSPCs. Comparing the proportion of the cells doesn't show the real impact on HSPCs.

Thank you for your insightful point. In the revision, we compared the numbers, not percentages, of HSPCs and found no difference in the number of cells in the major HSPC fractions in Lin-. The figure has been revised (revised Figure S6C) and the corresponding description has been added (page 10, lines 296-299).

Author response image 20.

Minor:

1. In S1 F-G is not indicated in which day post 5FU injection is done the analysis. I assume on day 6 but it should be indicated in the figure legend and/or text.

Thank you for pointing this out. As you assumed, the analysis was performed on day 6. The description has been added to the legend of the revised Figure S1G.

1. S1K is not described in the text. What are proliferative and quiescence-maintaining conditions? The analyses are done by flow using LKS SLAM markers after culture? How long was the culture?

Thank you for your comments. First, the figure citation on line 250 was incorrect and has been corrected to Figure S1N. Regarding the proliferative and quiescence-maintaining conditions, we have previously reported on these 8. In brief, these are culture conditions that maintain HSC activity at a high level while allowing for the proliferation or maintenance of HSCs in quiescence, achieved by culturing under fatty acid-rich, hypoxic conditions with either high or low cytokine concentrations. Analysis was performed after one week of culture, with the HSC number determined by flow cytometry based on the LSK-SLAM marker. While these are mentioned in the Methods section, we have added a description in the main text to highlight these points for the reader (page 7, lines 214-217).

1. In Figure 5G, why does the blue line (PFKFB3 inhibitor) go up in the end of the real-time monitoring? Does it mean that other compensatory pathway is turned on?

As you have pointed out, we cannot rule out the possibility that other unknown compensatory ATP production pathways were activated. We have added a note in the Discussion section to address this (page 18, lines 555-556).

1. In Figure S6H&J, the reduction is marginal. Does it mean that PKM2 is not important for ATP production in HSCs?

The activity of the inhibitor is essential in the GO-ATeam2 analysis. The commercially available PKM2 inhibitors have a higher IC50 value (IC50 = 2.95 μM in this case). Nevertheless, the effect of reducing the ATP concentration was observed in progenitor cells, but not in HSCs. The report by Wang et al. 9 on the analysis using a PKM2-deficient model suggests a stronger effect on progenitor cells than on HSCs. Our results are similar to those of the previous report.

(Specific comments)

Specifically, there are several major points that rise concerns about the claims:

1. The gating strategy to select HSCs with enlarged Sca1 gating is not convincing. I understand the rationale to have a sufficient number of cells to analyze, however this gating strategy should be applied also in the control group. From the FACS plot seems that there are more HSCs upon 5FU treatment (Figure S1b). How that is possible? Is it because of the 20% more of cycling cells at day 6? To prove that this gating strategy still represents a pure HSC population, authors should compare the blood reconstitution capability of this population with a "standard" gated population. If the starting population is highly heterogeneous then the metabolic readout could simply reflect cell heterogeneity.

Thank you for pointing this out. First, we did not enlarge the Sca-1 gating in this study. We apologize for any confusion caused by the incomplete description. The gating of c-Kit is based on that shown by Umemoto et al (Figure EV1A) 2, who used 250 mg/kg 5-FU, so their c-Kit reduction is more pronounced than ours.

We followed this study and compared c-Kit expression in the Lin-Sca-1+CD150+CD48-EPCR+ gates to BMMNCs on day 6 after 5-FU administration (150 mg/kg). The results are shown below.

Author response image 21.

Since the MFI of c-Kit was downregulated, we used gating that extended the c-Kit gate to lower expression regions on day 6 after 5-FU administration (revised Figure S1C).

At other time points, LSK gating was the same as in the PBS-treated group, as noted in the Methods.

The reason why the number of HSCs appears to be higher in the 5-FU group is because most of the differentiated blood cells were lost due to 5-FU administration and the same number of cells as in the PBS group were analyzed by FACS, resulting in a relatively higher number of HSCs. The legend of Figure S1 shows that the number of HSCs in both the PBS and 5-FU groups appeared to increase because the same number of BMMNCs was obtained at the time of analysis (page S22, lines 596-598).

Regarding cellular heterogeneity, from a metabolic point of view, the heterogeneity in HSCs is rather reduced by 5-FU administration. As shown in Figure S3A–C, this is simulated under stress conditions, such as after 5-FU administration or during OXPHOS inhibition, where the flux variability in each enzymatic reaction is significantly reduced. GO-ATeam2 analysis after 5-FU treatment showed no increase in cell population variability. After 2-DG treatment, ATP concentrations in HSCs were widely distributed from 0 mM to 0.8 mM in the PBS group, while more than 80% of those in the 5-FU group were less than 0.4 mM (Figures 4B, D). HSCs may have a certain metabolic diversity at a steady state, but under stress conditions, they may switch to a more specialized metabolism with less cellular heterogeneity in order to adapt.

1. S2 does not show major differences before and after sorting. However, a key metabolite like Lactate is decreased, which is also one of the most present. Wouldn't that mean that HSCs once they move out from the hypoxic niche, they decrease lactate production? Do they decrease anaerobic glycolysis? How can quiescent HSC mostly rely on OXPHOS being located in hypoxic niche?

2. Since HSCs in the niche are located in hypoxic regions of the bone marrow, would that not mimic OxPhos inhibition (oligomycin)? Would that not mean that HSCs in the niche are more glycolytic (anaerobic glycolysis)?

3. In Figure 5B, the orange line (Glucose+OXPHOS inhibition) remains stable, which means HSCs prefer to use glycolysis when OXPHOS is inhibited. Which metabolic pathway would HSCs use under hypoxic conditions? As HSCs resides in hypoxic niche, does it mean that these steady-state HSCs prefer to use glycolysis for ATP production? As mentioned before, mitochondrial inhibition can be comparable at the in vivo condition of the niche, where low pO2 will "inhibit" mitochondria metabolism.

Thank you for the first half of comment 2 on the technical features of our approach. First, as you have pointed out, there is minimal variation and stable detection of many metabolites before and after sorting (Figure S2A), suggesting that isolation from the hypoxic niche and sorting stress do not significantly alter metabolite detection performance. This is consistent with a previous report by Jun et al. 10. Meanwhile, lactate levels decreased by sorting. Therefore, if the activity of anaerobic glycolysis was suppressed in stressed HSCs, it may be difficult to detect these metabolic changes with our tracer analysis. However, in this study, several glycolytic metabolites, including an increase in lactate, were detected in HSCs from 5-FU-treated mice compared with HSCs from PBS-treated mice that were similarly sorted and prepared, suggesting an increase in glycolytic activity. In other words, we may have been fortunate to detect the stress-induced activation of the glycolytic system beyond the characteristic of our analysis system that lactate levels tend to appear lower than they are. Given that damage to the bone marrow hematopoiesis tends to alleviate the low-oxygen status of the niche 11, we postulate that this upregulated aerobic glycolysis arises intrinsically in HSCs rather than from external conditions.

The second half of comment 2, and comments 7 and 10, are essential and overlapping comments and will be answered together. Although genetic analyses have shown that HSCs produce ATP by anaerobic glycolysis in low-oxygen environments 9,12, our GO-ATeam2 analysis in this study confirmed that HSCs also generate ATP via mitochondria. This is also supported by Ansó's prior findings where the knockout of the Rieske iron–sulfur protein (RISP), a constituent of the mitochondrial electron transport chain, impairs adult HSC quiescence and bone marrow repopulation 13. Bone marrow is a physiologically hypoxic environment (9.9–32.0 mmHg 11). However, the p50 value of mitochondria (the partial pressure of oxygen at which respiration is half maximal) is below 0.1% oxygen concentration at atmospheric pressure (less than 1 mmHg) 7. This suggests that OXPHOS can retain sufficient activity even under physiologically hypoxic conditions. We are currently initiating efforts to discern ATP concentrations in vivo within the bone marrow under physiological hypoxia. This will be reported in a separate project in the future. Admittedly, when we began this research, we did not anticipate the significant mitochondrial reliance of HSCs. As we previously reported, the metabolic uncoupling of glycolysis and mitochondria 12 may enable HSCs to activate only glycolysis, and not mitochondria, under stress conditions such as post-5-FU administration, suggesting a unique metabolic trait of HSCs. We have included these technical limitations and perspectives in the Discussion section (page 17, lines 520-523).

1. The authors performed challenging experiments to track radiolabeled glucose, which are quite remarkable. However, the data do not fully support the conclusions. Mitochondrial metabolism in HSCs can be supported by fatty acid and glutamate, thus authors should track the fate of other energy sources to fully discriminate the glycolysis vs mito-metabolism dependency. From the data on S2 and Fig1 1C-F, the authors can conclude that upon 5FU treatment HSCs increase glycolytic rate.

2. FIG.2B-C: Increase of Glycolysis upon oligomycin treatment is common in many different cell types. As explained before, other radiolabeled substrates should be used to understand the real effect on mitochondria metabolism.

Thank you for your suggestion. While this is essential for future studies, we believe it is not the primary focus of the paper. We are planning another research project on tracer analysis using labeled fatty acids and glutamates, which we will report on in the near future. We have clearly stated in the Abstract and Introduction of the revised manuscript that the focus of this study is on changes in glucose metabolism when HSCs are stressed (page 3, line 75 and 87, page 5, lines 135).

Instead, we have added the following analyses of metabolic changes in fatty acids and glutamate using the GO-ATeam2 system: HSCs derived from GO-ATeam2 mice treated with PBS or 5-FU were used to measure changes in ATP concentrations after exposure to the fatty acid beta-oxidation (FAO) inhibitor etomoxir and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON). Etomoxir was used at 100 µM, a concentration that inhibits FAO without inhibiting mitochondrial electron transfer complex I, as previously reported 5. DON was used at 2 mM, a concentration that sufficiently inhibits the enzyme as the Ki for glutaminase is 6 µM. In this experiment, etomoxir alone, DON alone, or etomoxir and DON in combination did not decrease the ATP concentration of HSCs in the PBS and 5-FU groups (revised Figures S7J–M), suggesting that FAO and glutaminolysis were not essential for ATP production in HSCs in the short term. Thus, according to the analysis using the GO-Ateam2 system, HSCs exposed to acute stresses change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. Since there are reports that FAO and glutaminolysis are required for HSC maintenance in the long term 5,6, compensatory pathways may be able to maintain ATP levels in the short term. A description of these points has been added to the Discussion (page 17, lines 525-527).

Author response image 22.

Fatty acid β-oxidation activity was also measured in 5-FU-treated HSCs using the fluorescent probe FAOBlue and was unchanged compared to PBS-treated HSCs (revised Figure S7N).

Author response image 23.

Notably, the addition of 100 µM etomoxir plus glucose and Pfkfb3 inhibitors resulted in a rapid decrease in ATP concentration in HSCs (revised Figures S7O–P). This indicates that etomoxir partially mimics the effect of oligomycin, suggesting that at a steady state, OXPHOS is driven by FAO, but can be compensated by the acceleration of the glycolytic system by Pfkfb3. Meanwhile, the exposure of HSCs to Pfkfb3 inhibitors in addition to 2 mM DON did not reduce ATP (revised Figures S7O–P). This suggests that ATP production from glutaminolysis is limited in HSCs at a steady state.

Author response image 24.

These points suggest that OXPHOS is driven by fatty acids at a steady state, but unlike the glycolytic system, FAO is not further activated by HSCs after 5-FU treatment. The results of these analyses and related descriptions are included in the revised manuscript (page 11, lines 332-344).

1. In Figure S1, 5-FU leads to the induction of cycling HSCs and in figure 1, 5-FU results in higher activation of glycolysis. Would it be possible to correlate these two phenotypes together? For example, by sorting NBDG+ cells and checking the cell cycle status of these cells?

We appreciate the reviewer’s insightful comments. We administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than HSCs with low 2-NBDG uptake and were comparable to HSCs after 5-FU treatment, although the overall population of HSCs that exited the G0 phase and entered the G1 phase increased after 5-FU treatment. In both PBS/5-FU-treated groups, these profound differences in cell cycle glucose utilization suggest a direct link between HSC proliferation and glycolysis activation. Descriptions of the above findings and perspectives have been added to the Results and Discussion section (page 7, lines 208-214, page 20, lines 607-610).

Author response image 25.

1. Why are only ECAR measurements (and not OCR measurements) shown? In Fig.2G, why are HSCs compared with cKit+ myeloid progenitors, and not with MPP1? The ECAR increased observed in HSC upon oligomycin treatment is shared with many other types of cells. However, cKit+ cells have a weird behavior. Upon oligo treatment cKit+ cells decrease ECAR, which is quite unusual. The data of both HSCs and cKit+ cells could be clarified by adding OCR curves. Moreover, it is recommended to run glycolysis stress test profile to assess the dependency to glycolysis (Glucose, Oligomycin, 2DG).

In addition to the ECAR data of the Mito Stress test (original Figures 2G–H), OCR data were added in the revised manuscript (revised Figures 2H, S3D). Compared to c-Kit+ myeloid progenitors (LKS- cells), HSC exhibited a similar increase in ECAR, while the decrease in OCR was relatively limited. This may be because glycolytic and mitochondrial metabolism are coupled in c-Kit+ myeloid progenitors, whereas they are decoupled in HSCs. This is also suggested by the glucose plus oligomycin experiment in Figures 5B, C, and S6A–D (orange lines). In summary, in HSCs, glycolytic and mitochondrial ATP production are decoupled and can maintain ATP levels by glycolytic ATP production alone, whereas in progenitors including GMPs, the two ATP production systems are constantly coupled, and glycolysis alone cannot maintain the ATP concentration. While we could not conduct a glycolysis stress test, we believe that Pfkfb3-dependent glycolytic activation, which is evident in the oligomycin+glucose+Pfkfb3i experiment, is only apparent in HSCs when subjected to glucose+oligomycin treatment (original Figures 5F–I). We have added descriptions of these points in the Results and Discussion section (page 8, lines 240-243, page 18, lines 558-561).

Author response image 26.

FIG.3 A-C. As mentioned previously, the flux analyses should be integrated with data using other energy sources. If cycling HSCs are less dependent to OXPHOS, what happen if you inhibit OXHPHOS in 5-FU condition? Since the authors are linking OXPHOS inhibition and upregulation of Glycolysis to increase proliferation, do HSCs proliferate more when treated with oligomycin?

First, please see our response to comments 3 and 5 regarding the first part of this comment about the flux analysis of other energy sources. According to the analysis using the GO-Ateam2 system, stressed HSCs change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. The change in ATP concentration after OXPHOS inhibition for 5-FU-treated HSCs is shown in Figures 4C and E, suggesting that the activity of OXPHOS itself does not increase. HSCs after oligomycin treatment and HSCs after 5-FU treatment are similar in that they activate glycolytic ATP production. However, inhibition of OXPHOS did not induce the proliferation of HSCs (original Figure S1K). This suggests that proliferation activates glycolysis and not that activation of the glycolytic system induces proliferation. This similarity and dissimilarity of glycolytic activation upon proliferation and OXPHOS inhibition is discussed in the Discussion section (page 16-17, lines 505-515).

1. FIG.4 shows that in vivo administration of radiolabeled glucose especially marks metabolites of TCA cycle and Glycolysis. The authors interpret enhanced anaerobic glycolysis, but I am not sure this is correct; if more glycolysis products go in the TCA cycle, it might mean that HSC start engaging mitochondrial metabolism. What do the authors think about that?

Thank you for pointing this out. We believe that the data are due to two differences in the experimental features between in vivo (Figure S5) and in vitro (Figures 1 and S2) tracer analysis. The first difference is that in in vivo tracer analysis, unlike in vitro, all cells can metabolize U-13C6-glucose. Another difference is that after glucose labeling in vivo, it takes approximately 120–180 minutes to purify HSCs to extract metabolites, and processing on ice may result in a gradual progression of metabolic reactions within HSCs. As a result, in vivo tracer analysis may detect an increased influx of labeled carbon derived from U-13C6-glucose into the TCA cycle over an extended period. However, it is difficult to interpret whether this influx of labeled carbon is derived from the direct influx of glycolysis or the re-uptake by HSCs of metabolites that have been metabolized to other metabolites in other cells. Meanwhile, as shown in Figure 4C using the GO-ATeam2 system, ATP production from mitochondria is not upregulated by 5-FU treatment. This suggests that even if the direct influx from glycolysis into the TCA cycle is increased, the rate of ATP production does not exceed that of glycolysis. Despite these technical caveats in interpretation, the results of in vivo and in vitro tracer analyses are considered essential. In particular, we consider the increased labeling of metabolites involved in glycolysis and nucleotide synthesis to be crucial. We have added a discussion of these points, including experimental limitations (page 17-18, lines 530-545).

1. FIG.4: the experimental design is not clear. Are BMNNCs stained and then put in culture? Is it 6-day culture or BMNNCs are purified at day 6 post 5FU? FIG-4B-C The difference between PBS vs 5FU conditions are the most significant; however, the effect of oligomycin in both conditions is the most dramatic one. From this readout, it seems that HSCs are more dependent on mitochondria for energy production both upon 5FU treatment and in PBS conditions.

We apologize for the incomplete description of the experimental details. The experiment involved dispensing freshly stained BMMNC with surface antigens into the medium and immediately subjecting them to flow cytometry analysis. For post-5-FU treatment HSCs, mice were administered with 5-FU (day 1), and freshly obtained BMMNCs were analyzed on day 6. The analysis of HSCs and progenitors was performed by gating each fraction within the BMMNC (original Figure S5A). We have added these details to ensure that readers can grasp these aspects more clearly (page S5, lines 155-158).

As pointed out by the reviewer, we understand that HSCs produce more ATP through OXPHOS. However, ATP production by glycolysis, although limited, is observed under steady-state conditions (post-PBS treatment HSC), and its reliance increases during the proliferation phase (post-5-FU treatment HSC) (original Figures 4B, D). Until now, discussions on energy production in HSCs have focused on either glycolysis or mitochondrial functions. However, with the GO-ATeam2 system, it has become possible for the first time to compare their contributions to ATP production and evaluate compensatory pathways. As a result, it became evident that while OXPHOS is the main source of ATP production, the reliance on glycolysis plastically increases in response to stress. This has led to a better understanding of HSC metabolism. These points are included in the Discussion as well (page 16, lines 479-488).

1. FIG.6H should be extended with cell cycle analyses. There are no differences between 5FU and ctrl groups. If 5FU induces HSCs cycling and increases glycolysis I would expect higher 2-NBDG uptake in the 5FU group. How do the authors explain this?

Thank you for your comments. In the original Figure 6H, we found that 2-NBDG uptake correlated with mPFKFB3 levels in both the 5-FU and PBS groups. mPfkfb3 levels remained low in the few HSCs with low 2-NBDG uptake in the 5-FU group.

In the revised manuscript, to directly relate glucose utilization to the cell cycle, we administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than those with low 2-NBDG uptake and are comparable to HSCs after 5-FU treatment, although the overall population of HSCs that exited the G0 phase and entered the G1 phase increased after 5-FU treatment. The large differences in glucose utilization per cell cycle observed in both PBS/5-FU-treated groups suggest a direct link between HSC proliferation and glycolysis activation. Descriptions of the above findings have been added to the Results and Discussion ((page 7, lines 208-214, page 20, lines 607-610).

Author response image 27.

1. In S7 the experimental design is not clear. What are quiescent vs proliferative conditions? What does it mean "cell number of HSC-derived colony"? Is it a CFU assay? Then you should show colony numbers. When HSCs proliferate, they need more energy thus inhibition of metabolism will impact proliferation. What happens if you inhibit mitochondrial metabolism with oligomycin?

Regarding the proliferative and quiescence-maintaining conditions, we have previously reported on these 8. In brief, these are culture conditions that maintain HSC activity at a high level while allowing for the proliferation or maintenance of HSCs in quiescence, achieved by culturing under fatty acid-rich, hypoxic conditions with either high or low cytokine concentrations. Analysis was performed after one week of culture, with the HSC number determined by flow cytometry based on the LSK-SLAM marker. While these are mentioned in the Methods section, we have added a description in the main text to highlight these points for the reader (page 7, lines 214-217).

In vitro experiments with the oligomycin treatment of HSCs showed that OXPHOS inhibition activates the glycolytic system, but does not induce HSC proliferation (original Figure S1K). This suggests that proliferation activates glycolysis and not that activation of the glycolytic system induces proliferation. This similarity and dissimilarity of glycolytic activation upon proliferation and OXPHOS inhibition is discussed in the Discussion (page 16-17, lines 505-515).

1. In FIG 7 since homing of HSCs is influenced by the cell cycle state, should be important to show if in the genetic model for PFKFB3 in HSCs there's a difference in homing efficiency.

In response to the reviewer's comments, we knocked out PFKFB3 in HSPCs derived from Ubc-GFP mice, transplanted 200,000 HSPCs into recipients (C57BL/6 mice) post-8.5Gy irradiation, and harvested the bone marrow of recipients after 16 h to compare homing efficiency (revised Figure S10H). Even with the knockout of PFKFB3, no significant difference in homing efficiency was detected compared to the control group (Rosa knockout group). These results suggest that the short-term reduction in chimerism due to PFKFB3 knockout is not due to decreased homing efficiency or cell death by apoptosis (Figure 7K) but a transient delay in cell cycle progression. We have added descriptions regarding these findings in the Results and Discussion sections (page 15, lines 470-471, page 19, lines 576-578).

Author response image 28.

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Author Response

We would like to thank the Editors and Reviewers for their comprehensive review of the manuscript. We appreciate your feedback, and we will carefully consider all your comments in the revision of the manuscript. Below are our provisional responses to your comments.

eLife assessment

This manuscript reveals important insights into the role of ipsilateral descending pathways in locomotion, especially following unilateral spinal cord injury. The study provides solid evidence that this method improves the injured side's ability to support weight, and as such the findings may lead to new treatments for stroke, spinal cord injuries, or unilateral cerebral injuries. However, the methods and results need to be better detailed, and some of the statistical analysis enhanced.

Thank you for your assessment. We will incorporate various textual enhancements in the final version of the manuscript to address the weaknesses you have pointed out. The specific improvements are outlined below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript provides potentially important new information about ipsilateral cortical impact on locomotion. A number of issues need to be addressed.

Strengths:

The primary appeal and contribution of this manuscript are that it provides a range of different measures of ipsilateral cortical impact on locomotion in the setting of impaired contralateral control. While the pathways and mechanisms underlying these various measures are not fully defined and their functional impacts remain uncertain, they comprise a rich body of results that can inform and guide future efforts to understand cortical control of locomotion and to develop more effective rehabilitation protocols.

Weaknesses:

1. The authors state that they used a cortical stimulation location that produced the largest ankle flexion response (lines 102-104). Did other stimulation locations always produce similar, but smaller responses (aside from the two rats that showed ipsilateral neuromodulation)? Was there any site-specific difference in response to stimulation location?

We derived motor maps in each rat, akin to the representation depicted in Fig 6. In each rat, alternative cortical sites did, indeed, produce distal or proximal contralateral leg flexion responses. Distal responses were more likely to be evoked in the rostral portion of the array, similarly to proximal responses early after injury. This distribution in responses across different cortical sites is reported in this study (Fig. 6) and is consistent with our prior work. The Results section will be revised to provide additional clarification and context for the data presented in Figure 6.

1. Figure 2: There does not appear to be a strong relationship between the percentage of spared tissue and the ladder score. For example, the animal with the mild injury (based on its ladder score) in the lower left corner of Figure 2A has less than 50% spared tissue, which is less spared tissue than in any animal other than the two severe injuries with the most tissue loss. Is it possible that the ladder test does not capture the deficits produced by this spinal cord injury? Have the authors looked for a region of the spinal cord that correlates better with the deficits that the ladder test produces? The extent of damage to the region at the base of the dorsal column containing the corticospinal tract would be an appropriate target area to quantify and compare with functional measures.

In Fig. S6 of our 2021 publication "Bonizzato and Martinez, Science Translational Medicine", we investigated the predictive value of tissue sparing in specific sub-regions of the spinal cord for ladder performance. Specifically, we examined the correlation between the accuracy of left leg ladder performance in the acute state and the preservation of the corticospinal tract (CST). Our results indicated that dorsal CST sparing serves as a mild predictor for ladder deficits, confirming the results obtain in this study.

1. Lines 219-221: The authors state that "phase-coherent stimulation reinstated the function of this muscle, leading to increased burst duration (90{plus minus}18% of the deficit, p=0.004, t-test, Fig. 4B) and total activation (56{plus minus}13% of the deficit, p=0.014, t-test, Fig. 3B). This way of expressing the data is unclear. For example, the previous sentence states that after SCI, burst duration decreased by 72%. Does this mean that the burst duration after stimulation was 90% higher than the -72% level seen with SCI alone, i.e., 90% + -72% = +18%? Or does it mean that the stimulation recovered 90% of the portion of the burst duration that had been lost after SCI, i.e., -72% * (100%-90%)= -7%? The data in Figure 4 suggests the latter. It would be clearer to express both these SCI alone and SCI plus stimulation results in the text as a percent of the pre-SCI results, as done in Figure 4.

Your assessment is correct; we intended to report that the stimulation recovered 90% of the portion of the burst duration that had been lost after SCI. This point will be addressed in the revision of the manuscript.

1. Lines 227-229: The authors claim that the phase-dependent stimulation effects in SCI rats are immediate, but they don't say how long it takes for these effects to be expressed. Are these effects evident in the response to the first stimulus train, or does it take seconds or minutes for the effects to be expressed? After the initial expression of these effects, are there any gradual changes in the responses over time, e.g., habituation or potentiation?

The effects are immediately expressed at the very first occurrence of stimulation. We never tested a rat completely naïve to stimuli, as each treadmill session involves prior cortical mapping to identify a suitable active site for involvement in locomotor experiments. Yet, as demonstrated in Supplementary Video 1 accompanying our 2021 publication on contralateral effects of cortical stimulation, "Bonizzato and Martinez, Science Translational Medicine," the impact of phase-dependent cortical stimulation on movement modulation is instantaneous and ceases promptly upon discontinuation of the stimulation. We did not quantify potential gradual changes in responsiveness over time, but we cannot exclude that for long stimulation sessions (e.g., 30 min or more), stimulus amplitude may need to be slightly increased over time to compensate habituation.

1. Awake motor maps (lines 250-277): The analysis of the motor maps appears to be based on measurements of the percentage of channels in which a response can be detected. This analytic approach seems incomplete in that it only assesses the spatial aspect of the cortical drive to the musculature. One channel could have a just-above-threshold response, while another could have a large response; in either case, the two channels would be treated as the same positive result. An additional analysis that takes response intensity into account would add further insight into the data, and might even correlate with the measures of functional recovery. Also, a single stimulation intensity was used; the results may have been different at different stimulus intensities.

We confirm that maps of cortical stimulation responsiveness may vary at different stimulus amplitudes. To establish an objective metric of excitability, we identified 100µA as a reliable stimulation amplitude across rats and used this value to build the ipsilateral motor representation results in Figure 6. This choice allows direct comparison with Figure 6 of our 2021 article, related to contralateral motor representation. The comparison reveals a lack of correlation with functional recovery metrics in the ipsilateral case, in contrast to the successful correlation achieved in the contralateral case.

Regarding the incorporation of stimulation amplitudes into the analysis, as detailed in the Method section (lines 770-771), we systematically tested various stimulation amplitudes to determine the minimal threshold required for eliciting a muscle twitch, identified as the threshold value. This process was conducted for each electrode site. Upon reviewing these data, we considered the possibility of presenting an additional assessment of ipsilateral cortical motor representation based on stimulation thresholds. However, the representation depicted in the figure did not differ significantly from the data presented in Figure 6A. Furthermore, this representation introduced an additional weakness, as it was unclear how to represent the absence of a response in the threshold scale. We chose to arbitrarily designate it as zero on the inverse logarithmic scale, where, for reference, 100 µA is positioned at 0.2 and 50 µA at 0.5.

In conclusion, we believe that the conclusions drawn from this analysis align substantially with those in the text. The addition of the threshold analysis, in our assessment, would not contribute significantly to improving the manuscript.

Author response image 1.

Threshold analysis

Author response image 2.

Original occurrence probability analysis, for comparison.

1. Lines 858-860: The authors state that "All tests were one-sided because all hypotheses were strictly defined in the direction of motor improvement." By using the one-sided test, the authors are using a lower standard for assessing statistical significance that the overwhelming majority of studies in this field use. More importantly, ipsilateral stimulation of particular kinds or particular sites might conceivably impair function, and that is ignored if the analysis is confined to detecting improvement. Thus, a two-sided analysis or comparable method should be used. This appropriate change would not greatly modify the authors' current conclusions about improvements.

Our original hypothesis, drawn from previous studies involving cortical stimulation in rats and cats, as well as other neurostimulation research for movement restoration, posited a favorable impact of neurostimulation on movement. Consistent with this hypothesis, we designed our experiments with a focus on enhancing movement, emphasizing a strict direction of improvement.

It's important to note that a one-sided test is the appropriate match for a one-sided hypothesis, and it is not a lower standard in statistics. Each experiment we conducted was constructed around a strictly one-sided hypothesis: the inclusion of an extensor-inducing stimulus would enhance extension, and the inclusion of a flexion-inducing stimulus would enhance flexion. This rationale guided our choice of the appropriate statistical test.

We acknowledge your concern regarding the potential for ipsilateral stimulation to have negative effects on locomotion, which might not be captured when designing experiments based on one-sided hypotheses. This concern is valid, and we will explicitly mention it in the statistics section. Nonetheless, even if such observations were made, they could serve as the basis for triggering an ad-hoc follow-up study.

Reviewer #2 (Public Review):

Summary:

The authors' long-term goals are to understand the utility of precisely phased cortex stimulation regimes on recovery of function after spinal cord injury (SCI). In prior work, the authors explored the effects of contralesion cortex stimulation. Here, they explore ipsilesion cortex stimulation in which the corticospinal fibers that cross at the pyramidal decussation are spared. The authors explore the effects of such stimulation in intact rats and rats with a hemisection lesion at the thoracic level ipsilateral to the stimulated cortex. The appropriately phased microstimulation enhances contralateral flexion and ipsilateral extension, presumably through lumbar spinal cord crossed-extension interneuron systems. This microstimulation improves weight bearing in the ipsilesion hindlimb soon after injury, before any normal recovery of function would be seen. The contralateral homologous cortex can be lesioned in intact rats without impacting the microstimulation effect on flexion and extension during gait. In two rats ipsilateral flexion responses are noted, but these are not clearly demonstrated to be independent of the contralateral homologous cortex remaining intact.

Strengths:

This paper adds to prior data on cortical microstimulation by the laboratory in interesting ways. First, the strong effects of the spared crossed fibers from the ipsi-lesional cortex in parts of the ipsi-lesion leg's step cycle and weight support function are solidly demonstrated. This raises the interesting possibility that stimulating the contra-lesion cortex as reported previously may execute some of its effects through callosal coordination with the ipsi-lesion cortex tested here. This is not fully discussed by the authors but may represent a significant aspect of these data. The authors demonstrate solidly that ablation of the contra-lesional cortex does not impede the effects reported here. I believe this has not been shown for the contra-lesional cortex microstimulation effects reported earlier, but I may be wrong. Effects and neuroprosthetic control of these effects are explored well in the ipsi-lesion cortex tests here.

In the revised version of the manuscript, we will incorporate various text improvements to address the points you have highlighted below. Additionally, we will integrate the suggested discussion topic on callosal coordination related to contralateral cortical stimulation.

Weaknesses:

Some data is based on very few rats. For example (N=2) for ipsilateral flexion effects of microstimulation. N=3 for homologous cortex ablation, and only ipsi extension is tested it seems. There is no explicit demonstration that the ipsilateral flexion effects in only 2 rats reported can survive the contra-lateral cortex ablation. We agree with this assessment. The ipsilateral flexion representation is here reported as a rare but consistent phenomenon, which we believe to have robustly described with Figure 7 experiments. We will underline in the text that the ablation experiment did not conclude on the unilateral-cortical nature of ipsilateral flexion effects.

Some improvements in clarity and precision of descriptions are needed, as well as fuller definitions of terms and algorithms.

Likely Impacts: This data adds in significant ways to prior work by the authors, and an understanding of how phased stimulation in cortical neuroprosthetics may aid in recovery of function after SCI, especially if a few ambiguities in writing and interpretation are fully resolved.

The manuscript text will be revised in its final version, and we seek to eliminate any ambiguity in writing, data interpretation and algorithms.

Reviewer #3 (Public Review):

Summary:

This article aims to investigate the impact of neuroprosthesis (intracortical microstimulation) implanted unilaterally on the lesion side in the context of locomotor recovery following unilateral thoracic spinal cord injury.

Strength:

The study reveals that stimulating the left motor cortex, on the same side as the lesion, not only activates the expected right (contralateral) muscle activity but also influences unexpected muscle activity on the left (ipsilateral) side. These muscle activities resulted in a substantial enhancement in lift during the swing phase of the contralateral limb and improved trunk-limb support for the ipsilateral limb. They used different experimental and stimulation conditions to show the ipsilateral limb control evoked by the stimulation. This outcome holds significance, shedding light on the engagement of the "contralateral projecting" corticospinal tract in activating not only the contralateral but also the ipsilateral spinal network.

The experimental design and findings align with the investigation of the stimulation effect of contralateral projecting corticospinal tracts. They carefully examined the recovery of ipsilateral limb control with motor maps. They also tested the effective sites of cortical stimulation. The study successfully demonstrates the impact of electrical stimulation on the contralateral projecting neurons on ipsilateral limb control during locomotion, as well as identifying important stimulation spots for such an effect. These results contribute to our understanding of how these neurons influence bilateral spinal circuitry. The study's findings contribute valuable insights to the broader neuroscience and rehabilitation communities.

Thank you for your assessment of this manuscript. The final version of the manuscript will incorporate your suggestions for improving term clarity and will also enhance the discussion on the mechanism of spinal network engagement, as outlined below.

Weakness:

The term "ipsilateral" lacks a clear definition in the title, abstract, introduction, and discussion, potentially causing confusion for the reader. In the next revision of the manuscript, we will provide a clear definition of the term "ipsilateral."

The unexpected ipsilateral (left) muscle activity is most likely due to the left corticospinal neurons recruiting not only the right spinal network but also the left spinal network. This is probably due to the joint efforts of the neuroprosthesis and activation of spinal motor networks which work bilaterally at the spinal level. However, in my opinion, readers can easily link the ipsilateral cortical network to the ipsilateral-projecting corticospinal tract, which is less likely to play a role in ipsilateral limb control in this study since this tract is disrupted by the thoracic spinal injury.

We agree with your assessment. The discussion section paragraph presenting putative mechanisms of cortico-spinal transmission in the effects presented in the results will be enhanced to reflect these suggestions.

Author Response

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

eLife assessment

This paper reports valuable results regarding the potential role and time course of the prefrontal cortex in conscious perception. Although the sample size is small, the results are clear and convincing, and strengths include the use of several complementary analysis methods. The behavioral test includes subject report so the results do not allow for distinguishing between theories of consciousness; nevertheless, results do advance our understanding of the contribution of prefrontal cortex to conscious perception. We appreciate very much for editor and reviewers encouraged review opinion. Particularly, we thank three reviewers very much for their professional and constructive comments that help us to improve the manuscript substantially.

Public Reviews:

Reviewer #1 (Public Review):

This is a clear and rigorous study of intracranial EEG signals in the prefrontal cortex during a visual awareness task. The results are convincing and worthwhile, and strengths include the use of several complementary analysis methods and clear results. The only methodological weakness is the relatively small sample size of only 6 participants compared to other studies in the field. Interpretation weaknesses that can easily be addressed are claims that their task removes the confound of report (it does not), and claims of primacy in showing early prefrontal cortical involvement in visual perception using intracranial EEG (several studies already have shown this). Also the shorter reaction times for perceived vs not perceived stimuli (confident vs not confident responses) has been described many times previously and is not a new result.

We appreciate very much for the reviewer’s encouraged opinion. We are going to address reviewer’s specific questions and comments point-by-point in following.

‘The only methodological weakness is the relatively small sample size of only 6 participants compared to other studies in the field.’

We agree that the sample size is relatively small in the present study. To compensate such shortcoming, we rigorously verified each result at both individual and population levels, resembling the data analysis method in non-human primate study.

Interpretation weaknesses that can easily be addressed are claims that their task removes the confound of report (it does not),

Thank you very much for your comment. We agree that our task does not remove the confound of report entirely. However, we believe that our task minimizes the motor confounds by dissociating the emergence of awareness from motor in time and balanced direction of motor between aware and unaware conditions. We have modified the text according to reviewer’s comment in the revised manuscript as following: “This task removes the confound of motor-related activity”.

..and claims of primacy in showing early prefrontal cortical involvement in visual perception using intracranial EEG (several studies already have shown this).

We agree that several iEEG studies, including ERP and HFA, have shown the early involvement of prefrontal cortical in visual perception. However, in these studies, the differential activity between conscious and unconscious conditions was not investigated, thus, the activity in prefrontal cortex might be correlated with unconscious processing, rather than conscious processing. In present study, we compared the neural activity in PFC between conscious and unconscious trials, and found the correlation between PFC activity and conscious perception. Although one iEEG study(Gaillard et al., 2009) reported awareness-specific PFC activation, the awareness-related activity started 300 ms after the onset of visual stimuli, which was ~100 ms later than the early awareness related activity in our study. Also, due to the limited number of electrodes in the previous study (2 patients with 19 recording sites mostly in mesiofrontal and peri-insular regions), it was restricted while exploring the awareness-related activity in PFC. In the present study, the number of recording sites (245) were much more than previous study and covered multiple areas in PFC. Our results further show earlier awareness-related activity (~ 200 ms after visual stimuli onset), including ERP, HFA and PLV, which sheds new light on understanding of the role of PFC in conscious perception.

We have added this discussion in the MS (lines 522-536);

Also the shorter reaction times for perceived vs not perceived stimuli (confident vs not confident responses) has been described many times previously and is not a new result. Thank you very much for your comment. We agree that the reaction time is strongly modulated by the confident level, which has been described previously (Broggin, Savazzi, & Marzi, 2012; Marzi, Mancini, Metitieri, & Savazzi, 2006). However, in previous studies, the confident levels were usually induced by presenting stimulus with different physical property, such as spatial frequency, eccentricity and contrast. It is well known that the more salient stimuli will induce the faster process of visual information and speed up the process of visuomotor transformation, eventually shorten the reaction time (Corbetta & Shulman, 2002; Posner & Petersen, 1990). Therefore, the dependence of visual processing on the salience of visual stimulus confounds with the effect of visual awareness on the reaction time, which is hard to attribute the shorter reaction time in more salient condition purely to visual awareness. In contrast, we create a condition (near perceptual threshold) in the present study, in which the saliency (contrast) of visual stimulus is very similar in both aware and unaware conditions in order to eliminate the influence of stimulus saliency in reaction time. We think that the difference in reaction time in our study is mainly due to the modulation of awareness state, which was not reported previously.

We have added the discussion in the MS (lines 497-507).

Reviewer #1 (Recommendations For The Authors):

Specific comments follow:

Abstract: "we designed a visual awareness task that can minimize report-related confounding" and in the Introduction lines 112-115: "Such a paradigm can effectively dissociate awareness-related activity from report-related activity in terms of time... and report behavior"; Discussion lines 481-483 "even after eliminating the influence of the confounding variables related to subjective reports such as motion preparation" and other similar statements in the manuscript should be removed. The task involves report using eye movements with every single stimulus. The fact that there is report for both perceived and not perceived stimuli, that the direction of report is not determined until the time of report, and that there is delay between stimulus and report, does not remove the report-related post-perceptual processing that will inevitably occur in a task where overt report is required for every single trial. For example, brain activity related to planning to report perception will only occur after perceived trials, regardless of the direction of eye movement later decided upon. This preparation to respond is different for perceived and not perceived stimuli, but is not part of the perception itself. In this way the current task is not at all unique and does not substantially differ from many other report-based tasks used previously.

The objective of present study is to assess whether PFC is involved in the emergence of visual awareness. To do so, it is crucial to determine the subjective awareness state as correct as possible. Considering the disadvantage of non-report paradigms in determining the subjective awareness state (Tsuchiya et al. TiCS, 2015; Mashour et al, Neuron, 2020), we employed a balanced report paradigm. It has been argued (Merten & Nieder, PNAS, 2011) that, in the balanced report paradigms, subjects could not prepare any motor response during the delay period because only the appearance of a rule cue (change color of fixation point at the end of delay period) informed subjects about the appropriate motor action. In this case, the post-perceptual processing during delay period might reflect the non-motor cognitive activity. Alternatively, as being mentioned by reviewer, the post-perceptual processing might relate to planning to report perception, which is different for perceived and not perceived stimuli. Therefore, up to date, the understanding of the post-perceptual processing remains controversial. According to reviewer’s comment, we have modified the description of our task as following: “we designed a visual awareness task that can minimize report-related motor confounding”. Also, have changed “report-related” to “motorrelated” in the text of manuscript.

Figures 3, 4 changes in posterior middle frontal gyri suggest early frontal eye field involvement in perception. This should be interpreted in the context of many previous studies showing FEF involvement in signal detection. The authors claim that "earlier visual awareness related activities in the prefrontal cortex were not found in previous iEEG studies, especially in the HG band" on lines 501-502 of the Discussion. This statement is not true and should be removed. The following statement in the Discussion on lines 563-564 should be removed for the same reasons: "our study detected 'ignition' in the human PFC for the first time." Authors should review and cite the following studies as precedent among others:

Blanke O, Morand S, Thut G, Michel CM, Spinelli L, Landis T, Seeck M (1999) Visual activity in the human frontal eye field. Neuroreport 10 (5):925-930. doi:10.1097/00001756-19990406000006

Foxe JJ, Simpson GV (2002) Flow of activation from V1 to frontal cortex in humans. A framework for defining "early" visual processing. Exp Brain Res 142 (1):139-150. doi:10.1007/s00221-001-0906-7

Gaillard R, Dehaene S, Adam C, Clemenceau S, Hasboun D, Baulac M, Cohen L, Naccache L (2009) Converging intracranial markers of conscious access. Plos Biology 7 (3):e61

Gregoriou GG, Gotts SJ, Zhou H, Desimone R (2009) High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324:1207-1210

Herman WX, Smith RE, Kronemer SI, Watsky RE, Chen WC, Gober LM, Touloumes GJ, Khosla M, Raja A, Horien CL, Morse EC, Botta KL, Hirsch LJ, Alkawadri R, Gerrard JL, Spencer DD, Blumenfeld H (2019) A Switch and Wave of Neuronal Activity in the Cerebral Cortex During the First Second of Conscious Perception. Cereb Cortex 29 (2):461-474.

Khalaf A, Kronemer SI, Christison-Lagay K, Kwon H, Li J, Wu K, Blumenfeld H (2022) Early neural activity changes associated with stimulus detection during visual conscious perception. Cereb Cortex. doi:10.1093/cercor/bhac140

Kwon H, Kronemer SI, Christison-Lagay KL, Khalaf A, Li J, Ding JZ, Freedman NC, Blumenfeld H (2021) Early cortical signals in visual stimulus detection. Neuroimage 244:118608.

We agree that several iEEG studies, including ERP and HFA, have shown the early involvement of prefrontal cortical in visual perception. However, in these studies, the differential activity between conscious and unconscious conditions was not investigated, thus, the activity in prefrontal cortex might be correlated with unconscious processing, rather than conscious processing. In present study, we compared the neural activity in PFC between conscious and unconscious trials, and found the correlation between PFC activity and conscious perception. Although one iEEG study reported awareness-specific PFC activation, the awareness-related activity started 300 ms after the onset of visual stimuli, which was ~100 ms later than the early awareness related activity in our study. Also, due to the limited number of electrodes in the previous study (2 patients with 19 recording sites mostly in mesiofrontal and peri-insular regions), it was restricted while exploring the awareness-related activity in PFC. In the present study, the number of recording sites (245) were much more than previous study and covered multiple areas in PFC. Our results further show earlier awareness-related activity (~ 200 ms after visual stimuli onset), including ERP, HFA and PLV, which sheds new light on understanding of the role of PFC in conscious perception.

We have added this discussion in the MS (lines 522-533);

Minor weakness that should be mentioned in the Discussion: The intervals for the FP (fixation period) and Delay period were both fixed at 600 ms instead of randomly jittered, so that subjects likely had anticipatory activity predictably occurring with each grating and cue stimulus.

Thank you very much for your comment. We agree that subjects might have anticipatory activity during experiment. Actually, the goal for us to design the task in this way is to try to balance the effect of attention and anticipation between aware and unaware conditions. We have added this discussion in the MS (lines 467-469);

The faster reaction times for perceived/confident responses vs not perceived/unconfident responses has been reported many times previously in the literature and should be acknowledged rather than being claimed as a novel finding. Authors should modify p. 163 lines 160-162, first sentence of the Discussion lines 445-446 "reaction time.. shorter" claiming this was a novel finding; same for lines 464-467. Please see the following among others:

Broggin E, Savazzi S, Marzi CA (2012) Similar effects of visual perception and imagery on simple reaction time. Q J Exp Psychol (Hove) 65 (1):151-164. doi:10.1080/17470218.2011.594896

Chelazzi L, Marzi CA, Panozzo G, Pasqualini N, Tassinari G, Tomazzoli L (1988) Hemiretinal differences in speed of light detection in esotropic amblyopes. Vision Res 28 (1):95-104 Marzi CA, Mancini F, Metitieri T, Savazzi S (2006) Retinal eccentricity effects on reaction time to imagined stimuli. Neuropsychologia 44 (8):1489-1495. doi:10.1016/j.neuropsychologia.2005.11.012

Posner MI (1994) Attention: the mechanisms of consciousness. Proceedings of the National Academy of Sciences of the United States of America 91 (16):7398-7403

Sternberg S (1969) Memory-scanning: mental processes revealed by reaction-time experiments. Am Sci 57 (4):421-457

Thanks. We have cited some of these papers in the revised manuscript due to the restricted number of citations.

Methods lines 658-659: "results under LU and HA conditions were classified as the control group and were only used to verify and check the results during calculation." However the authors show these results in the figures and they are interesting. HA stimuli show earlier responses than NA stimuli. This is a valuable result which should be discussed and interpreted in light of the other findings.

We thank very much for reviewer’s comment. We have made discussion accordingly in the revised MS (lines 535-536).

General comment on figures: Many of the figure elements are tiny and the text labels and details can't be seen at all, especially single trial color plots, and the brain insets showing recording sites.

We have modified the figures accordingly.

Other minor comments: Typo: Figure 2 legend, line 169 "The contrast level resulted in an awareness percentage greater than 25%..." is missing a word and should say instead something like "The contrast level that resulted in an awareness percentage greater than 25%..."

Thanks. We have corrected the typo accordingly.

Figure 2 Table description in text line 190 says "proportions of recording sites" but the Table only shows number of recording sites and number of subjects, not "proportions." This should be corrected in the text.

Thanks. We have corrected the error.

Figure 3, and other figures, should always label the left and right hemispheres to avoid ambiguity.

Thanks. We have made correction accordingly. In caption of Figure 2D (line 189), we modified the sentence as ‘In all brain images, right side of the image represents the right side of the brain’.

Methods line 666. The saccadic latency calculations paragraph should have a separate heading before it, to separate it from the Behavioral data analysis section.

Thanks. It has been corrected in line 725.

Reviewer #2 (Public Review):

The authors attempt to address a long-standing controversy in the study of the neural correlates of visual awareness, namely whether neurons in prefrontal cortex are necessarily involved in conscious perception. Several leading theories of consciousness propose a necessary role for (at least some sub-regions of) PFC in basic perceptual awareness (e.g., global neuronal workspace theory, higher order theories), while several other leading theories posit that much of the previously reported PFC contributions to perceptual awareness may have been confounded by task-based cognition that co-varied between the aware and unaware reports (e.g., recurrent processing theory, integrated information theory). By employing intracranial EEG in human patients and a threshold detection task on low-contrast visual stimuli, the authors assessed the timing and location of neural populations in PFC that are differentially activated by stimuli that are consciously perceived vs. not perceived. Overall, the reported results support the view that certain regions of PFC do contribute to visual awareness, but at time-points earlier than traditionally predicted by GNWT and HOTs.

Reply: We appreciate very much for the reviewer’s encouraged opinion.

Major strengths of this paper include the straightforward visual threshold detection task including the careful calibration of the stimuli and the separate set of healthy control subjects used for validation of the behavioral and eye tracking results, the high quality of the neural data in six epilepsy patients, the clear patterns of differential high gamma activity and temporal generalization of decoding for seen versus unseen stimuli, and the authors' interpretation of these results within the larger research literature on this topic. This study appears to have been carefully conducted, the data were analyzed appropriately, and the overall conclusions seem warranted given the main patterns of results.

Reply: We appreciate very much for the reviewer’s encouraged opinion.

Weaknesses include the saccadic reaction time results and the potential flaws in the design of the reporting task. This is not a "no report" paradigm, rather, it's a paradigm aimed at balancing the post-perceptual cognitive and motor requirements between the seen and unseen trials. On each trial, subjects/patients either perceived the stimulus or not, and had to briefly maintain this "yes/no" judgment until a fixation cross changed color, and the color change indicated how to respond (saccade to the left or right). Differences in saccadic RTs (measured from the time of the fixation color change to moving the eyes to the left or right response square) were evident between the seen and unseen trials (faster for seen). If the authors' design achieved what they claim on page 3, "the report behaviors were matched between the two awareness states ", then shouldn't we expect no differences in saccadic RTs between the aware and unaware conditions? The fact that there were such differences may indicate differences in post-perceptual cognition during the time between the stimulus and the response cue. Alternatively, the RT difference could reflect task-strategies used by subjects/patients to remember the response mapping rules between the perception and the color cue (e.g., if the YES+GREEN=RIGHT and YES+RED=LEFT rules were held in memory, while the NO mappings were inferred secondarily rather than being actively held in memory). This saccadic RT result should be better explained in the context of the goals of this particular reporting-task.

The objective of present study is to assess whether PFC is involved in the emergence of visual awareness. To do so, it is crucial to determine the subjective awareness state as correct as possible. Considering the disadvantage of non-report paradigms in determining the subjective awareness state (Tsuchiya et al, TiCS, 2015; Mashour et al, Neuron, 2020), we employed a balanced report paradigm. It has been argued (Merten & Nieder, PNAS, 2011) that, in the balanced report paradigms, subjects could not prepare any motor response during the delay period because only after the appearance of a rule cue (change color of fixation point at the end of delay period) subjects were informed about the appropriate motor action. In this case, the post-perceptual processing during delay period might reflect the non-motor cognitive activity, such as working memory (Mashour et al. Neuron, 2020). Alternatively, as being mentioned by reviewer, the postperceptual processing might relate to planning to report perception, which is different for perceived and not perceived stimuli (Aru et al. Neurosci Biobehav Rev, 2012 ). Therefore, up to date, the understanding of the post-perceptual processing remains controversial. Considering reviewer’s comment together with other opinions, we have modified the description of our task as following: “we designed a visual awareness task that can minimize report-related motor confounding”. Also, we have changed “report-related” to “motor-related” in the rest of manuscript.

Regarding the question whether the saccadic RT in our balanced response paradigm should be expected to be similar between aware and unaware condition, we think that the RT should be similar in case if the delay period is long enough for the decision of “no” to be completed. In fact, in a previous study (Merten & Nieder, PNAS, 2011), the neuronal encoding of “no” decision didn’t appear until 2s after the stimulus cue onset. However, in our task, the delay period lasted only 600 ms that was long enough to form the “yes” decision, but was not enough to form the “no” decision. It might be the reason that our data show shorter RT in aware condition than in unaware condition.

We totally agree reviewer’s comment about the alternative interpretation for RT difference between aware and unaware condition in our study, i.e., reflecting task-strategies used by subjects/patients to remember the response mapping rules between the perception and the color cue (e.g., if the YES+GREEN=RIGHT and YES+RED=LEFT rules were held in memory, while the NO mappings were inferred secondarily rather than being actively held in memory). We have made additional discussion about these questions in the revised manuscript (lines 492496).

Nevertheless, the current results do help advance our understanding of the contribution of PFC to visual awareness. These results, when situated within the larger context of the rapidly developing literature on this topic (using "no report" paradigms), e.g., the recent studies by Vishne et al. (2023) Cell Reports and the Cogitate consortium (2023) bioRxiv, provide converging evidence that some sub-regions of PFC contribute to visual awareness, but at latencies earlier than originally predicted by proponents of, especially, global neuronal workspace theory.

We appreciate very much for the reviewer’s encouraged opinion.

Reviewer #2 (Recommendations For The Authors):

Abstract: "the spatiotemporal overlap between the awareness-related activity and the interregional connectivity in PFC suggested that conscious access and phenomenal awareness may be closely coupled." I strongly suggest revising this sentence. The current results cannot be used to make such a broad claim about p-consciousness vs. a-consciousness. This study used a balanced trial-by-trial report paradigm, which can only measure conscious access.

We thank reviewer for this comment. We have withdrawn this sentence from the revised manuscript.

Task design: A very similar task was used previously by Schröder et al. (2021) J Neurosci. See specifically, their Figure 1, and Figure 4B-C. Using almost the exact same "matching task", the authors of this previous study show that they get a P3b for both the perceived and not-perceived conditions, confirming that post-perceptual cognition/report confounds were not eliminated, but instead were present in (and balanced between) both the perceived/not-perceived trials due to the delayed matching aspect of the design. This previous paper should be cited and the P3b result should be considered when assessing whether cognition/report confounds were addressed in the current study.

Thank you very much for your reminding about the study of Schröder et al. We are sorry for not citing this closely related study in our previous manuscript. Schröder et al. found while P3b showed significant difference between perceived and not-perceived trials in direct report task, the P3b was presented in both perceived/not-perceived trials and not significantly different in the matched task. Based on these findings, Schröder et al. argued that P3b represented the task specific post-perceptual cognition/report rather than the emergence of awareness per se. Considering the similarity of tasks between Schröder et al. and ours, we agree that our task is not able to totally eliminate the confound of post-perceptual cognition/report related activity with awareness related activity. Nevertheless, our task is able to minimize the confound of motorrelated activity with the emergence of awareness by separating them in time and balancing the direction of responsive movements. Therefore, we modified the term of “report-related” to “motor-related” in the text of revised manuscript.

On page 2, lines 71-75, the authors' review of the Frassle et al. (2014) experiment should be revised for accuracy. In this study, all PFC activity did not disappear as the authors claim. Also, the main contrast in the Frassle et al. study was rivalry vs. replay. However, in both of these conditions, visual awareness was changing with the main difference being whether there was sensory conflict between the two eyes or not. Such a contrast would presumably subtract out the common activity patterns related to visual awareness changes, while isolating rivalry (and the resulting neural competition) vs. non-rivalry (and the lack of such competition) which is not broadly relevant for the goal of measuring neural correlates of visual awareness which are present in both sides of the contrast (rivalry and replay).

Thank you very much for your suggestion. We agree that and revised in the MS (lines 71-76).

‘For instance, a functional magnetic resonance imaging (fMRI) study employing human binocular rivalry paradigms found that when subjects need to manually report the changing of their awareness between conflict visual stimuli, the frontal, parietal, and occipital lobes all exhibited awareness-related activity. However, when report was not required, awareness-related activation was largely diminished in the frontal lobe but remained in the occipital and parietal lobes’

On page 2, lines 76-78, the authors write, "no-report paradigm may overestimate unconscious processing because it cannot directly measure the awareness state". This should be reworded for clarity, as report paradigms also do not "directly measure the awareness state". All measures of awareness are indirect, either via subjects verbal or manual reports, or via behaviors or other physiological measures like OKN, pupillometry, etc. It's also not clear as written why no-report paradigms might overestimate unconscious processing.

Thank you very much for your suggestion. We agreed and modified the description. In lines 76-80:

‘Nevertheless, the no-report paradigm may overestimate the neural correlates of awareness by including unconscious processing, because it infers the awareness state through other relevant physiological indicators, such as optokinetic nystagmus and pupil size(Tsuchiya, Wilke, Frassle, & Lamme, 2015). In the absence of subjective reports, it remains controversial regarding whether the presented stimuli are truly seen or not.’

However, the no-report paradigm may overestimate the neural correlates of awareness, because it infers the awareness state through other relevant physiological indicators, such as optokinetic nystagmus and pupil size(Tsuchiya et al., 2015) , in the absence of subjective reports and it remains controversial that whether the stimuli presented in such paradigm are truly seen as opposed to being merely potentially visible but unattended.

On page 5, line 155, there is a typo. This should be Figure 2C, not 2B.

Thanks. We have modified the description.

On page 5, lines 160-162, the authors state, "The results showed that the saccadic reaction time in the aware trials was systematically shorter than that in the unaware trials. Such results demonstrate that visual awareness significantly affects the speed of information processing in the brain." I don't understand this. If subjects can never make a saccade until the fixation cross changes color, both for Y and N decisions, why would a difference in saccadic reaction times indicate anything about visual awareness affecting the speed of information processing in the brain? Doesn't this just show that the Red/Green x Left/Right response contingencies were easier to remember and execute for the Yes-I-did-see-it decisions compared to the No-I-didn't-see-it decisions?

We agree and have made additional discussion about these questions in the revised manuscript (lines 492-496).

‘An alternative interpretation for RT difference between aware and unaware condition in our study is that the difference in task-strategies used by subjects/patients to remember the response mapping rules between the perception and the color cue (e.g., if the YES+GREEN=RIGHT and YES+RED=LEFT rules were held in memory, while the NO mappings were inferred secondarily rather than being actively held in memory).’

In Figure 3B (and several other figures) due to the chosen view and particular brain visualization used, many readers will not know whether the front of brain is up and back of brain down or vise versa (there are no obvious landmarks like the cerebellum, temporal sulcus, etc.). I suggest specifying this in the caption or better yet on the figure itself.

Thanks. We have added these descriptions in the caption of Figure 2D.

Line 189 ‘In all brain images, right and up sides of each image represent the right and up sides of the brain’.

In Figure 3B, the color scale may confuse some readers. When I first inspected this figure, I immediately thought the red meant positive voltage or activation, while the blue meant negative voltage or deactivation. Only later, I realized that any color here is meaningful. Not sure if an adjustment of the color scale might help, or perhaps not normalizing (and not taking absolute values of the voltage diffs, but maintaining the +/- diffs)?

Thanks for reviewer’s comment. We are sorry for not clearly describing the reason why we normalized the activity in absolute value and chose the color scale from 0 to 20. The major reason is that it is not clearly understood so far regarding the biological characteristics of LFP polarity (Einevoll et al, Nat Rev Neurosci, 2013). To simplify such complex issue, we consider the change in magnitude of LFP during delay period in our task represents awareness related activity, regardless its actual value being positive or negative. Therefore, we first calculated the absolute value of activity difference between aware and unaware trials in individual recording site, then used Shepard's method (see Method for detailed information) to calculate the activity in each vertex and projected on the surface of brain template as shown in Fig. 3B.

We have added the description in the MS (lines 794-800).

We have tried to adjust the color scale from -20 to 20 according to reviewer’s suggestion. However, the topographic heatmap showed less distinguishable between brain regions with different strength of awareness related activity. Thus, we would like to keep the way as we used to analyze and present these results.

Figure 3B: Why choose seemingly arbitrary time points in this figure? What's the significance of 247 and 314 and 381ms (why not show 200, 250, 300, etc.)? Also, are these single time-points or averages within a broader time window around this time-point, e.g., 225-275ms for the 250ms plot?

Thank reviewer for this helpful comment. We are sorry for not clearly describing why we chose the 8 time points to demonstrate the spatiotemporal characteristics of awareness related activity in Fig. 3B. To identify the awareness related activity, we analyzed the activity difference between aware and unaware trials during delay period (180-650 ms after visual stimulus onset). The whole dynamic process has been presented in SI with a video (video S1). Here, we just sampled the activity at 8 time points (180 ms, 247 ms, 314 ms, etc.) that equally divided the 430 ms delay period.

We have added the description in the MS (lines 213-215).

Figure 3D: It's not clear how this figure panel is related to the data shown in Fig3A. In Fig3A, the positive amplitude diffs all end at around 400ms, but in Fig3D, these diffs extend out to 600+ms. I suggest adding clarity about the conversion being used here.

Thanks for reviewer’s comment. We are sorry for not clearly describing the way to analyze the population activity (Fig. 3D) in the previous version of manuscript. Since it is not clearly understood so far regarding the biological characteristics of LFP polarity, to simplify such complex issue, we consider the change in magnitude of LFP during delay period in our task is awareness related activity, regardless its actual value being positive or negative. Therefore, while analyzing the awareness related population activity, we first calculate the absolute value of activity difference between aware and unaware trials in individual recording site, then pool the data of 43 recording sites together and calculate the mean and standard error of mean (SEM)(Fig. 3D). As you can see in Fig. 3A, the activity difference between aware (red) and unaware (blue) trials lasts until/after the end of delay period. Thus, the awareness related population activity in Fig 3D extends out to 600 ms.

We have added the description in the MS (lines 769-777).

Figure 6D could be improved by making the time labels much bigger, perhaps putting them on the time axis on the bottom rather than in tiny text above each brain.

Thanks for reviewer’s comment. We have modified it accordingly.

Page 18, line 480: "our results show that the prefrontal cortex still displays visual awareness-related activities even after eliminating the influence of the confounding variables related to subjective reports such as motion preparation" This is too strong of a statement. It's not at all clear whether confounding variables related to subjective reports (especially the cognition needed to hold in mind the Y/N decision about seeing the stimulus prior to the response cue) were eliminated with the design used here. In other places of the manuscript, the authors use "minimized" which is more accurate.

Thanks for reviewer’s comment. We have modified it accordingly.

Page 19, section starting on line 508: The authors should consider citing the study by Vishne et al. (2023), which was just accepted for publication recently, but has been posted on bioRxiv for almost a year now: https://www.biorxiv.org/content/10.1101/2022.08.02.502469v1 . And on page 20, line 563, the authors claim that to the best of their knowledge, they were the first to detect "ignition" in PFC in human subjects. Consider revising this statement, now that you know about the Vishne et al. paper.

We agree.

Thanks for your reminding about these papers. We have cited this study and made discussion in the revised manuscript (line 522-533). We agree that several iEEG studies have shown the early involvement of PFC in visual perception (Vishne et al. 2023; Khalaf et al. 2023; Kwon et al. 2021). However, in these studies, authors did not compare the neural activity between conscious and unconscious conditions, leaving the possibility that the ERP and HFA were correlated with the unconscious information processing rather than awareness-specific processing. In the present study, we compared the neural activity in PFC between conscious and unconscious trials, and found that the activity of PFC specifically correlated with conscious perception. As we mentioned in the previous version of manuscript, there is one iEEG study (Gaillard et al. 2009) that reported awareness-specific activity in PFC. However, the awareness related activity started more than 300 ms after the onset of visual stimuli, which was about 100 ms longer than the early awareness related activity in our study. Nevertheless, according to reviewer’s comment, we modified our argument as following in lines 621-623:

‘However, as discussed above, in contrast with previous studies, our study detected earlier awareness-specific ‘ignition’ in the human PFC, while minimizing the motor-related confounding.’

Experimental task section of Methods: Were any strategies for learning the response cue matching task suggested to patients/subjects, and/or did any patients/subjects report which strategy they ended up using? For example, if I were a subject in this experiment, I would remember and mentally rehearse the rules: "YES+GREEN = RIGHT" and "YES+RED = LEFT". For trials in which I didn't see anything, I wouldn't need to hold 2 more rules in mind, as they can be inferred from the inverse of the YES rules (and it's much harder to hold 4 things in mind than 2). This extra inference needed to get to the NO+GREEN = LEFT and NO+RED = RIGHT rules would likely cause me to respond slightly slower to the NO trials compared to the YES trials, leading to saccadic RT effects in the same direction the authors found. More information about the task training and strategies used by patients/subjects would be helpful.

We agree and discussed this in lines 492-496.

Reviewer #3 (Public Review):

The authors report a study in which they use intracranial recordings to dissociate subjectively aware and subjectively unaware stimuli, focusing mainly on prefrontal cortex. Although this paper reports some interesting findings (the videos are very nice and informative!) the interpretation of the data is unfortunately problematic for several reasons. I will detail my main comments below. If the authors address these comments well, I believe the paper may provide an interesting contribution to further specifying the neural mechanisms important for conscious access (in line with Gaillard et al., Plos Biology 2009).

Reply: We appreciate very much for the reviewer’s encouraged opinion.

The main problem with the interpretation of the data is that the authors have NOT used a so called "no-report paradigm". The idea of no report paradigms is that subjects passively view a certain stimulus without the instruction to "do something with it", e.g., detect the stimulus, immediately or later in time. Because of the confusion of this term, specifically being related to the "act of reporting", some have argued we should use the term no-cognition paradigm instead (Block, TiCS, 2019, see also Pitts et al., Phil Trans B 2018). The crucial aspect is that, in these types of paradigms, the critical stimulus should be task-irrelevant and thus not be associated with any task (immediately or later). Because in this experiment subjects were instructed to detect the gratings when cued 600 ms later in time, the stimuli are task relevant, they have to be reported about later and therefore trigger all kinds of (known and potentially unknown) cognitive processes at the moment the stimuli are detected in real-time (so stimulus-locked). You could argue that the setup of this delayed response task excludes some very specific report related processes (e.g., the preparation of an eye-movement), which is good, however this is usually not considered the main issue. For example when comparing masked versus unmasked stimuli (Gaillard et al., 2009 Plos Biology), these conditions usually also both contain responses but these response related processes are "averaged out" in the specific contrasts (unmasked > masked). In this paper, RT differences between conditions (that are present in this dataset) are taken care of by using this delayed response in this paper, which is a nice feature for that and is not the case for the above example set-up.

Given the task instructions, and this being merely a delayed-response task, it is to be expected that prefrontal cortex shows stronger activity for subjectively aware versus subjectively unaware stimuli. Unfortunately, given the nature of this task, the novelty of the findings is severely reduced. The authors cannot claim that prefrontal cortex is associated with "visual awareness", or what people have called phenomenal consciousness (this is the goal of using no-cognition paradigms). The only conclusion that can be drawn is that prefrontal cortex activity is associated with accessing sensory input: and hence conscious access. This less novel observation has been shown many times before and there is also little disagreement about this issue between different theories of consciousness (e.g., global workspace theory and local recurrency theories both agree on this).

We totally agree that the no-report/no-cognition paradigms contain less cognition within the post-perceptual processing than the report paradigms. We designed the balanced response task in order to minimize the motor related component from post-perceptual processing, even though this task does not eliminate the entire cognition from post-perceptual processing. Regarding reviewer’s comment that our task is not able to assess the involvement of PFC in the emergence of awareness, we have different opinion. As we mentioned in the manuscript, the findings of early awareness related activity (~200 ms) in PFC, which resemble the VAN activity in EEG studies, indicate the association of PFC with the emergence of visual awareness (phenomenal consciousness).

The best solution at this point seems to rewrite the paper entirely in light of this. My advice would be to state in the introduction that the authors investigate conscious access using iEEG and then not refer too much to no-cognition paradigm or maybe highlight some different strategies about using task-irrelevant stimuli (see Canales-Johnson et al., Plos Biology 2023; Hesse et al., eLife 2020; Hatamimajoumerd et al Curr Bio 2022; Alilovic et al., Plos Biology 2023; Pitts et al., Frontiers 2014; Dwarakanth et al., Neuron 2023 and more). Obviously, the authors should then also not claim that their results solve debates about theories regarding visual awareness (in the "no-cognition" sense, or phenomenal consciousness), for example in relation to the debate about the "front or the back of the brain", because the data do not inform that discussion. Basically, the authors can just discuss their results in detail (related to timing, frequency, synchronization etc) and relate the different signatures that they have observed to conscious access.

The objective of present study is to assess whether PFC is involved in the emergence of visual awareness (i.e., phenomenal consciousness). Interestingly, we found the early awareness related activity (~200 ms after visual stimulus onset), including ERP, high gamma activity and phase synchronization, in PFC, which indicate the association of PFC with the emergence of visual awareness. Therefore, we would like to keep the basic context of manuscript and make revision according to reviewers’ comments.

On the other hand, we totally agree reviewer’s argument that the report paradigm is more suitable to study the access consciousness. Indeed, we have found that the awareness related activity in PFC could be separated into two subgroups, i.e., early activity with shorter latency (~200 ms after stimulus onset) and late activity with longer latency (> 350 ms after stimulus onset). In addition, the early activity was declined to the baseline level within ~200 ms during delay period, whereas the late activity lasted throughout the delay period and reached to the next stage of task (change color of the fixation point). Moreover, the early activity occurs primarily within the contralateral PFC of the visual stimulus, whereas the late activity occurs within both contralateral and ipsilateral PFC. While the early awareness related activity resembles the VAN activity in EEG studies (associating with p-consciousness), the late awareness related activity resembles the P3b activity (associating with a-consciousness). We are going to report these results in a separated paper soon.

I think the authors have to discuss the Gaillard et al PLOS Biology 2009 paper in much more detail. Gaillard et al also report a study related to conscious access contrasting unmasked and masked stimuli using iEEG. In this paper they also report ERP, time frequency and phase synchronization results (and even Granger causality). Because of the similarities in approach, I think it would be important to directly compare the results presented in that paper with results presented here and highlight the commonalities and discrepancies in the Discussion.

Thanks for reviewer’s comment. We have made additional analysis and detailed discussion accordingly. In addition, we also extended discussion with other relevant studies in the revised manuscript.

In lines 528-549,

‘Although one iEEG study reported awareness-specific PFC activation, the awareness-related activity started 300 ms after the onset of visual stimuli, which was ~100 ms later than the early activity in our study. Also, due to the limited number of electrodes in PFC (2 patients with 19 recording sites mostly in mesiofrontal and peri-insular regions), their experiments were restricted while exploring the awareness-related activity in PFC. In the present study, the number of recording sites (245) were much more than previous study and covered more areas in PFC. Our results further show earlier awareness-related activity (~ 200 ms after visual stimuli onset), including ERP, HFA and PLV. These awareness-related activity in PFC occurred even earlier (~150 ms after stimulus onset) for the salient stimulus trials (Fig. 3A\D and Fig. 4A\D, HA condition).

However, the proportions are much smaller than that reported by Gaillard et al, which peaked at ~60%. We think that one possibility for the difference may be due to the more sampled PFC subregions in present study and the uneven distribution of awareness-related activity in PFC. Meanwhile, we noticed that the peri-insula regions and middle frontal gyrus (MFG), which were similar with the regions reported by Gaillard et al, seemed to show more fraction of awarenessrelated sites than other subregions during the delay period (0-650 ms after stimulus onset). To test such possibility and make comparison with the study of Gaillard et al. we calculated the proportion of awareness-related site in peri-insula and MFG regions. We found although the proportion of awareness-related site was larger in peri-insula and MFG than in other subregions, it was much lower than the report of Gaillard et al. One alternative possibility for the difference between these two studies might be due to the more complex task in Gaillard et al. Nevertheless, we think these new results would contribute to our understanding of the neural mechanism underlying conscious perception, especially for the role of PFC.’ In lines 601-603:

‘The only human iEEG study reported that the phase synchronization of the beta band in the aware condition also occurred relatively late (> 300 ms) and mainly confined to posterior zones but not PFC.’

As for the Granger Causality analysis between PFC and occipital lobe, while the aim of this study focused mainly on PFC and there were few recoding sites in occipital lobe, we would like to do this analysis in later studies after we collect more data.

In the Gaillard paper they report a figure plotting the percentage of significant frontal electrodes across time (figure 4A) in which it can be seen that significant electrodes emerge after approximately 250 ms in PFC as well. It would be great if the authors could make a similar figure to compare results. In the current paper there are much more frontal electrode contacts than in the Gaillard paper, so that is interesting in itself.

Thanks reviewer for this constructive comment. We made similar analysis as Gaillard et al. and plotted the results in the figure bellow. As you can see, the awareness related sites started to emerge about 200 ms after visual stimulus onset according to both ERP and HG activity. The proportion of awareness related sites reached peak at ~14% (8% for HG) in 300-400ms. However, the proportions are much smaller than that reported by Gaillard et al, which peaked at ~60%. We think that one possibility for the difference may be due to the more sampled PFC subregions in present study and the uneven distribution of awareness-related activity in PFC. Meanwhile, we noticed that the peri-insula regions and middle frontal gyrus (MFG), which were similar with the regions reported by Gaillard et al, seemed to show more fraction of awareness-related sites than other subregions during the delay period (0-650 ms after stimulus onset). To test such possibility and make comparison with the study of Gaillard et al. we calculated the proportion of awareness-related site in peri-insula and MFG regions. We found although the proportion of awareness-related site was larger in peri-insula and MFG than in other subregions, it was much lower than the report of Gaillard et al. One alternative possibility for the difference between these two studies might be due to the more complex task in Gaillard et al.

We have added this figure and discussion to the revised manuscript as a new result (Figure 4E & S2 and lines 537-549).

Author response image 1.

Percentage of awareness-related sites in ERP and HG analysis. n, number of recording sites in PFC.

Author response image 2.

Percentage of awareness-related sites in ERP and HG analysis at parsopercularis and middle frontal gyrus (MFG). n, number of recording sites.

In my opinion, some of the most interesting results are not highlighted: the findings that subjectively unaware stimuli show increased activations in the prefrontal cortex as compared to stimulus absent trials (e.g., Figure 4D). Previous work has shown PFC activations to masked stimuli (e.g., van Gaal et al., J Neuroscience 2008, 2010; Lau and Passigngham J Neurosci 2007) as well as PFC activations to subjectively unaware stimuli (e.g., King, Pescetelli, and Dehaene, Neuron 2016) and this is a very nice illustration of that with methods having more detailed spatial precision. Although potentially interesting, I wonder about the objective detection performance of the stimuli in this task. So please report objective detection performance for the patients and the healthy subjects, using signal detection theoretic d'. This gives the reader an idea of how good subjects were in detecting the presence/absence of the gratings. Likely, this reveals far above chance detection performance and in that case I would interpret these findings as "PFC activation to stimuli indicated as subjectively unaware" and not unconscious stimuli. See Stein et al., Plos Biology 2021 for a direct comparison of subjectively and objectively unaware stimuli.

We gratefully appreciate for reviewer’s helpful and valuable comments. We do notice that the activity of PFC in subjectively unawareness condition (stimulus contrast near perceptual threshold) is significantly higher than stimulus absent condition. Such results, by using sEEG recordings with much higher spatial resolution than brain imaging and scalp EEG, support findings of previous studies (citations). Considering the question of neural correlation of unawareness processing is a hot and interesting topic, after carefully considering, we would like to report these results in a separate paper, rather than add these results in the current manuscript in order to avoid the distraction.

According to reviewer’s comment about the objective detection performance of the stimuli in our task, we analyzed the signal detection theoretic d’. The values of d’ in patients and healthy subjects are similar (1.81±0.27 in patients and 2.12±0.37 in healthy subjects). Such results indicate that the objective detection performance of subjects in our task is well above the chance level. Since our task merely measures the subjective awareness, we agree reviewer’s comment about the interpretation of our results as “PFC activation to stimuli indicated the subjective unawareness rather than objective unawareness”. We will emphasize this point in our next paper.

We have added the d prime in the MS (lines149-150).

In Figure 7 of the paper the authors want to make the case that the contrast does not differ between subjectively aware stimuli and subjectively unaware stimuli. However so far they've done the majority of their analyses across subjects, and for this analysis the authors only performed within-subject tests, which is not a fair comparison imo. Because several P values are very close to significance I anticipate that a test across subjects will clearly show that the contrast level of the subjectively aware stimuli is higher than of the subjectively unaware stimuli, at the group level. A solution to this would be to sub-select trials from one condition (NA) to match the contrast of the other condition (NU), and thereby create two conditions that are matched in contrast levels of the stimuli included. Then do all the analyses on the matched conditions.

Thank reviewer for the helpful comment. Regarding reviewer’s comment “However so far they've done the majority of their analyses across subjects, and for this analysis the authors only performed within-subject tests, which is not a fair comparison imo”, if we understand correctly, reviewer considered that it was fair if the analysis of neural activity in PFC was done across subjects but the stimulus contrast analysis between NA and NU was done individually. Actually, it is not the case. In neural activity analysis, the significant awareness-related sites were identified firstly in each individual subject (Fig. 3A and Fig 4A, and Methods), same as the analysis of stimulus contrast (see Methods). Only in the neural population activity analysis, the activity of awareness-related sites was pooled together and made further analysis.

To further evidence the awareness related activity in PFC is not highly correlated with stimulus contrast, we compared the activity difference between two different stimulus contrast conditions, i.e., stimulus contrast difference between high-contrast aware (HA) and NA conditions (large difference, ~14%), and between NA and NU conditions (slight difference, ~0.2%). The working hypothesis is that, if PFC activity is closely correlated with the contrast of stimulus contrast, we expect to see the activity difference between HA and NA conditions is much larger than that between NA and NU conditions. To test this hypothesis, we analyzed data of two patients in which the previous analysis showed significant or near significant difference of stimulus contrast between NA and NU conditions (Author response image 1, below, patient #2 and 1). The results (Author response image 1) show that the averaged activity difference (0-650 ms after visual stimulus onset) between HA and NA was similar as the averaged activity difference between NA and NU trials, even though the stimulus contrast difference was much larger between HA and NA conditions than between NA and NU conditions. Such results indicate that the awareness-related activity in PFC cannot be solely explained by the contrast difference between NA and NU conditions. Based on these results, we think that it is not necessary to perform the analysis as reviewer’s comment “A solution to this would be to sub-select trials from one condition (NA) to match the contrast of the other condition (NU), and thereby create two conditions that are matched in contrast levels of the stimuli included. Then do all the analyses on the matched conditions”. Another reason that impedes us to do this analysis is due to the limited trial numbers in our dataset.

Author response image 3.

Relationship between stimulus contract and PFC activity. X axis represents the stimulus contrast difference between two paired conditions, i.e., aware versus unaware in near perceptual threshold conditions (NA – NU, red dots); aware in high contrast condition versus aware in near perceptual threshold condition (HA – NA, blue dots). Y axis represents the activity difference between paired stimulus conditions. The results show that activity difference is similar between two paired conditions regardless the remarkable contrast difference between two paired conditions. Such results indicate that the greater activity in NA trials than in NU trials (Fig. xx-xx) could not be interpreted by the slight difference in stimulus contrast between NA and NU trials.

Related, Figure 7B is confusing and the results are puzzling. Why is there such a strong below chance decoding on the diagonal? (also even before stimulus onset) Please clarify the goal and approach of this analysis and also discuss/explain better what they mean.

We have withdrawn Figure7B for the confusing decoding results on the diagonal.

I was somewhat surprised by several statements in the paper and it felt that the authors may not be aware of several intricacies in the field of consciousness. For example, a statement like the following "Consciousness, as a high-level cognitive function of the brain, should have some similar effects as other cognitive functions on behavior (for example, saccadic reaction time). With this question in mind, we carefully searched the literature about the relationship between consciousness and behavior; surprisingly, we failed to find any relevant literature." This is rather problematic for at least two reasons. First, not everyone would agree that consciousness is a highlevel cognitive function and second there are many papers arguing for a certain relationship between consciousness and behavior (Dehaene and Naccache, 2001 Cognition; van Gaal et al., 2012, Frontiers in Neuroscience; Block 1995, BBS; Lamme, Frontiers in Psychology, 2020; Seth, 2008 and many more). Further, the explanation for the reaction time differences in this specific case is likely related to the fact that subjects' confidence in that decision is much higher in the aware trials than in the unaware trials, hence the speeded response for the first. This is a phenomenon that is often observed if one explores the "confidence literature". Although the authors have not measured confidence I would not make too much out of this RT difference.

We agree that and modified accordingly in lines 492-507.

‘An alternative interpretation for RT difference between aware and unaware condition in our study, i.e., reflecting task-strategies used by subjects/patients to remember the response mapping rules between the perception and the color cue (e.g., if the YES+GREEN=RIGHT and YES+RED=LEFT rules were held in memory, while the NO mappings were inferred secondarily rather than being actively held in memory).

Another possibility is that the reaction time is strongly modulated by the confident level, which has been described in previous studies(Broggin et al., 2012; Marzi et al., 2006). However, in previous studies, the confident levels were usually induced by presenting stimulus with different physical property, such as spatial frequency, eccentricity and contrast. However, the dependence of visual process on the salience of visual stimulus confounds with the effect of visual awareness on the reaction time of responsive movements, which is hard to attribute the shorter reaction time in more salient condition purely to visual awareness. In contrast, we create a condition (near aware threshold) in the present study, in which the saliency (contrast) of visual stimulus is very similar in both aware and unaware conditions in order to eliminate the influence of stimulus saliency in reaction time. We think that the difference in reaction time in our study is mainly due to the modulation of awareness state, which was not reported previously.’

I would be interested in a lateralized analysis, in which the authors compare the PFC responses and connectivity profiles using PLV as a factor of stimulus location (thus comparing electrodes contralateral to the presented stimulus and electrodes ipsilateral to the presented stimulus). If possible this may give interesting insights in the mechanism of global ignition (global broadcasting), supposing that for contralateral electrodes information does not have to cross from one hemisphere to another, whereas for ipsilateral electrodes that is the case (which may take time). Gaillard et al refer to this issue as well in their paper, and this issue is sometimes discussed regarding to Global workspace theory. This would add novelty to the findings of the paper in my opinion.

We gratefully appreciate reviewer’s helpful and available suggestions. We have made the analysis accordingly. We find that the awareness-related ERP activation in PFC occurs earlier only in the contralateral PFC with latency about 200 ms and then occurs in both contralateral and ipsilateral PFC about 100 ms later. In addition, the magnitude of awareness-related activity is stronger in the contralateral PFC than in ipsilateral PFC during the early phase (200-400 ms), then the activity becomes similar between contralateral and ipsilateral PFC. Moreover, the awareness related HG activity only appears in the contralateral PFC. Such results show the spatiotemporal characteristics of visual awareness related activity between two hemispheres. We are going to report these results in a separate paper soon.

Reviewer #3 (Recommendations For The Authors):

Some of the font sizes in the figures are too small.

We have modified accordingly.

To me, the abbreviations are confusing, (NA/NU etc). I would try to come up with easier ones or just not use abbreviations.

We have modified accordingly and try to avoid to use the abbreviations.

The data/scripts availability statement states "available upon reasonable request". I would suggest that the authors make the data openly available when possible, and I believe eLife requires that as well.

Thanks for reviewer’s suggestions. Due to several ongoing studies based on this dataset, we would like to open our data after complete these studies if there is no restriction from national policy.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Many drugs have off-target effects on the gut microbiota but the downstream consequences for drug efficacy and side effect profiles remain unclear. Herein, Wang et al. use a mouse model of liver injury coupled to antibiotic and microbiota transplantation experiments. Their results suggest that metformin-induced shifts in gut microbial community structure and metabolite levels may contribute to drug efficacy. This study provides valuable mechanistic insights that could be dissected further in future studies, including efforts to identify which specific bacterial species, genes, and metabolites play a causal role in drug response. Importantly, although some pilot data from human subjects is shown, the clinical relevance of these findings for liver disease remain to be determined.

Thank you for reviewing our manuscript. We appreciate your valuable feedback. We agree that the downstream consequences of off-target effects on the gut microbiota by various drugs remain unclear. Our study aimed to shed light on this aspect by utilizing a mouse model of liver injury and conducting antibiotic and microbiota transplantation experiments. Our findings suggest that shifts in the structure and metabolite levels of the gut microbial community induced by metformin play a role in the drug’s efficacy. We believe that these mechanistic insights provide a strong foundation for further investigations. Specifically, future studies could focus on identifying the specific bacterial species, genes, and metabolites that have a causal role in drug response. While we have included some pilot data from human subjects, we acknowledge that the clinical relevance of our findings in the context of liver disease still requires further determination. In fact, we focused on the alteration of microbiota and metabolism caused by metformin in human bodies, which could capture the characteristics of changes in a more composite clinical direction, elucidating the potential role of metformin. We appreciate your attention to this aspect and thank you again for your thoughtful review and valuable suggestions.

The major strength of this work is its scope, including detailed mouse phenotyping, inter-disciplinary methods, and numerous complementary experiments. The antibiotic depletion and FMT experiments provide support for a role of the gut microbiota in this mouse model.

A major limitation is the lack of studies narrowing down which microbes are responsible. Sequencing data is shown, but no follow-up studies are done with bacterial isolates or defined communities.

We acknowledge the limitation of our study in not narrowing down the specific microbes responsible for the observed effects. We hold the opinion that metformin exerts its effects through modulation of specific metabolic pathways unique to the microbial community. Previous study has shown that metformin can inhibit microbial folate metabolism, leading to longevity-promoting effects that are not attributed to a single colony or strain[1]. Similarly, the impact of metformin on amino acid metabolism in the microbial community appears to be widespread. While further investigations with bacterial isolates or defined communities are needed, our findings suggest that metformin's effects on microbial metabolism are complex and involve multiple members of the microbial community.

The link to GABA is also somewhat tenuous. While it does match the phenotypic data, there are no targeted experiments in which GABA producing microbial communities/strains are compared to a control community/strain. As such, it seems difficult to know how much of the effects in this model are due to GABA vs. other metabolites.

We agree with your point regarding the tenuous link to GABA in our study. While we did observe an increase in GABA as the only amino acid following metformin treatment, and this finding has not been reported previously, we acknowledge the need for targeted experiments comparing GABA-producing microbial communities/strains to control communities/strains. Previous literatures suggest that metformin's modulation of the microbiota can vary significantly depending on the disease context, with different microbial populations exhibiting differential responses[2-4]. Given this complexity, we opted to study the overall microbial community response to metformin rather than focusing on specific strains. Additionally, our detection of key enzymes involved in GABA synthesis at the community level further supports our findings.

My major recommendation would be to revise the title, abstract, and discussion to provide more qualification and to consider alternative interpretations.

We appreciate your feedback and understand your concern regarding the need for more qualification and consideration of alternative interpretations. We hope to have more specific and detailed suggestions you may have to enhance the clarity and qualification of our title and abstract. Furthermore, we have tried to revise discussion in order to enhance the scientific rigor and logical coherence of our study. If you have any specific recommendations or insights, we would be more than willing to make further revisions to address those concerns.

Some key controls are also missing, which could be addressed by repeat experiments in the mouse model.

We appreciate your suggestion to include additional key controls in the mouse model experiments. We have conducted repeat experiments to test the effect of antibiotics in the absence of metformin to differentiate between the effects of the model itself and the interaction of metformin with antibiotics. As results of liver injury indicators shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (Figure 1).

Author response image 1.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

The antibiotic depletion experiment would be improved by testing the effect of antibiotics in the absence of metformin, to see if the effect is just driven by the model itself as opposed to an interaction between metformin and antibiotics.

For the antibiotic depletion experiment, we had used antibiotics (Abx) for the mice of modeling, and the survival rate and liver function detection suggested that Abx had no extra effect on liver, which demonstrated that the effect is just driven by the model itself as opposed to an interaction between metformin and antibiotics (Figure 2).

Author response image 2.

Figure2 a: Survival rate between IR and IR + Abx group; b: Serum ALT level; c: Serum AST level.

References

[1] CABREIRO F, AU C, LEUNG K Y, et al. Metformin Retards Aging in C. elegans by Altering Microbial Folate and Methionine Metabolism [J]. Cell, 2013, 153(1): 228-39.

[2] LIANG H, SONG H, ZHANG X, et al. Metformin attenuated sepsis-related liver injury by modulating gut microbiota [J]. Emerg Microbes Infect, 2022, 11(1): 815-28.

[3] SUN L, XIE C, WANG G, et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin [J]. Nat Med, 2018, 24(12): 1919-29.

[4] ZHAO H Y, LYU Y J, ZHAI R Q, et al. Metformin Mitigates Sepsis-Related Neuroinflammation via Modulating Gut Microbiota and Metabolites [J]. Frontiers in Immunology, 2022, 13:797312.

Reviewer #2 (Public Review):

The authors examine the use of metformin in the treatment of hepatic ischemia/reperfusion injury (HIRI) and suggest the mechanism of action is mediated in part by the gut microbiota and changes in hepatic ferroptosis. While the concept is intriguing, the experimental approaches are inadequate to support these conclusions.

The histological and imaging studies were considered a strength and reveal a significant impact of metformin post-HIRI.

Thank you for reviewing our paper titled “Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis”. We appreciate your insightful comments and suggestions, which have provided valuable insights into improving the quality and credibility of my research. We agree with your assessment that the experimental approaches used in this study may have limitations in supporting the conclusions drawn, and we appreciate your recognition of the strength of our histological and imaging studies, which clearly demonstrate the impact of metformin post-HIRI.

Weaknesses largely stem from the experimental design. First, use of the iron chelator DFO would be strengthened using the ferroptosis inhibitor, liproxstatin.

Your suggestion to employ the ferroptosis inhibitor, liproxstatin, in addition to the iron chelator DFO is well-taken. Incorporating liproxstatin into our experimental setup would provide a more comprehensive understanding of the involvement of hepatic ferroptosis in the mechanism of action of metformin. Therefore, we employed liproxstatin to inhibit HIRI and detected some core indicators of liver injury. As figure 3 shown, liproxstatin can reduce liver injury, restore liver GSH level and inhibit Fe accumulation, suggesting that ferroptosis plays an important role in HIRI. We hope this modification will enhance the credibility of our conclusions.

Author response image 3.

Figure3 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level; d: Liver GSH level; e: Liver Fe level.

Second, the impact of metformin on the microbiota is profound resulting in changes in bile acid, lipid, and glucose homeostasis. Throughout the manuscript no comparisons are made with metformin alone which would better capture the metformin-specific effects.

Thank you for raising an important point regarding the impact of metformin on the microbiota and its potential effects on bile acid, lipid, and glucose homeostasis. It has well known that that the effects of metformin on normal blood glucose and lipid metabolism are minimal. Metformin primarily exerts its effects in cases of impaired glucose tolerance, which is why it is widely used for non-diabetic conditions. Regarding the changes in bile acid metabolism and chronic cholesterol and lipid elevation, these associations are typically observed in chronic liver disease models. Since our study focuses on an acute model of HIRI, we did not specifically investigate these changes.

Lastly, the absence of proper controls including germ free mice, metformin treated mice, FMT treated mice, etc make it difficult to understand the outcomes and to properly reproduce the findings in other labs.

Lastly, we acknowledge your concern regarding the absence of proper controls, including germ-free mice, metformin-treated mice, and FMT -treated mice. We understand that these controls are essential for robustly interpreting and reproducing our findings. Therefore, we have added a batch of experiments for verification. As results shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (Figure 1). We hope the result of these controls could address your valid point and provide a more comprehensive framework for understanding the outcomes.

Author response image 4.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

Overall, while the concept is interesting and has the potential to better understand the pleiotropic functions of metformin, the limitations with the experimental design and lack of key controls make it challenging to support the conclusions.

We genuinely appreciate your constructive criticism and the time you have taken to evaluate my work. Your feedback has shed light on the limitations of our experimental design and the need for key controls, which we have addressed in revised manuscript. If you have any further recommendations or concerns, we would be more than willing to incorporate them into my future work.

Reviewer #3 (Public Review):

The study presented in this paper explores the role of gut microbiota in the therapeutic effect of metformin on HIRI, as supported by fecal microbiota transplantation (FMT) experiments. Through high throughput sequencing and HPLC-MS/MS, the authors have successfully demonstrated that metformin administration leads to an increase in GABA-producing bacteria. Moreover, the study provides compelling evidence for the beneficial impact of GABA on HIRI.

Thank you for your valuable feedback on our paper exploring the role of gut microbiota in the therapeutic effect of metformin on hepatic ischemia-reperfusion injury (HIRI). We appreciate your positive remarks and suggestions for improvement. In response to your comments, we have revised the manuscript accordingly. We have included additional details on the high throughput sequencing and HPLC-MS/MS methods used to analyze the gut microbiota and GABA levels. This should provide readers with a clearer understanding of our experimental approach and the evidence supporting our findings.

Regarding your suggestion to further investigate the mechanisms underlying the beneficial impact of GABA on HIRI, we agree that this is an important direction for future research. We plan to conduct additional studies to explore the specific mechanisms by which GABA exerts its protective effects on HIRI in the future. We also supplemented discussion of potential therapeutic strategies targeting GABAergic pathways in the discussion section.

Thank you once again for your insightful comments. We believe that these revisions have strengthened the manuscript and improved its scientific rigor. We hope that you find the revised version to be satisfactory and look forward to your further feedback.

Reviewer #1 (Recommendations For The Authors):

The writing could be improved. Multiple typos are found throughout and there is an overuse of adverbs like "expectedly". You should let the reader decide what is or is not expected. Try to avoid terms like "confirmed" or "validated", which only applies if you knew the result a priori. Remove underscores in species names. The Results section is also very difficult to interpret given the lack of explanation of experimental design. For example, the human study is only briefly mentioned within a larger paragraph on mouse data, without any explanation as to the study design. Similar issues are true for the transcriptomics and amplicon sequencing - it would help the reader to explain what samples were processed, the timepoints, etc.

Thank you for your valuable feedback on our manuscript entitled “Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis” We appreciate your constructive comments and insightful suggestions for improvement.

We have carefully reviewed your comments and have made several revisions to enhance the clarity and readability of the manuscript. We have addressed the issue of multiple typos and have removed the overuse of adverbs, such as “expectedly,” to allow readers to draw their own conclusions from the results. Additionally, we have eliminated terms like “confirmed” or “validated” that may imply a priori knowledge of the results.

We apologize for the lack of clarity regarding the experimental design in the Results section. We have now provided a more detailed explanation of the study design for the human study, transcriptomics, and amplicon sequencing experiments. This includes information on the samples processed, timepoints, and other relevant details, to aid readers in understanding the experimental procedures.

In response to your comment about removing underscores in species names, we have revised the text accordingly to ensure consistency and accuracy in the species nomenclature used throughout the manuscript.

Once again, we sincerely appreciate your valuable input, which has helped us improve the quality of our manuscript. We hope that the revised version now meets your expectations and look forward to any further feedback you may have.

Thank you for your time and attention.

Line 53 - prebiotics aren't "microbial agents"

We apologize for this error, which we have corrected. (line 55: “Microbial agents, such as synbioticsprebiotics and probiotics…”)

Line 88 - sequencing doesn't "verify the critical role of gut microbiota"

We apologize for this error, which we have corrected. (line 90: “In order to verifyclarify the critical role of gut microbiota in the pleiotropic actions of metformin,22-24 fecal samples were collected from the mice to perform 16S rRNA sequencing.

Line 92 - missing a citation for the "microbiota-gut-liver axis theory"

We have corrected it in manuscript. (line 93: “Next, as the microbiota-gut-liver axis theory indicates,25 HIRI-induced dysfunction of the gut barrier may aggravate liver damage by disrupting the gut microbiota.”)

Line 112 - it's very surprising to me that FMT led to lower alpha diversity, which seems impossible.

We understand your surprise regarding the observed decrease in alpha diversity after FMT. Our findings indeed deviate from the commonly observed pattern of increased alpha diversity post-FMT. We have carefully re-examined our data and conducted additional analyses to ensure the accuracy of our results. After thorough investigation, we have identified a potential reason for this unexpected outcome, which we believe could shed light on this phenomenon. We hypothesize that the lower alpha diversity observed in our study might be attributed to the specific characteristics of the donor microbiota used for FMT. While the donor microbiota exhibited certain beneficial properties associated with the therapeutic effect on HIRI, it could have presented a limited diversity compared to the recipient’s original gut microbiota. This discrepancy in diversity could have contributed to the observed decrease in alpha diversity following FMT.

To further support our hypothesis, we have included a discussion on this unexpected finding in the revised manuscript. We believe that this addition will provide a more comprehensive understanding of the results and help contextualize the observed decrease in alpha diversity following FMT.

Line 117 - Antibiotics don't "identify the function of gut microbes." Need to specify which antibiotics were used and for how long.

We have corrected it in manuscript. (line 119: “To further identify the function of gut microbes, experiments were designed, and combination treatment of antibiotics (1 mg/mL penicillin sulfate, 1 mg/mL neomycin sulfate, 1 mg/mL metronidazole and 0.16 mg/mL gentamicin) and metformin were employed for 1 week before IR treated.”)

Line 120 - this experiment shows that the gut microbiota (or antibiotics more precisely) matters, not the "reshaped gut microbiota"

We have corrected it in manuscript. (line 124: “The results confirmed that reshaped gut microbiota is critical for the effect of metformin against HIRI.”)

Line 122 - need to reword this subheading and the concluding sentence. The main takeaway is that the FMT improved markers of ferroptosis, but no additional causal links are provided here.

We have revised in manuscript. (line 125: “FMT alleviates HIRI-induced ferroptosis through reshaped fecal microbiota.”)

Line 141 - need to explain what transcriptomics data was generated and how it was analyzed.

We have revised in manuscript. (line 144: “To elucidate the molecular mechanisms through which pathway participates metformin-treated IR injury, we analysed gene expression profiles of each group mice. Transcriptome sequencing analysis revealed that 9697 genes were in common among four groups (Supplementary Figure 6). Therefore, we used these common genes for KEGG analysis, showing that The transcriptome analysis of liver tissues showed that similar mRNA changes between Met group and FMT group are mainly concentrated in the three top pathways: lipid metabolism, carbohydrate metabolism, and amino acid metabolism (Fig 4a).”)

Line 150 - change to "16S rRNA gene sequencing". Typo: "mice microbes".

We have revised in manuscript. (line 156: “Moreover, it was observed that the genus of Bacteroides had a significant increase based on the 16s rRNA gene sequencing of metformin-treated mice microbes.”)

Line 152 - upregulated refers to gene expression, change to enriched.

We have revised in manuscript. (line 171: “Detailedly, the species of Bacteroides containing Bacteroides thetaiotaomicron, Bacteroides unifomis, and Bacteroides salyersiae, were enriched in human gut after metformin administration (Fig. 4i).”)

Line 159 - typo: "prokaryotes"

We have revised in manuscript. (line 165: “In order to further identify the increased GABA originates from gut microbiota, two key enzymes of prokaryotes protokaryotic GABA synthesis, GAD and PAT, were detected on DNA level, finding that both of them are significantly increased in the feces from IR+Met and IR+FMT groups (Fig. 4h).”)

Line 161 - the human study should be under a new sub-heading and provide more details.

We have revised in manuscript. (line 168: In order to clarify the specific effects of metformin on microbiota, given the big safety margin, healthy volunteers were recruited for a 1 week of daily oral 500mg dose of metformin trial. Fecal samples were collected before and after oral administration of metformin for metagenomic analysis .”)

Line 197 - It's unclear why the current study conflicts with prior literature. Is it due to the disease model, the starting microbiota, something else? Please add more discussion.

Thank you for bringing this important point to our attention, and we appreciate your valuable input. We agree that it is important to discuss the potential reasons for the discrepancy between our findings and prior literature on metformin-reshaped microbiota. In our study, we used a disease model of HIRI, which may have unique characteristics compared to other disease models. It is possible that the specific disease model influenced the response of the gut microbiota. Additionally, the starting microbiota of the recipients and the characteristics of the donor microbiota used for FMT could also play a role in the disparity. We have expanded the discussion section of our revised manuscript to further address these potential factors and their implications. We hope that this additional information will provide a more comprehensive explanation for the discrepancy between our study and prior literature.

Figure 1a - change to Kaplan Meier not ANOVA. Specify the contrast - which groups are being compared?

We have revised in Figure 1a.

Figure 1e, alpha diversity - relabel "sobs" with "observed OTUs". Change to 3 bars with error and add statistics.

We have revised in Figure 1e.

Figure 1e, PCA - this should be a separate panel (1f). Color of big red circle doesn't match the points. Add PERMANOVA p-value/R2. Change to OTUs not genera. Better yet, use amplicon sequence variants from DADA2.

We have revised in Figure 1e..

Figure 2a - Change to Kaplan Meier. Also, it's unclear if residual metformin could be in the donor samples.

We have revised in Figure 2a.

Figure 2f, alpha diversity - relabel "sobs" with "observed OTUs". Change to 3 bars with error and add statistics.

We have revised in Figure 2f.

Figure 2f, PCA - this should be a separate panel (2g). Color of big orange circle doesn't match the points. Add PERMANOVA p-value/R2. Change to OTUs not genera. Better yet, use amplicon sequence variants from DADA2.

We have revised in Figure 2f.

Figure 4b - check units, shouldn't this be ng/mg (i.e. weight not volume).

We have revised in Figure 4b.

Figure 4c,d - need more explanation in the legend and Results as to what is shown here.

We have revised in Figure 4c,d.

Figure 4d - unclear why only Bacteroides are shown here or if the p-values are adjusted for multiple comparisons.

Thank you for your comment regarding Figure 4d in our manuscript. We apologize for the confusion caused. The reason why only Bacteroides is shown in Figure 4d is because we specifically wanted to investigate the changes in Bacteroides abundance following metformin treatment.

In the mouse experiments, we observed a significant increase in Bacteroides after metformin treatment. To investigate if a similar change occurs in healthy volunteers, we examined the levels of Bacteroides in fecal samples before and after oral administration of metformin. We found that the abundance of Bacteroides also increased in the human gut after metformin administration, consistent with the results from the animal experiments. Regarding the p-values, we apologize for not mentioning whether they were adjusted for multiple comparisons in the figure legend. In our revised manuscript, we have provided a clarification stating that the p-values were adjusted using the appropriate method. We appreciate your feedback and hope that this explanation clarifies the rationale behind Figure 4d. Thank you for your valuable input.

Reviewer #2 (Recommendations For The Authors):

Below I've listed several suggestions to improve the paper.

1. Controls - the authors should include metformin only treated mice, FMT only treated mice, etc. Additionally, germ free mice treated with metformin and HIRI would be helpful to better implicate the gut microbiome in these beneficial effects.

Thank you for your suggestion regarding the inclusion of additional control groups in our study. We agree that including metformin only treated mice, FMT only treated mice, and germ-free mice treated with metformin and HIRI would provide valuable insights into the role of the gut microbiome in the observed beneficial effects.

Therefore, we have included metformin only treated mice, FMT only treated mice and Abx only treated mice as supplement to better assess the specific contribution to the observed effects. As results shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (figure1).

We appreciate your input and believe that the inclusion of these additional control groups will strengthen our study and provide a more comprehensive understanding of the role of the gut microbiome in the therapeutic effects observed.

Author response image 5.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

1. More thorough characterization of metabolite pools. Metformin is known to influence many pathways including bile acids and lipids. These important molecules should be measures as they likely play a key role in the observed protective effect. In fact, many of the key changes displayed in Figure 3H are involved in lipid metabolism.

Thank you for your valuable feedback regarding the characterization of metabolite pools in our study. We appreciate your suggestion to measure the influence of metformin on bile acids and lipid metabolism, as they are crucial pathways that may play a significant role in the observed protective effect.

Regarding bile acids, we agree that they are important in the context of metformin’s influence on metabolic pathways. However, it is important to note that the impact of metformin on bile acids appears to be more prominent in chronic liver disease models. In our acute model, the changes in bile acids were not as significant. Instead, our results primarily indicate a close association between lipid changes and hepatic ferroptosis. Metformin significantly modulates lipid metabolism, thereby alleviating liver ferroptosis.

Additionally, we have conducted metagenomic sequencing on the gut microbiota of healthy volunteers before and after oral administration of metformin. While analyzing the data, we did not observe significant changes in key genes involved in regulating bile acid variations. This might be attributed to the healthy volunteers used in our study, where significant changes in bile acids were not induced.

We appreciate your insightful comments and suggestions, which have shed light on the importance of characterizing bile acids and lipid metabolism in our study. While the impact of bile acids may be more evident in chronic liver disease models, our findings highlight the significant influence of metformin on lipid metabolism, closely related to hepatic ferroptosis. We will take your suggestions into account for future studies to further explore the role of bile acids and their regulation by metformin.

1. Imaging of lipid ROS is not quantitative. The authors should conduct more standard assays with BODIPY 581/591 C11 using cell lysates.

We appreciate your suggestion to conduct more standard assays using BODIPY 581/591 C11 with cell lysates.

We would like to clarify that we did indeed utilize assays with BODIPY 581/591 C11 to detect and measure lipid ROS in our study. The detailed description of these assays can be found in the Methods section of our paper. We followed established protocols and guidelines to ensure accurate and reliable measurements of lipid ROS levels.

We acknowledge that imaging techniques may have limitations in providing quantitative data. However, we employed BODIPY 581/591 C11 assays as a widely accepted and commonly used method to assess lipid ROS levels. This allowed us to obtain qualitative and semi-quantitative information on the changes in lipid ROS levels in response to metformin treatment.

1. Liproxstatin may be a better drug choice or at the very least should be used to compare with the DFO data

Thank you for your suggestion. We have taken your advice into consideration and conducted an evaluation of Liproxstatin as a ferroptosis inhibitor. Our findings indicate that Liproxstatin significantly improves HIRI (Figure C). We believe that incorporating Liproxstatin in our research will provide valuable insights and allow for a comprehensive comparison with the DFO data.

Author response image 6.

Figure3 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level; d: Liver GSH level; e: Liver Fe level.

1. The rationale for how GABA was selected is not clear. I am surprised that there were not more significant metabolite changes. It might be better to show a volcano plot of heatmap of the significantly changed features.

Thank you for raising an important question regarding the rationale for selecting GABA as the focus metabolite in our study. Initially, we also had concerns about the limited number of significant metabolite changes observed. However, through our comprehensive metabolomic profiling, we identified GABA as the most significantly altered metabolite following HIRI.

It is worth noting that we specifically focused on the measurement of 22 essential amino acids in our analysis. While it is possible that changes in non-essential amino acids may have occurred, we did not examine them in this study. Nevertheless, we have since used additional methods to validate the upregulation of GABA levels, and the biological effects observed support the specific role of GABA in protecting against HIRI. Based on the fact that GABA was the only significant amino acid, the volcano plot was of little significance, so we did not supplement this plot.

We appreciate your valuable input and thank you for bringing up this important issue.

1. The manuscript needs to be proofread and edited. There are a variety of typos and grammar issues throughout.

Thank you for your feedback. We acknowledge that the manuscript requires proofreading and editing, as we have identified several typos and grammar issues. We will try to ensure that the necessary revisions are made to improve the overall quality of the manuscript.

Reviewer #3 (Recommendations For The Authors):

However, I have some major concerns for the manuscript.

1. Line 26 16S rRNA and metagenomic sequencing alone can't accurately confirm the improvement effect of GABA producing bacteria on HIRI. In fact, transcriptome analysis, HPLC-MS/MS and other methods were also used in this paper, so the language expression here is not appropriate

Thank you for pointing out the language expression issue in line 26 of the manuscript. We apologize for any confusion caused. You are correct in stating that 16S rRNA and metagenomic sequencing alone may not accurately confirm the improvement effect of GABA-producing bacteria on HIRI. In our study, we employed a combination of multiple methods, including transcriptome analysis, HPLC-MS/MS, especially detection of bacteria GABA key synthetases, PAT and GAD, to comprehensively investigate the impact of GABA-producing bacteria on HIRI.

We have revised the language in line 26 to reflect the broader range of methods used in our study to support the conclusions regarding the improvement effect of GABA-producing bacteria on HIRI.

1. The Introduction section needs to add a description of the previous research on the association between HIRI and ferroptosis

Thank you for your suggestion regarding the inclusion of a description of the association between HIRI and ferroptosis in the Introduction section. We agree that this is an important aspect to address. However, upon further consideration, we have decided to move the discussion of ferroptosis and its potential role in HIRI to the Discussion section, as it aligns better with the logical flow of the manuscript. This allows us to discuss the potential implications and future directions in a more organized and coherent manner.

1. Authors should provide quantified figure or table next to the results of western blot that are more convenient to understand.

We have revised in manuscript. (See sfigure 7)

1. In this paper, FMT experiments are used to verify that metformin remodeled gut microbiota can play a role in improving HIRI. The operation steps of FMT should be described more specifically in the method part

*What is the fecal donor information for FMT?

*Line272 Did the IR + FMT group put the transplanted microbiota of FMT directly into the drinking water like the other treatment groups? Will such an operation affect the quality and quantification of the transplanted microbiota and lead to the loss of microbiota species? It is crucial for the authors to provide a clear and thorough clarification regarding these matters within the context of their FMT experiment.

Thank you for your feedback regarding the need for a more detailed description of the fecal microbiota transplantation (FMT) procedure and clarification regarding the IR + FMT group in our manuscript. We appreciate your suggestions and we have taken them into consideration.

In our study, the fecal donor for FMT was obtained from mice that had been orally administered metformin. The fecal microbiota was collected and processed to remove any residual metformin before transplantation. Specifically, the microbiota for the IR + FMT group was administered through gavage, as stated in line 272. This method does not affect the quality or quantity of the transplanted microbiota, nor does it lead to a loss of microbiota species. We understand the importance of providing clear and thorough clarification regarding these matters. Therefore, we have included additional specific details of the FMT procedure in the revised version of the manuscript. We hope that this clarification addresses your concerns and provides a more comprehensive understanding of our FMT experiment.

1. The presentation of transcriptomic analysis results in the manuscript is insufficiently comprehensive and specific, as they are solely depicted through Fig 4a. Relying solely on Fig 4a is inadequate to establish the definitive roles of the met group and FMT group in ferroptosis compared to other groups. Therefore, the authors should provide additional transcriptomic analysis results to ascertain the specific effects of the met group and FMT group in ferroptosis, as well as their comparison with other groups.

Thank you for your feedback regarding the comprehensiveness of our transcriptomic analysis results in the manuscript. We understand your concerns and appreciate your suggestion. In our study, we have provided additional data beyond Fig 4a to support the specific effects of the met group and FMT group in ferroptosis, as well as their comparison with other groups. Specifically, in Figure 3, we have included Western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR) data to confirm the involvement of ferroptosis in HIRI and the role of metformin in attenuating ferroptosis. Moreover, we have presented transcriptomic analysis results in Figure 3h, which includes a heatmap of genes related to lipid metabolism. These findings can strengthen our conclusions regarding the importance of ferroptosis in HIRI and the protective effects of metformin against ferroptosis. We hope that these data address your concerns and provide a more comprehensive understanding of our research findings.

Author Response

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

eLife assessment

This fundamental study provides compelling evidence to explain how chemical variations within a set of kinase inhibitors drive the selection of specific Erk2 conformations. Conformational selection plays a critical role in targeting medically relevant kinases such as Erk2 and the findings reported here open new avenues for designing small molecule inhibitors that block the active site while also steering the population of the enzyme into active or inactive conformations. Since protein dynamics and conformational ensembles are essential for enzyme function, this work will be of broad interest to those working in drug development, signal transduction, and enzymology.

Public Reviews:

Reviewer #1 (Public Review):

Summary: The authors set out to determine how chemical variation on kinase inhibitors determines the selection of Erk2 conformations and how inhibitor binding affects ERk2 structure and dynamics.

Strengths: The study is beautifully presented both verbally and visually. The NMR experiments and the HDX experiments complement each other for the study of Erk2 solution dynamics. X-ray crystallography of Erk2 complexes with inhibitors shows small but distinct structural changes that support the proposed model for the impact of inhibitor binding.

Weaknesses: A discussion of compound residence time for the different compounds and kinase constructs and how it could affect the very slow HDX rates might be helpful. For example, could any of the observed effects in Figure 4 be due to slow compound dissociation rather than slowed down kinase dynamics? What would be the implications?

Response: Rate constants for kon and koff were estimated for three inhibitors using surface plasmon resonance:

Author response table 1.

SPR estimates of Kd for selected inhibitors ranged between 0.03-3 nM. All HDX time courses involved prebinding of 20 µM inhibitor and 17 µM ERK2 for 30 min (predicted occupancy 99.9%), followed by deuteration time courses with 20 µM inhibitor and 1.7 µM ERK2. Estimated rates of dissociation were ~0.0003-0.007 s-1 and rates of binding were 20-100 s-1 for the inhibitors tested. Because the binding rates are faster than the intrinsic H-D exchange rate at pD 7 (~1 s-1), we expect ligands to rebind and form the enzyme:ligand complex faster than the free enzyme undergoes exchange. Therefore, HDX rates should mostly reflect deuteration of the inhibitor-bound enzyme for all inhibitors.

Reviewer #2 (Public Review):

Erk2 is an essential element of the MAP kinase signaling cascade and directly controls cell proliferation, migration, and survival. Therefore, it is one of the most important drug targets for cancer therapy. The catalytic subunit of Erk2 has a bilobal architecture, with the small lobe harboring the nucleotide-binding pocket and the large lobe harboring the substrate-binding cleft. Several studies by the Ahn group revealed that the catalytic domain hops between (at least) two conformational states: active (R) and inactive (L), which exchange in the millisecond time scale based on the chemical shift mapping. The R state is a signature of the double phosphorylated Erk2 (2P-Erk2), while the L state has been associated with the unphosphorylated kinase (0P-Erk2). Interestingly, the X-ray structures reveal only minimal differences between these two states, a feature that led to the conclusion that active and inactive states are structurally similar but dynamically very different. The Ahn group also found that ATP-competitive inhibitors can steer the populations of Erk2 either toward the R or the L state, depending on their chemical nature. The latter opens up the possibility of modulating the activity of this kinase by changing the chemistry of the ATP-competitive inhibitor. To prove this point, the authors present a set of nineteen compounds with diverse chemical substituents. From their combined NMR and HDX-Mass Spec analyses, fourteen inhibitors drive the kinase toward the R state, while four compounds keep the kinase hopping between the R and L states. Based on these data, the authors rationalize the effects of these inhibitors and the importance of the nature of the substituents on the central scaffold to steer the kinase activity. While all these inhibitors target the ATP binding pocket, they display diverse structural and dynamic effects on the kinase, selecting a specific structural state. Although the inhibited kinase is no longer able to phosphorylate substrates, it can initiate signaling events functioning as scaffolds for other proteins. Therefore, by changing the chemistry of the inhibitors it may be possible to affect the MAP cascade in a predictable manner. This concept, recently introduced as proof of principle, finds here its significance and practical implications. The design of the next-generation inhibitors must be taken into account for these design principles. The research is well executed, and the data support the author's conclusions.

Reviewer #3 (Public Review):

Summary: Anderson et al utilize an array of orthogonal techniques to highlight the importance of protein dynamics for the function and inhibition of the kinase ERK2. ERK2 is important for a large variety of biological functions.

Strengths: This is a thorough and detailed study that uses a variety of techniques to identify critical molecular/chemical parameters that drive ERK2 in specific states.

Weaknesses: No details rules were identified so that novel inhibitors could be designed. Nevertheless, the mode of action of these existing inhibitors is much better defined.

Response: As recommended we added a sentence to the Discussion suggesting that inhibitors that perturb the β1-β2-β3 sheet in such a way that moves helix αC and αL16 away from the binding site might confer R-state selection. We view this as a preliminary model for predicting conformation selection in ERK2.

Reviewer #1 (Recommendations For The Authors):

Maybe the authors can comment on how the HDX timescale and the NMR timescale relate to each other and how such different timescales can report on the same event. In particular, the HDX timescale appears to be on the scale on minutes to tens hours (e.g. 2P state). How would inhibitor dissociation and rebinding affect the observed HDX signal? Is it worth considering compound residence time for the different compounds/kinase states?

Response: The HDX-MS and NMR experiments report different processes therefore their timescales do not necessarily match. For native state proteins at neutral pH, HDX-MS reports fluctuations that allow solvent exposure of backbone amide N-H, reflecting conformational mobility of the main chain. This is often modeled as a two-state interconversion between “closed” (HDX protected) and “open” (HDX accessible) states. Because the µs-ms timescale of main chain fluctuations is faster than the intrinsic rate of HDX (kexch, ~1 s-1), the observed HDX rate (kobs) can be approximated by the ratio of kopen/kclosed x kexch = Kop x kexch. Therefore, kobs can be considered a thermodynamic measurement that reflects Kop.

The [methyl 13C,1H] NMR CPMG experiment that we used to identify global exchange behavior in Xiao et al (PNAS, 2014) modeled the 2P-ERK2 apoenzyme by a two-state equilibrium (L⇌R) between methyl-ILV conformers, yielding rate constants kL→R 240 s-1 and kR→L 60 s-1. Some methyls had large enough chemical shifts between L and R that they appeared as separate peaks in HMQC spectra that matched the L and R populations estimated by CPMG. In this study, the HMQC peaks shown in Figures 1, 6, and 9 are those that report shifts in L vs R populations and conformation selection for the R-state by VTX11e, BVD523 and triazolopyridine inhibitors.

Where HDX and NMR agree is in their ability to report changes in populations of L and R in 2P-ERK2. This was first shown when both HDX and NMR measurements reported perturbations at the activation loop induced by inhibitors with differential selection for the R- vs L-states (Pegram et al. PNAS, 2019). CPMG measurements then confirmed that methyl probes in the activation loop are included in the global exchange process (Iverson et al., Biochemistry, 2020). Therefore, the HDX and NMR experiments reflect shifts in the equilibrium between L and R conformers, rather than motions with specific timescales.

Reviewer #2 (Recommendations For The Authors):

I believe the paper is suitable for the special issue of Elife dedicated to protein kinases after the authors address minor concerns/comments.

a) Introduction, page 3: "[..] But within the ATP binding site, the conserved residues ...are largely overlapping." Do the authors mean that the residues are overlapping in the X-ray structures? If so, what is the rmsd among the X-ray structures?

Response: The overlap between conserved residues K52, E69, D147, N152 and D165 in 2P- and 0P-ERK2 is presented in Fig. S1C, which shows an overlay between their apoenzyme crystal structures (PDBID: 2ERK, 5UMO). The RMSD of atoms in each residue are: K52 0.63 Å (9 atoms); E69 0.15 Å (9 atoms); D147 0.055 Å (8 atoms); D165 0.88 Å (8 atoms). As recommended, this information was added to the legend to Suppl. Fig. S1.

b) Introduction, page 5: "[...] For example binding of VTX11 partially inhibits...[..]" Please provide a citation.

Response: As recommended we added a citation at end of this sentence (Pegram et al. PNAS, 2019).

c) Introduction, page 5: "[...] N-lobe deformities..." What do the authors mean by deformities? Are there frustrated conformations?

Response: We used the term “deformities” to mean conformational differences, which may be but are not necessarily due to frustration. To avoid confusion, we removed the term “deformities” and replaced it with “conformational changes”.

d) Supplementary Information. The authors report the chemical shift perturbations for several inhibitors. Does the extent of the chemical shift perturbation reflect the strength of the binding for each inhibitor? In other words, do the largest chemical shift perturbations correspond to the highest binding affinity?

Response: The concentrations used in the NMR ligand binding experiments (150 µM ERK2, 180 µM inhibitor) allow 99.9+% complex formation over the 0.03 - 3 nM range of Ki for all inhibitors. Therefore, the chemical shifts report changes in electronic environment between bound and free enzyme. These can be ascribed to first or second sphere contacts with ligand or distal allosteric effects. But they are not likely to reflect differences in binding affinity.

New Suppl. Fig. S3 now adds HMQC titrations of VTX11e and GDC0994 into 2P-ERK2, which confirm binding saturation based on the disappearance of free enzyme peaks.

e) Do the authors have any evidence for the dynamic effects of the different inhibitors? Of course, a systematic analysis of the protein dynamics by NMR will require a significant amount of time and effort beyond this work. However, did the authors measure the effects of the inhibitors on the linewidths of the methyl groups distal from the binding site?<br /> Response: As recommended, we examined linewidths of selected peaks in the presence and absence of inhibitors. The results show no significant systematic differences between bound and free ERK2. Therefore dynamic effects of different inhibitors are not indicated by the available data.

f) The authors identified the b3-aC loop as a critical element for the internal network of interactions. Can this structural element be targeted by small molecules as well?

Response: Yes, in fact the X-ray structures of 0P-ERK2 bound to the inhibitor, SCH772984, and 2P-ERK2 bound to the related compound, SCHCPD336, both show inhibitor occupying a pocket between between strand β3 and helix αC, leading to disruption of β3-αC contacts (Chaikaud et al., NSMB 2014; Pegram et al., PNAS 2019). To the extent that β3-αC contacts are important for conformation selection to the R-state, this may explain why SCH772984 favors the L-state. We revised the Discussion to add this point.

g) The authors should mention a recent paper suggesting that it is possible to control substrate-binding affinity by changing the nature of the ATP-binding inhibitors ((DOI: 10.1126/sciadv.abo0696).

Response. As recommended we added this point and citation to the Discussion.

Reviewer #3 (Recommendations For The Authors):

3.1. The manuscript is well written, but very long and sometimes repetitive. Some parts of the introduction are repeated in the result section and parts of the result section are repeated in the discussion. It will be easy to shorten the work to make it easier to read.

Response: As recommended we streamlined the Discussion to remove some of the repetitive elements, while trying to retain the main conclusions and rationale for readers who are not well versed in kinase structure.

3.2. Only specific residues are shown for the NMR spectra figures - while this is helpful to understand the concept, full spectra need to be shown to allow for direct comparison of the data quality (i.e. in supplemental material). If statements are made that measurements are done under full saturation - it should be shown that saturation is achieved in the measurements. All relaxation data should be made available - similar to CSPs.

Response: As recommended, new Suppl. Figs. S2 and S9 were added to show the full spectra of each inhibitor complex analyzed by NMR. New Suppl. Fig. S3 now adds titrations of 2P-ERK2 with VTX11e and GDC0994.The results confirm binding saturation based on the disappearance of free enzyme peaks.

3.3. No validation report was provided, nor a PDB number - so it is unclear if the crystal structures have been submitted - they need to be submitted in order to also access an mtz file, which is critical to understanding the quality of the structure (especially the ligand). This makes it difficult to assess the quality of the structures.

Response: Table S1 has been revised to show data collection and refinement parameters for PDBID: 8U8K (2PERK2:Inh#8, Fig. 8C) and 8U8J (2P-ERK2:Inh#16, Fig. 8D). RCSB validation reports are attached and PDB depositions have been approved and will be released upon VOR assignment.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Goetz et al. takes a new perspective on sensory information processing in cells. In contrast to previous studies, which have used population data to build a response distribution and which estimate sensory information at about 1 bit, this work defines sensory information at the single cell level. To do so, the authors take two approaches. First, they estimate single cells' response distributions to various input levels from time-series data directly. Second, they infer these single-cell response distributions from the population data by assuming a biochemical model and extracting the cells' parameters with a maximum-entropy approach. In either case, they find, for two experimental examples, that single-cell sensory information is much higher than 1 bit, and that the reduction to 1 bit at the population level is due to the fact that cells' response functions are so different from each other. Finally, the authors identify examples of measurable cell properties that do or do not correlate with single-cell sensory information.

The work brings an important and distinct new insight to a research direction that generated strong interest about a decade ago: measuring sensory information in cells and understanding why it is so low. The manuscript is clear, the results are compelling, and the conclusions are well supported by the findings. Several contributions should be of interest to the quantitative biology community (e.g., the demonstration that single cells' sensory information is considerably larger than previously implied, and the approach of inferring single-cell data from population data with the help of a model and a maximum-entropy assumption).

We thank the reviewer for the excellent summary of our research.

Reviewer #2 (Public Review):

In this paper the authors present an existing information theoretic framework to assess the ability of single cells to encode external signals sensed through membrane receptors.

The main point is to distinguish actual noise in the signaling pathway from cell-cell variability, which could be due to differences in their phenotypic state, and to formalize this difference using information theory.

After correcting for this cellular variability, the authors find that cells may encode more information than one would estimate from ignoring it, which is expected. The authors show this using simple models of different complexities, and also by analyzing an imaging dataset of the IGF/FoxO pathway.

The implications of the work are limited because the analysed data is not rich enough to draw clear conclusions. Specifically,

• the authors do not distinguish what could be methodological noise inherent to microscopy techniques (segmentation etc), and actual intrinsic cell state. It's not clear that cell-cell variability in the analyzed dataset is not just a constant offset or normalization factor. Other authors (e.g. Gregor et al Cell 130, 153-164) have re-centered and re-normalized their data before further analysis, which is more or less equivalent to the idea of the conditional information in the sense that it aims to correct for this experimental noise.

We thank the reviewer for the comment. However, we do not believe our analysis is a consequence of normalization artifacts. Prior to modeling the single cell data, we removed well-dependent background fluorescence. This should take care of technical variation related to overall offsets in the data. We agree with the reviewer that background subtraction may not fully account for technical variability. For example, some of the cell-to-cell variability may potentially be ascribed to issues such as incorrect segmentation. Unfortunately, however, attempting to remove this technical variability through cell-specific normalization as suggested by the reviewer1 will diminish to a very large extent the true biological effects related to extensivity (cell size, total protein abundance). We note that these effects are a direct function of cell state-variables (see for example Cohen-Saidon et al.2 who use cell-state specific normalization to improve signaling fidelity). Therefore, an increase in mutual information after normalization does not only reflect removal of technical noise but also accounts for effect of cell state variables.

Nonetheless, as the reviewer suggested, we performed a cell-specific normalization wherein the mean nuclear FoxO levels in each cell (in the absence of IGF) were normalized to one. Then, for each ligand concentration, we collated FoxO response across all cells and computed the channel capacity corresponding to cell-state agnostic mutual information ICSA. As expected, ICSA increases from ∼0.9 bits to ∼1.3 bits when cell-specific normalization was performed (Author response image 1). However, this value is significantly lower than the average ∼1.95 of cell-state specific mutual information ⟨ICee⟩. Finally, we note that the cell specific normalization does not change the calculations of channel capacity at the single cell level as these calculations do not depend on linear transformations of the data (centering and normalization). Therefore, we do not think that our analysis of experimental data suffers from artifacts related to microscopy.

Author response image 1.

Author response image 1. Left: nuclear FoxO response averaged over all cells in the population across different ligand concentration. Right: nuclear FoxO response was first normalized at the single cell level and then averaged over all cells in the population across different ligand concentrations.

• in the experiment, each condition is shown only once and sequentially. This means that the reproducibility of the response upon repeated exposures in a single cell was not tested, casting doubt on the estimate of the response fidelity (estimated as the variance over time in a single response).

The reviewer raises an excellent question about persistence of cell states. To verify that cell states are indeed conserved at the time scale of the experiment, we reanalyzed data generated by Gross et al.3 wherein cells were perturbed with IGF (37.5 pM), followed by a washout which allowed the cells to reach pre-stimulation nuclear FoxO levels, followed by a re-perturbation with the same amount of IGF. Nuclear FoxO response was measured at the single cell level after 90 minutes with IGF exposure both these times. Since the response x to the same input u was measured twice in the same cell (x1 and x2), we could evaluate the intrinsic variability in response at the single cell level. We then compared this intrinsic variability to the extrinsic cell-state dependent variability in the population.

To do so, we computed for each cell δ=x1-x2 the difference between the two responses. reviewer Figure 2 show the histogram p(δ) as computed from the data (pink) and the same computed from the model that was trained on the single cell data (blue). We also computed p(δ0) which represented the difference between responses of two different cells both from the data and from the model.

As we see in Author response image 2, the distribution p(δ) is significantly narrower than p(δ0) suggesting that intracellular variability is significantly smaller than across-population variability and that cells’ response to the same stimuli are quite conserved, especially when compared to responses in randomly picked pairs of cells. This shows that cell states and the corresponding response to extracellular perturbations are conserved, at least at the time scale of the experiment. Therefore, our estimates of cell-to-cell variability signaling fidelity are stable and reliable. We have now incorporated this discussion in the manuscript (lines 275-281).

Author response image 2.

Author response image 2. Left: Cells were treated with 37.5 pM of IGF for 90 minutes, washed out for 120 minutes and again treated with 37.5 pM of IGF. Nuclear FoxO was measured during the treatment and the washout. The distributions on the left show the difference in FoxO levels in single cells after the two 90 minutes IGF stimulations (pink: data, blue: model). Right: Distribution of difference in FoxO levels in two randomly picked cells after 90 minutes of exposure to 37.5 pM IGF.

• another dataset on the EGF/EGFR pathway is analyzed, but no conclusion can be drawn from it because single-cell information cannot be directly estimated from it. The authors instead use a maximum-entropy Ansatz, which cannot be validated for lack of data.

We thank the reviewer for this comment. We agree with the reviewer that we have not verified our predictions for the EGF/EGFR pathway. That study was meant to show the potential generality of our analysis. We look forward to validating our predictions for the EGF/EGFR pathway in future studies.

Reviewer #3 (Public Review):

Goetz, Akl and Dixit investigated the heterogeneity in the fidelity of sensing the environment by individual cells in a population using computational modeling and analysis of experimental data for two important and well-studied mammalian signaling pathways: (insulin-like growth factor) IGF/FoxO and (epidermal growth factor) EFG/EFGR mammalian pathways. They quantified this heterogeneity using the conditional mutual information between the input (eg. level of IGF) and output (eg. level of FoxO in the nucleus), conditioned on the "state" variables which characterize the signaling pathway (such as abundances of key proteins, reaction rates, etc.) First, using a toy stochastic model of a receptor-ligand system - which constitutes the first step of both signaling pathways - they constructed the population average of the mutual information conditioned on the number of receptors and maximized over the input distribution and showed that it is always greater than or equal to the usual or "cell state agnostic" channel capacity. They constructed the probability distribution of cell state dependent mutual information for the two pathways, demonstrating agreement with experimental data in the case of the IGF/FoxO pathway using previously published data. Finally, for the IGF/FoxO pathway, they found the joint distribution of the cell state dependent mutual information and two experimentally accessible state variables: the response range of FoxO and total nuclear FoxO level prior to IGF stimulation. In both cases, the data approximately follow the contour lines of the joint distribution. Interestingly, high nuclear FoxO levels, and therefore lower associated noise in the number of output readout molecules, is not correlated with higher cell state dependent mutual information, as one might expect. This paper contributes to the vibrant body of work on information theoretic characterization of biochemical signaling pathways, using the distribution of cell state dependent mutual information as a metric to highlight the importance of heterogeneity in cell populations. The authors suggest that this metric can be used to infer "bottlenecks" in information transfer in signaling networks, where certain cell state variables have a lower joint distribution with the cell state dependent mutual information.

The utility of a metric based on the conditional mutual information to quantify fidelity of sensing and its heterogeneity (distribution) in a cell population is supported in the comparison with data. Some aspects of the analysis and claims in the main body of the paper and SI need to be clarified and extended.

1. The authors use their previously published (Ref. 32) maximum-entropy based method to extract the probability distribution of cell state variables, which is needed to construct their main result, namely p_CeeMI (I). The salient features of their method, and how it compares with other similar methods of parameter inference should be summarized in the section with this title. In SI 3.3, the Lagrangian, L, and Rm should be defined.

We thank the reviewer for the comment and apologize for the omission. We have now rewritten the manuscript to include references to previous reviews of works that infer probability distributions4 of cell state variables (lines 156-168). Notably, as we argued in our previous work5, no current method can efficiently estimate the joint distribution over parameters that is consistent with measured single cell data and models of signaling networks. Therefore, we could not use multiple approaches to infer parameter distributions. We have now expanded our discussion of the method in the supplementary information sections.

1. Throughout the text, the authors refer to "low" and "high" values of the channel capacity. For example, a value of 1-1.5 bits is claimed to be "low". The authors need to clarify the context in which this value is low: In some physically realistic cases, the signaling network may need to simply distinguish between the present or absence of a ligand, in which case this value would not be low.

We agree with the reviewer that small values of channel capacities might be sufficient for cells to carry out some tasks, in which case a low channel capacity does not necessarily indicate a network not performing its task. Indeed, how much information is needed for a specific task is a related but distinct question from how much information is provided though a signaling network. Both questions are essential to understand a cell's signaling behavior, with the former being far less easy to answer in a way which is generalizable. In contrast, the latter can be quantitatively answered using the analysis presented in our manuscript.

1. Related to (2), the authors should comment on why in Fig. 3A, I_Cee=3. Importantly, where does the fact that the network is able to distinguish between 23 ligand levels come from? Is this related to the choice (and binning) of the input ligand distribution (described in the SI)?

We thank the reviewer for the comment. The network can distinguish between all inputs used in the in silico experiment precisely because the noise at the cellular level is small enough that there is negligible overlap between single cell response distributions. Indeed, the mutual information will not increase with the number of equally spaced inputs in a sub-linear manner, especially when the input number is very high.

1. The authors should justify the choice of the gamma distribution in a number of cases (eg. distribution of ligand, distribution cell state parameters, such as number of receptors, receptor degradation rate, etc.).

We thank the reviewer for the comment. We note that previous works in protein abundances and gene expression levels (e.g. see6) have reported distributions with positive skews that can be fit well with gamma distributions or log-normal distributions. Moreover, many stochastic models of protein abundance levels and signaling networks are also known to result in abundances that are distributed according to a negative binomial distribution, the discrete counterpart of gamma distribution. Therefore, we chose Gamma distributions in our study. We have now clarified this point in the Supplementary Information. At the same time, gamma distribution only serves as a regularization for the finite data and in principle, our analysis and conclusion do not depend on choice of gamma distribution for abundances of proteins, ligands, and cell parameters.

1. Referring to SI Section 2, it is stated that the probability of the response (receptor binding occupancy) conditioned on the input ligand concentration and number of receptors is a Poisson distribution. Indeed this is nicely demonstrated in Fig. S2. Therefore it is the coefficient of variation (std/mean) that decreases with increasing R0, not the noise (which is strictly the standard deviation) as stated in the paper.

We thank the reviewer of the comment. We have now corrected our text.

1. In addition to explicitly stating what the input (IGF level) and the output (nuclear GFP-tagged FoxO level) are, it would be helpful if it is also stated what is the vector of state variables, theta, corresponding to the schematic diagram in Fig. 2C.

We thank the reviewer of the comment. We have now corrected our text in the supplementary material as well as the main text (Figure 2 caption).

1. Related to Fig. 2C, the statement in the caption: "Phosphorylated Akt leads to phosphorylation of FoxO which effectively shuttles it out of the nucleus." needs clarification: From the figure, it appears that pFoxO does not cross the nuclear membrane, in which case it would be less confusing to say that phosphorylation prevents reentry of FoxO into the nucleus.

We thank the reviewer of the comment. We have now corrected our text (Figure 2 caption).

1. The explanations for Fig. 2D, E and insets are sparse and therefore not clear. The authors should expand on what is meant by model and experimental I(theta). What is CC input dose? Also in Fig. 2E, the overlap between the blue and pink histograms means that the value of the blue histogram for the final bin - and therefore agreement or lack thereof with the experimental result - is not visible. Also, the significance of the values 3.25 bits and 3 bits in these plots should be discussed in connection with the input distributions.

We thank the reviewer of the comment. We have now corrected our text (Figure 2 caption and lines 249-251).

1. While the joint distribution of the cell state dependent mutual information and various biochemical parameters is given in Fig. S7, there is no explanation of what these results mean, either in the SI or main text. Related to this, while a central claim of the work is that establishing this joint distribution will allow determination of cell state variables that differentiate between high and low fidelity sensing, this claim would be stronger with more discussion of Figs. 3 and S7. The related central claim that cell state dependent mutual information leads to higher fidelity sensing at the population level would be made stronger if it can be demonstrated that in the limit of rapidly varying cell state variables, the I_CSA is retrieved.

We thank the reviewer for this excellent comment. We have now added more discussion about interpreting the correlation between cell state variables and cell-state specific mutual information (lines 294-306). We also appreciate the suggestion about a toy model calculation to show that dynamics of cell state variables affects cell state specific mutual information. We have now performed a simple calculation to show how dynamics of cell state variables affects cells’ sensing ability (lines 325-363). Specifically, we constructed a model of a receptor binding to the ligand wherein the receptor levels themselves changed over time through a slow process of gene expression (Author response image 3, main text Figure 4). In this model, the timescales of fluctuations of ligand-free receptors on the cell surface can be tuned by speeding up/slowing down the degradation rate of the corresponding mRNA while keeping the total amount of steady state mRNA constant. As shown in Author response image 3, the dependence of cell-specific mutual information on cell state variable diminishes when the time scale of change of cell state variables is fast.

Author response image 3.

Author response image 3. Cell state dynamics governs cell state conditioned mutual information. A. In a simple stochastic model, receptor mRNA is produced at a constant rate from the DNA and the translated into ligand-free receptors. The number of ligand-bound receptors after a short exposure to ligands is considered the output. B. A schematic showing dynamics of receptor numbers when mRNA dynamics are slower compared to signaling time scales. C. Conditioning on receptor numbers leads to differing abilities in sensing the environment when the time scale of mRNA dynamics τ is slow. In contrast, when the mRNA dynamics are fast (large τ-1), conditioning on cell state variables does not lead to difference in sensing abilities.

Reviewer #1 (Recommendations For The Authors):

My major concerns are mainly conceptual, as described below. With proper attention to these concerns, I feel that this manuscript could be a good candidate for the eLife community.

Major concerns:

1. The manuscript convincingly demonstrates that cells good sensors after all, and that heterogeneity makes their input-output functions different from each other. This raises the question of what happens downstream of sensing. For single-celled organisms, where it may be natural to define behavioral consequences at the single-cell level, it may very well be relevant that single-cell information is high, even if cells respond differently to the environment. But for cells in multicellular organisms, like those studied here, I imagine that most behavioral consequences of sensing occur at the multicellular level. Thus, many cells' responses are combined into a larger response. Because their responses are different, their high-information individual responses may combine into a low-information collective response. In fact, one could argue that a decent indicator of the fidelity of this collective response is indeed the population-level information measure estimated in previous works. Thus, a fundamental question that the authors must address is: what is the ultimate utility of reliable, but heterogeneous, responses for a multicellular system? This question has an important bearing for the relevance of their findings.

We thank the reviewer for this thought-provoking comment. We agree that the fidelity with which cells sense their environment, especially those in multicellular organisms, may not always need to be very high. We speculate that when the biological function of a collection of cells can be expressed as an average over the response of individual cells; high-information but heterogeneous cells can be considered equivalent to low-information homogeneous cells. An example of such a function is population differentiation to maintain relative proportions of different cell types in a tissue or producing a certain amount of extracellular enzyme.

In contrast, we believe that when the biological function involves collective action, spatial patterning, or temporal memory, the difference between reliable but heterogeneous population and unreliable homogeneous population will become significant. We plan to explore this topic in future studies.

1. The authors demonstrate that the agreement is good between their inference approach and the direct estimation of response distributions from single-cell time series data. In fact, the agreement is so good that it raises the question of why one would need the inference approach at all. Is it because single-cell time series data is not always available? Is that why the authors used it for one example and not the other? The validation is an asset, but I imagine that the inference approach is complicated and may make assumptions that are not always true. Thus, its utility and appropriate use must be clarified.

We thank the reviewer for the comment. As the reviewer correctly pointed out, live cell imaging data is not always available and has limited scope. Specifically, optical resolution limits measurements of multiple targets. Moreover, typical live cell measurements measure total abundance or localization and not post-translational modification (phosphorylation, methylation, etc.) which are crucial to signaling dynamics. The most readily available single cell data such those measured using single cell RNA sequencing, immunofluorescence, or flow cytometry are necessarily snapshots. Therefore, computational models that can connect underlying signaling networks to snapshot data become essential when imputing single cell trajectories. In addition, the modeling also allows us to identify network parameters that correlate most strongly with cellular heterogeneity. We have now clarified this point in the manuscript (lines 366-380).

Minor comments:

1. I would point out that the maximum values in the single-cell mutual information distributions (Fig 2D and E) correspond to log2 of the number of inputs levels, corresponding to perfect distinguishability of each of the equally-weighted input states. It is clear that many of the mutual information values cluster toward this maximum, and it would help readers to point out why.

We thank the reviewer for the comment. We have now included a discussion about the skew in the distribution in the text (lines 251-260).

1. Line 216 references Fig 2C for the EGF/EGFR pathway, but Fig 2C shows the FoxO pathway. In fact, I did not see a schematic of the EGF/EGFR pathway. It may be helpful to include one, and for completeness perhaps also one for the toy model, and organize the figures accordingly.

We thank the reviewer for the comment. We did not include three separate schematics because the schematics of the EGF/EGFR model and the toy model are subsets of the schematic of the IGF/FoxO model. We have now clarified this point in the manuscript (Figure 2 caption).

Reviewer #2 (Recommendations For The Authors):

• the simple model of Fig. 2A would gain from a small cartoon explaining the model and its parameters.

We thank the reviewer for the comment. We did not include a schematic for the toy model as it is a subset of the schematic of the IGF/FoxO model. The schematic of the toy model is included in the supplementary information.

• L should be called u, and B should be called x, to be consistent with the rest of the notations in the paper.

We have decided to keep the notation originally presented in the manuscript.

• legend of 2E and D should be clarified. "CC input dose" is cryptic. The x axis is the input dose, the y axis is its distribution at the argmax of I. CC is the max of I, not its argmax. Likewise "I" in the legend for the colors should not be used to describe the insets, which are input distributions.

We have now changed this in the manuscript.

• the data analysis of the IGF/FoxO pathway should be explained in the main text, not the SI. Otherwise it's impossible to understand how one arrives at, or how to intepret, figure 2E, which is central to the paper. For instance the fact that p(x|u,theta) is assumed to be Gaussian, and how the variance and mean are estimated from the actual data is very important to understand the significance of the results.

While we have added more details in the manuscript in various places, for the sake of brevity and clarity, we have decided to keep the details of the calculations in the supplementary materials.

• there's no Method's section. Most of the paper's theoretical work is hidden in the SI, while it should be described in the methods.

We thank the review of the comment. However, we believe that adding a methods section will break the narrative of the paper. The methods are described in detail in the supplementary materials with sufficient detail to reproduce our results. Additionally, we also provide a link to the github page that has all scripts related to the manuscript.

PS: please submit a PDF of the SI for review, so that people can read it on any platform (as opposed to a word document, especially with equations)

We have now done this.

Reviewer #3 (Recommendations For The Authors):

1. Subplots in Fig. 1, inset in Fig. 3 are not legible due to small font.

We have now increased the font.

1. Mean absolute error in Fig. S5 and relative error in related text should be clarified.

We have now clarified this in the manuscript.

1. Acronyms (MACO, MERIDIAN) should be defined.

We have now made these changes.

References

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2. Cohen-Saidon C, Cohen AA, Sigal A, Liron Y, Alon U. Dynamics and Variability of ERK2 Response to EGF in Individual Living Cells. Mol Cell. 2009;36(5):885-93. doi: 10.1016/j.molcel.2009.11.025. PubMed PMID: WOS:000272965400020.

3. Gross SM, Dane MA, Bucher E, Heiser LM. Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis. Cell Syst. 2019;9(6):580-8 e4.

4. Loos C H, J. Mathematical modeling of variability in intracellular signaling. Current Opinion in Systems Biology. 2019;16:17-24.

5. Dixit PD, Lyashenko E, Niepel M, Vitkup D. Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks. Cell Syst. 2020;10(2):204-12 e8.

6. Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, Emili A, Xie XS. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010;329(5991):533-8. doi: 10.1126/science.1188308. PubMed PMID: 20671182; PMCID: PMC2922915.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The proposed study provides an innovative framework for the identification of muscle synergies taking into account their task relevance. State-of-the-art techniques for extracting muscle interactions use unsupervised machine-learning algorithms applied to the envelopes of the electromyographic signals without taking into account the information related to the task being performed. In this work, the authors suggest including the task parameters in extracting muscle synergies using a network information framework previously proposed. This allows the identification of muscle interactions that are relevant, irrelevant, or redundant to the parameters of the task executed.

The proposed framework is a powerful tool to understand and identify muscle interactions for specific task parameters and it may be used to improve man-machine interfaces for the control of prostheses and robotic exoskeletons.

With respect to the network information framework recently published, this work added an important part to estimate the relevance of specific muscle interactions to the parameters of the task executed. However, the authors should better explain what is the added value of this contribution with respect to the previous one, also in terms of computational methods.

It is not clear how the well-known phenomenon of cross-talk during the recording of electromyographic muscle activity may affect the performance of the proposed technique and how it may bias the overall outcomes of the framework.

We thank reviewer 1 for their useful commentary on this manuscript.

Reviewer #2 (Public Review):

This paper is an attempt to extend or augment muscle synergy and motor primitive ideas with task measures. The authors idea is to use information metrics (mutual information, co-information) in 'synergy' constraint creation that includes task information directly. By using task related information and muscle information sources and then sparsification, the methods construct task relevant network communities among muscles, together with task redundant communities, and task irrelevant communities. This process of creating network communities may then constrain and help to guide subsequent synergy identification using the authors published sNM3F algorithm to detect spatial and temporal synergies.

The revised paper is much clearer and examples are helpful in various ways. However, figure 2 as presented does not convincingly show why task muscle mutual information helps in separating synergies, though it is helpful in defining the various network communities used in the toy example.

The impact of the information theoretic constraints developed as network communities on subsequent synergy separation are posited to be benign and to improve over other methods (e.g., NNMF). However, not fully addressed are the possible impacts of the methods on compositionality links with physiological bases, and the possibility remains of the methods sometimes instead leading to modules that represent more descriptive ML frameworks that may not support physiological work easily. Accordingly, there is a caveat. This is recognized and acknowledged by the authors in their rebuttal of the prior review. It will remain for other work to explore this issue, likely through testing on detailed high degree of freedom artificial neuromechanical models and tasks. This possible issue with the strategy here likely needs to be fully acknowledged in the paper.

The approach of the methods seeks to identify task relevant coordinative couplings. This is a meta problem for more classical synergy analyses. Classical analyses seek compositional elements stable across tasks. These elements may then be explored in causal experiments and generative simulations of coupling and control strategies. However, task-based understanding of synergy roles and functional uses is significant and is clearly likely to be aided by methods in this study.

Information based separation has been used in muscle synergy analyses using infomax ICA, which is information based at core. Though linear mixing of sources is assumed in ICA, minimized mutual information among source (synergy) drives is the basis of the separation and detects low variance synergy contributions (e.g., see Yang, Logan, Giszter, 2019). In the work in this paper, instead, mutual information approaches are used to cluster muscles and task features into network communities preceding the SNM3F algorithm use for separation, rather than using minimized information in separation. This contrast of an accretive or agglomerative mutual information strategy here used to cluster into networks, versus a minimizing mutual information source separation used in infomax ICA epitomizes a key difference in approach here.

Physiological causal testing of synergy ideas is neglected in the literature reviews in the paper. Although these are only in animal work (Hart and Giszter, 2010; Takei and Seki, 2017), the clear connection of muscle synergy analysis choices to physiology is important, and eventually these issues need to be better managed and understood in relation to the new methods proposed here, even if not in this paper.

Analyses of synergies using the methods the paper has proposed will likely be very much dependent on the number and quality of task variables included and how these are managed, and the impacts of these on the ensuing sparsification and network communities used prior to SNM3F. The authors acknowledge this in their response. This caveat should likely be made very explicit in the paper.

It would be useful in the future to explore the approach described with a range of simulated data to better understand the caveats, and optimizations for best practices in this approach.

A key component of the reviewers’ arguments here is their reductionist view of muscle synergies vs the emergentist view presented in our work here. In the reductionist lens, muscle groupings are the units (‘building blocks’) of coordinated movement and thus the space of intermuscular interactions is of particular interest for understanding movement construction. On the other hand, the emergentist view suggests that muscle groupings emerge from interactions between constituent parts (as quantified here using information theory, synergistic information is the information found when both activities are observed together). This is in line with recent work in the field showing modular control at the intramuscular level, exemplifying a scale-free phenomena. Nonetheless, we consider these approaches to muscle synergy research as complementary and beneficial for the field overall going forward.

Reviewer #3 (Public Review):

In this study, the authors developed and tested a novel framework for extracting muscle synergies. The approach aims at removing some limitations and constraints typical of previous approaches used in the field. In particular, the authors propose a mathematical formulation that removes constraints of linearity and couples the synergies to their motor outcome, supporting the concept of functional synergies and distinguishing the task-related performance related to each synergy. While some concepts behind this work were already introduced in recent work in the field, the methodology provided here encapsulates all these features in an original formulation providing a step forward with respect to the currently available algorithms. The authors also successfully demonstrated the applicability of their method to previously available datasets of multi-joint movements.

Preliminary results positively support the scientific soundness of the presented approach and its potential. The added values of the method should be documented more in future work to understand how the presented formulation relates to previous approaches and what novel insights can be achieved in practical scenarios and confirm/exploit the potential of the theoretical findings.

In their revision, the authors have implemented major revisions and improved their paper. The work was already of good quality and now it has improved further. The authors were able to successfully:

• improve the clarity of the writing (e.g.: better explaining the rationale and the aims of the paper);

• extend the clarification of some of the key novel concepts introduced in their work, like the redundant synergies;

• show a scenario in which their approach might be useful for increasing the understanding of motor control in patients with respect to traditional algorithms such as NMF. In particular, their example illustrates why considering the task space is a fundamental step forward when extracting muscle synergies, improving the practical and physiological interpretation of the results.

We thank reviewer 3 for their constructive commentary on this manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Figure 3 should report the distances between reaching points in panel A and the actual length distances of the walking paths in panel C.

The caption of fig.3 concerning the experimental setup of the datasets analysed has been updated with the following for dataset 1: “(A) Dataset 1 consisted of participants executing table-top point-to-point reaching movements (40cm distance from starting point P0) across four targets in forward (P1-P4) and backwards (P5-P8) directions at both fast and slow speeds (40 repetitions per task) [25]. The muscles recorded included the finger extensors (FE), brachioradialis (BR), biceps brachii (BI), medial-triceps (TM), lateral-triceps (TL), anterior deltoid (AD), posterior deltoid (PD), pectoralis major (PE), latissimus dorsi (LD) of the right, reaching arm.”. For dataset 3, to the best of the authors knowledge, this information was not given in the original paper.

Figure 4, what is the unit of the data shown?

The unit of bits is now mentioned in the toy example figure caption and in the caption of fig.5

Figure 4, the characteristics of the interactions are not fully clear, and the graphical representation should be improved.

We have made steps to improve the clarity of the figures presented.

For dataset 3, τ was the movement kinematics, but it is not specified how the task parameters were formulated. Did the authors use the data from all 32 kinematic markers, 4 IMUs, and force plates? If yes, it should be specified why all these signals were used. For sure, there will be signals included that are not relevant to the specific task. Did the authors select specific signals based on their relevance to the task (e.g., ankle kinematics)?

We have now clarified this in the text as follows: “For datasets 1 and 2, we determine the MI between vectors with respect to several discrete task parameters representing specific task attributes (e.g. reaching direction, speed etc.), while for dataset 3 we determined the task-relevant and -irrelevant muscles couplings in an unassuming way by quantifying them with respect to all available kinematic, dynamic and inertial motion unit (IMU) features.”

How did the authors endure that crosstalk did not affect their analysis, particularly between, e.g., finger extensors and brachioradialis and posterior deltoid and anterior deltoid (dataset 1)?

We have addressed this point in the previous round of reviews and made an explicit statement regarding cross-talk in the discussion section: “Although distinguishing task-irrelevant muscle couplings may capture artifacts such as EMG crosstalk, our results convey several physiological objectives of muscles including gross motor functions [66], the maintenance of internal joint mechanics and reciprocal inhibition of contralateral limbs [19,51].”

It would be informative to add some examples of not trivial/obvious task-related synergistic muscle combinations that have been extracted in the three datasets. Most of the examples reported in the manuscript are well-known biomechanically and quite intuitive, so they do not improve our understanding of synergistic muscle control in humans.

Our framework improves our understanding of synergistic motor control by enabling the formal quantification of synergistic muscle interactions, a capability not present among current approaches. Regarding the implications of this advance in terms of concrete examples, we have further clarified our examples presented in the results section, for example:

“Across datasets, many the muscle networks could be characterised by the transmission of complementary task information between functionally specialised muscle groups, many of which identified among the task-redundant representations (Fig.9-10 and Supp. Fig.2). The most obvious example of this is the S3 synergist muscle network of dataset 2 (Fig.11), which captures the complementary interaction between task-redundant submodules identified previously (S3 (Fig.9)).”

The description shows how our framework can extract the cross-module interactions that align with the higher-level objectives of the system, here the synergistic connectivity between the upper and lower body modules. Current approaches can only capture redundant and task-irrelevant interactions. Thus our framework provides additional insight into movement control.

The number of participations in dataset 2 is very limited and should be increased. We appreciate the reviewer's comment and would like to point out that for dataset 2 our aim was to increase the number of muscles (30), tasks (72) and trials for each task (30) which produced a very large dataset for each participant. This came at the expense of low number of participants, however all our statistical analyses here can be performed at the single-participant level. Furthermore, dataset 3 includes 25 participants and it enables us to demonstrate the reliability of the findings across participants.

Reviewer #2 (Recommendations For The Authors):

I believe it is important in the future to explore the approach proposed with a range of simulation data and neuromechanical models, to explore the issues I have raised and that you have acknowledged, though I agree it is likely out of scope for the paper here.

We agree with the reviewer that this would be valuable future work and indeed plan to do this in our future research.

The Github code for this paper should likely include the various data sets used in the paper and figures, appropriately anonymized, in order to allow the data to be explored and analyses replicated and package demonstrated to be exercised fully by a new user.

We thank the reviewer for this suggestion. Dataset3 is already available online at https://doi.org/10.1016/j.jbiomech.2021.110320. We will also make the other 2 datasets publicly available on our lab website very soon. Until then, as stated in the manuscript, we will make them available to anyone upon reasonable request.

Reviewer #3 (Recommendations For The Authors):

I have the following open points to suggest to the authors:

First, I recommend improving the quality of the figures: in the pdf version I downloaded, some writings are impossible to read.

We fully agree with the reviewer and note that in the pdf version of the paper, the figures are a lot worse than in the submitted word document submitted. Nevertheless, we will make further improvements on the figures as requested.

Even though the manuscript has improved, I still feel that some points were not addressed or were only partially addressed. In particular:

• The proposed comparison with NMF helps understanding why incorporating the task space is useful (and I fully agree with the authors about this point as the main reason to propose their contribution). However, the comparison does not help the reader to understand whether the synergies incorporating the task space are biased by the introduction of the task variables.

This question can be also reformulated as: are muscle synergies modified when task space variables are incorporated? Is the "weight" on task coefficients affecting the composition of muscle synergies? If so, the added interpretational power is achieved at the cost of losing the information regarding the neural substrate of synergies? I understand this point is not immediate to show, but it would increase the quality of the work.

• Reference to previous approaches that aimed at including task variables into synergy extraction are still missing in the paper. Even though it is not required to provide quantitative comparisons with other available approaches, there are at most 2-3 available algorithms in the literature (kinematics-EMG; force-EMG), that should not be neglected in this work. What did previous approaches achieve? What was improved with this approach? What was not improved?

Previous attempts of extracting synergies with non-linear approaches could also be described more.

In the latest version of the manuscript, we have referenced both the mixed NMF and autoencoders based algorithms. In both the introduction and discussion section of the manuscript, we also specify that our framework quantifies and decomposes muscle interactions in a novel way that cannot be done by other current approaches. In the results section we use examples from 3 different datasets to make this point clear, providing intuition on the use cases of our framework.

Author Response

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

Response to review.

We thank the editors and reviewers for their time in assessing our manuscript. We changed the title to remove the word “all” because we realized that was hyperbolic. Corrections in response to review are in blue text throughout the manuscript document (other minor corrections are not highlighted).

eLife assessment

This study presents valuable insights into the evolution of the gasdermin family, making a strong case that a GSDMA-like gasdermin was already present in early land vertebrates and was activated by caspase-1 cleavage. Convincing biochemical evidence is provided that extant avian, reptile, and amphibian GSDMA proteins can still be activated by caspase-1 and upon cleavage induce pyroptosis-like cell death - at least in human cell lines. The caspase-1 cleavage site is only lost in mammals, which use the more recently evolved GSDMD as a caspase-1 cleavable pyroptosis inducer. The presented work will be of considerable interest to scientists working on the evolution of cell death pathways, or on cell death regulation in non-mammalian vertebrates.

We thank the editor for their time in evaluating our manuscript. We agree with the eLife assessment and with the comments of the reviewers.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors start out by doing a time-calibrated gene/species tree analysis of the animal gasdermin family, resulting in a dendrogram showing the relationship of the individual gasdermin subfamilies and suggesting a series of gene duplication events (and gene losses) that lead to the gasdermin distribution in extant species. They observe that the GSDMA proteins from birds, reptiles, and amphibians do not form a clade with the mammalian GSDMAs and notice that the non-mammalian GSDMA proteins share a conserved caspase-1 cleavage motif at the predicted activation site. The authors provide several series of experiments showing that the non-mammalian GSDMA proteins can indeed be activated by caspase-1 and that this activation leads to cell death (in human cells). They also investigate the role of the caspase-1 recognition tetrapeptide for cleavage by caspase-1 and for the pathogen-derived protease SpeB.

We thank the reviewer for their time in evaluating our manuscript.

Strengths:

The evolutionary analysis performed in this manuscript appears to use a broader data basis than what has been used in other published work. An interesting result of this analysis is the suggestion that GSDMA is evolutionarily older than the main mammalian pyroptotic GSDMD, and that birds, reptiles, and amphibians lack GSDMD but use GSDMA for the same purpose. The consequence that bird GSDMA should be activated by an inflammatory caspase (=caspase1) is convincingly supported by the experiments provided in the manuscript.

We thank the reviewer for their assessment of the manuscript.

Weaknesses:

1. As a non-expert in phylogenetic tree reconstruction, I find the tree resulting from the authors' analysis surprising (in particular the polyphyly of GSDMA) and at odds with several other published trees of this family. The differences might be due to differences in the data being used or due to the tree construction method, but no explanation for this discrepancy is provided.

We agree, and we have modified the text to add more context to explain why our analysis generated a different topology: “In comparison to previously published studies, we used different methods to construct our gasdermin phylogenetic tree, with the result that our tree has a different topology. The topology of our tree is likely to be affected by our increased sampling of gasdermin sequences; we included 1,256 gasdermin sequences in comparison to 300 or 97 sequences used in prior studies. Prior studies used maximum likelihood tree building techniques, whereas we used a more computationally intensive Bayesian method using BEAST with strict molecular clocks that allows us to provide divergence time estimates, which we calibrated using mammal fossil estimated ages. We think that this substantially increased sampling paired with time calibration allow us to produce a more accurate phylogeny of the gasdermin protein family.”

To explain and further support our method in a more technical manner, in our phylogenetic tree, non-mammal GSDMAs are paralogous to mammals GSDMAs whereas others have found that non-mammal GSDMAs are orthologous to mammal GSDMAs. We obtained moderate support for the non-mammal GSDMA placement with Bayesian posterior 0.42 and with maximum likelihood bootstrap support of 0.96. Angosto-Bazarra et al. has for their placement a Bayesian posterior of 0.66 and maximum likelihood bootstrap support of 0.98. These are good results, but they arise from significantly fewer sequences than are included in our tree. However, in Fig S2 of Angosto-Bazarra et al. the support drops to 0.08. That the posteriors in both are not 1 indicate the presence of phylogenetic conflicts (i.e., a significant fraction of alternative trees), which means that the tree of our study or Angosto-Bazarra could be incorrect. That said, our tree is supported by biological support, and our dataset is substantially larger. To better characterize this node, further sampling with even more species would be required. We exhausted the current available sequences at the time our tree was generated.

Differences between our study and previous studies:

Author response table 1.

1. While the cleavability of bird/reptile GSDMA by caspase-1 is well-supported by several experiments, the role of this cleavage for pyroptotic cell killing is addressed more superficially. One cell viability assay upon overexpression of GSDMA-NTD in human HEK293 cells is shown and one micrograph shows pyroptotic morphology upon expression in HeLa cells. It is not clear why these experiments were limited to human cells…

We did include one more experiment in human cells which is Figure 4B, in which we express full length chicken GSDMA with dimerizable caspase-1, and show that LDH release requires the cleavage site aspartate, D244. That said, we agree that our use of only human cell lines is a weakness of the paper. We thought that the best way to definitively show the interaction of caspase-1 and GSDMA was to perform experiments in chicken macrophages. Therefore, we generated a custom-raised anti-chicken-GSDMA antibody. Unfortunately, the quality of the antibody was insufficient to detect endogenous GSDMA in chicken bone marrow-derived macrophages. Off target binding prevented the observation of chicken GSDMA bands. We added a section to the discussion acknowledge the need for further studies: “In future studies, the association of bird/amphibian/reptile GSDMA and caspase-1 should be confirmed in native cells from each of these animals.”

…and why two different cell types were used for the two complementary results.

In the paper we used 293T cells and HeLa cells as generic cell types that have distinct benefits. In general, we used 293T/17 cells for experiments where high transfection efficiency was most critical, as it is simple to achieve 90% or higher transfection efficiency in this line. However, 293T/17s have poor spreading in culture and thus are not as useful for morphologic studies. 293T/17 cells do display pyroptotic ballooning upon gasdermin activation, however, the images are less pronounced in comparison to other cell types that have more distinct morphology. Therefore, we used HeLa cells for the microscopy experiments because they are more adherent and larger than 293T/17s which make for easier visualization of pyroptotic ballooning. We have added the following statement to the text to make our rationale for the use of different cell line more apparent: “In these experiments, 293T/17s were used for their high transfection efficiency, and HeLas were used for microscopy studies for their larger size and improved adherence.”

1. The introduction mentions as a motivation for this work our lack of knowledge of how human GSDMA is activated. This is indeed an interesting and pressing question, but it is not really addressed in the manuscript. This is particularly true when believing the authors' dendrogram results that the bird and mammalian GSDMA families do not form a clade.

As a consequence, the significance of this finding is mostly limited to birds and reptiles.

Our aspirations were to discover hidden facets of mammal GSDMA by using a molecular evolutionary analysis. bird/amphibian/reptile GSDMA. Although we did not learn the identity of a host protease that activates mammalian GSDMA, we serendipitously discovered the evolutionary history of the association of caspase-1 with the gasdermin family. We think this manuscript provides an important and interesting advance in the field to reveal the process of evolution at work in the gasdermin family, and that the association of caspase-1 with a gasdermin to cause pyroptosis is an unbroken pairing throughout evolution. It is surprising to us that the specific gasdermin partner has changed over time.

Reviewer #2 (Public Review):

Summary:

The authors investigated the molecular evolution of members of the gasdermin (GSDM) family. By adding the evolutionary time axis of animals, they created a new molecular phylogenetic tree different from previous ones. The analyzed result verified that non-mammalian GSDMAs and mammalian GSDMAs have diverged into completely different and separate clades. Furthermore, by biochemical analyses, the authors demonstrated non-mammalian GSDMA proteins are cleaved by the host-encoded caspase-1. They also showed mammalian GSDMAs have lost the cleavage site recognized by caspase-1. Instead, the authors proposed that the newly appeared GSDMD is now cleaved by caspase-1.

We thank the reviewer for their time in evaluating our manuscript.

Through this study, we have been able to understand the changes in the molecular evolution of GSDMs, and by presenting the cleavage of GSDMAs through biochemical experiments, we have become able to grasp the comprehensive picture of this family of molecules. However, there are some parts where explanations are insufficient, so supplementary explanations and experiments seem to be necessary.

Strengths:

It has a strong impact in advancing ideas into the study of pyroptotic cell death and even inflammatory responses involving caspase-1.

We thank the reviewer for the critical consideration of the phylogeny presented.

Weaknesses:

Based on the position of mammalian GSDMA shown in the molecular phylogenetic tree (Figure 1), it may be difficult to completely agree with the authors' explanation of the evolution of GSDMA.

1. Focusing on mammalian GSDMA, this group, and mammalian GSDMD diverged into two clades, and before that, GSDMA/D groups and mammalian GSDMC separated into two, more before that, GSDMB, and further before that, non-mammalian GSDMA, when we checked Figure 1. In the molecular phylogenetic tree, it is impossible that GSDMA appears during evolution again. Mammalian GSDMAs are clearly paralogous molecules to non-mammalian GSDMAs in the figure. If they are bona fide orthologous, the mammalian GSDMA group should show a sub-clade in the non-mammalian GSDMA clade. It is better to describe the plausibility of the divergence in the molecular evolution of mammalian GSDMA in the Discussion section.

We appreciate the reviewer’s careful consideration of our phylogeny. We agree that we did not make this clear enough in the discussion. Indeed, this is a confusing point, and is a critical concept in the paper. This is among our most important findings, so we have added a line addressing this finding to the abstract. We think about these concepts starting from the oldest common ancestor of a group, and then think about how genes duplicate over time. To the discussion we now begin with the following:

We discovered that GSDMA in amphibians birds and reptiles are paralogs to mammal GSDMA. Surprisingly, the GSDMA genes in both the amphibians/reptiles/birds and mammal groups appear in the exact same locus. Therefore, this GSDMA gene was present in the common ancestor of all these animals. In mammals, this GSDMA duplicated to form GSDMB and GSDMC. Finally, a new gene duplicate, GSDMD, arose in a different chromosomal location. Then this GSDMD gene became a superior target for caspase-1 after developing the exosite. Once GSDMD had evolved, we speculate that the mammalian GSDMA became a pseudogene that was available to evolve a new function. This new function included a new promoter to express mammalian GSDMA primarily in the skin, and perhaps acquisition of a new host protease that has yet to be discovered.

In further support of the topology of our Bayesian tree in Figure 1, we also performed a maximum likelihood analysis, which also placed the GSDMA genes into similarly distinct clades (Figure 1-S3). Finally, we have biological evidence to support this reasoning, where caspase-1 cleaves non-mammal GSDMAs and also mammal GSDMD (and no longer can cleave mammal GSDMA).

1. Regarding (1), it is recommended that the authors reconsider the validity of estimates of divergence dates by focusing on mammalian species divergence. Because the validity of this estimation requires a recheck of the molecular phylogenetic tree, including alignment.

Our reconstructed evolution of gasdermins is consistent with the mammal tree of life. We constrained Bayesian estimation of divergences using soft calibrations from mammal fossil estimated ages. We have included the fossil calibration of mammalian gasdermins to the results section and to our methods.

1. If GSDMB and/or GSDMC between non-mammalian GSDMA and mammalian GSDMD as shown in the molecular phylogenetic tree would be cleaved by caspase-1, the story of this study becomes clearer. The authors should try that possibility.

It is known that mammal GSDMB and GSDMC cannot be activated by caspase-1. We propose that GSDMA was cleaved by caspase-1 only in extinct mammals that had not yet associated GSDMD with caspase-1. Such an extinct mammal could have encoded a GSDMA cleaved by caspase-1, a GSDMB cleaved by granzyme A, and GDSMC cleaved by caspase-8. Later, the GSDMA gene was again duplicated to form GSDMD. After GSDMD was targeted by caspase-1, then GSDMA was free to gain its current function in barrier tissues.

Reviewer #1 (Recommendations For The Authors):

As a non-expert on phylogenetic tree construction, I found the "time-calibrated maximum clade credibility coalescent tree" hard to digest. I would have liked to see an explanation of how this method is different from what has been used before and why the authors consider it to be better. This is particularly important when considering that the resulting tree shown in Figure 1 is quite different from other published trees of the same family (e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742441 where the GSDMA family appears monophyletic).

Please see response to Reviewer 1 weaknesses above. Also, we have moved the text “time-calibrated maximum clade credibility coalescent tree” to the figure legend.

In the bioinformatical analysis of the conserved caspase-1 cleavage motif in bird GSDMA sequences, I would recommend also addressing the residue behind the cleavage site Asp, as this position has an unusually high conservation (mostly Gly) in bird GSDMA.

This is a great observation. We suspect that this may reflect a need for flexibility in the secondary structure to allow the cleavage site to enter the enzymatic pocket of the caspase. This residue is also similarly enriched in mammal GSDMD, which is also cleaved by caspase-1. We also note high conservation of a P2' proline residue in birds with the FASD tetrapeptide, which could also be important for displaying the tetrapeptide to the caspase.

This comment prompted us to search the literature for evidence of these residues in caspase-1 substrate preference studies. Remarkably, a P1' glycine and P2` proline are among the most enriched residues in human caspase-1 targets. This supports our hypothesis that caspase-1 cleaves GSDMA in non-mammals. We added the following to the results section: “Additionally, the P1' residue in amphibian, bird and reptile GSDMA was often a glycine, and the P2' residue was often a proline, especially in birds with FASD/FVSD tetrapeptides (Fig. 2B). A small P1' residue is preferred by all caspases. By using a peptide library, glycine has been determined to be the optimal P1' residue for caspase-1 and caspase-4. Further, in a review of the natural substrates of caspase-1, glycine was the second most common P1' residue, and proline was the most common P2' residue. These preferences were not observed for caspase-9.”

Finally, I would like the authors to at least explain why the cell viability assays were done in 293T cells while the micrographs were done in HeLa cells. Why not show both experiments for both cell types?

In the paper we used 293T cells and HeLa cells as generic cell types that have distinct benefits. In general, we used 293T/17 cells for experiments where high transfection efficiency was most critical, as it is simple to achieve 90% or higher transfection efficiency in this line. However, 293T cells have poor spreading in culture and thus are not as useful for morphologic studies. 293T/17 cells do display pyroptotic ballooning upon gasdermin activation, however, the images are less pronounced in comparison to other cell types that have more distinct morphology. Therefore, we used HeLa cells for the microscopy experiments because they are more adherent and larger than 293T/17s which make for easier visualization of pyroptotic ballooning. We have added the following statement to the text to make our rationale for the use of different cell line more apparent: “In these experiments, 293T/17s were used for their high transfection efficiency, and HeLas were used for microscopy studies for their larger size and improved adherence.”

There are a number of minor points related to language and presentation:

• the expressions "pathogens contaminate the cytosol", "mammals can encode..", "an outsized effect" are unusual and might be rephrased.

We changed these to:

“manipulate the host cell, sometimes contaminating the cytosol with pathogen associated molecular patterns, or disrupting aspects of normal cell physiology”,

“Only mammals encode GSDMC and GSDMD alongside the other four gasdermins.”,

and

“greater effect”

• in line 87 the abbreviation "GSDMEc" is first used without explanation (of the "c").

This is an important distinction, as GSDMEc proteins were only recently uncovered. To remedy this, we have added the following text following line 87: “This gasdermin was recently identified as an ortholog of GSDMA.

It was called GSDMEc, following the nomenclature of other duplications of GSDME in bony fish that have been named GSDMEa and GSDMEb.”

• line 89 grammar problem.

Corrected

• line 186ff the sentence "We believe..." does not appear to make sense.

We revised the text to make this clear, changing the text to now read “We hypothesized that activating pyroptosis using separate gasdermins for caspase-1 and caspase-3 is a useful adaptation and allows for fine-tuning of these separate pathways. In mammals, this separation depends on the activation of GSDMD by caspase-1 and the activation of GSDME by caspase-3.”

• many figures use pictures rather than text to represent species groups. These pictures are not always intuitive. As an example, in Figure 6 the 'snake' represents amphibians. After reading the text, I understand that these should probably be the caecilian amphibians, but not every reader might know what these critters look like. In Figure 7, I have no idea what the black blob (2nd image from top) is supposed to be.

In crafting the manuscript, we found the use of text to denote the various species to be cumbersome. The species silhouettes are a standard graphical depiction used in evolutionary biology, which we think aids readability to the figures. For example, in a paper cited in our manuscript, these same silhouettes were used to depict the evolution of GSDMs (https://doi.org/10.3389/fcell.2022.952015 Figure 1A, Figure 3D, Figure 4G). However, we agree that many readers will not know that caecilians are legless amphibians that resemble snakes in their body morphology, but are not close to snakes by phylogeny. We think it is important to use an image of a caecilian amphibian because the more iconic amphibians (frogs, salamanders) do not encode GSDMA. To increase clarity, we have mentioned the morphology of caecilians in the legend of Figure 2, Figure 6, and Figure 7 when caecilican amphibians are first introduced.

In Figure 2: “Note, that caecilians morphologically are similar to snakes in their lack of legs and elongated body, however, this is an example of convergent evolution as caecilians are amphibians and are thus more closely related to frogs and salamanders than snakes.”

In Figure 6: “M. unicolor is an amphibian despite sharing morphological similarity to a snake.”

In Figure 7: “In caecilian amphibians, which are morphologically similar to snakes, birds, and reptiles, GSDMA is cleaved by caspase-1.”

The black blob is the mollusk Lingula anatina, which unfortunately has an indistinct silhouette. To clarify this, we have added text to label the images in Figure 7.

Reviewer #2 (Recommendations For The Authors):

1. Line 214, in "(Fig. 3-S2) Human and mouse ..", it is necessary to type a period.

2. Line 238, in the subtitle, GSMA should be amended to GSDMA.

These have both been corrected.

Author Response

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

We thank the reviewers for their careful, critical, and insightful evaluation of our manuscript.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The preprint by Laganowsky and co-workers describes the use of mutant cycles to dissect the thermodynamic profile of specific lipid recognition by the ABC transporter MsbA. The authors use native mass spectrometry with a variable temperature source to monitor lipid binding to the native protein dimer solubilized in detergent. Analysis of the peak intensities (that is, relative abundance) of 1-3 bound lipids as a function of solution temperature and lipid concentration yields temperature-dependent Kds. The authors use these to then generate van't Hoff plots, from which they calculate the enthalpy and entropy contributions to binding of one, two, and in some cases, three lipids to MsbA.

The authors then employ mutant cycles, in which basic residues involved in headgroup binding are mutated to alanine. By comparing the thermodynamic signatures of single and double (and in one instance triple) mutants, they aim to identify cooperativity between the different positions. They furthermore use inward and outward locking conditions which should control access to the different binding sites determined previously.

The main conclusion is that lipid binding to MsbA is driven mainly by energetically favorable entropy increase upon binding, which stems from the release of ordered water molecules that normally coordinate the basic residues, which helps to overcome the enthalpic barrier of lipid binding. The authors also report an increase in lipid binding at higher temperatures which they attribute to a non-uniform heat capacity of the protein. Although they find that most residue pairs display some degree of cooperativity, particularly between the inner and outer lipid binding sites, they do not provide a structural interpretation of these results.

Strengths:

The use of double mutant cycles and mass spectrometry to dissect lipid binding is novel and interesting. For example, the observation that mutating a basic residue in the inner and one in the outer binding site abolishes lipid binding to a greater extent than the individual mutations is highly informative even without having to break it down into thermodynamic terms (see "weaknesses" section). In this sense, the method and data reported here opens new avenues for the structure/activity relationship of MsbA. The "mutant cycle" approach is in principle widely applicable to other membrane proteins with complex lipid interactions.

Weaknesses:

The use of double mutant cycles to dissect binding energies is well-established, and has, as the authors point out, been employed in combination with mass spectrometry to study protein-protein interactions. Its application to extract thermodynamic parameters is robust in cases where a single binding event is monitored, e.g. the formation of a complex with well-defined stoichiometry, where dissociation constants can be determined with high confidence. It is, however, complicated significantly by the fact that for MsbA-lipid interactions, we are not looking at a single binding event, but a stochastic distribution of lipids across different sites. Even if the protein is locked in a specific conformation, the observation of a single lipid adduct does not guarantee that the one lipid is always bound to a specific site. In some of the complexes detected by MS, the lipid is likely bound somewhere else. Lipid binding Kds from mass spectrometry, although helpful in some instances as a proxy for global binding affinities, should therefore be taken with a grain of salt.

We agree with the reviewer in that while we will measure binding of lipid (mass shift) we do not know the binding location(s). Given this issue, we have added to the discussion section on this important point and elaborate more broadly on this problem in the context of membrane protein-lipid interactions. Tackling this issue represents a frontier challenge for the field.

The authors analyze the difference in binding upon mutating binding sites (ddG etc). Here, another complicating factor comes into play, the fact that mutation of a binding site (which the authors show reduces lipid binding) may instead allow the lipid to bind to a lower-affinity site elsewhere. Unfortunately, the authors do not specify the protein concentration, but assuming it is in the single-digit micromolar range, as common for native MS experiments, lipid and protein concentrations are almost equal for most of the data points, resulting in competition between binding sites for free lipids. As a rule of thumb, for Kd measurements, the concentration of the constant component, the protein, should be far below the Kd, to avoid working in the "titration" regime rather than the "binding" regime (see Jarmoskaite et al, eLife 2020). I cannot determine whether this is the case here. The way I understand the double mutant cycle approach, reliable Kd measurements are required to accurately determine dH and TdS, so I would encourage the authors to confirm their Kd values using complementary methods before in-depth interpretations of the thermodynamic components.

The reviewer references an article in eLife by Jarmoskaite and co-workers describing “titration” vs “binding” regimes. Below we paste a snippet from this article:

Author response image 1.

Equation 4a is an expression for the fraction of protein bound to ligand, which universally holds, i.e., if we know the concentration of molecules at equilibrium (including those unbound or free) then one can obtain the special ratio or equilibrium constant at a given temperature. Jarmoskaite et al. note that in practice (using traditional biophysical approaches) one cannot readily distinguish protein that is free or bound to ligand (see highlighted part above). While this assumption is basis of their eLife assessment, it does NOT apply to native mass spectrometry data. It is important to realize that the mole fraction (or concentration) of apo and each lipid bound states, i.e., [P], [PL], [PL2], …, [PLn+1], can readily be obtained directly from the deconvoluted mass spectrum. This is unlike other biophysical methods that are ensemble measurements, which measures the amount of heat or fraction of total ligand bound to protein. Since we can discern each lipid bound state, including the free protein and free ligand concentrations, the equilibrium binding constants can be directly calculated, and the protein and ligand concentration becomes irrelevant. In principle, equilibrium constants for protein-lipid interactions can be calculated from one mass spectrum. To increase transparency, we have updated the results section to highlight the important difference of the native MS approach compared to less robust traditional approaches that are riddled with underlying issues/assumptions.

We appreciated the reviewer’s suggestion of using complementary methods to confirm Kd values. In our previous report [1], we determined binding thermodynamics for soluble protein-ligand interactions using native MS, surface plasmon resonance (SPR), and isothermal calorimetry (ITC) and found the techniques yield similar binding constants and thermodynamic parameters. The use of soluble proteins with defined ligand binding studies was rather straightforward to carry out a complementary study. We have also shown consistent findings for native MS and SPR of membrane protein interaction with a soluble, regulatory protein [2]. However, in the case of membrane proteins they can bind the first few lipids very specifically and, with the addition of more lipid, bind even more lipids that represent rather weak binding. Thus, traditional approaches would report on the ensemble of lipids bound to membranes and specific lipid binding sites (such as inner and outer LPS binding sites in MsbA) are saturable but also additional binding will be observed, i.e., doesn’t follow traditional soluble protein-ligand binding studies. In the past we have used a fluorescent-lipid competition binding assay [3] to corroborate native MS results for Kir3.2, which showed a direct correlation. The disadvantage of this complementary approach is using a non-natural, fluorescent-modified lipid. Unfortunately, there is no commercial source for a fluorophore modified KDL.

It is somewhat counterintuitive that for many double mutants, and the triple mutant, the entropic component becomes more favorable compared to the WT protein. If the increase in entropy upon lipid binding comes from the release of ordered water molecules around the basic residues (a reasonable assumption) why does this apply even more in proteins where several basic residues have been changed to alanine, which coordinate far fewer water molecules?

There are many factors that contribute to the change in entropy of the system, beyond solvation entropy, and deciphering the entropic contributions of the various components remains a challenging task. We have revised the manuscript to emphasize that solvation is one component of the entropic term and other components are likely at play.

The authors could devote more attention to the fact that they use detergent micelles as a vehicle for lipid binding studies. To a limited extent, detergents compete with lipids for binding, and are present in extreme excess over the lipid. The micelle likely changes its behavior in response to temperature changes. For example, the packing around the protein loosens up upon heating, which may increase the chance for lipids to bind. In this case, the increase in binding at higher temperatures may not be related to a change in heat capacity. This question could be addressed by MD simulations, if it's not already in the literature.

The detergent and its concentration are consistent for all the different MsbA proteins in this study. In fact, we observe linear van’t Hoff plots with positive and negative slopes as well as non-linear curves that are convex or concave. The MsbA protein (wt or mutant), trapped or not, all display unique temperature-dependent responses. The reviewers comment of increasing temperature to loosen packing of detergent to promote lipid binding is clearly NOT that simple. If detergent was significantly influencing lipid binding (as suggested by reviewer) then increasing its concentration should impact lipid binding. In a previous study, we found no difference in membrane protein-lipid thermodynamics even when the concentration of detergent was increased five-fold [1]. We repeated similar experiments for MsbA and find the increased detergent concentration does not impact the abundances of lipid bound states. The figure to the right shows MsbA in the presence of lipid in 2x CMC (panel a and b) and 10x CMC (panel c and d). As you will see, no appreciably difference in the lipid bound signal is observed.

Author response image 2.

We applaud the suggestion of MD simulation. However, it is far beyond the scope of this paper and its not clear what will really be learned.

Reviewer #2 (Public Review):

Summary:

This is a solid study that dissects the thermodynamics of lipopolysaccharide (LPS) transporter MsbA and LPS. Native ESI-MS and the novel strategies developed by the authors were employed to quantify the affinities of LPS-MsbA interactions and its temperature dependence. Here, the equilibrium of lipid-protein interactions occurs in the micellar phase. The double-/triple-mutant cycle analysis and van't Hoff analysis allowed a full thermodynamic description of the lipid-protein interactions and the analysis of thermodynamic coupling between LPS binding sites. The most notable result would be that LPS-MsbA interaction is largely driven by entropy involving the negative heat capacity, a signature of the solvent reorganization effect (here authors attribute the solvent effect to "water" reorganization). The entropy driven lipid binding has been previously reported by the same authors for Kir1,2-PIP2 interactions.

Strengths:

1. This is overall a very thorough and rigorous study providing the detailed thermodynamic principles of LPS-MsbA interaction.

2. The double and triple-mutant cycle approaches are newly applied to lipid-protein interactions, enabling detailed thermodynamics between LPS binding sites.

3. The entropy-driven protein-lipid interaction is surprising. The binding seems to be mainly mediated by the electrostatic interaction between the positively charged residues on the protein and the negatively charged or polar headgroup of LPS, which could be thought of as "enthalpic" (making of a strong bond relative to that with solvent).

Weaknesses:

1. This study is a good contribution to the field, but it was difficult to find novel biological insights or methodological novelty from this study.

1a. Thermodynamic analysis of lipid-protein interactions, an example of entropy-driven lipid-protein interactions, and the cooperativity between lipid binding sites have been reported by the author's group. Also, the cooperativity between binding sites in general have been reported from numerous studies of biomolecular interactions.

We appreciate the reviewer for highlighting our previous work. Of course, a single study does not establish a pattern, such as entropy-driven lipid-protein interactions.

While we agree with the reviewer that cooperativity in biomolecular interactions has been established for many soluble protein systems, by no means do we have a detailed understanding of membrane protein-lipid interactions. This work is an important contribution to expanding on classical work on soluble protein systems to more challenging membrane protein systems and their interactions with lipids.

1b. It is not clear how this study provides new insights into the understanding of LPS transport mechanisms. Probably, authors could strengthen the Discussion by providing biological insights-how the residue coupling.

The thermodynamics provides us with a deeper insight into the chemical principles that drive specific membrane protein-lipid interactions. We have revised the discussion to highlight the importance of thermodynamics and the implication of individual residues to KDL binding, and the inner and outer LPS binding sites appear to be coupled, something that is new.

1. One to three LPS molecules bind to MsbA, but it is unclear whether bound KDL occupies inner or outer cavities, or both and how a specific mutation affects the affinity of specific LPS (i.e., to inner or to outer cavities). Based on the known structures, the maximal number of LPS is three. It is possible that the inner and outer cavities have different LPS affinities. Also, there can be multiple one-LPS-bound states, two-LPS-bound states if LPS strictly binds to the binding sites indicated by the structures. This aspect is beyond the scope of this study and difficult to address, but without this information, it seems hard to tell what is going on in the system.

In our response above, we note that lipids will bind to membrane proteins at specific site(s) and weaker sites, often described as non-annular lipids. The revision includes this discussion point.

1. If a single mutation is introduced to the inner cavity, its effect will be "doubled" because the inner cavity is shared by two identical subunits. This effect needs to be clarified in the result section.

Great point. In addition, an outer mutant will also impact not one but both outer binding site(s)s. The revised manuscript makes note of this point.

1. In the result section, "Mutant cycle analysis of KDL binding to vanadate-trapped MsbA.":

4a. It seems necessary to show the mass spectra for Msb-ADP-vanadate complex as well as its lipid bound forms.

In the original submission, the mass spectra of vanadate trapped MsbA with KDL binding was provided in Supplementary Figures 10 and 11.

4b. The rationale of this section (i.e., what mechanistic insights can be obtained from this study) is unclear. For example, it is not sure what meaningful information can be obtained from a single type (ADP/vanadate) of the bound state regarding the ATP-driven function of MsbA.

MsbA is a dynamic, populates different conformations. Trapping with vanadate locks the transporter in an outwardfacing state with NDB interacting. This provides the opportunity to characterize binding to the exterior site. We revised the manuscript to note this point.

Reviewer #3 (Public Review):

Summary:

In this paper presented by Liu et al, native MS on the lipid A transporter MsbA was used to obtain thermodynamic insight into protein-lipid interactions. By performing the analyses at different lipid A concentrations and temperatures, dissociation constants for 2-3 lipid A binding sites were determined, as well as enthalpies were calculated using nonlinear van't Hoff fitting. Changes in free Gibb's energies were then calculated based on the determined dissociation constants, and together with the enthalpy values obtained via van' t Hoff analysis, the entropic contribution to lipid binding (DeltaS*T) was indirectly determined.

Strengths:

This is an extensive high quality native MS dataset that provides unique opportunities to gain insights into the thermodynamic parameters underlying lipid A binding. In addition, it provides coupling energies between mutations introduced into MsbA, that are implicated in lipid A binding.

Weaknesses:

The data all rely on the accuracy of determining KD values for lipid binding to MsbA. For the weaker binding sites, the range of lipid concentrations probed were in fact too low to generate highly accurate data. Another weakness is a lack of clear evidence, which KD values belong to which of the possible lipid A binding sites.

See our detailed response to reviewer 1 regarding Kd determination using native MS compared to other techniques. We chose to focus on the first three lipid binding events and adjusted the concentrations accordingly to titrate these three. As noted above, the Kd values can be determined from one mass spectrum. For rigor, we include different titration points and fit sequential binding model to the data – the fits are shown in supplemental and quite reasonable.

Regarding multiple lipids binding to different site(s), we have been able to distinguish high-affinity vs low-affinity PIP binding to Kir3.2 in a previous study [4]. This was apparent by the mole fraction curves for some lipid bound states not returning back to zero. We agree binding to multiple sites can be an issue. However, other techniques report on the ensemble of binding and, hence, no real useful information is obtained. Native MS enables one step in the right direction by dissecting the different lipid bound states. Future directions will need to further address this forefront question in the field, which we make point of now in discussion.

Reviewer #1 (Recommendations For The Authors):

Experiments/analysis: In short, there should be a proof of principle experiment that the thermodynamic constants determined by MS are accurate. Once that is done, the authors can add a more engaging structural interpretation of the results from the mutant cycles (which they seem to consciously avoid in the present manuscript?). How are cooperative residues coupled? Why?

See our detailed response to reviewer 1 above.

The manuscript is well-written, but Figures 3-5 are somewhat repetitive and require a lot of time to understand. Schematics of the main findings in each figure would help the uninitiated reader.

We agree the illustrations are complex but there is rich data being shown.

Figure 2 C contains an x-axis label error.

Corrected.

Reviewer #2 (Recommendations For The Authors):

1. Lines 128-129: "Like other mutant cycle studies, we assume the single- and double-mutations do not disrupt binding at specific sites on MsbA."

This statement is obscure and needs to be clarified. Does this mean that the mutations still allow binding of KDL, or the mutations do not disrupt the conformational integrity of the binding sites?

This statement has been removed.

1. Lines 137-139: "More specifically, R78 coordinates one of the characteristic phosphoglucosamine (P-GlcN) substituents of KDL whereas K299 interacts with a carboxylic acid group in the headgroup of KDL."

Two identical subunits form a dimer interface that forms an LPS binding site. Thus, a single mutation on the inner cavity will disrupt two binding sites on LPS. One R78 to P-ClcN and the other to a sugar backbone. Also, one K299 interacts with a carboxylic acid group in the headgroup and the other to an unknown (not clear in the figure).

Also noted above, mutation of the outer site will also impact the two outer sites. We have made note of this caveat.

1. Lines 171-172: "leading to an increase in ΔG by ~4 kJ/mol (Fig. 2d)"

Relative to what?

Corrected.

1. Lines 172-173: "Mutant cycle analysis indicates a coupling energy (ΔΔGint) of 1.7 (plus minus) 0.4 kJ/mol that contributes to the stability of KDL-MsbA complex."

The sign of DDG (DDH,DDS)_int is a bit confusing. I recommend that authors define the meaning of negative or positive sign of DDG_int (DDH,DDS) at this point. Here, a positive sign means favorable cooperation between the two mutated residues. Sometimes, researchers designate a positive cooperativity as a negative sign.

The literature on mutant cycles does not appear to follow a consensus on the sign. Here, we have revised the manuscript to note positive sign means favorable cooperation and follow the formalism recently described by Horovitz, Sharon, and co-workers [5].

1. Lines 182-185: "Enthalpy and entropy for KDL binding MsbA R188A was largely similar to the wild-type protein (Fig 3a). However, the R243A mutation resulted in an increase in entropy, compensated for by an increase in positive enthalpy (Fig 3a)."

The thermodynamic parameters for R243A mutation change in a similar manner to WT and R188A. It is R238A, not R243A, whose DH-DS interplay shows a distinct pattern from WT. Please, reword this sentence.

The sentence has been revised.

1. Lines 252-253: Solvation of polar groups in aqueous solvent has been ascribed to positive heat capacities whereas negative for apolar solvation.

This statement is not precise. More precisely, the collapse of apolar molecules from their solvated state leads to the negative "change" in heat capacity.

The sentence has been corrected.

1. Line 262-267: "These hydrophilic patches will be highly solvated, which will be desolvated upon binding lipids contributing favorably to entropy. In the case of MsbA, the selected lysine and arginine residues (based alpha carbon position) are separated by about 9 to 18 Å (PDB 8DMM). This distance could result in overlap of solvation shells that collectively contribute to the positive coupling enthalpy observed for MsbA-KDL interactions."

This statement is too speculative without presenting the degree of solvation of the residues targeted for mutation. More quantitative arguments seem to be needed.

We have removed the speculative statement.

Reviewer #3 (Recommendations For The Authors):

In this paper presented by Liu et al, native MS on the lipid A transporter MsbA was used to obtain thermodynamic insight into protein-lipid interactions. By performing the analyses at different lipid A concentrations and temperatures, dissociation constants for 2-3 lipid A binding sites were determined, as well as enthalpies were calculated using nonlinear van't Hoff fitting.

Changes in free Gibb's energies were then calculated based on the determined dissociation constants, and together with the enthalpy values obtained via van' t Hoff analysis the entropic contribution to lipid binding (DeltaS*T) was indirectly determined.

Correction – In the case on linear van’t Hoff plots, dH and dS were determined directly from the plot. For the nonlinear form of the van’t Hoff equation, which does not include an entropy fitting parameter, we back calculated dS using dH and dG at a given temperature.

The authors then included single, double and triple mutants of residues known based on cryo-EM and X-ray structures to interact with Lipid A either in the large inward-facing cavity or at a secondary binding site accessible at the surface of outward-facing MsbA, and determined the thermodynamic parameters of these mutants alone and combined to gain access to coupling energies of pairwise interactions. This method has its roots in studying pair-wise interactions of protein-protein interfaces, generally known as thermodynamic mutant cycle analysis.

Having the main expertise in ABC transporter structure-function, I will judge the paper mostly from the standpoint of what I can learn as a transporter expert from this study and whether the insights are of value for researchers with average biophysical knowledge.

My overall impression of the manuscript is that, while it contains a wealth of experimental data using the innovative and unique method of native mass spectrometry, it is hard to understand what one can learn from this analysis beyond their interesting key finding that entropy plays an important role in lipid binding (but only at certain temperatures). In particular, the lessons learned from the coupling energy analysis of the introduced mutations is hard to grasp/digest for me with regards to what I can learn from these numbers (other than learning that there are such coupling effects).

We agree the thermodynamic data is rich. Often a ddGint of zero is reported as having no coupling/significance but here the value is due to compensating ddH and d-dTS terms. In our view, this work forms the foundation of additional studies to better understand the coupling energetic terms, beyond ddGint.

In some instances, the text/figure legends are a bit unclear or contain some typos; but this part can easily be handled in a revision. The discussion is well written and embeds the main findings in the (still rather limited) literature on thermodynamic analyses of lipid binding of membrane proteins.

Major points

1. The authors may have clarified the following point in a previous paper; but at least in this paper, it is unclear to me how they purified MsbA without lipid A. The reason I am asking is that in our experience, if one purifies MsbA expressed from E. coli with standard detergents (e.g. beta-DDM) one will find a perfect density for Lipid A when determining an inward-facing structure by cryo-EM. According to the Methods, MsbA is purified initially in DDM, and rebuffered to C10E5 during size exclusion chromatography. When looking at Fig. 2b, the authors state (or assume?) that if no lipid A is added, MsbA has 0 % lipid A bound.

We have previously reported details of MsbA sample prep and optimization [6]. The revised manuscript makes note of this previous work and refers the reader to the publication. Yes, we see no appreciable signal for lipid A bound to MsbA (see Fig 2b).

We also note that samples of MsbA prepared using DDM is highly heterogenous, contaminated by a battery of small molecules (that we suspect are co-purified lipids). These contaminants will inadvertently impact biochemical studies.

1. A second topic where further clarification is in my view needed is the question of the conformations that were probed and the lipid binding sites. If I get the experimental rationale correctly, most of the data were determined in the absence of nucleotides, and only a small subset (Fig. 5) of data were determined in the presence of ATP-vanadate. However, structural evidence for the cytosolic lipid A binding site has been only determined for outward-facing MsbA (PDB: 8DMM), but has thus far not been seen in any of the inward-facing cryo-EM structures of MsbA, including recent well-resolved cryo-EM structures showing excellent density for the lipid A bound to the inward-facing cavity (PDB: 7PH2). Further, there is only one lipid A molecule that can be accommodated by the inward-facing cavity, whereas (owing to the symmetry of the homodimer) two lipid A can be bound sideways to outward-facing MsbA. Now, my understanding problem is why one does see up to three lipid A molecules bound to inward-facing apo MsbA, e.g. Fig. 2b and elsewhere. Where are they expected to bind? And what is the evidence supporting these additional binding sites?

See our detailed response to reviewer 1. If we add more lipid, we see more lipid binding to MsbA, like every other membrane protein we have studied. This data clearly indicates that there are more KDL binding site(s) – deciphering the affinity of these site(s) represents a problem on the horizon.

A further question is which lipid A binding sites are present in vanadate-trapped MsbA. Here, there are two identical binding sites (at the surface of each MsbA molecule), and it is therefore surprising to see that the affinities for the first and the second binding site are so different (see e.g. Supplementary Fig. 13).

Great point. A logical explanation (described for other biochemical systems) is the two exterior LPS binding sites display negative cooperativity i.e., binding at one site weakens the affinity at the other site.

Finally, what is the evidence that in vanadate-trapped MsbA, all molecules have closed NBDs and thus assume the outward-facing conformation? It is not uncommon that vanadate trapping leads to NBD closure only in a subfraction of all transporters (hence not in 100 % of them).

Yes, the native mass spectrum shows no appreciable signal for MsbA not trapped with vanadate/ADP. In our previous cryoEM study [6], using the vanadate-trapped transporter, we did not observe particles with NDBs dissociated in space. Regarding samples from other labs, a native mass spectrum could shed light into the population of untrapped protein – however, most studies use SDS-PAGE for quality control of their purified samples. This technology is not sufficient to address underlying biochemical issues.

We do have a new report in preparation describing a new discovery regarding trapping efficiency of MsbA.

1. The key parameter that is underlying the entire thermodynamic analysis of wt and mutant MsbA is the dissociation/association constant, which are used to calculate free Gibb's energy and, via van't Hoff analysis, enthalpy. Entropy is not determined directly, but in fact indirectly from these two numbers both depending on the measurement quality of dissociation/association constant. Now, when looking at the fitted curves as shown in Figure 2b (and in the supplement), determination of the dissociation constant for KDL1 (blue curves) look reasonable and the determined KDs are within the range of measured points. However, for KDL2 (red) and even more so KDL3 (yellow), the determined KD values (Supplementary Table 5), the measured KD values are typically higher than highest KDL conc used in the assay (1.5 uM). For this reason, and despite the fact that error bars of the fits look reasonably small, I still have doubts about the reliability of these KD values for KDL2 and KDL3.

Hence, the surprisingly strong changes of enthalpy/entropy values for different mutants/temperatures may have their origin in incorrectly determined KD values.

The increase in binding affinity of subsequent lipid binding events is consistent with many reports from our group [1, 2, 4, 6-9] and that of Prof. Robinson [10, 11] on this topic. As noted above, we indeed observe linear van’t Hoff plots with positive and negative slopes as well as non-linear curves that are convex or concave. The MsbA protein (wt or mutant), trapped or not, all display unique temperature-dependent responses. If the reviewer suggestion that the Kd values are incorrectly or randomly determined, then none of the binding data should follow thermodynamic van’t Hoff equations. This is simply not the case - the error bars and fits are reasonable. Backing up even further, looking the raw native mass spectra (see supplemental figure 1-3 and 10-11) one can see different temperature-dependence of lipid binding.

Minor points

1. Lines 116-131: this section reads as an extended introduction/aims, and does not contain any results.

This section has been moved to introduction.

1. Lines 137-139: suggested to check whether these interactions are also present in recently determined cryo-EM structures determined at fairly high resolution (PDB: 7PH2)

The interactions of MsbA and LPS (bound at the interior site) are comparable for PDB 7PH2 and 6BPL.

1. Lines 144-146: suggested to elude in more detail on the fitting procedure here, as the KD values determined in this way are the foundation of all quantitative assessments.

Details of data analysis and the fitting procedure are provided in methods.

1. Figure legend, Fig. 2: Technically, MsbA was solubilized and purified in DDM and detergent exchange was done on SEC to C10E5.

Corrected.

1. Figure legend, Fig. 4: description in a) on deconvoluted mass spec data is incorrect. Letter below needs to be adjusted accordingly.

Corrected.

1. Figure legend, Fig. 5: suggested to mention in Figure legend title that here we look at ADP-vanadate trapped MsbA.

Corrected.

References 1. Cong, X., et al., Determining Membrane Protein–Lipid Binding Thermodynamics Using Native Mass Spectrometry. Journal of the American Chemical Society, 2016. 138(13): p. 4346-4349.

1. Cong, X., et al., Allosteric modulation of protein-protein interactions by individual lipid binding events. Nat Commun, 2017. 8(1): p. 2203.

2. Qiao, P., et al., Insight into the Selectivity of Kir3.2 toward Phosphatidylinositides. Biochemistry, 2020. 59(22): p. 2089-2099.

3. Qiao, P., et al., Entropy in the Molecular Recognition of Membrane Protein-Lipid Interactions. J Phys Chem Lett, 2021. 12(51): p. 12218-12224.

4. Sokolovski, M., et al., Measuring inter-protein pairwise interaction energies from a single native mass spectrum by double-mutant cycle analysis. Nat Commun, 2017. 8(1): p. 212.

5. Lyu, J., et al., Structural basis for lipid and copper regulation of the ABC transporter MsbA. Nat Commun, 2022. 13(1): p. 7291.

6. Patrick, J.W., et al., Allostery revealed within lipid binding events to membrane proteins. Proc Natl Acad Sci U S A, 2018. 115(12): p. 2976-2981.

7. Schrecke, S., et al., Selective regulation of human TRAAK channels by biologically active phospholipids. Nature Chemical Biology, 2021. 17(1): p. 89-95.

8. Zhu, Y., et al., Cupric Ions Selectively Modulate TRAAK-Phosphatidylserine Interactions. J Am Chem Soc, 2022. 144(16): p. 7048-7053.

9. Tang, H., et al., The solute carrier SPNS2 recruits PI(4,5)P(2) to synergistically regulate transport of sphingosine1-phosphate. Mol Cell, 2023. 83(15): p. 2739-2752 e5.

10. Yen, H.Y., et al., PtdIns(4,5)P(2) stabilizes active states of GPCRs and enhances selectivity of G-protein coupling. Nature, 2018. 559(7714): p. 423-427.

Author Response

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

Reviewer #1 (Public Review):

In this manuscript, Butkovic et al. perform a genome-wide association (GWA) study on Arabidopsis thaliana inoculated with the natural pathogen turnip mosaic virus (TuMV) in laboratory conditions, with the aim to identify genetic associations with virus infection-related parameters. For this purpose, they use a large panel of A. thaliana inbred lines and two strains of TuMV, one naïve and one pre-adapted through experimental evolution. A strong association is found between a region in chromosome 2 (1.5 Mb) and the risk of systemic necrosis upon viral infection, although the causative gene remains to be pinpointed.

This project is a remarkable tour de force, but the conclusions that can be reached from the results obtained are unfortunately underwhelming. Some aspects of the work could be clarified, and presentation modified, to help the reader.

(Recommendations For The Authors):

• It is important to note that viral accumulation and symptom development do not necessarily correlate, and that only the former is a proxy for "virus performance". These concepts need to be clear throughout the text, so as not to mislead the reader.

This has been explained better in line 118-120, “Virus performance has been removed.

• Sadly, only indirect measures of the viral infection (symptoms) are used, and not viral accumulation. It is important to note that viral accumulation and symptom development do not necessarily correlate and that only the former is a proxy for "virus performance". These concepts need to be clear throughout the text, so as not to mislead the reader. The mention of "virus performance" in line 143 is therefore not appropriate, nor is the reference to viral replication and movement in the Discussion section.

"Virus performance" was removed. Also, the reference to viral replication and movement in the Discussion section has been removed.

Now we mention: “We did not measure viral accumulation, but note this is significantly correlated with intensity of symptoms within the Col-0 line (Corrêa et al. 2020), although it is not clear if this correlation occurs in all lines.”

• Since symptoms are at the center of the screen, images representing the different scores in the arbitrary scales should ideally be shown.

Different Arabidopsis lines would look different and this could mislead a reader not familiar with the lines. In order to make a representation of our criteria to stablish the symptoms, we believe that a schematic representation is clearer to interpret. Here are some pictures of different lines showing variating symptoms:

Author response image 1.

• Statistical analyses could be added to the figures, to ease interpretation of the data presented.

Statistical analysis can be found in methods. We prefer to keep the figure legend as short as possible.

• The authors could include a table with the summary of the phenotypes measured in the panel of screened lines (mean values, range across the panel, heritability, etc.).

These data are plotted in Fig. 1. We believe that repeating this information in tabular form would not contribute to the main message of the work. Phenotype data and the code to reproduce figure 1 are available at GitHub (as stated in Data Availability), anyone interested can freely explore the phenotypes of the screened lines.

• The definition of the association peak found in chromosome 2 could be explained further: is the whole region (1.5 Mb) in linkage disequilibrium? How many genes are found within this interval, and how were the five strong candidates the authors mention in line 161 selected? It is also not clear which are these 5 candidates, apart from AT2G14080 and DRP3B - and among those in Table 1 (which, by the way, is cited only in the Discussion and not in the Results section)? Why were AT2G14080 and DRP3B in particular chosen?

We have replaced Table 1 with an updated Table S1 listing all genes found within the range of significant SNPs for each peak. We now highlight a subset of these genes as candidate genes if they have functions related to disease resistance or defence, and mentioned them explicitly in the text (lines 173-179. We have explicitly described how this table was constructed in the methods (lines 525-538).

• Concerning the validation of the association found in chromosome 2 (line 169 and onward): the two approaches followed cannot be considered independent validations; wouldn't using independent accessions, or an independent population (generated by the cross between two parental lines, showing contrasting phenotypes, for example) have been more convincing?

We aim to compare the hypothesis that the association is due to a causal locus to the null hypothesis that the observed association is a fluke due to, for example, the small number of lines showing necrosis. If this null hypothesis is true then we would not expect to see the association if we run the experiment again using the same lines. An alternative hypothesis is that the genotype at the QTL and disease phenotypes are not directly causally linked, but are both correlated with some other factor, such as another QTL, or maternal effects. We agree that an independent sample would be required to exclude the latter hypothesis, but argue that the former is the more pertinent. We have edited the text to be explicit about the hypothesis we are testing, and altered the language to shift the focus from ‘validation’ to ‘confirming the robustness’ of the association (line 182).

• Regarding the identification of the transposon element in the genomic region of AT2G14080: is the complementation of the knock-out mutant with the two alleles (presence/absence of the transposon) possible to confirm its potential role in the observed phenotype?

This could be feasible but we cannot do it as none of the researchers can continue this project.

• On the comparison between naïve and evolved viral strains: is the evolved TuMV more virulent in those accessions closer to Col-0?

This is not something we have looked at but would certainly be an interesting follow-up investigation.

• The Copia-element polymorphism is identified in an intron; the potential functional consequences of this insertion could be discussed. In the example the authors provide, the transposable element is inserted into the protein-coding sequence instead.

We now state explicitly that such insertions are expected to influence expression; beyond that we can only speculate. We have removed the reference to the insertion in the coding sequence.

• The authors state in line 398 that "susceptibility is unquestionably deleterious" - is this really the case? Are the authors considering susceptibility as the capacity to be infected, or to develop symptoms? Viral infections in nature are frequently asymptomatic, and plant viruses can confer tolerance to other stresses.

We have tone down the expression and clarify our wording: “Given that potyvirus outbreaks are common in nature (Pagán et al., 2010) and susceptibility to symptomatic infection can be deleterious”

Additional minor comments:

• In Table 1, Wu et al., 2018 should refer to DRP2A and 2B, not 3B.

We have removed Table 1 altogether.

• Line 126: a 23% increase in symptom severity is mentioned, but how is this calculated, considering that severity is measured in four different categories?

This is the change in mean severity of symptoms between the two categories.

• Figure 1F: "...symptoms"

Fixed.

• Line 179: "...suggesting an antiviral role..."

Changed.

• Lines 288-300: This paragraph does not fit into the narrative and could be omitted.

It has been removed and some of the info moved to the last paragraph of the Intro, when the two TuMV variants were presented.

• Lines 335-337: The rationale here is unclear since DRP2B will also be in the background - wouldn't DRPB2B and 3B be functionally redundant in the viral infection?

Our results suggest that DRPB3B is redundant with DRPB2B for the ancestral virus but not for the evolved viral strain. We speculate that the evolved viral isolate may have acquired the capacity to recruit DRPB3B for its replication and hence it produces less symptoms when the plant protein is missing.

We have spotted a mistake that may have add to the confusion. Originally the text said “In contrast, loss of function of DRP3B decreased symptoms relative to those in Col-0 in response to the ancestral, but not the evolved virus”. The correct statement is “In contrast, loss of function of DRP3B decreased symptoms relative to those in Col-0 in response to the evolved, but not the ancestral virus.”  

Reviewer #2 (Public Review):

The manuscript presents a valuable investigation of genetic associations related to plant resistance against the turnip mosaic virus (TuMV) using Arabidopsis thaliana as a model. The study infects over 1,000 A. thaliana inbred lines with both ancestral and evolved TuMV and assesses four disease-related traits: infectivity, disease progress, symptom severity, and necrosis. The findings reveal that plants infected with the evolved TuMV strain generally exhibited more severe disease symptoms than those infected with the ancestral strain. However, there was considerable variation among plant lines, highlighting the complexity of plant-virus interactions.

A major genetic locus on chromosome 2 was identified, strongly associated with symptom severity and necrosis. This region contained several candidate genes involved in plant defense against viruses. The study also identified additional genetic loci associated with necrosis, some common to both viral isolates and others specific to individual isolates. Structural variations, including transposable element insertions, were observed in the genomic region linked to disease traits.

Surprisingly, the minor allele associated with increased disease symptoms was geographically widespread among the studied plant lines, contrary to typical expectations of natural selection limiting the spread of deleterious alleles. Overall, this research provides valuable insights into the genetic basis of plant responses to TuMV, highlighting the complexity of these interactions and suggesting potential avenues for improving crop resilience against viral infections.

Overall, the manuscript is well-written, and the data are generally high-quality. The study is generally well-executed and contributes to our understanding of plant-virus interactions. I suggest that the authors consider the following points in future versions of this manuscript:

1. Major allele and minor allele definition: When these two concepts are mentioned in the figure, there is no clear definition of the two words in the text. Especially for major alleles, there is no clear definition in the whole text. It is recommended that the author further elaborate on these two concepts so that readers can more easily understand the text and figures.

We agree that the distinction between major/minor alleles and major/minor associations in our previous manuscript may have been confusing. In the current manuscript we now define the minor allele at a locus as the less-common allele in the population (line 167). We have removed references to major/minor associations, and instead refer to strong/weak associations.

1. Possible confusion caused by three words (Major focus / Major association and major allele): Because there is no explanation of the major allele in the text, it may cause readers to be confused with these two places in the text when trying to interpret the meaning of major allele: major locus (line 149)/ the major association with disease phenotypes (line 183).

See our response to the previous comment.

1. Discussion: The authors could provide a more detailed discussion of how the research findings might inform crop protection strategies or breeding programs.

We would prefer to restrain speculating about future applications in breeding programs.

(Recommendations For The Authors):

1. Stacked bar chart for the Fig 1F. It is recommended that the author use the form of a stacked bar chart to display the results of Fig 1F. On the one hand, it can fit in with the format of Fig 1D/E/G, on the other hand, it can also display the content more clearly.

We think the results are easier to interpret without the stacked bar chart.

1. Language Clarity: While there are no apparent spelling errors, some sentences could be rewritten for greater clarity, especially when explaining the results in Figure 1 and Figure 2.

We have reviewed these sections and attempted to improve clarity where that seemed appropriate.

There are some possibilities to explore in the future. For example: clarity of mechanisms for the future. While the study identifies genetic associations, it lacks an in-depth exploration of the underlying molecular mechanisms. Elaborating on the mechanistic aspects would enhance the scientific rigor and practical applicability of the findings.

Yes, digging into the molecular mechanisms is an ongoing task and will be published elsewhere. It was out of the scope of this already dense manuscript.  

Reviewer #3 (Public Review):

Summary of Work

This paper conducts the largest GWAS study of A. thaliana in response to a viral infection. The paper identifies a 1.5 MB region in the chromosome associated with disease, including SNPs, structural variation, and transposon insertions. Studies further validate the association experimentally with a separate experimental infection procedure with several lines and specific T-DNA mutants. Finally, the paper presents a geographic analysis of the minor disease allele and the major association. The major take-home message of the paper is that structural variants and not only SNPs are important changes associated with disease susceptibility. The manuscript also makes a strong case for negative frequency-dependent selection maintaining a disease susceptibility locus at low frequency.

Strengths and Weaknesses

A major strength of this manuscript is the large sample sizes, careful experimental design, and rigor in the follow-up experiments. For instance, mentioning non-infected controls and using methods to determine if geographic locus associations were due to chance. The strong result of a GWAS-detected locus is impressive given the complex interaction between plant genotypes and strains noted in the results. In addition to the follow-up experiments, the geographic analysis added important context and broadened the scope of the study beyond typical lab-based GWAS studies. I find very few weaknesses in this manuscript.

Support of Conclusions

The support for the conclusions is exceptional. This is due to the massive amount of evidence for each statement and also due to the careful consideration of alternative explanations for the data.

Significance of Work

This manuscript will be of great significance in plant disease research, both for its findings and its experimental approach. The study has very important implications for genetic associations with disease beyond plants.

(Recommendations For The Authors):

Line 41 - Rephrase, not clear "being the magnitude and sign of the difference dependent on the degree of adaptation of the viral isolate to A. thaliana."

Now it reads: “When inoculated with TuMV, loss-of-function mutant plants of this gene exhibited different symptoms than wild-type plants, where the scale of the difference and the direction of change between the symptomatology of mutant and wild-type plants depends on the degree of adaptation of the viral isolate to A. thaliana.”

Line 236 - typo should read: "and 21-fold"

Changed.

Author Response

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

In this manuscript, Xie et al report the development of SCA-seq, a multiOME mapping method that can obtain chromatin accessibility, methylation, and 3D genome information at the same time. This method is highly relevant to a few previously reported long read sequencing technologies. Specifically, NanoNome, SMAC-seq, and Fiber-seq have been reported to use m6A or GpC methyltransferase accessibility to map open chromatin, or open chromatin together with CpG methylation; Pore-C and MC-3C have been reported to use long read sequencing to map multiplex chromatin interactions, or together with CpG methylation. Therefore, as a combination of NanoNome/SMAC-seq/Fiber-seq and Pore-C/MC-3C, SCA-seq is one step forward. The authors tested SCA-seq in 293T cells and performed benchmark analyses testing the performance of SCA-seq in generating each data module (open chromatin and 3D genome). The QC metrics appear to be good and the methods, data and analyses broadly support the claims. However, there are some concerns regarding data analysis and conclusions, and some important information seems to be missing.

1. The chromatin accessibility tracks from SCA-seq seem to be noisy, with higher background than DNase-seq and ATAC-seq (Fig. 2f, Fig. 4a and Fig. S5). Also, SCA-seq is much less sensitive than both DNase-seq and ATAC-seq (Figs. 2a and 2b). This and other limitations of SCA-seq (high background, high sequencing cost, requirement of specific equipment, etc) need to be carefully discussed.

We thank the reviewer for the important comment about noisy GpC methylation signal in SCA-seq. We acknowledge that the SCA-seq signal presented in Fig. 2f, Fig. 4a, and Fig. S5 in our first draft was indeed noisy, as we present the raw 1D genomic signal. In this revision, we have taken steps to reduce the noise in GpC methylation signal by identifying the accessible regions on each segment of every single molecule. For each segment, we performed the sliding window analysis (50bp window sliding by a 10 bp step) with binomial test to identify accessible windows that significantly deviate from background GpC methylation ratio. The overlapping accessible windows (p < 0.05 for binomial test and contain at least two GpC sites) on the single fragments are merged as accessible region. Then we retain the GpC methylation signal inside the accessible region to reduce the background noise (Sfig 5ab). The details of the noise filtering steps are described in the Methods section (page 22 lines 13-23).

Visually, we can observe from the updated exemplary view of 1D signal track that the noise is dramatically reduced in filtered SCA-seq GpC methylation signal compared to the raw signal (Sfig5c). The clean SCA-seq GpC methylation 1D signals were also updated (Fig2f and Fig4a). We have observed an increase in the TSS enrichment score, which is a commonly used metric for assessing the signal-to-noise ratios in ATAC-seq data quality control. Specifically, the TSS enrichment score increased to 2.74 when using the filtered signal, compared to 1.93 when using the raw signal (Sfig5d). After noise filtering, 80% of SCA-seq 1D peaks overlaps with peaks called by ATAC-seq and/or DNase-seq (Fig2ab), compared to 74% from the raw signal in the first draft.

We thank the reviewer for raising up the concern about the sequencing cost and requirement of specific equipment. The sequencing cost is approximately 1300 USD per sample to sequence 30X depth human sample and obtain saturated GpC methylation signal (Sfig4d) as well as loop signal similar to the NGS-based Hi-C (Fig3gh). Considering that SCA-seq simultaneously provides higher-order chromatin structure and chromatin accessibility at single molecule resolution, we believe the cost is acceptable. However, it is worth noting that SCA-seq requires a regular Oxford nanopore sequencer with R9.4.1 chip, which is currently available but might be discontinued by Oxford Nanopore in the future. We have addressed all these concerns in the discussion section.

1. In Fig. 2f, many smaller peaks are present besides the major peaks. Are they caused by baseline DNA methylation? How many of the small methylation signals are called peaks? In Fig. 4a, it seems that the authors define many more enhancers from SCA-seq data than what will be defined from ATAC-seq or DHS. Are those additional enhancers false positives? Also, it is difficult to distinguish the gray "inaccessible segments" from the light purple "accessible segments.

We thank the reviewer for bringing up these concerns.

Regarding the smaller peaks in the 1D genomic GpC methylation signal, we have addressed this issue by implementing the noise filtering in this revision, the small peaks on 1D tracks are greatly reduced (Fig2f, Sfig5c). It is important to note that SCA-seq generates accessibility signals specifically on ligation junctions, which differs from the one-dimensional (1D) signals obtained through ATAC-seq or DNase-seq. The presence of remaining small peaks in the SCA-seq data can be attributed to the varied sequencing depth, which is influenced by the enriched spatial interactions occurring in regions of the genome that are enriched with ligation junctions. In general, the SCA-seq 1D peaks are well correlated with the high confidence peaks from 1D track of ATAC-seq and DNase-seq (Fig2b).

We apologize for the lack of clarity in our enhancer annotation. The enhancer regions were obtained from The Ensembl Regulatory Build (PMID: 25887522). We have now included this information in the method section (page 24 line 16).

We thank the reviewer for pointing out this visualization problem. The color scheme has been revised, with purple now representing the inaccessible segments and yellow representing the accessible segments.

1. For 3D genome analysis, it is important to provide information about data yield from SCA-seq. With 30X sequencing depth, how many contacts are obtained (with long-read sequencing, this should be the number of ligation junctions)? How is the number compared to Hi-C.

We thank the reviewer for raising up this crucial point about the sequencing yield that we missed. We have now included this information in the revised result section (page 11, lines 11-14).

We have checked the public data of a successful HEK293T Hi-C run (PMID: 34400762). The Hi-C experiment produced 699,464,541 reads (105G base), and we obtained 388,031,859 contacts.

From 100G bases of HEK293T SCA-seq data, we obtained 81,229,369 ligation junctions and 378,848,187 virtual pairwise contacts (3.8M pairwise contacts per Gb). The SCA-seq performance of virtual pairwise contact number per Gb is similar to that of PORE-C (PMID: 35637420).

1. Fig 3j. Because SCA-seq only do GpC methylation, the capability to detect the footprint at individual CTCF peaks depends on the density of GpC nearby. Have the authors taken GpC density into account when defining CTCF sites with or without footprint?

We appreciate the reviewer for bringing up the concern about the GpC site density at CTCF site. We would like to highlight that Battaglia et al. have demonstrated the feasibility of identifying transcription factor binding events using GpC labeling (PMID: 36195755). In our study, we have implemented a high-resolution sliding window approach to enhance the sensitivity of CTCF binding detection. We have taken GpC density into account by performing a sliding window (50 bp window, 10 bp step) binomial test on every single molecule overlapping with CTCF site to call accessible region. The detailed steps to call accessible region has been described in the answer of the first question. Based on the pattern in Fig3j, we identify CTCF footprints if the accessible regions are called nearby the CTCF sites (at least 20 bp away from the center of CTCF sites) but not on the CTCF sites.

To ensure that the GpC site density is sufficient for binomial test of each sliding window of the regions around CTCF site genome-wide, we examined the number of GpC sites in each window. Our analysis revealed that GpC sites are evenly distributed, and over 87% of the windows contain at least 2 GpC sites, which qualifies them for a binomial test (Author response image 1). This indicates that we are able to detect the CTCF footprint at most of the CTCF sites, taking into consideration the GpC density.

Author response image 1.

Genome wide GpC site density at CTCF site centered region. Distribution of the number of GpC sites (y-axis) at each 50 bp sliding window region (x-axis) was presented in violin plots.

1. This study only performs higher resolution chromatin interaction analysis based on individual read concatenates. It is unclear to me if the data have enough depth to perform loop analysis with Hi-C pipelines.

We thank the reviewer for highlighting this important concern about the depth of data for performing loop analysis. We have performed Aggregate peak analysis for SCA-seq and Hi-C side-by-side using hiccups function in Juicer (v1.9.9) (PMID: 27467249). We acknowledge that the level of loop signal enrichment is relatively weaker (one-fold less) in SCA-seq compared to Hi-C (Fig3h). This difference can be attributed to the lower sequencing yield per Gb in SCA-seq, which resulted in 4.93M pairwise contacts per Gb, compared to the 7M contacts per Gb in Hi-C. Despite this discrepancy, we were still able to observe the clear genome-wide loop enrichment pattern in SCA-seq (Fig3gh).

1. It appears that SCA-seq is of low efficiency in detecting chromatin interactions. As shown in Fig. S7a, 65.4% of sequenced reads contained only one restriction enzyme (RE) fragment/segment (with no genomic contact), which is much higher than that reported in published PORE-C methods. In addition, Fig. S7g is very confusing and in conflict with Fig. S7a. For example, in Fig. S7g, 21.4% and 22.2% of CSA-seq concatemers contain one and two segments, whereas the numbers are 65.4% and 14.7% in Fig. S7a, respectively. Please explain.

We apologize for the confusion in sfig7a and sfig7g.

Sfig7a was intended to illustrate the cardinality count of concatemers with only chr7 segments included, representing the intra-chromosome cardinality instead of the genome-wide cardinality. We have revised sfig7a and its corresponding figure legend to clarify that the figure describes segments of intra-chromosome interactions.

On the other hand, sfig7g shows the concatemers including both intra-chromosome and inter-chromosome segments, which explains the differences in the percentages of different cardinality ranges compared to Figure S7a. Moreover, the percentages reported in Figure S7g are similar to what is typically reported in PORE-C methods when considering both intra- and inter-chromosome interactions.

To provide a comprehensive view of the genome-wide concatemer cardinality distribution, we have also included a histogram in Fig3k, which demonstrates the detailed distribution of cardinality for genome-wide concatemers.

1. I disagree with the rationale of the entire Fig. S9. Biologically there is no evidence that chromatin accessibility will change due to genome interactions (the opposite is more likely), therefore the definition of "expected chromatin accessibility" is hard to believe. If the authors truly believe this is possible, they will need to test their hypothesis by deleting cohesin and check if the chromatin accessibility driven by "power center" are truly abolished. The math in Fig. S9 is also confusing. Firstly, the dimension of the contact matrix in Fig. S9 appears to be wrong, it should have 8 rows. Secondly, I don't understand why the interaction matrix is not symmetric. Third, if I understand correctly the diagonal of the matrix should be all 1, it is also hard to understand why the matrix only has 1, 0 or -1. It appears that the authors assume that the observed accessibility is a simple sum of the expected accessibility of all its interacting regions; this is wrong. In my opinion, the whole Fig. S9 should be deleted unless the authors can make sense of it and ideally also provide more evidence.

I apologize for any confusion caused by the rationale and figures in Fig. S9. The purpose of the hypothesis presented in the figure is to explore the potential relationship between chromatin accessibility and genome interactions. While there is currently no direct biological evidence supporting this hypothesis, it is a possibility that warrants further investigation.

Regarding the suggestion to delete Fig. S9 unless more evidence is provided, it is important to note that this paper primarily focuses on the methodology and theoretical framework. Experimental validation of the hypothesis falls outside the scope of this particular study.

We have made corrections to the schematic matrix in Fig. S9 to accurately represent the dimensions and symmetry. The numbers in the matrix represent mean accessible values of the contacts. Specifically, accessible-accessible contacts are represented by 2, accessible-inaccessible contacts are represented by 0, and inaccessible-inaccessible contacts are represented by -2.

Minor concerns:

1. The authors may want to clearly demonstrate the specificity and sensitivity of the ATAC part and the efficiency of the Hi-C part of SCA-seq.

We appreciate the reviewer’s suggestion to demonstrate the specificity and sensitivity of the ATAC-seq part and the efficiency of the Hi-C part in SCA-seq.

We considered the non-peak region genomic bins shared by ATAC-seq and DNase-seq as true negatives and the overlapping peaks of ATAC-seq and DNase-seq as true positives. Based on these criteria, the specificity of SCA-seq 1D peaks is calculated as TN / N, where TN represents the number of true negatives (89107) and N represents the sum of true negatives and false positives (89107 + 9345). The resulting specificity is 0.91. The sensitivity of SCA-seq 1D peaks is calculated as TP / P, where TP represents the number of true positives (33190) and P represents the sum of true positives and false negatives (33190 + 11758). The resulting sensitivity is 0.73.

We evaluate the efficiency of spatial interaction by the restriction enzyme digested fragments recovered in the pairwise contacts that contain ligation junctions. In SCA-seq, the efficiency is calculated as the number of dpnII digested fragments recovered by pairwise contacts (5625908) divided by the total number of in silico dpnII digested fragments (7127633). The resulting efficiency is 0.79.

We have now included this information in the revised result section (page 8 lines 15-18)

1. Fig 4g, colors with apparent differences might be used to clearly discriminate the three types of interactions (I-I, I-A and A-A).

We appreciate the reviewer for bringing up the issue regarding the visualization in Fig 4g. The color scheme has been revised, with purple now representing I-I interactions, orange representing I-A interactions, and red representing A-A interactions. We believe that these modifications have significantly improved the clarity.

1. Fig. 4c, when fitting an unknown curve, R-square becomes meaningless.

We appreciate the reviewer for pointing out the issue regarding the interpretation of R-square. We have removed the R-square value from Fig. 4c.

1. Fig 5a, "oCGIs comprised 65% CGIs that did not directly contact enhancers or promoters". Should it be "oCGIs comprised 65% of all CGIs"?

We appreciate the reviewer for pointing out the clarification needed in Fig 5a. We have revised the phrase in the figure legend to accurately state that “oCGIs comprised 65% of all CGIs”. Thank you for bringing this to our attention.

1. Page 15 lines 5-8, "By examining the methylation status on reads, as expected, these read segments demonstrated lower CpG methylation and higher chromatin accessibility (GpC methylation), which further supports their roles in gene activation (Fig 5b)". This statement seems to be inconsistent with the figure legend.

We appreciate the reviewer for pointing out the inconsistency in the legend of Fig 5b. We have revised the legend of Fig 5b to accurately highlight the low CpG methylation on oCGI regions. Thank you for bringing this to our attention.

1. Language editing and proof reading are needed.

I apologize for any errors or mistakes in the language. We have carefully reviewed the manuscript and made the necessary language editing and proofreading revisions to ensure its quality for publication.

Author Response

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

We thank the reviewers for collectively highlighting our study as “interesting and timely” and as making significant advances regarding the functional role of Orai in the activity of central dopaminergic neurons underlying the development of Drosophila flight behaviour. We hope that based on the revisions detailed below the data supporting our findings will be considered complete.

Reviewer 1:

• In this revision, the authors have addressed most points using text changes but there is still one important issue that continues to be inadequately addressed. This relates to point 1.

If Set2 is acting downstream of SOCE, it is not clear to me how STIM1 over expression rescues Set2-dependent downstream responses in flies that do not have Set2. It seems that if STIM1 over-expression, which would presumably enhance SOCE, largely rescues Set2-dependent effector responses in the Set2RNAi flies, then the proposed pathway cannot be true (because if Set2 is downstream of SOCE, it shouldn't matter whether SOCE is boosted in flies that lack Set2). This discrepancy is not explained. Does STIM1 over-expression somehow restore Set2 expression in the Set2RNAi flies?

Ans: Based on the requirement of Orai-mediated Ca2+ entry for Set2 expression (THD’>OraiE180A neurons, Figure 2C) we had indeed proposed that rescue of flight in Set2RNAi flies by STIMOE is because Set2 expression in Set2RNAi flies is restored by STIMOE. However, we agree that this has not been tested experimentally. Since these data are supportive but not essential to our findings here, we have removed data demonstrating flight rescue of Set2RNAi by STIMOE from Figure 2 – supplement 5 and associated text from the revised manuscript. We plan to investigate the effect of STIMOE on Set2 in the context of Drosophila dopaminergic neurons in the future.

Reviewer 2:

The manuscript analyses the functional role of Orai in the excitability of central dopaminergic neurons in Drosophila. The authors answer the previous concerns, but several important issues have not been experimentally tested. Especially, the lack of characterization of SOCE or calcium release from the intracellular calcium stores limits considerably the impact of the study. They comment on a number of technical problems but, taking into account the nature of the study, based on Orai and SOCE, the lack of these experimental data reduces the relevance of the study. Below are some specific comments:

1. The response to question 1 is unconvincing. The authors do not demonstrate experimentally that STIM over-expression enhances SOCE or how excess SOCE might overcome the loss of SET2.

Ans: The reason we have not performed experiments in this manuscript to investigate SOCE in STIM overexpression condition is two-fold. Firstly, extensive characterisation of SOCE by STIM overexpression in Drosophila pupal neurons forms part of an earlier publication (Chakraborty and Hasan, Front. Mol. Neurosci, 2017). A graph from Chakraborty and Hasan, 2017 where SOCE was measured in primary cultures of pupal neurons from an IP3R mutant (S224F/G1891S) of Drosophila. Reduced SOCE in IP3R mutant neurons (red trace) was restored by overexpression of STIM (black trace). The green trace is of wild-type neurons with STIM overexpression and the grey trace with STIMRNAi. Similar experiments were performed with Orai+STIM overexpression and the rescue in SOCE was compared with STIM overexpression in pupal neurons of wild type and IP3R mutant S224F/G1891S. See Chakraborty and Hasan, 2017 (Front. Mol. Neurosci. 10:111. doi: 10.3389/fnmol.2017.00111)

2) Secondly, rescue by STIMOE is supportive but not essential to the findings of this manuscript which relate primarily to the analysis of an Orai-dependent transcriptional feed-back mechanism acting via Trl and Set2 in flight promoting dopaminergic neurons (See Fig 2C where we demonstrate that OraiE180A expression in THD’ neurons brings down Set2 expression).

We agree that we have not demonstrated how loss of Set2 can be compensated by STIM overexpression. Therefore, we have now removed the supplementary data relating to STIM rescue of Set2RNAi (THD’>Set2RNAi; STIMOE) flight phenotypes since as mentioned above it was supportive but not essential to the main theme of the manuscript. Consistent with this, we have also removed rescue of flight in TrlRNAi by STIMOE (Figure 4C).

1. The authors do not present a characterization of SOCE in the cells investigated expressing native Orai or the dominant negative OraiE180A mutant yet. They comment on some technical problems for in situ determination or using culture cells but, apparently, in previous studies they have reported some results.

Ans: We respectfully submit that characterisation of SOCE in cells expressing native Orai and OraiE180A from primary cultures of Drosophila pupal dopaminergic neurons, form part of an earlier publication (Pathak, T., et al., (2015). The Journal of Neuroscience, 35, 13784–13799. https://doi.org/10.1523/jneurosci.1680-15.2015). As mentioned in lines 80-84 the dopaminergic neurons studied here (THD’) are a subset of the dopaminergic neurons studied in the Pathak et al., 2015 publication (TH). As evident in Figure 2 panels B-D expression of OraiE180A in dopaminergic neurons abrogates SOCE.

In this study we have focused on identifying the molecular mechanism by which OraiE180A expression and concomitant loss of cellular Ca2+ signals (Figure 3B, 3C) affects dopaminergic neuron function. In lines 270-274 (page 10) we have stated the technical reason why Ca2+ measurements made in this study from ex-vivo brain preps measure a composite of ER-Ca2+ release and SOCE. Our observation that the measured Ca2+ response is significantly attenuated in cells expressing OraiE180A leads us to the conclusion that we are indeed measuring an SOCE component in the ex-vivo brain preps. This is also explained in ‘Limitations of the study’.

1. Concerning the question about the STIM:Orai stoichiometry the authors answer that "We agree that STIM-Orai stoichiometry is essential for SOCE, and propose that the rescue backgrounds possess sufficient WT Orai, which is recruited by the excess STIM to mediate the rescue"; however, again, this is not experimentally tested.

Ans: To address this point we have now measured relative stoichiometries of STIM and Orai mRNA by qPCR under WT conditions in Drosophila THD’ neurons at 72 hr APF. The observed stoichiometry as per these measurements is STIM:Orai =1.6:1 (~8:5). These data are in relative agreement with the normalised read counts of STIM and Orai in THD’ neurons in the RNAseq performed and described in Fig 1F. The qPCR (A) and RNAseq (B) measures of STIM and Orai are appended below.

Author response image 1.

In comparison to the numerous studies investigating structural, biophysical and cellular characterisation of Orai channels in heterologous systems, there are fewer studies which have traced systemic implications of Orai function through multiple tiers of investigation including organismal behaviour. Leveraging the wealth of genetic resources available in Drosophila, we have attempted this here. While we respectfully agree that questions pertaining to the stoichiometries of STIM/Orai proteins are indeed relevant to cellular regulation of SOCE, we submit they may be better suited for investigation in heterologous systems involving cell culture, or with in-vitro systems with purified recombinant proteins, or indeed using computational and modelling approaches. None of these methods fall within the scope of our current investigation which is to understand how by Orai mediated Ca2+ entry regulates developmental maturation of Drosophila flight promoting dopaminergic neurons.

eLife assessment

In Drosophila melanogaster, the SOCE channel Orai is required for the development of flight promoting dopaminergic neurons. The Hasan laboratory has previously shown that disabling Orai function impairs Drosophila flight due to aberrant neuronal development at the pupal stage. In this fundamental study, Mitra et al show that SOCE drives a transcriptional feedback loop via the homeobox transcription factor, 'Trithorax-like' (Trl), and histone modifiers, Set2 and E(z), to regulate the expression of key genes required for the function of dopaminergic flight neurons, including the muscarinic acetylcholine receptor and the inositol 1,4,5-trisphosphate receptor. This solid study is carefully performed with validated methodology and most of the analyses are rigorous.

Author Response

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

We thank the Editor and the referees for their questions and remarks. In this document we provide a point-by-point response to revisions requested by the reviewers.

Public Reviews:

Reviewer #1 (Public Review):

Jafarinia et al. have made an interesting contribution to unravelling the molecular mechanisms underlying pathological phenotypes of repeat expansion of the C9orf72 gene. The repeat expression leads to the expression of polyPR proteins. Using coarse-grained molecular dynamics simulations, the authors identify putative binding partners involved in nucleocytoplasmic transport (NCT), and that conjecture that polyPR affects essential processes by binding to NCT-related proteins. The results are well-reported, but only putative, and need experimental support to be more conclusive. Also, a comparison with results from all-atom MD simulations in explicit water could help verify the results. But even without these, the work is very useful as a first step to unravel the role of polyPR and related peptides.

We greatly appreciate the reviewer's positive assessment of our work and the suggestions. We acknowledge the need for more experimental validation of the binding behavior of some of the transport components. Our results coincide with the experimental findings of Hutten et al. [1] ([16] in our paper) for example regarding the binding of polyPR to Kapβs and Impαs, but experimental validation of additional transport components, especially for RanGAP, would be valuable. We hope that our work will inspire colleagues from the field to actually perform such experiments.

We also agree with the reviewer's suggestion that all-atom simulations can provide further details on the molecular conformations at the local NTR-PR binding regions. Nonetheless, such simulations for all transport components, particularly for interactions involving large conformational flexibility of longer polyPR chains such as PR50, would require significant computational expenses. In a recent publication (Jafarinia et al. [2]) we reported on the close resemblance in binding behavior between our coarse-grained MD data and the all-atom MD simulations of (Nanaura et al. [3]), both showing polyPR binding to a negatively-charged cavity of Kapβ2. We expect future MD simulations to elucidate more atomistic detail with the continuously increasing power of high-performance computing clusters.

Reviewer #2 (Public Review):

This study used coarse-grained molecular dynamics simulation to explain how the binding of polyPR might interfere with distinct stages of the transport cycle. This finding shows that the interaction between polyPR and transport components is driven by electrostatic interactions and is correlated with the salt concentration and the length of polyPR, providing an important basis for subsequent exploration of the impact of C9orf72 R-DPRs on NCT disruption.

We appreciate the reviewer's positive feedback and the recognition of the significance of our work.

Reviewer #3 (Public Review):

Onck and co-workers present in this work the identification of binding partners and sites of polyPR on various nuclear transport components and elucidate how polyPR might potentially influence the transport process. It's interesting to note that some interaction sites on transport components also serve as their inherent/functional binding sites. The difference in the effects between short polyPR (PR7) and long polyPR (PR50) is also evident, although the authors might need to clarify the mechanisms better. Overall, the manuscript is well organized and concisely written, and it would greatly enhance our understanding of the toxicity induced by polyPR. In general, the 1-bead per atom force field model used in the study is well-tuned for studying the interactions between polyPR and proteins, as the essential cation-pi interactions (between Arg and Phe/Tyr/Trp) were included using an 8-6 LJ model.

We thank the reviewer for recognizing the suitability of our 1-bead-per-amino-acid force field for studying R-DPRs' interactions with transport components and for acknowledging our work's contribution to understanding polyPR toxicity mechanisms. Below we comment on the mechanisms describing the difference between short and long polyPR molecules.

Recommendations for the authors:

1) Regarding Figure 2 (also see below for more specific comments), there is a major concern that the dipole moment is not included in Fig 2b (as the correlation is better with f=0), but the authors still conclude that this is generally important (lines 258-261). As a minimum, this needs to be discussed more carefully. Is f (i..e. the importance of dipole moment for binding) dependent on the specific binding partner, or what is going on? Maybe, there is a good explanation?

Indeed, the significance of the dipole moment depends on the specific type of transport component involved. Our analysis reveals that for Kapβs, see figure 2b, the best-fit is obtained with f=0, indicating that the separation of charge within Kapβs has a relatively minor effect on their interaction with polyPR. Instead, the primary determinant for polyPR-Kapβ interaction appears to be the net charge per residue (NCPR), with a more negative NCPR leading to stronger interactions.

We attribute this behavior to the structural characteristics of Kapβs, particularly the superhelical structure which features inner and outer surfaces with differing charge distributions. Importantly, this structural arrangement creates an inner surface characterized by a negative electrostatic potential. As demonstrated in our previous work, polyPR predominantly binds to this negatively charged cavity within Kapβs. Consequently, the separation of charges on the Kapβ surface becomes less influential compared to the overall charge. Other transport components, however, depicted in figure 2a, do not share this feature and the distribution of charges over the surface becomes a more critical factor in polyPR interactions. We have now added this explanation to page 6, and emphasized in the conclusion section that the effect of dipole moment is only observed for the transport components in figure 2a.

2) Write out nucleoporin, Nup, at first appearance (line 51).

We have changed it in line 51.

3) Fig 1: a (representative) CG structure of polyPR (PR7,PR20 and PR70) would be very useful.

We have added a CG representation of PR7 and PR20 to figure 1.

4) Please use chi-square, not R-square, to evaluate the fit, as chi-square takes experimental errors into account.

We use R-square as a standard measure to assess the quality of the fit in the simulations, as it considers the summation of residuals. This choice aligns with the methodology we have used in our previous publications and therefore prefer to use this measure here as well.

5) Please use a dot (not a full stop) for multiplication in line 151 and Figure 2 legend.

We made the adjustment in line 151, the caption of figure 2, and the y-axis label of figure S2.

6) 330: it is very unconventional to plot half the std dev as an error bar. Please plot the std dev (standard error) of the mean.∙

We made the suggested change and now the error bars in figure 2 are standard errors of the mean (SEM) calculated from block averaging with three blocks at equilibrium. We also amended the caption of figure 2 and the Methods section.

7) Please write an explicit equation for the linear relation that is plotted in Figure 2. Something like: C_t = a(NCPR - fM/Rg)+b ? That would make it easier to read.

We have now added the linear equation of the fit to a new table S4, and included a reference to it in the caption of figure 2.

8) Fig 2: why is the fit to PR7 not reported/shown?

The fits for PR7 resulted in R2 values of 0.89 (a) and 0.83 (b) for 200M and of 0.7 (a) and 0.59 (b) for 100 mM. Because of the low R2 values for 100 mM, the fits for PR7 are not shown. We have added this explanation to the caption of figure 2.

9) Fig 4: isn't the blue shape KapB (and not importin)?

We changed "importin" to "Kapβ Imp" for consistency.

10) In the interest of reproducibility, a recommendation is to make the scripts for setting up, running, and analyzing the simulations freely available, e.g. at GitHub. This will increase reproducibility and transparency.

At the moment we do not have the scripts available on GitHub. However, codes can be provided by the authors upon reasonable request, as also mentioned in the data availability statement in the paper.

11) Can the authors explain the salient advances in this article versus the one published last year?

In our previous work, we showed that polyPR binds to the Kapβ family of nuclear transport receptors (NTRs), consistent with experimental findings. While this provided valuable insights, it was essential to broaden our investigation as C9orf72 toxicity not only affects the Kapβ family of NTRs but also disrupts other key regulators of NCT. For instance, recent literature (see lines 87-91 in our paper) showed that Ran and its regulators RanGAP and RanGEF are mislocalized in cells expressing R-DPRs, and genetic screening studies have identified several nucleocytoplasmic transport genes as modifiers of R-DPR-mediated toxicity.

In the present study, we therefore delved deeper into the underlying mechanisms of polyPR-modification of NCT. We focused on exploring whether polyPR directly interacts with Impα isomers, CAS/Cse1, RanGEF, RanGAP, Ran, and NTF2. By doing so, we unveiled a network of direct interactions between polyPR and a remarkably wide range of NCT components. This newfound insight is valuable for interpreting existing experimental findings, such as the mislocalization of RanGAP. We also demonstrate that polyPR binding is influenced not only by factors such as the net charge per residue and the polyPR chain length, as previously observed for Kapβs, but also by the spatial separation of charges, incorporated by an additional dependence on dipole moments in influencing the total number of contacts with polyPR. This sheds new light on how polyPR interacts with numerous targets within the cellular environment, providing a valuable reference for future (experimental) investigations of R-DPR-compromised nuclear transport. These points are explained in the last paragraph of the introduction and paragraphs 2,3 of the conclusion section. Paragraph 2 of the conclusion is also modified for clarification.

12) In Figure 2(a), the vertical coordinates of the first graph do not match the others.

We have now modified figure 2a left panel to match the others.

13) When the polyPR length is large enough, it seems that the binding of polyPR to RanGEF and NTF2 is not significantly improved.

The binding behavior depends on polyPR length, as well as on the net charge per residue and the dipole moment (expressed as NCPR-fM/R_g). We note that the number of contacts in figure 2 is normalized by the polyPR length so that for both NTF2 and RanGEF the total number of contacts increase with length (PR7 to PR20) when binding occurs. Specifically, for RanGEF, especially at lower ion concentrations (100 mM), PR7 and PR20 exhibit a similar number of contacts per unit length of polyPR. This implies that the absolute number of contacts between PR20 and RanGEF is higher than that of PR7. However, as we extend the polyPR length to PR50, there is a reduction in the number of contacts per unit length of polyPR. This phenomenon indicates that the more extended PR50 has regions that make little to no contact with RanGEF, resulting in a smaller number of contacts per unit length for PR50. Lines 188-195 are now modified to put more emphasis on the difference between number of contacts and number of contacts normalized by polyPR length.

14) The representation of the mechanism in Figure 4 is not intuitive enough and the color scheme still needs to be improved.

We have tried to improve clarity by including the names of each transport component next to their schematic representations.

15) Figure 3 shows that the longer polyPR exhibits a higher contact probability with individual residues compared to a shorter polyPR, is this result in conflict with Figure 2?

We re-iterate here that the number of contacts in figure 2 is normalized by the polyPR length, while the results in Fig. 3 are not.

Figure 3 and figure S4 demonstrate that as the length of polyPR increases, the contact probability of individual residues of transport components for interaction with polyPR also increases.

In figure 2, we have normalized the time-averaged number of contacts by the length of polyPR. For example, in the top-right panel of figure 2a, when comparing results for PR7 with PR50 interaction with RanGAP, a higher value for PR7 indicates that PR7 makes more contacts per unit of its length with RanGAP. In terms of absolute number of contacts, however, the PR50 chain makes more contacts with RanGAP, resulting in a higher contact probability. We now added a sentence (see lines 188-189) for clarification.

In summary, when a short polyPR strongly binds to a transport component (evidenced by a relatively large number of contacts), it makes more contacts per unit length than a large poyPR. This occurs because for shorter polyPRs most of the residues come into contact with the target protein. In contrast, for longer polyPRs, only certain parts of the chain are in contact with the transport components, while other regions make fewer or no contacts. This is explained in lines 188-195.

16) In S2 and S3, does the data require an error bar?

NCPR, defined as total charge divided by sequence length of the transport components, is a constant and therefore figure S3 does not require an error bar.

In figure S3 we have added error bars (standard deviation) for the dipole moment calculated from 2.5 us simulations of the isolated transport components.

17) What is the physiological significance when the salt concentration is 100 mM?

We conducted simulations at two different salt concentrations: 200 mM, which aligns with in vitro conditions as reported in Hutten et al. [1], and a lower 100 mM salt concentration. The inclusion of the 100 mM salt concentration enables us to assess the significance of salt concentration, and to confirm the dominance of electrostatic interactions in polyPR binding. We also note that this range of salt concentration is commonly used in in-vitro experiments [1, 4, 5].

18) Please introduce abbreviation NLS in the abstract.

We added the full name of NLS to the abstract.

19) Given the high number of Arg residues in its sequence, polyPR should interact with many proteins. It would be beneficial to discuss the frequency of binding/non-binding interactions of polyPR with nuclear transport components in comparison to general proteins.

We appreciate the reviewer's comment. While such a comparison is indeed interesting, our study primarily focused on elucidating the interactions between polyPR and crucial nuclear transport components, aiming to provide insights into potential defects in nucleocytoplasmic transport. The broader comparison of polyPR interactions with different protein classes in the proteome is indeed an interesting direction for future research, but out of the scope of the current manuscript.

20) The authors should provide a convergence check to determine whether the 2.5 µs simulations are sufficient for sampling the interaction modes, particularly with the long PR50.

We have included a new figure (figure S5) and additional text in the Methods section to verify that extending the simulation duration does not alter the contact probabilities (which are indicators of binding modes) presented in figure 3a, confirming convergence of our computations.

21) In reference to Figure 4, the upper panel merely summarizes the known transport mechanisms, while the lower part (A-H) provides potential novel insights from this study. Unfortunately, these novel insights are not sufficiently detailed. It is recommended to include more details to make these relevant plots clearer by expanding the corresponding discussions (currently, only the last paragraph in the Results section addresses these). If possible, the authors should also carry out some CG simulations of the most relevant processes to further elucidate the interference caused by polyPR.

We have taken the reviewer's feedback into consideration and made the suggested revisions. Specifically, we have expanded the last paragraph of the discussion to provide more detailed explanations of the insights derived from our computational model. For each mechanism, we begin by presenting the reader with the baseline understanding of normal function of the transport component. Subsequently, we discuss how the findings presented in figures 2 and 3 offer insights into polyPR's potential interference with the function of NCT components. Furthermore, we have made improvements to the schematic representation of mechanisms in figure 4 to enhance clarity.

At the moment, accurately capturing the binding of NCT components to their native binding targets and the competition with polyPR are best resolved by all-atom molecular dynamics simulations, which come with significant computational demands. This level of detail and computation-intensive analyses is beyond the scope of the current study, but we hope that our results will provide the groundwork for future, more detailed investigations.

References

1. Hutten, S., et al., Nuclear Import Receptors Directly Bind to Arginine-Rich Dipeptide Repeat Proteins and Suppress Their Pathological Interactions. Cell Rep., 2020. 33(12): p. 108538.

2. Jafarinia, H., E. Van der Giessen, and P.R. Onck, Molecular basis of C9orf72 poly-PR interference with the β-karyopherin family of nuclear transport receptors. Sci. Rep., 2022. 12(1): p. 21324.

3. Nanaura, H., et al., C9orf72-derived arginine-rich poly-dipeptides impede phase modifiers. Nat Commun, 2021. 12(1): p. 5301.

4. Brady, J.P., et al., Structural and hydrodynamic properties of an intrinsically disordered region of a germ cell-specific protein on phase separation. Proceedings of the National Academy of Sciences, 2017. 114(39): p. E8194-E8203.

5. Fisher, R.S. and S. Elbaum-Garfinkle, Tunable multiphase dynamics of arginine and lysine liquid condensates. Nat. Commun., 2020. 11(1): p. 4628.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

1. Experiments regarding the inducible expression of MukBEF: The authors should provide western blots or rt-qPCR for MukBEF expression at 40 min and 2H.

We provide now a western blot of MukB in non-induced and induced conditions as Figure 1-figure supplement 1D.

1. Experiments with RiTer and LiTer constructs:<br /> a. Authors compare the mukB deletion against wild type (Fig. 2C). It would be additionally informative if these comparisons are made for matP deletion and wild type as well. This will strengthen the conclusion that long-range interactions in ter do increase in the absence of matP.

We agree that the matP mutant may help the reader to compare the effect of the translocation in different backgrounds and have added it to the figure. This strengthens the conclusion that longrange interactions in ter do increase in the absence of matP in a rearranged chromosome, as observed in the WT configuration (Lioy et al., 2018).

b. Additionally, in Fig. 2C, it appears that there is some decrease in long-range interactions in the absence of mukB in ter1 (Riter). Is this a significant change?

The change observed is not significant. The results shown in Fig. 2C have been obtained using a 3C approach, which generated slightly more variability than Hi-C. Furthermore, we measured the range of contacts for the segment corresponding to Ter1 in RiTer (matS12-matS28), in different genetic contexts and different configurations. The results show that this level of variation is not significant (see graph below reporting two independent experiments).

Author response image 1.

Range of interactions measured on the interval matS12-matS18 in different genetic contexts and different configurations (MG1655 WT(1 and 2), ∆mukB, RiTer, RiTer ∆mukB).

1. Experiments with various matS organizations: These experiments are interesting and an important part of the paper. However, it is rather hard to visualize the chromosome conformations in the strains after transposition. To aid the reader (particularly with panel E), authors can provide schematics of the chromosome conformations and anticipated/ observed chromosomal interactions. Circular interaction plots would be useful here.

We thank the reviewer for this interesting remark; we have tried in the past to represent these interactions using a circular representation (see for example the web site of Ivan Junier; https://treetimc.github.io/circhic/index.html). However, this representation is not trivial to apprehend for nonspecialists, especially in strains with a rearranged chromosome configuration. Nonetheless, we have added graphical circular representations of the chromosome configurations to help the reader.

1. ChIP experiments:<br /> a. This section of the manuscript needs to be further strengthened. It is not clear whether the ChIP signal observed is significant (for example at T10 or T20 min, the peak value does not appear to go above 1.1 fold. Can the authors be sure that this small increase is not simply a consequence of increase in copy number of the loci around the origin, as replication has initiated?

The basal value of the ChIP on the non-replicated sequences (between 0-3.5 Mb for 10 minutes and 0-3 Mb for 20 minutes) is 0.8 and 0.7, respectively, whereas the mean value of the replicated sequence is 1.6 and 1.45. So the enrichment observed for these two points is about 2-fold, not 1.1 and it is 4 fold for t40min. These values were obtained by dividing the number of normalized reads in the ChIP (the number of reads at each position divided by the total number of reads) by the normalized reads of the input. Therefore, the increase in copy number is considered in the calculation. Furthermore, we added a supplementary figure (Figure Sup9) in which we performed a ChIP without tags on synchronized cells, and in this case, we did not observe any enrichment triggered by replication.

b. Authors make a conclusion that MukB loads behind the replication fork. However, the time resolution of the presented experiments is not sufficient to be certain of this. Authors would need to perform more time-resolved experiments for the same.

Reviewer 1 is correct; we attempted to discriminate whether the observed enrichment is (i) associated with the replication fork since we observed a decrease in the center of the enrichment at oriC as the maximum enrichment moves away with the replication fork after 20 and 40 minutes, or (ii) associated with the newly replicated sequence. To investigate this, we attempted to induce a single round of replication by shifting the cells back to 40°C after 10 minutes at 30°C. Unfortunately, replication initiation is not immediately halted by shifting the cells to 40°C, and we were unable to induce a single round of replication. To clarify our conclusions, we modified our manuscript to

“Altogether, these findings indicate that MukBEF is loaded into regions newly replicated either at the replication fork or even further behind it, except in the Ter region from which it would be excluded.”

c. Authors conclude that in the LiTer7 strain, MukB signal is absent from Ter2. However, when compared with the ChIP profiles by eye across panels in A and B, this does not seem to be significant. In the same results sections, authors state that there is a 3-fold increase in MukB signal in other regions. The corresponding graph does not show the same.

Rather than relying solely on the enrichment levels, which can be challenging to compare across different strains due to slight variations in replication levels, we believe there is a clear disruption in this profile that corresponds to the Ter2 sequence. Furthermore, this discontinuity in enrichment relative to the replication profile is also observable in the WT configuration. At T40min, MukB ChIPseq signals halt at the Ter boundary, even though Ter is actively undergoing replication, as evidenced by observations in the input data.

Regarding the fold increase of MukB, Reviewer 1 is correct; we overestimated this enrichment in the text and have now corrected it.

d. Authors should provide western blot of MukB-Flag.

We have added Supplementary Figure 1 D, which contains a Western blot of MukB-Flag.

1. The bioinformatic analysis of matS site distribution is interesting, but this is not followed upon. The figure (Fig 5) is better suited in the supplement and used only as a discussion point.

We acknowledge the reviewer's point, but we used this section to attempt to extend our findings to other bacteria and emphasize the observation that even though a few matS sites are necessary to inhibit MukBEF, the Ter domains are large and centered on dif even in other bacteria.

1. The discussion section is lacking many references and key papers have not been cited (paragraph 1 of discussion for example has no references).

The possibility that SMC-ScpAB and MukBEF can act independent of replication has been suggested previously, but are not cited or discussed. Similarly, there is some evidence for SMC-ScpAB association with newly replicated DNA (PMID 21923769).

We have added references to the suggested paragraph and highlighted the fact that MukBEF's activity independent of replication was already known. However, we believe that the situation is less clear for SMC-ScpAB in B. subtilis or C. crescentus. In a similar manner, we found no clear evidence that SMCScpAB is associated with newly replicated DNA in the referenced studies.

To clarify and enrich the discussion section, we have added a paragraph that provides perspective on the loading mechanisms of SMC-ScpAB and MukBEF.

1. There are minor typographical errors that should be corrected. Some are highlighted here:

a. Abstract: L5: "preferentially 'on' instead of 'in'"

b. Introduction: Para 1 L8: "features that determine"

c. Introduction: Para 2 L1: please check the phrasing of this line

d. Results section 2: L1: Ter "MD" needs to be explained

e. Page 8: Para 2: L6: "shows that 'a'"

g. Page 13: Para 2: "MukBEF activity...". This sentence needs to be fixed.

i. Figure 4: "input" instead of "imput"

We thank Reviewer 1 for pointing out all these grammatical or spelling mistakes. We have corrected them all.

f. Page 12: Para 2: "Xer" instead of "XDS"? *We added a reference to clarify the term.

h. Methods: ChIP analysis: Authors state "MatP peaks", however, reported data is for MukB

This description pertains to the matP peak detection shown in Supplementary Figure 3. We have incorporated this clarification into the text.

j. Supplementary figure legends need to be provided (currently main figure legends appear to be pasted twice)

Supplementary figure legends are provided at the end of the manuscript, and we have edited the manuscript to remove one copy of the figure legends.

k. Authors should ensure sequencing data are deposited in an appropriate online repository and an accession number is provided.

We waited for the appropriate timing in the editing process to upload our data, which we have now done. Additionally, we have added a data availability section to the manuscript, including sequence references on the NCBI.

Reviewer #2 (Recommendations For The Authors):

The authors largely avoid speculation on what might be the physiological relevance of the exclusion of MukBEF (and Smc-ScpAB) from the replication termination region (and the coordination with DNA replication). At this stage it would be helpful to present possible scenarios even if not yet supported by data. The authors should for example consider the following scenario: loop extrusion of a dif site in a chromosome dimer followed by dimer resolution by dif recombination leads to two chromosomes that are linked together by MukBEF (equivalent to cohesin holding sister chromatids together in eukaryotes but without a separase). This configuration (while rare) will hamper chromosome segregation. Is MatP particularly important under conditions of elevated levels of chromosome dimers? Could this even be experimentally tested? Other scenarios might also be entertained.

Even though we prefer to avoid speculations, we agree that we may attempt to propose some hypotheses to the reader. To do so, we have added a few sentences at the end of our discussion. “We may speculate, based on in vitro observations (Kumar et al., 2022), that MukBEF could interfere with TopIV activity and delay potential chromosome decatenation. Another possibility is that chromosome dimers resolved at the dif site may become trapped in loops formed by MukBEF, thus delaying segregation. But none of these possible scenarios are supported by data yet, and a major challenge for the future is to determine whether and how MukBEF may interfere with one or both of these processes.”

The manuscript text is well written. However, the labeling of strains in figures and text is sometimes inconsistent which can be confusing (LiTer Liter liter; e.g Riter Fig 2C). For consistency, always denote the number of matS sites in LiTer strains and also in the RiTer strain. The scheme denoting LiTer and RiTer strains should indicate the orientation of DNA segments so it is clear that the engineering does not involve inversion (correct?). Similarly: Use uniform labelling for time points: see T40mn vs 40mn vs T2H vs 2H

We have reviewed the manuscript to standardize our labeling. Additionally, we have included a schema in Figure 2, indicating the matS numbers at the Ter border to emphasize that the transposition events do not involve inversion.

matS sites do not have identical sequences and bind different levels of MatP (suppl fig 3). Does this possibly affect the interpretation of some of the findings (when altering few or only a single matS site). Maybe a comment on this possibility can be added.

We agree with the referee; we do not want to conclude too strongly about the impact of matS density, so we have added this sentence at the end of the section titled 'matS Determinants to Prevent MukBEF Activity':

“Altogether, assuming that differences in the matS sequences do not modify MatP's ability to bind to the chromosome and affect its capacity to inhibit MukBEF, these results suggested that the density of matS sites in a small chromosomal region has a greater impact than dispersion of the same number of matS sites over a larger segment”

Figure 5: show selected examples of matS site distribution in addition to the averaged distribution (as in supplemental figure)?

Figure 5 shows the median of the matS distribution based on the matS positions of 16 species as displayed in the supplementary figure. We believe that this figure is interesting as it represents the overall matS distribution across the Enterobacterales, Pasteurellales, and Vibrionales.

How do authors define 'background levels' (page 9)in their ChIP-Seq experiments? Please add a definition or reword.

We agree that the term 'background level' here could be confusing, so we have modified it to 'basal level' to refer to the non-replicating sequence. The background level can be observed in Supplementary Figure 9 in the ChIP without tags, and, on average, the background level is 1 throughout the entire chromosome in these control experiments.

This reviewer would naively expect the normalized ChIP-Seq signals to revolve around a ratio of 1 (Fig. 4)? They do in one panel (Figure 4B) but not in the others (Figure 4A). Please provide an explanation.

We thank the referee for this pertinent observation. An error was made during the smoothing of the data in Figure 4A, which resulted in an underestimation of the input values. This mistake does not alter the profile of the ChIP (it's a division by a constant) and our conclusions. We provide a revised version of the figure.

Inconsistent axis labelling: e.g Figure 4

Enterobacterals should be Enterobacterales (?)

KB should be kb

MB should be Mb

Imput should be Input

FlaG should be Flag

We have made the suggested modifications to the text.

'These results unveiled that fluorescent MukBEF foci previously observed associated with the Ori region were probably not bound to DNA' Isn't the alternative scenario that MukBEF bound to distant DNA segments colocalize an equally likely scenario? Please rephrase.

Since we lack evidence regarding what triggers the formation of a unique MukB focus associated with the origin and what this focus could represent, we have removed this sentence.

Reviewer #3 (Recommendations For The Authors):

The text is well-written and easy to follow, but I would suggest several improvements to make things clearer:

1. Many plots are missing labels or legends. (I) All contact plots such as Fig. 1C should have a color legend. It is not clear how large the signal is and whether the plots are on the same scale. (II)<br /> Ratiometric contact plots such as in Fig. 1D should indicate what values are shown. Is this a log ratio?

As indicated in the materials and methods section, the ratio presented on this manuscript was calculated for each point on the map by dividing the number of contacts in one condition by the number of contacts in the other condition. The Log2 of the ratio was then plotted using a Gaussian filter.

1. Genotypes and strain names are often inconsistent. Sometimes ΔmukB, ΔmatP, ΔmatS is used, other times it is just mukB, matP, matS; There are various permutations of LiTer, Liter, liter etc.

These inconsistencies have been corrected.

1. The time notation is unconventional. I recommend using 0 min, 40 min, 120 min etc. instead of T0, T40mn, T2H.

As requested, we have standardized and used conventional annotations.

1. A supplemental strain table listing detailed genotypes would be helpful.

A strain table has been added, along with a second table recapitulating the positions of matS in the different strains.

1. Fig. 1A: Move the IPTG labels to the top? It took me a while to spot them.

We have moved the labels to the top of the figure and increased the font size to make them more visible.

1. Fig 1C: Have these plots been contrast adjusted? If so, this should be indicated. The background looks very white and the transitions from diagonal to background look quite sharp.

No, these matrices haven't been contrast-adjusted. They were created in MATLAB, then exported as TIFF files and directly incorporated into the figure. Nevertheless, we noticed that the color code of the matrix in Figure 3 was different and subsequently adjusted it to achieve uniformity across all matrices.

7, Fig 1C: What is the region around 3 Mb and 4 Mb? It looks like the contacts there are somewhat MukBEF-independent.

The referee is right. In the presence of the plasmid pPSV38 (carrying the MukBEF operon or not), we repeatedly observed an increase of long range contacts around 3 Mb. The origin of these contacts is unknown.

1. Fig 1D: Have the log ratios been clipped at -1 and 1 or was some smoothing filter applied? I would expect the division of small and noisy numbers in the background region to produce many extreme values. This does not appear to be the case.

The referee is right, dividing two matrices generates a ratio with extreme values. To avoid this, the Log2 of the ratio is plotted with a Gaussian filter, as described before (Lioy et al., 2018).

1. Fig 1E: I recommend including a wild-type reference trace as a point of reference.

We have added the WT profile to the figure.

1. Fig 2: I feel the side-by-side cartoon from Supplemental Fig. 2A could be included in the main figure to make things easier to grasp.

We added a schematic representation of the chromosome configuration on top of the matrices to aid understanding.

1. Fig. 2C: One could put both plots on the same y-axis scale to make them comparable.

We have modified the axes as required.

1. Fig. 3C: The LiTer4 ratio plot has two blue bands in the 3-4.5 Mb region. I was wondering what they might be. These long-range contacts seem to be transposition-dependent and suppressed by MatP, is that correct?

The referee is right. This indicates that in the absence of MatP, one part of the Ter was able to interact with a distal region of the chromosome, albeit with a low frequency. The origin is not yet known.

1. Fig. 3E: It is hard to understand what is a strain label and what is the analyzed region of interest. The plot heading and figure legend say Ter2 (but then, there are different Ter2 variants), some labels say Ter, others say Ter2, sometimes it doesn't say anything, some labels say ΔmatS or ΔmatP, others say matS or matP, and so on.

We have unified our notation and add more description on the legend to clarify this figure :

“Ter” corresponds to the range of contacts over the entire Ter region, in the WT strain (WT Ter) or in the ΔmatP strain (ΔmatP Ter). The column WT matSX-Y corresponds to the range of contacts between the designated matS sites in the WT configuration. This portion of the Ter can be compared with the same Ter segment in the transposed strain (Ter2). Additionally, the matS20-28 segment corresponds to Ter2 in LiTer9, just as matS22-28 corresponds to Ter2 in LiTer7, and matS25-28 to Ter2 in LiTer4. The range of contacts of this segment was also measured in a ΔmatP or ΔmatS background.”

1. Fig. 4 and p.9: "Normalized ChIP-seq experiments were performed by normalizing the quantity of immuno-precipitated fragments to the input of MukB-Flag and then divide by the normalized ChIP signals at t0 to measure the enrichment trigger by replication."

This statement and the ChIP plots in Fig. 4A are somewhat puzzling. If the data were divided by the ChIP signal at t0, as stated in the text, then I would expect the first plot (t0) to be a flat line at value 1. This is not the case. I assume that normalized ChIP is shown without the division by t0, as stated in the figure legend.

The referee is right. This sentence has been corrected, and as described in the Methods section, Figure 4 shows the ChIP normalized by the input.

If that's true and the numbers were obtained by dividing read-count adjusted immunoprecipitate by read-count adjusted input, then I would expect an average value of 1. This is also not the case. Why are the numbers so low? I think this needs some more details on how the data was prepared.

The referee is right; we thank him for this remark. Our data are processed using the following method: the value of each read is divided by the total number of reads. A sliding window of 50 kb is applied to these normalized values to smooth the data. Then, the resulting signal from the ChIP is divided by the resulting signal from the input. This is what is shown in Figure 4. Unfortunately, for some of our results, the sliding window was not correctly applied to the input data. This did not alter the ChIP profile but did affect the absolute values. We have resolved this issue and corrected the figure.

Another potential issue is that it's not clear what the background signal is and whether it is evenly distributed. The effect size is rather small. Negative controls (untagged MukB for each timepoint) would help to estimate the background distribution, and calibrator DNA could be used to estimate the signal-to-background ratio. There is the danger that the apparent enrichment of replicated DNA is due to increased "stickiness" rather than increased MukBEF binding. If any controls are available, I would strongly suggest to show them.

To address this remark, a ChIP experiment with a non-tagged strain under comparable synchronization conditions has been performed. The results are presented as Supplementary Figure 9; they reveal that the enrichment shown in Figure 4 is not attributed to nonspecific antibody binding or 'stickiness’.

1. Fig. 4A, B: The y-axes on the right are unlabeled and the figure legends mention immunoblot analysis, which is not shown.

We labeled the y-axes as 'anti-Flag ChIP/input' and made corrections to the figure legend.

1. Fig. 4B: This figure shows a dip in enrichment at the Ter2 region of LiTer7, which supports the authors' case. Having a side-by-side comparison with WT at 60 min would be good, as this time point is not shown in Fig. 4A.

Cell synchronization can be somewhat challenging, and we have observed that the timing of replication restart can vary depending on the genetic background of the cells. This delay is evident in the case of LiTer7. To address this, we compared LiTer7 after 60 minutes to the wild type strain (WT) after 40 minutes of replication. Even though the duration of replication is 20 minutes longer in LiTer7, the replication profiles of these two strains under these two different conditions (40 minutes and 60 minutes) are comparable and provide a better representation of similar replication progression.

1. Fig. 4C: Highlighting the position of the replication origin would help to interpret the data.

We highlight oriC position with a red dash line

1. Fig. 4C: One could include a range-of-contact plot that compares the three conditions (similar to Fig. 1E).

We have added this quantification to Supplemental Figure 8

1. Supplemental Fig. 2A: In the LiTer15 cartoon, the flanking attachment sites do not line up. Is this correct? I would also recommend indicating the direction of the Ter1 and Ter2 regions before and after recombination.

In this configuration, attB and attR, as well as attL and attB', should be aligned but the remaining attR attL may not. We have corrected this misalignment. To clarify the question of sequence orientation, we have included in the figure legend that all transposed sequences maintain their original orientation.

1. Supplemental Fig. 3: One could show where the deleted matS sites are.

We added red asterisks to the ChIP representation to highlight the positions of the missing matS.

1. Supplemental Fig. 3B: The plot legend is inconsistent with panel A (What is "WT2")?

We have corrected it.

1. Supplemental Fig. 3C: The E-value notation is unusual. Is this 8.9 x 10^-61?

The value is 8.9 x 10-61; we modified the annotation.

23) Abstract: "While different features for the activity of the bacterial canonical SMC complex, SmcScpAB, have been described in different bacteria, not much is known about the way chromosomes in enterobacteria interact with their SMC complex, MukBEF."

Could this be more specific? What features are addressed in this manuscript that have been described for Smc-ScpAB but not MukBEF? Alternatively, one could summarize what MukBEF does to capture the interest of readers unfamiliar with the topic.

We modified these first sentences.

1. p.5 "was cloned onto a medium-copy number plasmid under control of a lacI promoter" Is "lacI promoter" correct? My understanding is that the promoter of the lacI gene is constitutive, whereas the promoter of the downstream lac operon is regulated by LacI. I would recommend providing an annotated plasmid sequence in supplemental material to make things clearer.

We modified it and replaced “ lacI promoter” with the correct annotation, pLac.

1. p. 5 heading "MukBEF activity does not initiate at a single locus" and p. 6 "Altogether, the results indicate that the increase in contact does not originate from a specific position on the chromosome but rather appears from numerous sites". Although this conclusion is supported by the follow-up experiments, I felt it is perhaps a bit too strong at this point in the text. Perhaps MukBEF loads slowly at a single site, but then moves away quickly? Would that not also lead to a flat increase in the contact plots? One could consider softening these statements (at least in the section header), and then be more confident later on.

We used 'indicate' and 'suggesting' at the end of this results section, and we feel that we have not overreached in our conclusions at this point. While it's true that we can consider other hypotheses, we believe that, at this stage, our suggestion that MukBEF is loaded over the entire chromosome is the simplest and more likely explanation.

1. p.7: "[these results] also reveal that MukBEF does not translocate from the Ori region to the terminus of the chromosome as observed with Smc-ScpAB in different bacteria."

This isn't strictly true for single molecules, is it? Some molecules might translocate from Ori to Ter. Perhaps clarify that this is about the bulk flux of MukBEF?

At this point, our conclusion that MukBEF does not travel from the ori to Ter is global and refers to the results described in this section. However, the referee is correct in pointing out that we cannot exclude the possibility that in a WT configuration (without a Ter in the middle of the right replicore), a specific MukBEF complex can be loaded near Ori and travel all along the chromosome until the Ter. To clarify our statement, we have revised it to 'reveal that MukBEF does not globally translocate from the Ori region to the terminus of the chromosome.' This change is intended to highlight the fact that we are drawing a general conclusion about the behavior of MukBEF and to facilitate its comparison with Smc-ScpAB in B. subtilis.

1. p. 10: The section title "Long-range contacts correlate with MukBEF binding" and the concluding sentence "Altogether, these results indicate that MukBEF promotes long-range DNA contacts independently of the replication process even though it binds preferentially in newly replicated regions" seem to contradict each other. I would rephrase the title as "MukBEF promotes long-range contacts in the absence of replication" or similar.

We agree with this suggestion and have used the proposed title.

1. p. 13: I recommend reserving the name "condensin" for the eukaryotic condensin complex and using "MukBEF" throughout.

We used MukBEF throughout.

Reviewer #1 (Public Review):

In this manuscript, Seba et al., investigate the mechanism of chromosome organization by the MukBEF complex in E. coli. They use a combination of Hi-C and ChIP analysis to understand the steps of MukBEF regulation: its unloading from DNA (how MukBEF activity is prevented in the terminus regions of the chromosome by MatP), and its loading onto DNA (how DNA replication influences MukBEF association with the chromosome). Seba et al., induce chromosomal rearrangements to flip the sections of the ter region, thus perturbing matS site numbers and position. They find that MukBEF activity is prevented around matS sites and that higher matS density has greater effect on MukBEF. Separately, using replication mutants and inducible MukBEF expression, they find that MukBEF can associate with the chromosome even in the absence of replication (as seen by the emergence of long-range contacts). However, ChIP data suggests that MukBEF binding to DNA is enriched on newly replicated DNA.

Altogether, this work provides a valuable and comprehensive view of MukBEF-mediated chromosome organization, with insights on the mechanism of the exclusion of MukBEF from the terminus region of the chromosome. The use of the programmed genetic rearrangements is powerful and allows the authors to provide clear and convincing evidence for MukBEF exclusion from ter by matS sites. It is particularly striking to see that MukBEF can promote long-range contacts even in chromosomal regions between two matS, but the complex is excluded from the matS 'zones'. Experiments using cells blocked for replication show that MukBEF can influence chromosome organization in the absence of replication as well. While previous studies have reported some evidences in support of both of the above conclusions, the experiments described here offer a clear and direct demonstration of the same.

Reviewer #2 (Public Review):

Summary:<br /> Chromosome organization in E. coli and related species ('transversal') deviates starkly from the pattern more commonly found in bacteria ('longitudinal'). The underlying mechanisms and the physiological roles, however, are not well understood. This manuscript by Seba et al. investigates the activity and regulation of MukBEF in chromosome folding in E. coli. Using a construct for inducible expression of MukBEF, the authors first demonstrate that the initiation of long-range chromosome contacts (likely by loop extrusion) is not restricted to few positions on the chromosome and rather widely distributed but excluding the replication terminus region. Using ChIP-Seq, the authors show that the distribution of MukBEF over the chromosome is consistent with widely distributed loading and moreover indicate a connection of chromosome folding and DNA replication with newly replicated DNA shower an increased tendency for MukBEF binding. To dissect this further, they then redistribute matS sites on the chromosome by a clever strategy based on large-scale transpositions. The results reveal that matS-free DNA segments undergo MukBEF dependent folding regardless of their position relative to the origin of replication, being consistent with a broad distributed loading of MukBEF. By fine-mapping with smaller transposition events, they show that few matS sites are sufficient to impede MukBEF activity. Surprisingly, however, E. coli and most related genomes harbor many matS sites, which are particularly highly concentrated near the chromosome dimer resolution dif site (Fig. 5).

This is a well-executed and well-presented study. The findings show that the MatP/matS system acts locally and independent of DNA replication to restrict MukBEF in the replication terminus region. Few of the many matS sites are sufficient for MukBEF restriction. The main conclusions of the work are clear and well supported by the data.

Reviewer #3 (Public Review):

Seba et al. investigate whether chromosomal recruitment of the E. coli SMC complex MukBEF is initiated at a single site, how MukBEF activity is excluded from the replication terminus region, and whether its recruitment and activity depend on DNA replication. Upon induction of MukBEF, the authors find that chromosomal long-range contacts increase globally rather than from a single site. Using large-scale chromosome rearrangements, they show that matS sites can insulate separate areas of high MukBEF activity from each other. This suggests that MukBEF loads at multiple sites in the genome. Finally, the authors propose that MukBEF associates preferentially with newly replicated DNA, based on ChIP-seq experiments after DNA replication arrest.

The conclusions of the paper are well supported by the data. The ratiometric contact analyses and range-of-contact analyses are compelling and nicely show the interplay between MukBEF and its proposed unloader MatP/matS. I particularly enjoyed the chromosome re-arrangement experiments, which lend strong support to the idea that MukBEF activity is independent of a centralized loading site.<br /> The enrichment of MukBEF in newly replicated regions is convincing, despite somewhat small effect sizes. The suggestion that matS density controls MukBEF activity is appealing, but will need additional support from more systematic studies. It is based on a comparison of only two strains (looking at different combinations of three matS sites), and the effect size is small. As it is, differences in matS sequence composition and genomic context cannot be factored out.

Overall, the work is an important advance in our understanding of bacterial chromosome organization. It will be of broad interest to chromosome biologists and bacterial cell biologists.

Author Response

Reviewer #1 (Public Review):

Summary:

Zhang et al. provide valuable data for understanding molecular features of the human spinal cord. The authors made considerable efforts to acknowledge and objectively address the limitations of Visium while attempting to overcome them by utilizing single-nucleus RNA sequencing (snRNA-seq) from the same tissue. By mapping snRNA-seq clusters to Visium data, they offer spatial information, complemented by RNA-ISH and immunofluorescence (IF) validation. They also discuss gender-related differences and the similarities between human and mouse data, aiming to establish a crucial foundation for experimental research. However, I have some comments below.

1) The observation of gender-related differences is interesting. The authors reported that SCN10A, associated with nociceptos, exhibited stronger expression in females. While they intend to validate this finding through IF, the quantitative difference is not clearly observed in the IF data (Figure 5f). It would be essential to provide validation through DAPI-based cell counts, demonstrating the difference in CHAT/SCNA10A co-expression.

Thank you for this important question! We have added panel G in Figure 5, which provided the quantitative analysis of the percentage of CHAT neurons that expressing SCN10A in male and female spinal cord.

2) It is meritorious that in novel features of the transcriptomic study, the authors considered gender-related differences and similarities between humans and mice. Nevertheless, despite the extensive bioinformatics-based analyses performed, the results mostly confirm what has been previously reported (Nguyen et al. 2021; Yadav et al. 2023; Jung et al. 2023).

Thank you! In addition to confirming the findings from previous studies, our results also provided new information regarding the difference between human and mouse. For example, we found that PVALB and SST showed broader expression across human DRG neuronal clusters than in mice, suggesting that genes are more selectively expressed in mice than in human DRGs. Moreover, we identified several genes associated with pain that were differentially expressed in motor neurons between sexes.

3) The study did not perform snRNA-seq in the DRG. The limitations of Visium in cell type separation are acknowledged, and the authors are aware that Visium alone has limitations in describing cell expression patterns. The authors need to validate their findings via analyses of public DRG snRNA-seq data (Jung et al. 2023 Ncom; Nguyen et al. 2021eLife) before drawing broad conclusions.

Thank you for this critical question! It is right that snRNA-seq has a higher resolution in describing cell expression patterns compared to the spatial transcriptomics. We acknowledged the limitation that we only performed spatial transcriptomics in human DRG without snRNA-seq. Nevertheless, our results of spatial transcriptomics in human DRG were similar to previously public snRNA-seq data of human DRG, suggesting a feasibility of using spatial transcriptomics in human DRG.

4) Figure 7's comparison between human Visium spot data and Renthal et al.'s mouse snRNA-seq may have limitations as Visium spot data could not provide a transcriptional profile at the single cell resolution. The authors need to clarify this point.

Thank you! We have clarified this in the limitation section.

5) Recent findings indicate that type 2 cytokines can directly stimulate sensory neurons. This includes the expression of IL-4RA, IL31RA, and IL13RA in DRG. These findings support the role of JAK kinase inhibitors in mediating chronic itch. Demonstrating the expression of these itch receptors in DRG would be valuable.

We have provided the expression patterns of IL-4RA, IL31RA, and IL13RA in human and mouse DRG (Figure 7-figure supplement 4), and cited the relevant paper.

6) Given that juxtacrine and paracrine signals operate from 0 to 200 um, spatial information is vital to understanding intercellular communication. The presentation of spatial information using Visium is meaningful, and more comprehensive analyses of potential interaction based on distance should be provided, beyond the top 10 interactions (Figure 8).

Thank you for this good question! In this study, we focused on the putative projections from DRG to spinal neuronal types, which may be an important future direction for research on sensory transduction. It will be interesting to determine the intercellular communication in the spinal spot using the spatial transcriptomics data in future studies.

7) The gender-related differences are interesting and, if possible, it would be interesting to explore whether age-related differences or degeneration-related factors exist. Using public data could allow the examination of age-related changes.

We agree with the reviewer that it is of great importance to identify the age-related differences using spatial transcriptomics and scRNA-seq data of human spinal cord. However, it is currently difficult to obtain comprehensive results due to the limited human spinal cord datasets regarding different ages.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors generated a comprehensive dataset of human spinal cord transcriptome using single-cell RNA sequencing and the Visium spatial transcriptomics platform. They employed Visium data to determine the spatial orientation of each cell type. Using single-cell RNA sequencing data, they identified differentially expressed genes by comparing human and mouse samples, as well as male and female samples.

Strengths:

This study offers a thorough exploration of both cellular and spatial heterogeneity within the human spinal cord. The resulting atlas datasets and analysis findings represent valuable resources for the neuroscience community.

Weaknesses:

The analysis of spatial transcriptomics data was conducted as it is single-cell RNAseq data. However, there are established tools for effectively integrating these two types of data. The incorporation of deconvolution methods could enhance the characterization of each spot's cell type composition.

Thank you very much for your positive comments and suggestions！Indeed, we have used deconvolution methods to incorporate the spinal snRNA-seq and spatial transcriptomics data.

Reviewer #3 (Public Review):

Summary:

Zhang et al sought to use spatial transcriptomics and single-nucleus RNA sequencing to classify human spinal cord neurons. The authors reported 17 clusters on 10x

Visium slides (6 donors) and 21 clusters by single-nucleus sequencing (9 donors). The authors tried to compare the results to those reported in mice and claimed similar patterns with some differing genes.

Strengths:

The manuscript provides a valuable database for the molecular and cellular organization of adult human spinal cords in addition to published datasets (Andersen, et al. 2023; Yadav, et al. 2023).

Weaknesses:

The results are largely observatory and lack quantitative analysis. Moreover, the assertions regarding the sex differences in motor neurons and the potential interactions between DRG and spinal cord neuronal subclusters appear preliminary and necessitate more rigorous validation.

Thank you very much! We have provided the quantitative analysis of the differential expression of SCN10A in male and female spinal cord motor neurons. Our sequencing data revealed putative projections from DRG to spinal neuronal types, which may be an important future direction for research on sensory transduction. We did not use animal models to verify these interactions between DRG and spinal cord neuronal subclusters, which is a major limitation in our study. Nevertheless, our analysis results will provide an important resource for future research to investigate the molecular mechanism underlying spinal cord physiology and diseases.

eLife assessment

This valuable manuscript follows up on previous findings from the same lab supporting the idea that deficits in learning due to enhanced synaptic plasticity are due to saturation effects. Convincing evidence is presented that behavioral learning deficits associated with enhanced synaptic plasticity in a transgenic mouse model can be rescued by manipulations designed to reverse the saturation of synaptic plasticity. In particular, the finding that a previously FDA-approved therapeutic can rescue learning could provide new insights for biologists, psychologists, and others studying learning and neurodevelopment.

Author Response

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

I greatly appreciate your time and attention on our manuscript. I have carefully considered the reviewers’ comments and made modifications. Below are my responses to each comment and the revisions I have made.

Reviewer #2 (Recommendations for The Authors):

1) The authors address well with most of my concerns. I am fine with most of the responses except question 8. Actin is also reported to be located in nuclear (PMID: 31481797). It would be better to utlize other markers, like GAPDH. Moreover, the author did not address the issue of LXRa. I strongly suggest that the authors repeat this experiment to get a more solid result.

Thank you for the comment! Actin is frequently used as a negative control for nucleus protein in many publications, such as DOI:10.1038/s41419-018-0428-x. Beta-actin is rich in cytoplasm protein that it only takes few seconds to reveal the strong band when performing western blot with cytoplasm. However, actin does not reveal when exposing western- blot with nucleus for minutes in many studies, including in this study. Even though as mentioned actin is also located in the nuclear, such a tiny amount in the nucleus may not be revealed in western blot with exposure in seconds. However, if nucleus protein is contaminated with total cell lysate, the action is quite easy to reveal. As a result, the use of actin as the nagtive control of nucleus protein is well-accepted.

Author response image 1.

2) In addition, the authors mentioned IL-1b but present IL-6 in the figure of Figure. 2F. Please correct.

We appreciate your attention on the detail. “IL-1b” is corrected to “IL-6”.

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

I greatly appreciate the time you and the reviewers have taken to review my paper and provide detailed feedback and suggestions. I have carefully considered the reviewers’ comments and made thorough modifications to the paper. Below are my responses to each comment and the revisions I have made.

Reviewer #1 (Recommendations for The Authors):

Although the paper has strengths in understanding better the pathway of activation leading to polarization, the mechanisms contributing to cytokine storm are weak. In the context of cellular in vitro changes, it would be very interesting to map these molecular changes to strengthen the pathways affected in this model. In vivo, stronger evidence is required to bridge the gap between the in vitro model and mechanisms regulating in vivo disease development. Reporting of experiments needs to be considerably strengthened. Individual data points are shown, however, it is unclear whether these represent biological or technical, or how many experiments have been undertaken. The addition of this information is essential for uznderstanding the robustness and repeatability of findings. Currently, these cannot be assessed from the information provided. Furthermore, it is unclear whether the error bars represent s.e.m or s.d. which greatly impacts data interpretation.

Answer: thank you for the valuable comments! We have added some in vivo experiments to strengthen the bridge between the in vitro and in vivo model. 1) The depletion of macrophage by clodronate-liposomes (CLL) i.v. injection was performed in endotoxemic mice with leucine. The alleviation of LPS-induced cytokine production by leucine was muted with macrophage depletion (Figure 2E, F), suggesting the anti-inflammatory effect of leucine was exerted via the regulation of macrophage. 2) The LXRα inhibitor, GSK2033, was applied to mice via i.v. injection prior to LPS-challenge. In GSK2033 treated mice, the effects of leucine on the serum levels of inflammatory cytokines were neutralized (Supplementary Figure 4), partially indicating the importance of LXRα in the regulation of cytokine release. We acknowledge the limitation of LXRα inhibition by GSK2033 in this study. In our future study, we plan to use monocyte specific LXRα knockout mice by LysM-cre to elucidate the importance of LXRα in the progression of CSS, and specifically focuse on the molecular mechanism how mTORC1 interacts with LXRα to modulate M2 macrophage polarization. Additionally, we made modifications in the manuscript to clarify that the error bars represented as the standard error of the mean (SEM) (line 416).

Reviewer #2 (Recommendations for The Authors):

1. The whole manuscript is based on the 2% leucine from feed and 5% leucine from water. Is there any rationale for using these two types of different concentrations in this study? Often, a dose-dependent treatment is utilized in vivo in pharmacological study. Therefore, the authors should at least test two different concentrations in each type to confirm the conclusion.

Answer: thank you for your comment and suggestion. The 2% leucine in feed and 5% leucine in water in this study were based on the literatures. In those studies, leucine was reported to activate mTORC1 and regulate metabolism at such types of different concentration as shown below, although there is lack of leucine in the regulation of macrophage activation. In this study, we found leucine supplementation in such types significantly increased the average body weight gain of mice, suggesting growth promoting and no toxicity of leucine on mice.

(1) Jiang X, Zhang Y, Hu W, Liang Y, Zheng L, Zheng J, Wang B, Guo X. 2021. Different Effects of Leucine Supplementation and/or Exercise on Systemic Insulin Sensitivity in Mice. Front Endocrinol (Lausanne) 12:651303. doi:10.3389/fendo.2021.651303

(2) Holler M, Grottke A, Mueck K, Manes J, Jücker M, Rodemann HP, Toulany M. 2016. Dual Targeting of Akt and mTORC1 Impairs Repair of DNA Double-Strand Breaks and Increases Radiation Sensitivity of Human Tumor Cells. PLoS One 11: e0154745. doi:10.1371/ journal. pone.0154745

1. The authors focus on macrophage polarization as the major cellular event affected by leucine treatment; however, they also report that the proportion of multiple immune cell types has been suppressed by leucine treatment. As some of these immune cells can also produce inflammatory cytokines, the authors should confirm the anti-inflammatory effects of leucine were mainly mediated by modulating macrophage polarization as they suggested in the manuscript. For example, the authors could utilize Anti-CSF1 or clodronate to deplete macrophage and observed whether leucine-reduced inflammatory cytokines production was largely diminished.

Answer: thank you for your valuable suggestion! We used clodronate-liposome (CLL) i.v. injection to deplete macrophages to further validate the specific contribution of macrophage polarization to the anti-inflammatory effects of leucine. The results revealed that clodronate treatment decreased blood monocyte counts and eliminated the effect of leucine in lowering serum inflammatory factors IL-6, IFN-γ and TNF-α (Figure 2E-F), suggesting the importance of leucine-mediacted macrophage activation on the anti-inflammation.

1. It would be important to examine whether 10 mM leucine would exhibit cytotoxicity to bone marrow derived monocytes/macrophages. This would confirm that leucine treatment directly suppresses inflammatory cytokines production or reduces cell viability to indirectly modulates inflammatory responses.

Answer: thank you for your valuable suggestion! We performed cell viability assays after treating BMDM with 2 mM and 10 mM leucine for 6h or 24h (consistent with the timing of leucine treatment in article). The results showed that at 6h, 2 mM leucine significantly increased cell viability, while 10 mM leucine had no significant effect on cell viability. At 24h, both 2 mM and 10 mM leucine significantly increased cell viability. In conclusion, 2 mM and 10 mM leucine were not cytotoxic to BMDM, and the anti-inflammatory effect of leucine was not derived from the reduction in cell viability (Supplementary Figure 2).

1. The authors found that leucine promotes mTORC1-LXRα for arginase-1 transcription and M2 polarization. The pathway the authors elucidated is not surprising, which has already been reported in other studies. What about the other M2 markers? The authors could examine whether arginiase-1 deficiency would deplete leucine-increased other M2 marker genes expression. Moreover, what about the molecular mechanism for leucine-reduced M1 polarization?

Answer: Thank you for the valuable comments! To clarify that Arginase-1 activity, mRNA expression of Fizz1, Mgl1, Mgl2, and Ym1 were well established markers for M2 macrophage. Specifically, Arginase-1 activity is important to define M2 functionality. These markers were used to define the level of M2 macrophage polarization. Only a few studies indicated the involvement of mTORC1 in the M2 polarization as shown below; however, there is no molecular mechanism about how mTORC1 modulates this process. In this study, we provide the evidence that LXRα mediated the mTORC1 associated M2 polarization, and leucine regulated mTORC1-LXRα to promote M2 polarization, which was in dependent of IL-4-induced STAT6 signaling. In our future study, we are focusing on the molecular mechanism how mTORC1 interacts with LXRα to modulate M2 macrophage polarization.

(1) Byles V, Covarrubias AJ, Ben-Sahra I, Lamming DW, Sabatini DM, Manning BD, Horng T. 2013. The TSC-mTOR pathway regulates macrophage polarization. Nat Commun 4:2834. doi:10.1038/ncomms3834

(2) Kimura T, Nada S, Takegahara N, Okuno T, Nojima S, Kang S, Ito D, Morimoto K, Hosokawa T, Hayama Y, Mitsui Y, Sakurai N, Sarashina-Kida H, Nishide M, Maeda Y, Takamatsu H, Okuzaki D, Yamada M, Okada M, Kumanogoh A. 2016. Polarization of M2 macrophages requires Lamtor1 that integrates cytokine and amino-acid signals. Nat Commun 7:13130. doi:10.1038/ncomms13130

1. In Fig. 1A, what's the P-value among these two groups? Moreover, what about the result with combination treatment as the authors performed in other panels?

Answer: thank you for the valuable comments from the reviewer! In Figure 1A, the P-value between the LPS and LPS+2% Leucine groups is 0.0031, and the P-value between the LPS and LPS+5% Leucine groups is 0.0009. I have marked the significance in Figure 1A accordingly. Due to the limited number of mice, we only treated mice in two different ways respectively. Initially, we performed survival experiment and observed that the addition of leucine prolonged survive of mice at lethal dose. Based on these findings, we further investigated whether a combination of the two methods would yield better results on the regulation of inflammation, but the combination exhibited the similar effect on cytokines production, and it is not necessary to repeat the survival experiment with the combination.

1. It seems not much difference could be observed between 2% leucine from feed and 5% leucine from water in the expression of inflammatory genes and anti-inflammation-related markers. However, it seems that 5% leucine from water would exhibit a better survival rate than 2% leucine from feed. The authors should explain potential reasons and at least examine it in vitro.

Answer: we appreciate the valuable comments from the reviewer! There are two possible reasons: 1) When lethal dose of LPS applied, mice were too weak to eat but still drank a small amount of water; 2) the absorption of leucine from the water were much easier than from the feed, thus leucine from the water exhibited much better efficiency in a short period of survival experiment. On the other hand, the cytokine levels and expressions were measure in non-lethal experiments, in which mice were in much better condition for lecine absorption.

1. In Fig. 4A, the authors examined the expression of p-mTOR. The authors should further examine the expression of p-AKT (S473, T308) and p-S6 to clarify whether mTORC1 or mTORC2 has been modulated. As reported, leucine should act on GATOR2 for mTORC1 activation. However, the authors reported that Torin, a mTORC1/mTORC2 inhibitor, inhibited M2 polarization more significantly compared to rapamycin, a mTORC1 inhibitor. These observations seem to indicate that leucine has other targets except mTORC1, such as mTORC2, which might raise novel mechanisms that have never been reported before.

Answer: thank you for the valuable comments! Akt-mTORC1 signaling integrates metabolic inputs to control macrophage activation. Wortamannin inhibition of AKT was followed by inhibition of M2 polarization, suggesting that AKT signaling is involved in M2 polarization. Studies reported that mTORC1 activation inhibits pAkt (T308), inhibition of mTORC1 in turn activate Akt (1), promoting M2 polarization as a feed back to compensate the inhibition of mTORC1 induced suppression of M2 polarization. mTORC2, directly phosphrlate Akt at S473, and inhibition of mTORC2 inhibits p-Akt (S473) (2), further inhibiting M2 porlarization. Torin1 is the inhibitor for both, while rapamycin is specially for mTORC1 (3). The explanation was included in Line 252-262

(1) Leontieva OV, Demidenko ZN, Blagosklonny MV. 2014. Rapamycin reverses insulin resistance (IR) in high-glucose medium without causing IR in normoglycemic medium. Cell Death Dis 5: e1214. doi:10.1038/cddis.2014. 178Byles.

(2) Holler M, Grottke A, Mueck K, Manes J, Jücker M, Rodemann HP, Toulany M. 2016. Dual Targeting of Akt and mTORC1 Impairs Repair of DNA Double-Strand Breaks and Increases Radiation Sensitivity of Human Tumor Cells. PLoS One 11: e0154745. doi:10.1371/journal. pone .0154745

(3) V, Covarrubias AJ, Ben-Sahra I, Lamming DW, Sabatini DM, Manning BD, Horng T. 2013. The TSC-mTOR pathway regulates macrophage polarization. Nat Commun 4:2834. doi:10.1038/ncomms3834.

1. In Fig.5B, frankly speaking, I do not observe much difference in LXRα expression. Also, the actin band is too poor to get any conclusion.

Answer: thank you for the valuable comments from the reviewer! In Fig. 5B, the extracted protein is specifically mentioned as nuclear protein in the text. It is stated that actin is expressed in the cytoplasm, while histone is expressed in the nucleus. The figure shows that actin expression is almost absent, which is mentioned to demonstrate the purity of the extracted nuclear protein.

1. In Fig. 5C and 5D, it is amazing that GSK2033 would reduce urea production even largely greater than the basal condition (lane 1). As GSK2033 normalized IL-4 or IL-4 combination with Leucine raised urea production in cells, how GSK2033 could reduce urea in medium. The authors should explain this discrepancy.

Answer: thank you for the valuable comments from the reviewer! In Fig. 5C, urea production was measured directly in the culture medium using a commercial assay kit, and GSK2033 indeed led to a significant decrease in urea production. In Fig. 5D, on the other hand, we assessed the activity of arginase-1 by lysing the cells, activating arginase-1, providing the substrate arginine, and then measuring urea production. In response to your question, the explanation is that in the assay measuring arginase-1 activity, we supplied a sufficient amount of substrate arginine, which may better reflect the enzyme’s activity and the results were consistent with our expectations. Additionally, when GSK2033 was used in combination with IL-4 or IL-4 plus leucine, it might interact with the IL-4 signaling pathway or leucine metabolism pathway, leading to an increase in urea production. This is just our preliminary explanation for the contradictory results, and we acknowledge that further research is needed to explore the mechanism of action of GSK2033 and its interactions with IL-4 or leucine.

1. Line 98, "INF-gamma" should be IFN-gamma.

Answer: We appreciate your attention to detail. We apologize for the error in line 98, where “INF-gamma” should indeed be corrected to “IFN-gamma (IFN-γ).” We will make the necessary correction in the revised version of the manuscript.