eLife Assessment

This is a useful study depicting the ultrastructural features of layer 1 of the human temporal cortex, the authors assess various synaptic parameters, astrocytic coverage, and mitochondrial morphology. High-quality data were collected using a solid methodology, however, the analysis of the functional vesicle pools is incomplete, technical issues remaining related to the identification of astrocytic processes and the measurement of vesicle diameters, and reliance solely on electron microscopy limits the scope of the work to structural observation. The work will be of interest to neuroscientists and computational researchers investigating cortical and network function.

Reviewer #1 (Public review):

Summary:

The Authors investigated the anatomical features of the excitatory synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of the synapse, the macular or the perforated appearance and the size of the synaptic active zone, the number and volume of the mitochondria, the number of the synaptic and the dense core vesicles, also differentiating between the readily releasable, the recycling and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The Authors conclude that the subcellular morphology of the layer 1 synapses is suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow the glutamate spillover from the synapses enhancing synpatic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable, since this is a highly time- and energy consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the Authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion.

Comments on latest version:

The corrected version of the article titled „Ultrastructural sublaminar specific diversity of excitatory synaptic boutons in layer 1 of the adult human temporal lobe neocortex" has been improved thanks to the comments and suggestions of the reviewers. The Authors implemented several of my comments and suggestions. However, many of them were not completed. It is understandable that the Authors did not start a whole new series of experiment investigating inhibitory synapses (as it was a misunderstanding affecting 2 reviewers from the three). But the English text is still very hard to understand and has many mistakes, although I suggested to extensively review the use of English. Furthermore, my suggestion about avoiding many abbreviations in the abstract, analyse and discuss more the perforated synapses, the figure presentation (Figure 3) and including data about the astrocytic coverage in the Results section were not implemented. My questions about the number of docked vesicles and p10 vesicles, as well as about the different categories of the vesicle pools have not been answered neither. Many other minor comments and suggestions were answered, corrected and implemented, but I think it could have been improved more if the Authors take into account all of the reviewers' suggestions, not only some of them. I still have several main and minor concerns, with a few new ones as well I did not realized earlier, but still think it is important.

Main concerns:

(1) Epileptic patients:<br /> As all patients were epileptic, it is not correct to state in the abstract that non-epileptic tissue was investigated. Even if the seizure onset zone was not in the region investigated, seizures usually invade the temporal lobe in TLE. If you can prove that no spiking activity occured in the sample you investigated and the seizures did not invade that region, then you can write that it is presumably non-epileptic. I would suggest to write „L1 of the human temporal lobe neocortical biopsy tissue". See also Methods lines 608-612. Write only „non-epileptic" or „non-affected" if you verified it with EcoG. If this was the case, please write a few sentences about it in the Methods.

(2) About the inhibitory/excitatory synapses.<br /> Since our focus was on excitatory synaptic boutons as already stated in the title we have not analyzed inhibitory SBs.<br /> Now, I do understand that only excitatory synapses were investigated. Although it was written in the title, I did not realized, since all over the manuscript the Authors were writing synapses, and were distinguishing between inhibitory and excitatory syanpses in the text and showing numerous excitatory and inhibitory synapses on Figure 2 and discussing inhibitory interneurons in the Discussion as well. Maybe this was the reason why two reviewers out of the three (including myself) thought you investigated both types of synapses but did not differentiated between them. So, please, emphasize in the Abstract (line 40), Introduction (for ex. line 92-97) and the Discussion (line 369) that only excitatory synaptic boutons were investigated.<br /> As this paper investigated only excitatory synaptic boutons, I think it is irrelevant to write such a long section in the Discussion about inhibitory interneurons and their functions in the L1 of the human temporal lobe neocortex. Same applies to the schematic drawing of the possible wiring of L1 (Figure 7). As no inhibitory interneurons were examined, neither the connection of the different excitatory cells, only the morphology of single synaptic boutons without any reference on their origin, I think this figure does not illustrate the work done in this paper. This could be a figure of a review paper about the human L1, but is is inappropriate in this study.

(3) Perforated synapses<br /> "the findings of the Geinismann group suggesting that perforated synapses are more efficient than non-perforated ones is nowadays very controversially discussed"<br /> I did not ask the Authors to say that perforated synapses are more efficient. However, based on the literature (for ex. Harris et al, 1992; Carlin and Siekievitz, 1982; Nieto-Sampedro et al., 1982) the presence of perforated synapses is indeed a good sign of synapse division/formation - which in turn might be coupled to synaptic plasticity (Geinisman et al, 1993), increased synaptic activity (Vrensen and Cardozo, 1981), LTP (Geinisman et al, 1991, Harris et al, 2003), pathological axonal sprouting (Frotscher et al, 2006), etc. I think it is worth mentioning this at least in the Discussion.

(4) Question about the vesicle pools<br /> Results, Line 271: Still not understandable, why the RRP was defined as {less than or equal to}10 nm and {less than or equal to}20nm. Why did you use two categories? One would be sufficient (for example {less than or equal to}20nm). Or the vesicles between 10 and 20nm were considered to be part of RRP? In this case there is a typo, it should be {greater than or equal to}10 nm and {less than or equal to}20nm.<br /> The answer of the Authors was to my question raised: We decided that also those very close within 10 and 20 nm away from the PreAZ, which is less than a SV diameter may also contribute to the RRP since it was shown that SVs are quite mobile.<br /> This does not clarify why did you use two categories. Furthermore, I did not receive answer (such as Referee #2) for my question on how could you have 3x as many docked vesicles than vesicles {less than or equal to}10nm. The category {less than or equal to}10nm should also contain the docked vesicles. Or if this is not the case, please, clarify better what were your categories.

(5) Astrocytic coverage<br /> On Fig. 6 data are presented on the astrocytic coverage derived from L1 and L4. In my previous review I asked to include this in the text of the Results as well, but I still do not see it. It is also lacking from the Results how many samples from which layer were investigated in this analysis. Only percentages are given, and only for L1 (but how many patients, L1a and/or L1b and/or L4 is not provided). In contrast, Figure 6 and Supplementary Table 2 (patient table) contains the information that this analysis has been made in L4 as well. Please, include this information in the text as well (around lines 348-360).<br /> About how to determine glial elements. I cannot agree with the Authors that glial elements can be determined with high certainty based only on the anatomical features of the profiles seen in the EM. „With 25 years of experience in (serial) EM work" I would say, that glial elements can be very similar to spine necks and axonal profiles.<br /> All in all, if similar methods were used to determine the glial coverage in the different layers of the human neocortex, than it can be compared (I guess this is the case). However, I would say in the text that proper determination would need immunostaining and a new analysis. This only gives an estimatation with the possibility of a certain degree of error.

(6) Large interindividual differences in the synapse density should be discussed in the Discussion.

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al examines the ultrastructural features of Layer 1 of human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as further from the epilepsy focus, and as such considered to be non-epileptic. The analyses has included 4 patients with different age, sex, medication and onset of epilepsy. The MS is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife<br /> Layer 5 - Yakoubi et al 2019 Cerebral Cortex,<br /> Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex

They find, the L1 synaptic boutons mainly have single active zone a very large pool of synaptic vesicles and are mostly devoid of astrocytic coverage.

Strengths:

The MS is well written easy to read. Result section gives a detailed set of figures showing many morphological parameters of synaptic boutons and surrounding glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in human brain are still very limited, the current MS has substantial relevance.<br /> The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analyses is clear and precise.

Weaknesses:

The authors made all the corrections required, answered most of my concerns, included additional data sets, and clarified statements where needed.

My remaining points are:

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections result in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Even it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming is also a prerequisite for release.<br /> It would help to call these pools as "putative" correlates of the morphological categories.

Reviewer #3 (Public review):

Summary:

Rollenhagen at al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue.

Strengths:

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented.

Weaknesses:

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles is also more complex than it is suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors investigated the anatomical features of the synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of each synapse, the macular or perforated appearance, the size of the synaptic active zone, the number and volume of the mitochondria, and the number of synaptic and dense core vesicles, also differentiating between the readily releasable, the recycling, and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The authors conclude that the subcellular morphology of the layer 1 synapses are suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow increased glutamate spillover from the synapses, enhancing synaptic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable since this is a highly time- and energy-consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion.

We would like to thank reviewer#1 for his very positive comments on our manuscript stating that such data about the fine structure of the human neocortex are are highly relevant.

Weaknesses:

There are several weaknesses in this work. First, the authors should check and review extensively for improvements to the use of English. Second, several additional analyses performed on the existing data could substantially elevate the value of the data presented. Much more information could be gained from the existing data about the functions of the investigated layer, of the cortical column, and about the information processing of the human neocortex. Third, several methodological concerns weaken the conclusions drawn from the results.

We would like to thank the reviewer for his critical and thus helpful comments on our manuscript. We took the first comment of the reviewer concerning the English and have thus improved our manuscript by rephrasing and shortening sentences. Secondly, according to the reviewer several additional analyses should be performed on the existing data, which could substantially elevate the value of the data presented. We will implement some of the suggestions in the improved version of the manuscript where appropriate. We will address a more detailed answer to the reviewer’s queries in her/his suggestions to the authors (see below). However, the reviewer states himself: “The techniques used to obtain the data, as well as the analyses and the statistics performed by the authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion”.

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al. examines the ultrastructural features of Layer 1 of the human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as far as possible from the epilepsy focus, and as such considered to be non-epileptic. The analyses included 4 patients with different ages, sex, medication, and onset of epilepsy. The manuscript is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife

Layer 5 - Yakoubi et al 2019 Cerebral Cortex

Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex.

They find, that the L1 synaptic boutons mainly have a single active zone, a very large pool of synaptic vesicles, and are mostly devoid of astrocytic coverage.

Strengths:

The manuscript is well-written and easy to read. The Results section gives a detailed set of figures showing many morphological parameters of synaptic boutons and glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in the human brain are still very limited, the current manuscript has substantial relevance. The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analysis is clear and precise.

We would like to thank the reviewer for his very positive evaluation of our paper and the comments that such data have a substantial relevance, in particular in the human neocortex. In contrast to reviewer#1, this reviewer’s opinion is that the manuscript is well written and easy to read.

Weaknesses:

One of the main findings of this paper is that "low degree of astrocytic coverage of L1 SBs suggests that glutamate spillover and as a consequence synaptic cross-talk may occur at the majority of synaptic complexes in L1". However, the authors only quantified the volume ratio of astrocytes in all 6 layers, which is not necessarily the same as the glial coverage of synapses. In order to strengthen this statement, the authors could provide 3D data (that they have from the aligned serial sections) detailing the percentage of synapses that have glial processes in close proximity to the synaptic cleft, that would prevent spillover.

We agree with the reviewer that we only quantified the volume ratio of the astrocytic coverage but not necessarily the percentage of synapses that may or not contribute to the formation of the ‘tripartite’ synapse. As suggested, we will re-analyze our material with respect to the percentage of coverage for individual synaptic boutons in each layer and will implement the results in the improved version of the manuscript. However, since this is a completely new analysis that is time-consuming we would like to ask the reviewer for additional time to perform this task.

A specific statement is missing on whether only glutamatergic boutons were analyzed in this MS, or GABAergic boutons were also included. There is a statement, that they can be distinguished from glutamatergic ones, but it would be useful to state it clearly in the Abstract, Results, and Methods section what sort of boutons were analyzed. Also, what is the percentage of those boutons from the total bouton population in L1?

We would like to thank the reviewer for this comment. Although our title clearly states, we focused on quantitative 3D-models of excitatory synaptic boutons, we will point out that more clearly in the Methods and Result chapters. Our data support recent findings by others (see for example Cano-Astorga et al. 2023, 2024; Shapson-Coe et al. 2024) that have evaluated the ratio between excitatory vs. inhibitory synaptic boutons in the temporal lobe neocortex, the same area as in our study, which was between 10-15% inhibitory terminals but with a significant layer and region specific difference. We will include the excitatory vs. inhibitory ratio and the corresponding citations in the Results section.

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections results in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly.

We partially disagree with the reviewer on this point. Using high-resolution transmission electron microscopy, we measured the distance from the outer-to-outer membrane only on those synaptic vesicles that were round in shape with a clear ring-like structure to avoid double counts and discarded all those that were only partially cut according to criteria developed by Abercrombie (1946) and Boissonnat (1988). We assumed that within a 55±5 nm thick ultrathin section (silver to gray interference contrast) all clear-ring-like vesicles were distributed in this section assuming a vesicle diameter between 25 to 40nm. For large DCVs, double-counts were excluded by careful examination of adjacent images and were only counted in the image where they appeared largest.

In addition, we have measured synaptic vesicles using TEM tomography and came to similar results. We will address this in Material and Methods that both methods were used.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool, and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Indeed, it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming are also a prerequisite for release.

We thank the reviewer for this comment. However, nobody before us tried to define a morphological correlate for the three functionally defined pools of synaptic vesicles since synaptic vesicles normally are distributed over the entire nerve terminal. As already mentioned above, after long and thorough discussions with Profs. Bill Betz, Chuck Stevens, Thomas Schikorski and other experts in this field we tried to define the readily releasable (RRP), recycling (RP) and resting pools by measuring the distance of each synaptic vesicle to the presynaptic density (PreAZ). Using distance as a criterion, we defined the RRP including all vesicles that were located within a distance (perimeter) of 10 to 20 nm from the PreAZ that is less than an average vesicle diameter (between 25 to 40 nm). The RP was defined as vesicles within a distance of 60-200 nm away, still quite close but also rapidly available on demand and the remaining ones beyond 200 nm were suggested to belong to the resting pool. This concept was developed for our first publication (Sätzler et al. 2002) and this approximation since then is very much acknowledged by scientist working in the field of synaptic neuroscience and computational neuroscientist. We were asked by several labs worldwide whether they can use our data of the perimeter analysis for modeling. We agree that our definition of the three pools can be seen as arbitrary but we never claimed that our approach is the truth but nothing as the truth. Concerning the debate whether only docked vesicles or also those very close the PreAZ should constitute the RRP we have a paper in preparation using our perimeter analysis, EM tomography and simulations trying to clarify this debate. Our preliminary results suggest that the size of the RRP should be reconsidered.

Tissue shrinkage due to aldehyde fixation is a well-documented phenomenon that needs compensation when dealing with density values. The authors cite Korogod et al 2015 - which actually draws attention to the problem comparing aldehyde fixed and non-fixed tissue, still the data is non-compensated in the manuscript. Since all the previous publications from this lab are based on aldehyde fixed non-compensated data, and for this sake, this dataset should be kept as it is for comparative purposes, it would be important to provide a scaling factor applicable to be able to compare these data to other publications.

We thank the reviewer for his suggestion. However, for several reasons we did not correct for shrinkage caused by aldehyde fixation. There are papers by Eyre et al. (2007) and the mentioned paper by Korogod et al. 2015 that have demonstrated that cryo-fixation reveals larger numbers of docked synaptic vesicles, a smaller glial volume, and a less intimate glial coverage of synapses and blood vessels compared to chemical fixation. Other structural subelements such as active zone size and shape and the total number of synaptic vesicles remained unaffected. In two further publications Zhao et al. (2012a, b) investigating hippocampal mossy fiber boutons using cryo-fixation and substitutions came to similar results with respect to bouton and active zone size and number and diameter of synaptic vesicles compared to aldehyde-fixation as described by Rollenhagen et al. 2007 for the same nerve terminal. This was one of the reasons not correcting for shrinkage. In addition, all cited papers state that chemical fixation in general provides a much better ultrastructural preservation of tissue samples when compared with cryo-fixation and substitution where optimal preservation is only regional within a block of tissue and therefore less suitable for large-scale ultrastructural analyses as we performed.

Reviewer #3 (Public review):

Summary:

Rollenhagen et al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology, and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue.

Strengths:

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented.

We are very thankful to the reviewer upon his very positive comments about our data analysis and presentation.

Weaknesses:

The study provides only morphological details, these can be useful in the future when combined with functional assessments or computational approaches. The authors emphasize the importance of their findings on astrocytic coverage and suggest important implications for glutamate spillover. However, the percentage of synapses that form tripartite synapses has not been quantified, the authors' functional claims are based solely on volumetric fraction measurements.

We thank the reviewer for his critical comments on our findings concerning the layer-specific astrocytic coverage as also suggested by reviewer#2. As already stated above we will analyze the astrocytic coverage and the layer-specific percentage of astrocytic contribution to the ‘tripartite’ synapse in more detail. We are, however, a bit puzzled about the comment that structural anatomists usually receive that our study only provides morphological details. Our thorough analysis of structural and synaptic parameters of synaptic boutons underlie and might even predict the function of synaptic boutons in a given microcircuit or network and will thus very much improve our understanding and knowledge about the functional properties of these structures, in particular in the human brain where such studies are still quite rare. The main goal of our studies in the human neocortex was the quantitative morphology of synaptic boutons and thus the synaptic organization of the cortical column, layer by layer which to our knowledge is the first such detailed study undertaken in the human brain. Our efforts have set a golden standard in the analysis of synaptic boutons embedded in different microcircuits und is meanwhile internationally very well accepted.

The distinction between excitatory and inhibitory synapses is not clear, they should be analyzed separately.

As already stated above in response to reviewer#1 our study focused on excitatory synaptic boutons since they represent the majority of synapses. However, in the improved version of our manuscript in the Material and Method section we included a paragraph with structural criteria to distinguish excitatory from inhibitory terminals (see also our comment to reviewer#1 concerning this point) including appropriate citations.

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles are also more complex than suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data.

We thank the reviewer for this comment. However, it has been shown by meanwhile numerous publications that the shape and size of the active zone together with the pool of synaptic vesicles and the astrocytic coverage critically determines synaptic transmission and synaptic strength, but can also contribute to the modulation of synaptic plasticity (see also citations within the text). It has been shown that synaptic boutons can switch upon certain stimulation conditions to different modes of release (uni- vs. multiquantal, uni- vs multivesicular release) and from asynchronous to synchronous release leading also to the modulation of synaptic short- and long-term plasticity. To the second comment: When we started with our first paper about the Calyx of Held – principal neuron synapse in the MNTB (Sätzler et al. 2002) we tried to define a morphological correlate for the three functionally defined pools. As already mentioned above in our reply to the other two reviewers, this is rather difficult since synaptic vesicles are normally distributed over the entire nerve terminal. After long and thorough discussions with Bill Betz, Chuck Stevens and other leading scientist in the field of synaptic neuroscience, we together with Bert Sakmann tried to define a morphological correlate for the functionally defined pools using a perimeter analysis. We defined the readily releasable pool as vesicles 10 to 20 nm away from the presynaptic active zone, the recycling pool as those in 60-200 nm distance and the remaining as those belonging to the resting pool. However, it has been shown by capacitance measurements (see for example Hallermann et al 2003), FM1-43 investigations (see for example Henkel et al. 1996) and high-resolution electron microscopy (see for example Schikorski and Stevens 2001; Schikorski 2014) that our estimate of the RRP nearly perfectly matches with the functionally defined pools at hippocampal and cortical synapses (Silver et al. 2003). In addition, in one of our own papers (Rollenhagen et al. 2018) we also estimated the RP functionally from trains of EPSPs using an exponential fit analysis and came to similar results upon its size using the perimeter analysis.

Of course, as stated by the reviewer the scenario could be more complex, using other criteria but we never claimed that our morphologically defined pools are the truth but nothing as the truth but we believe it offers a quite good approximation.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Abstract:

Avoid the numerous abbreviations in the abstract. The paragraph describing the results obtained in this study is too short. Include more results, such as the size of the active zone, the proportion of perforated synapses, the ratio of synapses terminating on dendrites/spines, the percentage of volume occupied by mitochondria, etc. In the last paragraph, compare the layer-specific data to other layers of the neocortex before writing the concluding sentence.

To meet the word limits of the abstract (150 words) defined by eLife we had to use abbreviations. We followed the suggestions by the reviewer and expanded our abstract by adding the proportion of macular vs. perforated active zone and the percentage of mitochondria within an SB. However, we did not include the comparison of structural parameters in the Abstract since this is discussed thoroughly in the MS at other places (see Results and Discussion).

Results:

First of all, wonderful data! Lots of work, very valuable quantitative electron microscopy results.

Main concerns:

Adding several analyses would give much more information about the cortical synaptic organization. It would be very useful to differentiate between excitatory and inhibitory terminals (and give their ratio) and include this information in all different analyses, such as in the SV number, SV pool analysis, mitochondrion analysis, etc., that would give functional information as well. You have all the data for this, and you know how to differentiate between inhibitory and excitatory synapses, it can be done. We could see the possible morphological differences between excitatory and inhibitory synapses (maybe one is larger/has more SVs, etc. than the other). Based on these possible differences conclusions could be drawn about functional hypotheses, such as one or the other is more efficient in inducing postsynaptic potentials, excitation or inhibition is more pronounced in layer 1, etc. Furthermore, looking at the ratio of perforated synapses, we could gain information about the formation of new synapses. Maybe there is a difference between excitatory and inhibitory circuits in this point of view.

To the first point: Since our focus was on excitatory synaptic boutons as already stated in the title we have not analyzed inhibitory SBs. To do so, we have to re-analyze our complete data which is time-consuming and an additional workload. However, we can give a ratio excitatory vs. inhibitory synaptic boutons which was between 10-15% but with layer-specific differences. Our finding are in good agreement with a recent publication in Science by the Lichtman group (Shapson-Coe et al. 2024) and work by the DeFelipe group (Cano-Astorga et al. 2023, 2024) estimating the number of inhibitory boutons in different layers of the temporal lobe neocortex as we did by 10-15%. We included a small paragraph about inhibitory synapses, their percentage and included the citations in our Results section. Concerning the ratio between macular, non-perforated vs. perforated active zones we stated the majority of synaptic boutons were of the macular, non-perforated type (~75%; see improved version of the MS). If perforated, this was found predominantly on the postsynaptic site, but quite rare in L1 SBs. Since GABAergic terminals had only a small or no clearly visible PSD this would be hard to look at.

To the last point, it has been demonstrated that the number of dense core vesicles and their fusion with the presynaptic density could be a critical factor in the build-up of the active zone. In addition, the findings of the Geinismann group suggesting that perforated synapses are more efficient than non-perforated ones is nowadays very controversially discussed since other factors such as size of the active zone (see for example Matz et al. 2010; Holderith et al. 2012) and the astrocytic coverage contribute to synaptic efficacy and strength.

Related to this topic: although in the case of rat CA1 pyramidal cells all inhibitory synapses terminated on dendritic shafts (Megias et al., Neuroscience 2001), please be aware that both excitatory and inhibitory synapses can terminate on both dendritic shafts and spines in humans (inhibitory synapses are though rare on spines, usually less than 10%, but they do exist, see for example Wittner et al, Neuroscience, 2001). Please, define the excitatory/inhibitory nature of the synapses based on morphological features (not on their postsynaptic target), i.e., flattened vesicles and thin postsynaptic density for GABAergic synapses, whereas larger, round vesicles and thick postsynaptic density for glutamatergic synapses. Anyway, the ratio of excitatory and inhibitory synapses on dendrites and spines in the two sublamina would also give useful information about the synaptic organization of the human neocortical layer 1.

We are aware that not all terminals targeting on spines are excitatory, in turn it has been shown that not all terminals on shafts were inhibitory as long thought (Silver et al. 2003). However, as stated by the reviewer their abundancy on spines is rather low. At the moment it is rather unclear which functional impact inhibitory terminals on spines have, despite a local inhibition (see for example Kubota et al. eLife 2015), and thus their role is rather speculative since excitatory synapses are the predominant class on dendritic spines. As already stated above the ratio of excitatory vs. inhibitory terminals is between 10-15% and not significantly different between the two sublaminae. We are willing to add this in the results section (see in the improved version of the manuscript).

(2) About the glial coverage: Please, specify how glial elements were determined. What were the morphological features specific to astroglial processes? In Figure 5, how could we know whether the glial element marked by green is not a spine neck? The lack of morphological features specific to glial processes makes this analysis weak. The most accurate would be to make it with the aid of GFAP staining. I know this is not possible with your existing data, but at least, provide information on how glial processes were identified.

We used the criteria first described by Peters et al. (1991) and Ventura and Harris (1999) identifying astrocytic profiles by their irregular stellate shape, relatively clear cytoplasm, numerous glycogen granules and bundles of intermediate filaments. After more than 20 years of structural investigations, we hope that the reviewers will believe us that we can identify astrocytic processes at the high-resolution TEM level. In some of our publications (Rollenhagen et al. 2007; 2015; 2018; Yakoubi et al. 2019a) we have used glutamine synthetase pre-embedding immunhistochemistry to identify astrocytic processes, but a disadvantage of this method is the reduction of the ultrastructural preservation of the tissue. We have included the criteria to identify astrocytic processes of glial coverage in our manuscript together with the two citations (see improved version of the manuscript).

(3) The authors state that the total number of SVs was very variable. How was the distribution of the number of SVs? Homogenous distribution suggests that different types of synapses cannot be distinguished based on their morphological features, whereas distribution with more than one peak would suggest that different types of synapses are present in L1, and that they can be differentiated by their appearance (number of SVs, for example). This might be also related to the type of synapse (i.e., excitatory or inhibitory). The same applies to the number of RP and resting pool SVs.

To look for differences in structural and synaptic parameters that can further classify synaptic boutons we have performed a hierarchical cluster and multivariance analysis. However, it turned out that according to structural and functional parameters no further classification into subtypes could be done.

(4) The authors should check and review extensively for improvements to the use of English. The Results and Discussion sections contain many sentences which are not easy to understand. They have either a too complicated structure, or they are incomplete and hard to follow. Few examples: "The RRP/PreAZ at p20 nm criterium was on average 19.05 {plus minus} 17.23 SVs (L1a: 25.04 {plus minus} 21.09 SVs and L1b: 13.07 {plus minus} 13.87SVs) and thus nearly 2-fold larger for L1a." If you take out the parenthesis, the sentence has no meaning. "The majority of SBs in L1 of the human TLN had a single at most three AZs that could be of the non-perforated macular or perforated type comparable with results for other layers in the human TLN but by ~1.5-fold larger than in rodent and non-human primates." Rephrase these types of sentences, please.

We partially agree with the reviewer. We have improved our manuscript by rephrasing and shortening sentences.

Other suggestions:

(1) Put the synaptic density part after the description of the neuronal and synaptic composition part, it is more logical this way (i.e., first qualitative description, the distinction between sublayers, then quantitative data). Please write down in the description of the neuronal and synaptic composition part how L1a and L1b were differentiated (see also my comment on Figure 1).

We agree with the reviewer and did the change according to the suggestion. For a better understanding, we have also expanded the neuronal and synaptic description of the two sublaminae in L1.

(2) Introduce a list of abbreviations at the beginning, that would help.

It is quite unusual to provide a list of abbreviations in eLife. However, when used first the full meaning of the abbreviations is now given.

(3) What is cleft width? Usually, it refers to the distance between the pre- and the postsynaptic membrane, but here, I think it refers to the size (diameter) of the active zone. Please, clarify in the Result section (as it appears earlier than the Methods section, where it is explained). I would probably use the expression "synaptic cleft size" instead of "synaptic cleft width" to avoid misunderstanding.

We thank the reviewer for the suggestion and used synaptic cleft size for better clarity and have transferred the sentence from the Material and Methods to the Results section.

(4) The description of the different SVs (RRP, RP, etc.) is not clear in lines 236-242. What does it mean, that RRP vesicles are located {less than or equal to}10 nm and {less than or equal to}20 nm from the active zone? Explain, why the two different distance criteria were used. Furthermore, how were the vesicles located at p20-p60 defined? Why were these vesicles not considered in the determination of the different pools?

As stated in the public review to the reviewers concern we have tried to define a morphological correlate to the three functionally defined pools. After thorough discussions, with leading scientists in the field of synaptic neuroscience we have decided to use the distance of individual vesicles from the PreAZ and sort vesicles upon these criteria. One can argue that this approach is random, however, these distance criteria were described by Rizzoli and Betz (2004, 2005) and Denker and Rizzoli (2010). As also stated in the public review there is still a controversial discussion whether only docked or omega-shaped SVs constitute the RRP. We decided that also those very close within 10 and 20 nm away from the PreAZ, which is less than a SV diameter may also contribute to the RRP since it was shown that SVs are quite mobile.

(5) Please, explain how the number of docked vesicles can be 3x larger in L1b, than the number of vesicles located at p10? Docked vesicles are the closest (with the membrane touching the PreAZ)... if this comes from the fact that another pool of boutons was used for the EM tomography analysis, then the entire pool of boutons analyzed, then it means that the selection of boutons for the EM tomography is highly biased. This also implies that EM tomography data are most probably not valid for the entire L1b. The difference might also come from the different ratios of dendrite/spine synapses included in the two different analyses. In this case, it would be helpful to distinguish between synapses terminating on dendrites/spines and analyse them separately (same as for inhibitory/excitatory, which is not exactly the same as dendrite/spine!). Different n numbers of synapses are given in the text (n=25, 25, 25 25) and in Table 2 (n=91, 98, 87, and 84) for the analysis of the docked vesicles, please, correct this.

This is a correct value and thus there is a nearly 3-fold difference. The TEM tomography was carried out on the same blocks that have been used for our 3D-volume reconstructions. To carry out TEM tomography we had to use thicker sections (250 nm) to look for complete SBs as we also did in our serial sections, but of course, we could not quantify the same SBs. The completeness of SBs was one of our main criteria to reconstruct structural and synaptic parameters. The second was that the synaptic cleft was cut perpendicular. Only SBs that met these criteria were chosen for further quantitative analysis. In this respect we are of course biased in both methods.

Secondly, as already stated we did not quantify inhibitory terminals in serial sections. However, we did not find significant differences between shaft vs. spine synapses.

Finally, in Table 2 the total number of ‘docked’ SVs is given analyzed from the total number of SBs analyzed.

Discussion:

Please include the recent findings of human L1 neurons, including the "rosehip" cells in the L1 neuronal network, see Boldog et al., Nat Neurosci 2018. It would be also useful to consider in the discussion the human-specific cortical synchrony and integration phenomena derived from in vitro data (Mansvelder, Lein, Tamas, Wittner, Larkum, Huberfeld labs, etc.), and how the synaptic morphology can be related to these.

We thank the reviewer and include the reference in our chapter functional significance.

Figures and Tables:

Figure 1: In the legend, it is written that CR cells are marked by an asterisk, but on the figure it is marked by arrowheads. H: I would put the dashed line slightly lower, just above the two neuronal cell bodies. Now it looks like in the middle of the astrocytic layer. One of the asterisks marking the CR cell is not above the nucleus of that cell. I: the gabaergic neuron is outside of the framed area. I would delete the frame, anyway, the arrowheads and the asterisk are enough to show what the authors want to show.

We have changed the Figure according to the suggestions raised by the reviewer.

Figure 3: The transparent yellow is not visible. It is a bit disturbing that the contours of the boutons are not visible, I would make the transparent yellow stronger (less transparent). The SVs in green/magenta will be still visible.

We wanted to highlight the internal subelements of SBs and thus made the covering transparent but we think it is still visible.

Figure 6C: The data concerning other layers than L1 are most probably taken from other publications of the research group. One is cited (for L6), but not the others. Please correct this, or if not, then write this in the Results and Methods.

We changed the citation in the improved version of the manuscript. We overlooked that the values for L4 and L5 were already published in Schmuhl-Giesen et al. 2022.

Table 1: What does central and lateral cleft width mean in Table 1? Furthermore, please, give the name for abbreviations CV and IQR in Tables 1 and 2.

The measurements of the synaptic cleft are now described in detail in the Results section. We now have given the full names for CV and IQR in the legends of tables 1 and 2.

Supplemental Figures 1 and 2: Why Hu01 and Hu02 are twice? What is the difference? Based on the figure legend, it is L1a and L1b? If yes, please, indicate on the figure or in the legend.<br /> Supplemental Table 1: What is TLE in the case of Hu_04? If it is temporal lobe epilepsy, then why age at epilepsy onset is missing?

Yes, Hu01 and Hu02 were selected for both L1a and L1b in separate serial sections preparations each. We indicated this now in the figure legend. Concerning Hu_04, unfortunately we do not have any further information about the medical background of the patient.

Supplemental Table 1 (Patient table), that there are many abbreviations explained which do not appear in the table (lBAZ: Brivaracetam CBZ: Carbamazepine; CLB: Clobazam; ESL: Eslicarbazepin; GGL: Ganglioglioma, etc.), please check and correct.

We have removed the unnecessary abbreviations.

Other minor suggestions:

What is Pr? Please, give the name a first appearance (line 368).

We explained Pr (release probability) when used for the first time.

Give the name for t-LDT, please (lines 442-443).

We explained t-LTD (timing-dependent long-term depression) when used for the first time.

Typo in line 169: DCW instead of DCV (dense core vesicle), DCV is used in the figure legends.

We changed DCW to DCV.

Typo in line 190: Yokoubi instead of Yakoubi (reference).

We changed Yokoubi to Yakoubi.

Typo in line 237: Rizzoloi instead of Rizzoli (reference).

We changed Rizzoloi to Rizzoli.

Line 229-230: One reference is not inserted properly - Piccolo and Bassoon.

The reference of Schoch and Gundelfinger and Murkherjee to the build-up of the active zone and the role of DCV containing Piccolo and Bassoon are properly cited in the text.

Typo in line 398: exit instead of exist.

Corrected

Typo in line 700: Reynolds (1063) instead of 1963.

Corrected

Reviewer #2 (Recommendations for the authors):

Abstract:

The last sentence seems far-fetched, and unrelated to the manuscript. How mostly single active zone boutons can "mediate, integrate and synchronize contextual and cross-modal information, enabling flexible and state-dependent processing of feedforward sensory inputs from other layers of the cortical column"? Which of the anatomical findings of the manuscript led to these conclusions?

According to the review by Schuman et al. (2021) layer 1 is regarded as a layer that mediate, integrate and synchronize contextual and cross-modal information, enabling flexible and state-dependent processing of feedforward sensory inputs from other layers of the cortical column to which the structural quantitative 3D- models of SBs contribute since they are an integral element connecting neurons and building networks.

I am also puzzled by the authors' statement in more than one place of the manuscript that "L1a can be characterized as a predominantly astrocytic sublamina". If the L1 contains the lowest measured volume ratio of glial processes (Figure 6), then this description does not seem to hold. Please rephrase.

The reviewer is right and we rephrased the sentences for more clarity in the improved version of our manuscript.

Results:

The authors find large inter-patient variability in the synapse density at L1, which raises the issue of what were the criteria to include certain patients in the analyses. Apparently, these are different from the ones analysed in their previous papers, and all the provided parameters were different (sex, age, medication, onset of epilepsy), and any of them can result in altered synapse density.

First, we have not used all patients for this study. Secondly, it was not possible to use all patients for all six layers.

It would be useful to add a panel for Figure 1 with synapse density across the different layers, as they provide this data in the Discussion.

We implemented a Supplementary Table 1 with the synaptic density values over all layers compared in the Discussion.

I cannot find Source Data 1 in the manuscript although it is referred to in more than 1 place (e.g. page 5 line 100).

Source data were uploaded when our manuscript was submitted directly to eLife as Supplemental Material. However, as stated by bioRxiv ‘any Supplemental Materials associated with this manuscript have not been transferred to bioRxiv to avoid the posting of potentially sensitive information’ all source data have not been uploaded to the preprint server.

Page 5 line 100 the correct value is 7.3*107 or rather 108?

We corrected the value in the improved version of the MS.

It would be nice to put the synapse density values into context by comparing them to e.g. mouse, rat, or monkey data.

Since we are working on the human temporal lobe neocortex we avoided to compare those data with those estimated in experimental animals. In addition as discussed by DeFelipe et al. (1999) different methods were used to quantify synaptic density in experimental animals so these results are difficult to compare.

Page 5 Line 117 CR-cells stands for Cayal-Retzius cells?

CR-cells is the abbreviation for Cajal-Retzius cells.

Page 6 Line 146 repeated sentence.

We deleted the repeated sentence.

Page 7 Line 154 "file-scale TEM" ??

We replaced file-scale by fine-scale.

Page 7 Line 164 "GABAergic synapses identified by the smaller more spherical SVs". With this fixation condition, GABAergic vesicles are more ovoid than glutamatergic ones. What were the criteria to distinguish them?

To our knowledge in meanwhile numerous publications using the same fixation inhibitory terminals contain more spherical and smaller and not roundish synaptic vesicles and showed no clear prominent PSDs as described in our paper. We have addressed that more clearly in the results section of the improved version of the MS.

Page 8 line 197 "The majority (~98%) of SBs in L1a and L1b had only a single (Figures 2C-E, 3A-C, E) at most two or three AZs" is in striking contrast with the other statement from page 7 Line 163 "Numerous SBs in both sublaminae were seen to establish either two or three synaptic contacts on the same spine or dendrite". Which of these statements is valid? Please provide exact quantification for this statement and decide which one is true.

It is true that the majority of synaptic boutons had a single active zone. However, for example on a spine not only a single but also two or three SBs can be found. We have rephrased this sentence for more clarity.

Page 9 Line 206 "L1 AZs did not show a large variability in size as indicated by the low SD, CV, and variance (Table 1)" Is this inter-patient variance of mean values? As in Supplementary Figure 1, both the SBs volume and PreAZ area show large variability in a given patient sample. Only the inter-patient variability of mean values seems low. Please state it clearly throughout the MS for other datasets as well.

For clarity concerning the variability between patients and structural parameters we have generated box plots (Suppl. Figures 1 and 2).

Page 9 Line 208 data is on Figure 5A and not 8A.

We thank the reviewer and corrected the citation of the Figure

Page 12 Line 295 how can the number of docked vesicles for L1b be larger than the one measured by the perimeter p10 nm? This later should contain the docked and PreAZ membrane proximal pool as well. This difference is even larger if we assume, that at EM tomography only partial AZs were analysed in a 200 nm thick section, not the entire AZ as for the perimeter measurement. Can the authors provide density estimates by dividing the docked / p10 nm vesicle numbers with the AZ area and comparing them?

This is a result comparing both methods. To the second concern: As stated in the text only synaptic boutons were the active zone can be followed from the beginning to its end and were the synaptic cleft was cut perpendicular were included in the TEM tomography sample as we also did in our 3D-volume reconstructions.

Methods:

Page 25 Line 624 While the PSD area can be equivocally measured, due to the dense appearance of the PSD on the EM images, the PreAZ is more difficult to outline due to lack of evident anatomical markers except the synaptic cleft (the dense material is much thinner). That is why in many publications the PreAZ area is considered to be identical to the PSD area. What are the anatomical criteria used here for the PreAZ? Why do the authors correct the PSD area, which is easy to measure with the PreAZ area that is much less certain to outline?

As stated in material and Methods both the pre- and postsynaptic densities are not defined by placing a closed contour in both densities because one can’t be certain that the dense accumulation of particles defining both areas since the impregnation (staining) and contrast of both structures critically depends on the uranyl and lead staining which could led to misinterpretation due to different staining results. That’s why we have drawn a contour line from the beginning to the end of the presynaptic density and extrapolated that for the postsynaptic density (for details see Material and Methods). In our samples both the pre- and postsynaptic densities were always clearly visible in those boutons further analyze.

Page 26 Line 640 vesicle density measurement: All the synaptic vesicles that are in the 50 nm thick section in their entirety are missed, and there are methods based on EM tomography to correct these estimations. One can not assume, that the error caused by "double counts" of vesicles cancels for the lost ones. There are stereological methods to estimate both types of error please include them and correct the values.

We would like to point out that the whole body of our work to structural analysis of vesicle pools is based on image data stemming from transmission electron microscopy (TEM) generating a projection of the entire volume of the ultra-thin section and NOT from scanning electron microscopy (SEM) where only a small volume close to the surface of the section would be captured. Operating in TEM mode ensures that no vesicle is missed only because it is embedded in its entirety in the section as postulated by the reviewer. Hence, EM tomography, which is basically a TEM operating from different incident angles in relation to the specimen or section, does not provide any advantage in detecting these vesicles. It does, however, help to better position a 3D object within the section volume itself and therefore allows to detect objects that could overlap from one viewing angle by using another angle. As the average vesicle diameter is of similar size compared to the section thickness, the possibility of a complete overlap to happen, however, is almost zero. And as we only count clear ring-like structures, a stereological correction factor calculated according to Abercrombie (1946) would underestimate real counts (see also Saetzler et al. 2002). If there is, however, relevant literature on "methods based on EM tomography" and "stereological methods to estimate both types of error" (over- and underestimates) that we are missing out on, we would appreciate the reviewer providing us with the corresponding references so that we can include such calculations in our paper.

Page 27 Line 664 and 665 "sections" are still tissue blocks, as sectioning comes after if the process is correctly written. Please correct.

We have corrected this according to the reviewer’s comment.

Page 43 Figure 4 D Data for L1b is missing, only the correlation line is visible.

Corrected in a new Figure.

Page 44 Figure 5 C arrowheads are in the correct places? Some of them do not seem to point to the edge of the synapse.

We carefully checked the Figure and adjusted the arrowheads.

Figure 5 E lower arrowhead labels something, that is difficult to identify but does not seem to be a vesicle.

We agree with the reviewer on this point and changed the figure accordingly.

Figure 5 F, the upper vesicle is at least 10 nm apart from the PreAZ membrane. Did the authors consider it as docked (indicated with arrowhead, according to the legend it labels docked vesicles)?

We agree with the reviewer on this point and changed the figure accordingly.

Page 45 Figure 6 B one of the 2 synaptic boutons (sb), sb2 has a tangential active zone that precludes the identification of the pre- and post-synaptic membranes, still 2 "docked vesicles" are labeled. How were they classified as docked? Please remove these tangential synapses from the dataset, as membranes can not be identified.

The reviewer is right that the active zone is tangentially cut, however, the two vesicles are associated with the AZ. In addition, we did not use this AZ for vesicle data analysis.

Page 46 Line 1124 interneuron axon labelled in green not brown.

Corrected as suggested by the reviewer.

Line 1129 SStC is missing.

Changed according to the reviewer’s comment.

Page 48 Table 2 Number of docked vesicles Median values are rounded to integer values? If yes why?

The statistic package used rounded to the given values.

Page 51 Supplementary Table 1 Hu_04 Histopathology, what does TLE stands for?

TLE: temporal lobe epilepsy. We included the abbreviation in the legend of Supplementary Table1, that is now table 2.

Reviewer #3 (Recommendations for the authors):

(1) Reanalysis of astrocytic coverage based on the % of synapses that form tripartite synapses.

We have reanalyzed the data concerning this point (new Figure 6D).

(2) Segregation of excitatory and inhibitory synapses.

We have now included a paragraph in our results section to distinguish between excitatory and inhibitory synapses.

(3) Better explanation of the limits of the study to assess functional parameters.

We disagree with the reviewer on this point and have not included an explanation concerning the limits of this study.

eLife Assessment

This valuable study uses high-field fMRI to test the hypothesized involvement of subcortical structures, particularly the striatum, in updating working memory. The study overcomes limitations of prior work by applying high-field imaging with a more precise definition of regions of interest in the brain. Thus, the empirical observations are of use to specialists interested in working memory gating or the reference back task specifically. The evidence is generally solid, but strong conclusions on dopaminergic contributions must await additional work using molecular imaging or related techniques.

Reviewer #1 (Public review):

Summary:

Trutti and colleagues used 7T fMRI to identify brain regions involved in subprocesses of updating the content of working memory. Contrary to past theoretical and empirical claims that the striatum serves a gating function when new information is to be entered into working memory, the relevant contrast during a reference-back task did not reveal significant subcortical activation. Instead, the experiment provided support for a role of subcortical (and cortical) regions in other subprocesses.

Strengths

The use of high-field imaging optimized for subcortical regions in conjunction with the theory-driven experimental design mapped well to the focus on a hypothetical striatal gating mechanism.

Consideration of multiple subprocesses and the transparent way of identifying these, summarized in a table, will make it easy for future studies to replicate and extend the present experiment.

Weaknesses:

The reference-back paradigm seems to only require holding a single letter in working memory (X or O; Fig 1). It remains unclear how such low demand on working memory influences associated fMRI updating responses. It is also not clear whether reference-switch trials with 'same' response truly taxes working-memory updating (and gate opening), as the working-memory content/representation does not need to be updated in this case. These potential design issues, together with the rather low number of experimental trials, raise concerns about the demonstrated absence of evidence for striatal gate opening. Adding an experiment with higher working-memory demand and additional trials could strengthen the evidence for the authors present claim

The authors provide a motivation for their multi-step approach to fMRI analyses. Still, the three subsections of fMRI results (3.2.1; 3.2.2; 3.3.3) for 4 subprocesses each (gate opening, gate closing, substitution, updating mode) made the Results section complex and it was not always easy to understand why some but not other approaches revealed significant effects (as the midbrain in gate opening).<br /> It could be helpful to readers to further revise the Results section and/or more clearly convey the analytic strategy.

The many references to the role of dopamine are interesting, but the discussion of dopaminergic pathways and signals remains speculative and must be confirmed in future studies (e.g., with PET imaging).

Several relevant studies were not cited (e.g., Dahlin et al., 2008, Science; Bäckman et al., 2011, Science).

Reviewer #2 (Public review):

Summary:

The study reported by Trutti et al. uses high-field fMRI to test the hypothesized involvement of subcortical structure, particularly striatum, in WM updating. Specifically, participants were scanned while performing the Reference Back task (e.g., Rac-Lubashevsky and Kessler, 2016), which tests constructs like working memory gate opening and closing and substitution. While striatal activation was involved in substitution, it was not observed in gate opening.

While there have been prior fMRI studies of the reference back task (Nir-Cohen et al., 2020), the present study overcomes limitations in prior work, particularly with regard to subcortical structures, by applying high-field imaging with more precise definition of ROIs. And, the fMRI methods are careful and rigorous, overall. Thus, the empirical observations here are useful and will be of interest to specialists interested in working memory gating or the reference back task specifically. I do not have additional concerns about this contribution.

Author response:

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

eLife Assessment 

This useful study uses high-field fMRI to test the hypothesized involvement of subcortical structure, particularly the striatum, in WM updating. It overcomes limitations in prior work by applying high-field imaging with a more precise definition of ROIs. Thus, the empirical observations are of use to specialists interested in working memory gating or the reference back task specifically. However, evidence to support the broader implications, including working memory gating as a construct, is incomplete and limited by the ambiguities in this task and its connection to theory. 

We would like to express our gratitude to the editor and the reviewers for their time and effort in providing insightful and valuable comments. We greatly value the critical perspective on the relationship between fMRI contrasts and the PBWM model. We hope to have addressed all the last critical points and changed the manuscript according to the reviewers’ suggestions. Furthermore, we would like to point out that the behavioral results section was edited, as a double-check of the results section revealed some erroneous descriptive statistics.

Public Reviews:

Reviewer #1:

Summary: 

Trutti and colleagues used 7T fMRI to identify brain regions involved in subprocesses of updating the content of working memory. Contrary to past theoretical and empirical claims that the striatum serves a gating function when new information is to be entered into working memory, the relevant contrast during a reference-back task did not reveal significant subcortical activation. Instead, the experiment provided support for the role of subcortical (and cortical) regions in other subprocesses. 

Strengths: 

The use of high-field imaging optimized for subcortical regions in conjunction with the theory-driven experimental design mapped well to the focus on a hypothetical striatal gating mechanism. 

Consideration of multiple subprocesses and the transparent way of identifying these, summarized in a table, will make it easy for future studies to replicate and extend the present experiment.   

Weaknesses: 

The reference-back paradigm seems to only require holding a single letter in working memory (X or O; Figure 1). It remains unclear how such low demand on working memory influences associated fMRI updating responses. It is also not clear whether reference-switch trials with 'same' response truly tax working-memory updating (and gate opening), as the working-memory content/representation does not need to be updated in this case. These potential design issues, together with the rather low number of experimental trials, raise concerns about the demonstrated absence of evidence for striatal gate opening. 

We acknowledge that a limitation of our study is that the task involved relatively low working memory demands. It remains to be clarified whether the same neural mechanisms would be engaged under a higher working memory load, and this is an important consideration for future research.

We also fully agree that it is uncertain whether reference-switch trials requiring a ‘same’ (or ‘match’ ) response truly engage working memory updating (or gate opening), as the working memory content or representation does not need to be altered in these cases. This concern is addressed in detail in the discussion section titled “No Support for Striatal Gate Opening” (see second paragraph).

Regarding our references to dopamine, we completely agree with the reviewer about the speculative nature of these discussions. In response, we thoroughly reviewed the manuscript and made revisions where necessary to ensure that we consistently emphasize the speculative nature of our commentary on dopamine and dopaminergic pathways.

Finally, we acknowledge the concerns about the design and the relatively low number of trials. However, our fMRI analyses of other reference-back task contrasts did reveal activity in the striatum and other subcortical ROIs. This suggests that our scanning protocol and task design are sufficiently sensitive to detect striatal activity, even with the limited number of trials.

The authors provide a motivation for their multi-step approach to fMRI analyses. Still, the three subsections of fMRI results (3.2.1; 3.2.2; 3.3.3) for 4 subprocesses each (gate opening, gate closing, substitution, updating mode) made the Results section complex and it was not always easy to understand why some but not other approaches revealed significant effects (as the midbrain in gate opening). 

We thank the reviewer for this important remark and the opportunity to clarify our approach. We conducted whole-brain general linear models (GLMs) to generate a comprehensive wholebrain map of brain activity for each contrast. However, the whole-brain statistical parametric mappings (SPMs) involve data smoothing, which–while improving signal detection–reduces spatial precision. This is especially problematic in smaller or closely adjacent regions, where spatial blurring can merge distinct activations or make localized signals appear more widespread.

Additionally, the statistical thresholds in whole-brain analyses may detect weak or borderline significant effects, whereas ROI-wise GLMs, which assume uniform behavior across the entire region, may miss the same effects if the signal is weak or inconsistent across the ROI.

Since our primary focus was on the subcortex, we relied more heavily on ROI-wise GLMs, which were limited to subcortical regions. We prioritized findings that were supported by either the ROI-wise GLMs or by both GLM analyses. For instance, the midbrain activations found in our whole-brain analysis but not in the ROI analysis may result from smoothing (where activation from neighboring regions spreads into midbrain voxels) or from functional heterogeneity within the ROI, which can obscure localized activations when averaged in the ROI-wise GLMs. Inferences from each GLM approach, along with their discrepancies, are discussed for each contrast throughout the discussion, with additional details on the clusterbased ROI analysis in the discussion section titled “Dopaminergic involvement in working memory substitution” (see third paragraph).

We acknowledge that the results section may seem complex, and we apologize for any inconvenience this may cause.

Reviewer #2:

Summary: 

The study reported by Trutti et al. uses high-field fMRI to test the hypothesized involvement of subcortical structure, particularly striatum, in WM updating. Specifically, participants were scanned while performing the Reference Back task (e.g., Rac-Lubashevsky and Kessler, 2016), which tests constructs like working memory gate opening and closing and substitution. While striatal activation was involved in substitution, it was not observed in gate opening. This observation is cited as a challenge to cortico-striatal models of WM gating, like PBWM (Frank and O'Reilly, 2005). 

Strengths: 

While there have been prior fMRI studies of the reference back task (Nir-Cohen et al., 2020), the present study overcomes limitations in prior work, particularly with regard to subcortical structures, by applying high-field imaging with a more precise definition of ROIs. And, the fMRI methods are careful and rigorous, overall. Thus, the empirical observations here are useful and will be of interest to specialists interested in working memory gating or the reference back task specifically. 

Weaknesses: 

I am less persuaded by the more provocative points regarding the challenge it presents to models like PBWM, made in several places by the paper. As detailed below, issues with conceptual clarity of the main constructs and their connection to models, like PBWM, along with some incomplete aspects of the results, make this stronger conclusion less compelling. 

(1) The relationship of the Nir-Cohen et al. (2020) task analysis of the reference back task, with its contrasts like gate opening and closing, and the predictions of PBWM is far from clear to me for several reasons. 

First, contrasts like gate opening and gate closing make strong finite state assumptions. As far as I know, this is not an assumption of PBWM, certainly not for gate opening. At a minimum, PBWM is default closed because of the tonic inhibition of cortico-thalamic dynamics by the globus pallidus. Indeed, this was even noted in the discussion of this paper, which seems to acknowledge this discrepancy, but then goes on to conclude that they have challenged the PBWM model anyway.  

We thank the reviewer for this remark and agree that the reference-back task contrasts do not perfectly align with the predictions of the PBWM model. In the discussion section "No support for striatal gate opening," we note that our data support the PBWM model by emphasizing the central role of the basal ganglia in working memory processes. However, we acknowledge that it may not have been sufficiently clear in the manuscript that the way the reference-back task is operationalised does not allow for a precise test of the PBWM's gating predictions. To address this, we have revised the manuscript to shift focus away from framing it as a direct challenge to the PBWM model. Below, some edits are highlighted.

‘This contrasts with the findings of Nir-Cohen et al. (2020) and raises questions about the relationship between the gate opening process in the reference back task and the indirect striatal gating mechanism described in the PBWM model (Frank et al., 2001; Hazy et al., 2007; O’Reilly & Frank, 2006) and other neurocomputational theories (Hazy et al., 2007; Jongkees, 2020). According to these models, a dopaminergic signal in the striatum is required to trigger gating. Although the orthogonal contrasts in the referenceback task are intended to isolate working memory subprocesses inspired by models of working memory, the two gating contrasts do not fully capture the gating mechanism as originally proposed in neurocomputational models (Frank et al., 2001; Hazy et al., 2007; O’Reilly & Frank, 2006).’ (line 721-730)

‘Another explanation for the lack of enhanced striatal activity in gate opening challenges the conceptualization of the gating mechanism in the reference-back task, which does not accurately map onto the PBWM predictions.’ (line 746)

‘Moreover, despite the lack of striatal involvement during gate opening, our findings do not rule out the possibility that the PBWM model's predictions about striatal gating in working memory are correct, given the misalignment between the gate opening contrast and the PBWM’s proposal regarding striatal gating. It remains unclear whether the absence of striatal activation during gate opening trials is specific to low-demand tasks, like the reference-back task, which does not require as much gating compared to high working memory-demand tasks involving preparation for updating. Or whether the gate opening contrast does not sufficiently capture the PBWM proposed gating mechanism. Further investigation is needed to determine whether (dopamine-driven) striatal gating occurs in high-demand working memory tasks, where the gating process plays a more critical role.’

Second, as far as I know, PBWM emphasizes go/no-go processes around constructs of input- and output-gating, rather than state shifts between gate opening and closing. While this relationship is less clear in reference back, substituting task-relevant items into working memory does appear to be an example of input gating, as modeled by PBWM. Thus, it is not clear to me why the substitution contrast would not be more of a test of input gating than the gate opening contrast, which requires assumptions that are not clear are required by the model, as noted above. 

We fully agree with the reviewer, which is why we proposed that neural mechanisms involving the midbrain and striatum are more likely to be observed in the substitution contrast rather than the gate opening contrast.

Third, PBWM relies on striatal mechanisms to solve the problem of selective gating, inputting, or outputting items in memory while also holding on to others. Selective gating contrasts with global gating, in which everything in memory is gated or nothing. The reference back task is a test of global gating. It is an important distinction because non-striatal mechanisms that can solve global gating, cannot solve selective gating. Indeed, this limitation of non-striatal mechanisms was the rationale for PBWM adding striatum. The connectivity of the striatum with the cortex permits this selectivity. It is not clear that the reference back task tests these selective demands in the first place. That limitation in this task was the rationale behind the recent Rac-Lubashevsky and Frank (2022) paper using the reference back 2 procedure that modifies the original reference back for selective gating. 

We thank the reviewer for highlighting this excellent reference. We believe it holds exciting potential for future high-field fMRI studies that explore the neural mechanisms underlying selective gating.

So, if the primary contribution of the paper is to test PBWM, as suggested by the first line of the abstract, then it is not clear that the reference back task in general, or the gate opening contrast in particular, is the best test of these predictions. Other contrasts (substitution), or indeed, tasks (reference back 2) would have been better suited. 

We agree with the reviewer that the gate opening contrast may not be the optimal test for the PBWM model predictions. However, previous studies have found evidence of striatal gateopening mechanisms using the reference-back task, which cannot be overlooked. We hypothesized that striatal mechanisms are likely active only when working memory content requires replacement, as seen in the substitution contrast in line with the PBWM model. Additionally, the reference-back 2 task (Rac-Lubashevsky & Frank, 2021) had not yet been published when we began data collection. Exploring this task in future studies, particularly with a 7 T fMRI protocol optimized for subcortical regions, would be an exciting avenue for further investigation.

Finally, in response to the reviewer’s remark, we have revised the abstract to remove the emphasis on challenging the PBWM model.

(2) In general, observations of univariate activity in the striatum have been notoriously variable in the context of WM. Indeed, Chatham et al. (2014) who tested working memory output gating - notably in a direct test of the predictions of PBWM - noted this variability. They too did not observe univariate activation in the striatum associated with selective output gating. Rather they found evidence of increased connectivity between the striatum and cortex during selective output gating. They argued that one account of this difference is that striatal gating dynamics emerge from the balance between the firing of both Go and NoGo cell populations that decide whether to gate or not. It is not always clear how this balance should relate to univariate activation in the striatum. Thus, the present study might also test cortico-striatal connectivity, rather than relying exclusively on univariate activation, in their test of striatal involvement in these WM constructs. 

We appreciate the reviewer’s insightful observation regarding the variability of univariate activity in the striatum, particularly in the context of working memory and the challenges noted by Chatham et al. (2014). We agree that striatal gating dynamics likely reflect a balance between Go and NoGo cell populations, which may not always manifest in univariate activation alone. In line with the reviewer’s suggestion, examining cortico-striatal connectivity could provide a more comprehensive understanding of striatal involvement in working memory processes, particularly selective gating.

While our current study focused primarily on univariate activity, we recognize the importance of connectivity-based approaches and plan to incorporate functional connectivity analyses in future studies to further explore these dynamics. Such an approach, especially when combined with ultra-high-field fMRI, may offer valuable insights into the interaction between the striatum and cortex during working memory tasks.

(3) It is concerning that there was no behavioral cost for comparison switch vs. repeat trials. This differs from with prior observations from the reference back (e.g., Nir-Cohen et al., 2020), and in general, is odd given the task switch/cue interpretation component. This failure to observe a basic behavioral effect raises a concern about how participants approached this task and how that might differ from prior reports of the reference back. If they were taking an unusual strategy, it further complicates the interpretation of these results and the implications they hold for theory. 

We understand the reviewer’s concern regarding the lack of behavioral response time costs for comparison switch versus repeat trials, which does indeed differ from previous findings in studies such as Nir-Cohen et al. (2020). It is possible that this results from our fMRI task design, such as increased inter-trial intervals compared to behavioral studies. While this is certainly a point of concern, we believe that the neural data still provide valuable insights into the mechanisms underlying working memory gating despite the absence of a clear behavioral effect.

In future studies, we aim to increase the number of trials and more closely align our task design with previous studies to mitigate this issue. We agree that further investigation is necessary to ensure the robustness of these effects and their theoretical implications.

In summary, the present observations are useful, particularly for those interested in the reference back task. For example, they might call into question verbal theories and task analyses of the reference back task that tie constructs like gate-opening to striatal mechanisms. However, given the ambiguities noted above, the broader implications for models like PBWM, or indeed, other models of working memory gating, are less clear.

eLife Assessment

This important study successfully applies an innovative chemogenetic tool to investigate cerebellar function to advance our understanding of the contributions of Purkinje cell populations to postural control in larval zebrafish. The evidence supporting the conclusions is convincing and supported by rigorous statistical analysis. The study highlights the power of combining genetically targeted perturbations with quantitative high-throughput behavioral analysis and original microscopy tools.

Reviewer #1 (Public review):

This study uses a variety of approaches to explore the role of cerebellum, and in particular Purkinje cells (PCs), in the development of postural control in larval zebrafish. A chemogenetic approach is used to either ablate PCs or disrupt their normal activity and a powerful, high-throughput behavioural tracking system then enables quantitative assessment of swim kinematics. Using this strategy, convincing evidence is presented that PCs are required for normal postural control in the pitch axis. Calcium imaging further shows that PCs encode tilt direction. Evidence is also presented that suggests the role of the cerebellum changes over the course of early development, although this claim is less robust. Finally, the authors build on their prior work showing that both axial muscles and pectoral fins contribute to "climbs" and show convincing evidence that PCs are required for speed-dependent engagement of the fins during this behavior. Overall, establishing a role for cerebellum in postural control is not very surprising. However, a clear motivation of this study was to establish a robust experimental platform to investigate the changing role of cerebellar circuits in the development of postural control in the highly experimentally accessible zebrafish larvae and in this regard the authors have certainly succeeded.

This revised version of the manuscript incorporates several improvements. In particular, additional analysis and methodological detail is provided regarding the chemogenetic manipulation, there is expanded analysis of the speed-dependency of pectoral fin engagement, and aspects of the decoding analysis are clearer. However, it is still not certain that the emergence of a dive phenotype over development (from 7 to 14 day post fertilisation) really represents changing role for the cerebellum as opposed to changing sensitivity of Purkinje cells to the chemogenetic treatment.

Reviewer #2 (Public review):

Franziska Auer et al. successfully applied the TRPV1/capsaicin tool to study the contribution of Purkinje cells to postural control. They leveraged the ability of this tool to both activate and ablate neurons within the same construct and tested its effects using their smart, high-throughput behavioral setup for postural control monitoring. With Purkinje cells ablated, balance did not appear to be disrupted; however, postural control was clearly modified along the pitch axis, with larval zebrafish maintaining, on average, a more nose-down posture compared to controls. While this effect is subtle, it is statistically robust and consistent with the group's previous findings using KillerRed-mediated ablation of Purkinje cells, where the observed postural angle change was explained by a disruption in cerebellar-mediated fin-trunk coordination. Here, the authors present a novel insight, demonstrating that this coordination is swim-speed dependent.

Furthermore, the authors convincingly activated Purkinje cells at 7 dpf, and reported modifications in posture pitch angle comparable to those observed when ablating Purkinje cells. The authors suggest a potential desynchronization of Purkinje cells to explain this observation. Future characterization and application of this activation method to other developmental time points could be of major interest. The authors successfully validated the transfer of the TRPV1/capsaicin method for targeted cell ablation and activation to the study of cerebellar functions and reinforced our current understanding of the role of Purkinje cells in postural control.

This study also explores the developmental evolution of cerebellar function in postural control by comparing the effects of Purkinje cell ablation at 7 dpf and 14 dpf. Interestingly, only dive bout posture showed differential effects across these time points, with no significant impact at 7 dpf but a significant change in postural pitch angle at 14 dpf. In contrast, the effect of Purkinje cell ablation on the climbing bout postural angle remained comparable at both ages. Including additional developmental time points would further strengthen this critical characterization of cerebellar maturation in the context of postural control.

To examine whether Purkinje cell activity encodes postural tilt angle, the authors performed calcium imaging on 31 cells from 8 fish using their Tilt In Place Microscope (TIPM). They found that tilt-angle could be decoded from individual neurons with highly tuned responses, as well as from neurons that were not obviously tuned when pooling their data. The authors refer to this effect as pseudo-population coding because recordings were performed non-simultaneously across animals.

This study successfully integrates cutting-edge genetic tools, high-throughput behavioral assays, and advanced optical microscopy to investigate the role of populations of Purkinje cells in postural control. The authors have not only validated these powerful tools but have also provided novel insights into the cerebellar involvement in postural control, including the swim-speed dependence of fin-trunk coordination.

This work represents an important step toward a detailed understanding of cerebellar contributions to postural control and highlights the potential of combining genetically targeted perturbation with quantitative behavioral analysis.

The authors have addressed my previous concerns, and I congratulate them for their excellent work.

Reviewer #3 (Public review):

Summary:

This paper uses a new chemogenetic tool to investigate the role of cerebellar Purkinje cells in postural control. Using a high-throughput behavioral assay, they show that activation or ablation of Purkinje cells affects various aspects of postural control in zebrafish larvae during spontaneous swimming, and that the effects are more pronounced at later developmental time points, where the Purkinje cell number is much greater. Using a sophisticated imaging assay, they record Purkinje cell activity in response to tilt of the fish, and show that some Purkinje cells are tuned to tilt direction, and that the direction can even be decoded from untuned neurons.

Strengths:

Overall the study is nice, using a variety of genetic tools and behavioral analysis to address a fundamental question about the role of the cerebellum in postural control in fish

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This study uses a variety of approaches to explore the role of the cerebellum, and in particular Purkinje cells (PCs), in the development of postural control in larval zebrafish. A chemogenetic approach is used to either ablate PCs or disrupt their normal activity and a powerful, high-throughput behavioural tracking system then enables quantitative assessment of swim kinematics. Using this strategy, convincing evidence is presented that PCs are required for normal postural control in the pitch axis. Calcium imaging further shows that PCs encode tilt direction. Evidence is also presented that suggests the role of the cerebellum changes over the course of early development, although this claim is rather less robust in the current version of the paper. Finally, the authors build on their prior work showing that both axial muscles and pectoral fins contribute to "climbs" and show evidence that suggests PCs are required for correct engagement of the fins during this behaviour. Overall, establishing a role for the cerebellum in postural control is not very surprising. However, a clear motivation of this study was to establish a robust experimental platform to investigate the changing role of cerebellar circuits in the development of postural control in the highly experimentally accessible zebrafish larvae, and in this regard, the authors have certainly succeeded.

Overall, I consider this an excellent paper, with some room for improvement in aspects of presentation, discussion, and some aspects of the data analysis..

We thank the reviewer for their kind comments and support. In the revision we have addressed their concerns regarding data presentation and analysis. Additionally, we have expanded our introduction and discussion to address questions of presentation.  

Reviewer #2 (Public Review):

Summary:

Franziska Auer et al. investigate the role of cerebellar Purkinje cells in controlling posture in larval zebrafish using the chemogenetic tool TRPV1/capsaicin to bidirectionally manipulate (i.e., activate or ablate) these cells. This tool has been developed for zebrafish previously but has not been applied to Purkinje cells.

High-throughput behavioral experiments are presented to monitor how body posture is affected by these perturbations. The analysis of postural control focuses on a specific subaspect of posture: the body tilt-angle relative to horizontal just before a swim bout is executed, quantified separately for pre-ascent and pre-dive bouts. They report a broad bimodal distribution of pre-ascent bout posture ranging from -20 to +40 degrees, while the pre-dive bout posture was more Gaussian, ranging between -40 and 0 degrees. The treatment effect is quantified as the change in the median of these distributions.

Purkinje cell activation and ablation in 7 days post-fertilization (dpf) fish shifted the median of the ascending bout posture distributions to positive values. The authors hypothesize that the stochastic nature of the activation process might desynchronize Purkinje cell activity, thus abolishing Purkinje cells' role in postural control, similar to ablation. However, this does not explain why dive bout posture decreased upon activation but was unaffected by ablation. 

To test whether the role of Purkinje cells in postural control matures over development, the authors repeated the ablation experiments at 14 dpf. They state that "at 14 dpf, the effects of Purkinje cell lesions on posture were more widespread than at 7 dpf." However, this effect size is comparable to that observed at 7 dpf, suggesting no further maturation of the role of Purkinje cells in pre-ascending bout postural control. The median pre-dive bout posture decreased at 14 dpf, contrasting with no effect at 7 dpf, yet this change was comparable in effect size to the activation effect on Purkinje cells at 7 dpf. The current data breadth may not be sufficient to conclude that signatures of emerging cerebellar control of posture across early development were uncovered.

The study's exploration of activating Purkinje cells in freely swimming fish using TRPV1/ capsaicin is of special interest, but the practicability of this method is unclear from the current presentation. It would be beneficial to present the distribution of the percentage of activatable Purkinje cells across animals and time points to provide insight into the method's efficiency. Discussing this limitation and potential improvements would aid in evaluating the method, especially since the authors report that the activation experiments were labor-intensive, limiting repeat experiments. This may explain why the activation experiment at 7 dpf is the only data presented with cell activation, with other analyses performed using the cell ablation capabilities of the TRPV1/capsaicin method.

Another data point at 14dpf would significantly strengthen the conclusions.

The authors analyze Purkinje cell-controlled fin-trunk coordination by examining ascending bout posture across different swim bout speeds. They make the important finding that pectoral fin movements contribute significant lift for median and fast swim bouts but not for slow ones, and that Purkinje cell ablation disrupts lift generation at all speeds.

Finally, the authors examined whether Purkinje cell activity encodes postural tilt-angle by performing calcium imaging on 31 cells from 8 fish using their Tilt In Place Microscope (TIPM). They report that they could decode the tilt-angle from individual neurons with a highly tuned response, and also from neurons that were not obviously tuned when pooling them and analyzing the population response. However, due to the non-simultaneous recordings across animals, definitive conclusions about populationlevel encoding should be made cautiously, it might be better to suggest potential population encoding that needs confirmation with more targeted experiments involving simultaneous recordings.

Strengths:

- The study introduces a novel application of the chemogenetic tool TRPV1/capsaicin to study cerebellar function in zebrafish.

- High-throughput behavioral experiments provide detailed analysis of postural control.

- The further investigation of Purkinje cell-controlled fin-trunk coordination offers new insights into motor control mechanisms.

- The use of calcium imaging to decode postural tilt-angle from Purkinje cell activity presents interesting preliminary results on neuronal population encoding.

Weaknesses:

- The term "disruption" for postural control effects may lead to misleading expectations.

- The supporting data show only subtle median shifts in postural angle, raising questions about the significance of observed effects. Statistical methods that account for the hierarchical structure of the data might be required to support the conclusions.

- The study's data breadth may not be sufficient to conclude emerging cerebellar postural control across early development.

- The current presentation does not adequately detail the practicability and efficiency of the TRPV1/capsaicin method for activating Purkinje cells, and the labor-intensive nature of these experiments constrains the ability to replicate and validate the findings.

- Non-simultaneous recordings in calcium imaging necessitate cautious interpretation of population-level encoding results.

We appreciate the reviewer's thoughtful and detailed feedback. In response, we have made several changes to highlight key points in our manuscript. We have adjusted our wording to more accurately reflect the scope of our findings. Finally, we have clarified and expanded the methods used.

Reviewer #3 (Public Review):

Summary:

This paper uses a new chemogenetic tool to investigate the role of cerebellar Purkinje cells in postural control. Using a high-throughput behavioral assay, they show that activation or ablation of Purkinje cells affects various aspects of postural control in zebrafish larvae during spontaneous swimming and that the effects are more pronounced at later developmental time points, where the Purkinje cell number is much greater. Using a sophisticated imaging assay, they record Purkinje cell activity in response to the tilt of the fish and show that some Purkinje cells are tuned to tilt direction and that the direction can even be decoded from untuned neurons.

Strengths:

Overall the study is nice, using a range of tools to address a fundamental question about the role of the cerebellum in postural control in fish.

Weaknesses:

(1) The data in Figure 1 that establishes the method seems to be based on a very small number of experiments and lacks some statistical analysis.

(2) The choice and presentation of the statistical and analysis methods used in Figures 2-5 could be improved.

We thank the reviewer for their comments.  We have added additional statistical analyses for the activation experiments, and improved data presentation .

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Overall I think this is a great paper.

* Introduction and Discussion.

The Introduction (and Discussion) do little to explain what is understood about cerebellar control of posture and what major outstanding questions remain. The first paragraph of the Introduction seems to argue that the role of the cerebellum in control of posture is well established and line 24 attempts to motivate the present study by virtue of the fact that terrestrial locomotion is "complex". This might be true but is not necessarily a major obstacle given the suite of powerful approaches available in rodent neuroscience. What are the major challenges that are hard to tackle in rodents and what specific questions can the larval zebrafish help to answer? What about development (which gets no mention at all)? I'm not suggesting a comprehensive review of every aspect of cerebellar physiology, but I think the Introduction should attempt to outline the current hypotheses in a little more detail and highlight what we still need to understand.

We take the Reviewer’s point that there is more to say in the Introduction. We feel that multi-dimensional limb biomechanics and proprioception are two aspects of terrestrial locomotion that support our use of the word “complexity.” However, we don’t dwell on this point because, as the reviewer correctly states, the suite of tools for rodent neuroscience & behavior is expansive and, in our opinion, not a limiting factor. Instead, we said what we felt we could regarding the potential contribution of the larval zebrafish in the last paragraph of the Discussion. In the revision, we have added details about the development of cerebellum to the introduction (though this, of course, is an expansive topic and well-beyond the scope of the Introduction), highlighted some of the historical limitations in rodent posture analysis, and set up the .

* Figure 2: 'Arrows denote the shift towards more nose-up postures'. I think the distribution is quite easy to interpret without these arrows; I suggest removing them.

We have removed the arrows.  

* IQR is sometimes stated as a single number and sometimes as a range. It should be consistent and unless eLife has guidance to the contrary, I suggest that it be the latter.

Thank you for pointing that out. We now report it as the value at the 25&75th %ile for all IQRs.  

* Figure S2: For 14 dpf fish the axes are labelled PC2/3 - is this an error?

We have changed it to a 3-dimensional plot for both 7 and 14 dpf data to show comparable plots for both ages (now Figure S5 F and G). For the analysis in the 14dpf fish the clearest separation was in the space defined by the 2nd and 3rd principal component.  

* In the methods, there is insufficient detail given about fluorescent imaging.

We added additional information to how the fluorescent imaging was performed to the ‘Confocal imaging’ section as well as to the ‘Functional imaging section’

* Abstract

In my opinion, the statement "Here, we used a powerful chemogenetic tool (TRPV1/ capsaicin) to *define the role of Purkinje cells*..." is too strong. Whilst the evidence that PCs are required for postural control is certainly strong, what exactly these cells do in the service of postural control is far from clear (as the authors indeed acknowledge in the Discussion). As such, I wouldn't say their role has been "defined".

We change the word to “describe” to better reflect our findings

* aldoca transgenic.

This appears to be a beautiful transgenic line but the data showing the extent of its expression and evidence that in the cerebellum it exclusively labels PCs isn't clear enough.

(i) Ideally Figure 1A would show an image of a whole animal to provide an overview of transgene expression but instead it seems to be (the legend is unclear) a cartoon with a confocal projection of part of the brain overlaid.

We have updated the figure legend to be clearer that we show a cartoon of a larval zebrafish with the confocal image overlaid. The aldoca promotor has been previously described and exclusively labels Purkinje cells (10.1523/JNEUROSCI.3352-10.2010)

(ii) Figure 1B shows expression in the cerebellum, but how are we to understand that all the labelled cells are PCs? Are all PCs labelled, or only a subset? Perhaps a double labelling with a PC in situ marker could be done to demonstrate colocalisation?

As above, the aldoca promotor has been previously described; to the best of our knowledge in the Hibi lab’s hands (and ours) it labels Purkinje cells exclusively, and it labels all of them (10.1523/JNEUROSCI.3352-10.2010)  

* Chemogenetic validation.

Overall, the chemogenetic approach to abrogate PC function looks to be very powerful. The authors state in several places that a contribution of this paper is in its "establishing the validity of TRPV1/capsaicin-mediated perturbations". However, the data in Figure 1, along with various comments in other parts of the paper raise some questions:

(i) For experiments depolarising PCs with 1µM CSn, the same size is tiny: Two transgenic animals and one control. Moreover, it is stated 'in one fish ... we observed a small number of neurons at the 9h timepoint with bright, speckled fluorescence suggestive of cell death". Was this one out of two transgenics?! In the discussion, I didn't understand the statement "ensure adequate brightness levels *to achieve sufficient depolarization without excitotoxicity*". Does this "excitotoxicity" relate to the specked fluorescence observation?

Overall, the very small sample size and comments about excitotoxicity and cell death raise concerns about the approach that I think warrant clearer treatment in the results (including information about the assessment of transgene expression, % embryos judged to have suitable expression), especially as this paper is seeking to establish the validity of the method.

We note first that the method has been previously validated (https://doi.org/10.1038/ nmeth.3691) and that we build on this work. For the experiment described, the point was to identify an acceptable duration for exposure. To that end, we analyzed 6 animals for up to 6h (including the washout experiments in Figure S1B) where we never observed any speckled fluorescence; we limited our behavioral experiments to 6h accordingly. We thought it would be worth including the observation of speckled fluorescence at 9h timepoint for future reference. To directly address the comment we have increased the number of analyzed cells and fish for the 1uM capsaicin experiments and added statistical analysis (lines 65-67).

When screening for transgene expression we selected for fish that had clearly visible expression, but that did not look overly bright, and used the same criteria when screening fish for the GCaMP imaging and for behavior. Around a quarter of the fish that had aldoca:TRPV1-tagRFP expression had a usable expression level for the activation experiment. We have added this information to the Results (line 62) and Methods (line 369-372)

(ii) The authors note "capsaicin could sporadically activate subsets of Purkinje cells" and further speculate about PC activity and synchrony in the discussion. Figure 1 seems to rely on single images at widely spaced time points but given that they are set up to do 2-photon calcium imaging, why didn't they collect continuous time series data and analyse the temporal patterns of activity across the transgenic PC population?

We have added time series data for calcium imaging after 1uM of Capsaicin in TRPV1-  and TRPV1+ cells to Supplementary Figure S1A. Here too we see sporadic increases in calcium levels at similar rates: 0% for TRPV1- and 15-19% for TRPV1+ (see also Figure S1 legend)

(iii) The axonopathy and cell death resulting from 10 µM Csn is quite dramatic.

However, here the authors do not appear to have included a TRPV1 negative control (although oddly they did for 1 µM treatment) so it is currently unclear whether or not a high conc of Csn alone might be cytotoxic.

Chen et al (https://doi.org/10.1038/nmeth.3691) have established the TRPV1/capsaicin method in zebrafish with broad neuronal label and did not see any effect with high doses of capsaicin in TRPV1 negative fish.  

* Behavioural assessment - stats

Overall, the disruption of postural stability after PC manipulations is convincing.

However, I have a few queries about the statistics:

(i) In this section, the statistical unit was not clear. The tables, which are otherwise very useful, give no indication of N. The legend text does report "8 repeats/149 control fish" and "across experimental repeats" suggesting the statistical unit might be the repeats rather than animals, but this should be clarified. In Figure 2G, individual data points should be plotted if N=8, or a representation of the distribution (eg violin or box and whisker plots) if N = 149.

We apologize for the confusion. Given the variable numbers of bouts, a single experimental repeat does not allow for an accurate estimate of expected value. Below we simulated how accurately the median can be estimated based on increasing sample sizes (Author response image 1). Given that large numbers of bouts are necessary to accurately estimate the median we pool the data for all experiments and use resampling statistics to estimate bias in our estimate.

Author response image 1.

Median estimation based on increasing sample size

(ii) Related to the above, I hope it might be easier to interpret the unexpected change in climb posture in ablation controls once the data for individual repeats is shown.

When we analyze the data as single repeats we see considerable variability between different repeats due to undersampling. We tested the medians for the single repeats for outliers to ensure that the shift is not due to a single repeat skewing the distribution. We did not detect any outliers in the pre-lesion control or in the post-lesion control group. (Outliers were determined as deviating more than 3 times the scaled median absolute deviation (MAD) from the median. A scaling factor of 1.4826 was used to ensure that MAD-based outlier detection is consistent with other methods like Z-scores.) We added this information to line 133-134 and the method section under Statistics. 

(iii) In some parts of this section, including the Tables, the authors report the 95% CI of the median, rather than IQR. In this case, they should report the z-value used for 95% CI estimation.

As we are using resampling to estimate the 95% confidence interval of the median there is no z-value as in a traditional normal distribution based confidence interval; Instead, we explicitly define the 2.5th and 97.5th percentiles from the bootstrapped sample distribution, which captures the middle 95% of the data, representing the 95% confidence interval.

* It is stated that "fish adopted more nose-up postures before *and throughout* climb bouts". Figure 2F seems to show posture before the climb, but where is the "throughout" data? It would be useful if Figure 2E, J could be extended to make a bit clearer these two phases of postural assessment.

We removed the phrase ‘throughout climb bouts’ as we are not showing the posture throughout the bout and to avoid over complicating the interpretation.  

* Why were PCs not activated at 14 dpf (eg using 1 µM Csn)?

Due to shifts in priorities the first author will not be continuing this series of experiments, and so this additional experiment will have to wait for someone to pick up this line of inquiry

* The authors appear to claim that the difference in phenotype in 7 versus 14 dpf animals following high conc Csn treatment is indicative of a changing role for cerebellar PCs over this developmental period. For instance, in reference to the 14 dpf ablation phenotype, the authors write "reveals the functional emergence of Purkinje cell control of dives" and in the abstract they talk about "emerging control of posture across early development". However, can they rule out that the phenotypic differences might instead reflect differential sensitivity of the relevant PC (sub)populations to CSn at the two ages? If this caveat cannot be discounted then I suggest it is acknowledged e.g. in the discussion.

As previously established, all Purkinje cells are labeled in the aldoca line (10.1523/ JNEUROSCI.3352-10.2010). Fluorescence is brighter at 14dpf compared to 7dpf, suggesting higher levels of TRPV1. We therefore assume that at 14 dpf, the high concentration of Csn is sufficient to ablate Purkinje cells. At 14 dpf, cerebellar damage is visible under a standard dissecting microscope.The preponderance of evidence therefore speaks against a previously undiscovered subpopulation of TRPV1expressing Purkinje cells that are, by mechanisms yet unknown, resistant to high doses of capsaicin. 

* Fin-body "coordination"

The ideas and data around fin-body coordination are very intriguing.

(i) The statement "fin engagement is speed-dependent" would benefit from a stats test to show this is indeed significant. The data in Figure 4B suggest a rather high degree of variance.

This is an important point; we appreciate the Reviewer’s attention. We have added statistics to show this is speed dependent to line 167-169 and show the corresponding plot in the supplement in Figure S4.  "Here, we observed that fin engagement is speeddependent, with faster bouts producing greater lift for a given axial rotation (Spearman correlation coefficient: control 0.2193; 10uM capsaicin: 0.0397; Z-test after ztransformation: p < 0.001)  

(ii) The statement "After capsaicin exposure, the slopes of the medium fast speed bins were significantly lower (Figure 4C), reflecting *a loss of speed-dependent modulation*" is not convincing. The slope is likely a function of both speed and Csn treatment, and the comparisons in Figure 4C appear to be testing the latter, not the former.

We understand the reviewer’s point. However, the slope for the slow bouts remains unchanged. We therefore conclude that the reduction in fin-body slope is speed dependent and not a speed independent reduction of slope overall. 

We have made this more clear by adding Supplementary Figure S4 and changing the text in line 177-179. 

(iii) I'd like to understand more about the phenotype of the fin-amputated animals. Were any "bout" parameters changed? Did the animals still attempt climbs and was the distribution of the upward rotation parameter similar to controls? The text states "the slope of the relationship between upward rotation and lift was indistinguishable from zero" but the stats reported in the text are comparisons between groups while Table 5 shows 95% CIs that don't span zero. Some clarification would be useful here.

We appreciate the Reviewer’s interest. We’ve studied climbing in fin-amputated animals at length here: https://doi.org/10.7554/eLife.45839 and here: https://doi.org/10.1016/ j.celrep.2023.112573 and have added these references in line 183.

(iv) The authors repeatedly refer to fin-body *coordination* but it is not clear whether the loss of lift after PC ablation is a result of an explicit coordination defect (i.e. changes in the relative timing and/or kinematics between fins and axial motion components), versus a simple reduction in pectoral fin engagement. Either result could be interesting, but this should be clarified.

Thank you for pointing that out. In the fastest speed bin, we observed an increase in upward rotation and a decrease in average fin lift. In contrast, the medium speed bin showed no significant changes in average fin lift or upward rotation (see Author response image 2 and Tables 4 and 5), yet already displayed coordination deficits. Based on these observations, we argue that Purkinje cell lesions primarily affect coordination, rather than simply reducing one specific parameter such as lift or rotation (line 293-298).

We have added fin lift and rotation values from Author response image 2 for all speed bins to tables 4 and 5.  

Author response image 2.

Fin lift and rotation for slow, medium and fast bouts

* PC activity and decoding of pitch direction.

The clever TIPM method is used to collect calcium data that convincingly shows that individual PCs can encode pitch-tilt direction. However, a population of "not tuned" cells are also identified, and here I found the analysis of their responses and the argument that they encode pitch direction at a population level difficult to follow.

(i) First, although the naming of the cells implies that individual neurons do not encode pitch direction, I did not find this convincing. Figures 5F/G suggest that several "not tuned" cells in fact show quite consistent differences in activity across trial types and indeed in terms of their average responses sit as far from the unity line as do several "tuned" cells.

The Reviewer’s comment helped us clarify some key points. First, tuned and untuned cells were categorized based on a Directionality Index threshold of 0.35; some cells might look similar in 5F/G but the highly variable responses of Purkinje cells have highly variable response so overall there was no consistent tuning. We have clarified this in the text in line 203-207 Below we have plotted the Up versus Down responses for the 10 least tuned cells (sorted by directionality index). While some cells have higher responses on average to one direction we think that the variability makes it difficult to support a claim for “tuning.” We have also tested the support vector machine on the least tuned cells to confirm that the chosen cutoff for tuned/untuned is not affecting our claim that untuned cells can encode position.(see also Author response image 4)

Author response image 3.

Trial-by-trial variability

(ii) It is therefore not very surprising that PCA (and the SVM decoder) distinguishes trial type. I would guess that PCA assigns the largest weights to these most tuned of the "not tuned" cells, and the 3-5 cell decoders do well when these cells happen to be sampled.

Author response image 4.

Decoding accuracy of the 3/5/7 least tuned cells

This was an interesting idea. To rule out that it is only the most tuned cells that contain the information, we tested the decoder on the 3/5/7 least tuned cells; here too, 5 and more cells are better able to accurately decode the direction. We have add the decoding accuracy to the text in line 221-224

(iii) As I understand the analysis, Figure 5G shows responses for "not tuned" cells over 21 trials (of each type) but these are not the same trials for the different cells? How then is population coding being assessed?

We have updated the text and refer to this data as a “pseudo-population” in lines 216 and 218 for all experiments where we combined cells from different fish. For technical reasons, when we perform TIPM at eccentric angles we must use sparsely labelled fish to ensure that we can find the same cells over a 60 degree range. We have repeated our analyses for TIPM centered at the horizon, where we can record from entire populations from a single fish.  

(iv) Furthermore, Figure S2 shows a somewhat different analysis with decoding accuracy measured on a fish-by-fish basis. In this case, are these decoders for simultaneously imaged neurons? Is this a cross-validated measure of decoding accuracy?

Yes, as above, Figure S4 (former S2) looks at fish-by-fish basis of simultaneous recorded neurons. Yes, it was 5-fold cross validated. We have updated the text in line 490-494.

Reviewer #2 (Recommendations For The Authors):

- Postural control involves various aspects such as balance, coordination, relative body part orientations, and stability. Discussing these and presenting in this context the specific subaspect characterized in this study would help clarify which aspect of postural control the work focuses on.

The Reviewer makes an interesting point, but we think their description of what constitutes postural control is overly broad. Specifically, control of “relative body part orientations in space” by definition requires coordination, and subserves balance and stability. We acknowledge, of course, that different aspects can be and often are treated independently. While interesting, a full treatment of what comprises “postural control” is beyond the scope of the paper, as it would require reconciling the terms across taxa, effectors, environments and well over a century of experiments.

We contend that posture — particularly underwater — is best defined as the relative orientation of body parts in space. For fish, those parts consist of predominantly axial muscles and secondarily fins. We present these definitions in the Introduction and thank the Reviewer for encouraging us to more clearly shape our findings.

- Disruption of posture or postural control: The use of the word "disruption" could lead to misleading expectations. While it may not be incorrect, it suggests a significant loss of equilibrium, an obvious increase in postural variability, or at least a noticeable effect when observing an individual animal's behavior. However, the supporting data show only a subtle median shift in postural angle within a very broad distribution averaged over many individuals. This effect was only significant when comparing fish with a control group, not when comparing fish posture before and after the treatment.

Replacing "disruption" with "modification" would be more cautious.

We take the Reviewer’s point and have adjusted our wording to "modifies postural control.” In lines 137, 266, and 283

- Statistical significance: Consider aligning the asterisk notation with conventional standards (e.g., * for p < 0.05, ** for p < 0.01, *** for p < 0.001) to enhance clarity for readers. On the other hand, the individual measurements might not be independent (e.g., measurements from the same fish, or the same tank are likely to be correlated), so using the Wilcoxon rank-sum test (Mann-Whitney U test) on pooled data might lead to incorrect conclusions. Methods that account for the hierarchical structure of the data might be required to support the conclusions.

We take the Reviewer’s point about the importance of conventions, however we have never found “more stars = more significant” to be all that helpful in evaluating claims. Instead, we’ve opted to have both a significance and effect size criteria; a “star” here reflects our considered confidence in the difference we observe. 

We agree that the hierarchical nature of pooled data is worth considering/presenting.

We performed a two-way analysis of variance (ANOVA) on the interquartile ranges (IQRs) of the single experimental repeats for the 7 days post-fertilization (dpf) activation, 7dpf lesion, and 14dpf lesion experiments. The ANOVA revealed no significant main effects, supporting the strategy of pooling experimental repeats to estimate distributions.

The results of the ANOVA, along with the IQRs for all experimental repeats, are presented in Tables 6-11. We have also clarified this in the methods section in lines 505-509.

- Data representation: All data of postural angles should be represented in the form of violin plots to show the underlying distributions of the postural angles, especially given that the effect size is small relative to the dispersion of the distribution of the postural angle and that this distribution is also not Gaussian but bimodal, and different before and after the treatments.

We take the Reviewer’s point that seeing the full distribution can be useful. We have added plots of the raw distributions for the data in Figure 3 as supplemental Figure S3.

- Showing the distributions will provide the necessary information for the reader to evaluate the importance of the effect. For all data shown in Table 1, the distributions should be presented in the supplementary information.

As requested, we have added the distributions of the data in Table 1 to the supplement (Figure S2)

- Roll posture: A statement about whether roll posture is perturbed by Purkinje cell manipulation would be a piece of important additional information helping to understand how strong the 'disruption' of posture is.

We haven’t assessed roll posture, as this is not practical in the current version of the SAMPL apparatus. We have added this limitation to the results (line 116) but also note that as our manipulations are bilateral, we don’t anticipate any systematic changes to roll.   

- Comparison with other methods: Add a discussion on how the TRPV1/capsaicin method compares with other methods, such as using nitroreductase (Ntr) for targeted pharmaco-genetic ablation of cells by treatment with metronidazole or the the possibility to to ablate Purkinje cells by KillerRed as the author lab has done previously. Both methods have been applied to ablate Purkinje cells in larval zebrafish. What are the advantages of the TRPV1 method compared to these when neglecting the activation possibility?

Thank you for that suggestion, we have added a section to the discussion where we compare the TRPV1/capsaicin lesion to other lesion methods (lines 334-336)

- Describe the decoding algorithm: The decoding algorithm used could be described more in detail in the methods section.

We have described the decoding algorithm in more detail in the methods under ‘Functional GCaMP imaging in Purkinje cells.’ Line 488+ 

We used a support vector machine (SVM) with a linear kernel. The SVM model was trained using k-fold cross-validation, which splits the data into k subsets (folds). At each iteration, the model was trained on k-1 folds and tested on the remaining fold, ensuring that the model performance was evaluated on unseen data in each fold. Permutations were performed on randomized trial identity as a null hypothesis (5-fold cross-validation; 100 shuffles for randomization). Accuracy was calculated as 1 minus the classification loss.  

- Availability of code: The link to the data and code repository is not working.

Thank you for pointing that out, we have fixed it now. In the lower right of the page you can see the history of all changes to the repository, including the entry on 2023-09-08 where the corresponding author set it to “public.” When we checked thanks to your comment, it had been set to “private,” without any record of when/why. We have reset it 2024-10-17. We will continue to check it periodically in the future and apologize in advance if it is unavailable; this is the first time we’ve seen that happen.

- Electrophysiological Control: Including an electrophysiological characterization of the activation of Purkinje cells by the TRPV1/capsaicin would significantly strengthen the validity of the method.

We take the Reviewer’s point that electrophysiological characterization is a way to strengthen the validity of the method. However, Chen et al (h"ps://doi.org/10.1038/ nmeth.3691) have performed electrophysiology during neuronal activation and concluded that TRPV1 activation with capsaicin indeed increases neuronal activity and firing rates increased. Our calcium imaging and lesion experiments amply demonstrate that Purkinje cells are sensitive to TRPV1-mediated currents. We therefore do not believe that the additional information gained by arduous electrophysiological evaluation is merited here.

- Describe more in detail how climb and dive bouts are defined. The height difference between consecutive bouts measured 250ms before the bout of executions.

Climb and Dive bouts are split by the angle of their trajectory. If the fish moves up (i.e. trajectory larger 0) it is considered a climb bout and vice versa for dive bouts. 250ms prior to the maximum speed is roughly the time the fish initiate a bout, so the pre-bout posture is measured when at this point. The time-courses of bouts are dissected extensively in Zhu et. al. 2023. We have added a definition for climb and dive bouts to the method section under ‘Behavior analysis’ line 453 and 454.  

- Figure 1H: Why can't you ablate all Purkinje cells but only about 80%?

This is an excellent question. We opted for an extremely conservative count, and included everything that was still resembling a cell, even if it might not be functional/ already dying. Our counts are therefore likely an underestimate of the percentage of cells that were lost. We have added this point to the text in lines 393 395

- Figure 2C: The method is not fully clear. At 8dpf 0.1uM capsaicin is added to the chamber. At what time after the application of capsaicin did the behavioral recording start?

We recorded after about 10-15min after adding the 1uM Csn to the chambers. The fish were fed after the 6h in capsaicin. We have added this information to the method section line 404 - 408.

- Figure 2F: What indicates the shown confidence interval? Also median with a 95% confidence interval calculated over the experiments in parallel?

The distributions shown in Figure 2F take data from all experiments pooled. We use resampling methods to determine the variability in our estimates. The distribution plots are showing the median and the 25th and 75th percentile of the resampled distribution. We have added this information to the figure legends.

- Figure 3: Subtitles on panel D and E indicating <climb bout posture> and would facilitate reading.

We have added the subtitles to those panels.

- Figure 4: Describe in the methods how recordings from individual fish were mapped onto each other to superimpose the Purkinje cell locations recorded from the 8 fish.

We have added the respective section to the methods: Line 481 - 483

“To map the anatomical locations of the recorded cells, we imaged overview stacks for each fish. These stacks were manually aligned in Illustrator, and the cells included in the analysis were reidentified and color-coded according to their tuning properties.”

Reviewer #3 (Recommendations For The Authors):

Major points:

(1) Lines 74-81. The data presented here and in later experiments to argue for an effect of capsaicin on neural activity lacks statistical rigor because of the apparently very small numbers of animals/cells assessed. For example, the control appears to involve 4 cells assessed from 1 animal, and the experimental group is just 2 animals. Given that the interpretation of the paper depends upon this result, it is worthwhile to show the result more clearly, and with some statistical analysis. They argue in the discussion that "Our imaging assay established that 1 µM of capsaicin would stochastically activate subsets of Purkinje cells" which seems a stretch from the data as presented.

We appreciate this point, which was shared by Reviewer 1. We have added more data and performed statistical analysis (line 63 - 67 as well as Figure S1A)

(2) I found the practice of sorting effects by a mixture of effect size and p-value to be a little arbitrary, although in this case, it seems likely that it identified the most relevant effects. I would have preferred to see some attempt to correct for multiple comparisons (e.g. by resampling with the identities of fish shuffled to estimate the distribution of each measurement for this population size), followed by filtering for effect size after establishing a corrected threshold for significance.

We take the Reviewer’s point, though we note that critical values for effect size and pvalue are inevitably “a little arbitrary.” We can’t do the exact analysis the Reviewer suggests as we do not measure data from individual fish for these experiments. However, we did calculate new critical p-values (added to the Tables) that account for multiple comparisons using Šidák’s method.

(3) Figure 4. The data here is a little strange in that the slope in the control condition for medium speed is given as much larger than for slow, but the data in the two cases appears largely overlapping for most of the range of behavior, only diverging for the most extreme rotations. It seems perhaps that the measurement of slope is strongly dependent on these most extreme values. The authors might want to consider the use of robust regression methods which might mitigate these effects.

This is an interesting observation and we appreciate the Reviewer’s thoughtful suggestion. We now use a robust regression method (bisquare weighting of residuals).

We have adjusted all values in lines 175 - 177  and added the regression method to the Methods section line 520.

(4) Figure 5. The 'principal component analysis' description is extremely unclear. The text says that PCA 'showed near-complete segregation of trial types' but it is not explained how this was achieved with PCA or how this was quantified. Figure panels show the data plotted using different pairs of PCs showing visual evidence of segregation. In the methods, it is stated that "We performed principal component analysis" and that "cells were used for principal component analysis and subsequent support vector machine decoding analysis". What is meant exactly by 'performed PCA'? Was PCA used in a dimensionality reduction step? And if so, how many and which PCs were chosen and why? For visualization of the separation, the authors show arbitrary pairs of PCs. Could it be better to use a method more suited to that purpose such as linear discriminant analysis?

PCA was used to define a subspace to qualitatively evaluate if different trials could be separated. Once it became clear that it could, we next trained a binary decoder on the complete dataset (i.e. no dimensionality reduction). We did not perform linear discriminant analysis as the unsupervised PCA already showed separation of trial types.  We have made this clearer in lines 212 - 214.

(5) Why does the decoding analysis use only untuned cells? Isn't it equally, or more, interesting to know how well tilt can be encoded using all cells? It is unclear to me what we learn by selecting only untuned cells for this analysis (although I agree it is interesting that this does work).

We focused exclusively on untuned cells because including even a single highly tuned cell for the population coding will lead to excellent results. By using untuned cells we test if there is some directionality information that is not visible just by looking at the up/ down responses of single cells. We have made this clear in lines 217 - 218

Minor points and corrections:

(1) Maybe consider losing the words 'powerful' (I think it is overused and not well defined) and 'reagent'. Reagent is normally used for something that participates in a reaction. It is a bit odd to use it to refer to a transgenic animal. Later it is called a 'tool' which seems better.

We have changed the wording and refer to it as tool for the whole paper.  

(2) Figure 1D. Please use a color bar to indicate the scale.

We have added a color scale to the panel

(3) Saying that 'posture' increases is confusing, although the meaning can be inferred from the overall context and the definitions in the Methods - could Posture be capitalized to indicate a specific definition is being used rather than the general meaning?

This suggestion agrees with those made by Reviewer 2. We have changed the wording to “postural angle.” 

(4) The arrowheads in Figure 2FHK are unnecessary and confusing (why are some horizontal and some vertical?).

Thank you for that suggestion, we have removed the arrowheads.

(5) Figure 3 The legend should indicate that the image is shown with an inverted lookup table.

We have updated the legend

(6) Figure 3 D and E Titles would be helpful, so it is not necessary to refer to the legend to understand the difference.

We have added titles to the figure panels

(7) The dwell time for the 2-photon experiments is given in the manuscript, but I think the authors meant microseconds?

Thank you for pointing that out. We have corrected it to microseconds.

eLife Assessment

This important study provides convincing evidence that developing neurons in the neocortex regulate glial cell development. The data demonstrates that the transcription factor FOXG1 negatively regulates gliogenesis by controlling the expression of a member of the FGF ligand family and by suppressing the receptor for this ligand in developing neurons. This study leads to a new understanding of the cascade of events regulating the timing of glial development in the neocortex.

Reviewer #1 (Public review):

Summary:

In this paper, Bose et al. investigated the role of Foxg1 transcription factor in the progenitors at late stages of cerebral cortex development.

They discover that Foxg1 is a repressor of gliogenesis and has a dual function, first as a repressor of Fgfr3 receptor in progenitors, and second as a suppressor of the Fgf ligands in young neurons.

They found that the inactivation of Foxg1 in cortical progenitors causes premature astrogliogenesis at the expense of neurogenesis. They identify Fgfr3 as a novel FOXG1 target. They show that suppression of Fgfr3 by FOXG1 in progenitors is required to maintain neurogenesis. On the other hand, they also show that FOXG1 negatively regulates the expression of Fgf gliogenic secreted factors in young neurons suppressing gliogenesis cells extrinsically.

Strengths:

The authors used time-consuming in vivo experiments utilizing several mouse strains including Foxg1-MADM in combination with RNA-Seq and ChIP to convincingly show that Foxg1 acts upstream of FGF signalling in the control of gliogenesis onset. The conclusions of this paper are mostly well supported by data.

Reviewer #2 (Public review):

Summary:

We have known for some time that neural progenitors in the cerebral cortex switch their output from cortical neurons to glia at late embryonic stages, however little is known about how this switch is regulated at the molecular level. Bose et al present a convincing set of findings, demonstrating that the transcription factor Foxg1 plays a key role in this process, mediated through FGF signalling. Foxg1 cell-autonomously inhibits gliogenesis in progenitor cells (thereby promoting neuronal identity), and lower Foxg1 expression in postnatal neurons leads to increased expression of FGF ligand, promoting glial development from nearby progenitors.

Strengths:

The study is very well designed, having a systematic, thorough, and logical approach. The data is convincing. The authors make full use of a range of existing transgenic strains, published 'omics data, and elegant genetic approaches such as MADM. This combination of approaches is particularly rigorous, lending significant weight to the study. The manuscript is well-written, clear, and easy to follow.

Impact

This manuscript identifies a previously unknown role for Foxg1 in forebrain development and a mechanism underlying the neurogenic-to-gliogenic switch that occurs at late embryonic stages of cortex development. These findings will stimulate further research to uncover more details of how this important switch is controlled and may provide useful insight into some of the symptoms experienced by children with FOXG1 Syndrome.

Author response:

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

Weaknesses (Reviewer 1):

The role of Fgf signaling in gliogenesis and Foxg1 in neurogenesis is well known. It is not clear if Fgf18 is a direct target of Foxg1.

We agree with the reviewer- Fgf signaling is an established pro-gliogenic pathway (Duong et al 2019) and Foxg1 overexpression is known to promote neurogenesis in cultured neural stem cells (Branacaccio et al 2019). Our study links these two mechanisms, as the Reviewer has summarized: (a) we demonstrate that FOXG1 works via modulating Fgf signaling cell-autonomously within progenitors by regulating the levels of Fgfr3. (b) Loss of Foxg1 in postmitotic neurons results in the upregulation of Fgf ligand expression (possibly via indirect mechanisms) and this non-cell autonomously increases Fgf signaling in progenitors_. Our study is entirely performed _in vivo.

Revision: We have revised the manuscript to reflect that Fgf18 may be an indirect target of FOXG1 in postmitotic neurons.

Weaknesses (Reviewer 2):

It wasn't clear to me why the authors chose postnatal day 14 to examine the effects of Foxg1 deletion at E15 - this is a long time window, giving time for indirect consequences of Foxg1 deletion to influence development and thereby potentially complicating the interpretation of findings. For example, the authors show that there is no increased proliferation of astrocytes or death of neurons lacking Foxg1 shortly after cre-mediated deletion, but it remains formally possible (if perhaps unlikely) that these processes could be affected later during the time window. The rationale underlying the choice of this time point should be explained.

I don't agree with the statement in the very last sentence of the results section that "neurogenesis is not possible in the absence of [Foxg1]" as there are multiple reports in the literature demonstrating the presence of neurons in Foxg1-/- mice (eg: Xuan et al., 1995; Hanashima et al., 2002, Martynoga et al., 2005, Muzio and Mallamaci 2005). Perhaps the statement refers specifically to late-born cortical neurons. This point also arises in the discussion section.

Revisions:

(a) We have revised the manuscript to explain why we chose postnatal day 14 to examine the effects of Foxg1 deletion at E15.

●  We have examined the transcriptomic dysregulation after Foxg1 deletion at E17.5, which is a reasonable period to identify potential direct targets. Furthermore, FOXG1 occupies the Fgfr3 locus in ChIP-seq performed at E15.5. Together, these support the interpretation that Fgfr3 is a direct target of Foxg1.

● As the Reviewer notes, we have investigated the possibility of increased proliferation of astrocytes and death of neurons and found no evidence suggesting these phenomena occur in the 3 days after loss of Foxg1. Cortical neurons are postmitotic and differentiated by E18.5, the stage at which we examined CC3 staining and found no difference in cell death in control and mutants (Supplementary Figure S2C, C’). The majority of progenitors (PAX6+ve cells) that lose Foxg1 at E15.5 express the gliogenic transcription factor NFIA by E18.5 (Figure 2C, C’), but hardly any express intermediate (neurogenic) progenitor marker TBR2 (Supplementary Figure S2B, B’). It is therefore unlikely that neurons are born from Foxg1 mutant progenitors and then die at a later stage.

● The cellular consequences of loss of Foxg1 require additional time to detect e.g. it takes ~ 5 days for GFAP to be detected in astrocytes once they are born. The P14 timepoint permits the assessment of oligogenesis which begins after astrogliogenesis and therefore permits a comprehensive assessment of the lineage of E15.5 Foxg1 null progenitors.

(b) Thank you for pointing out that the last sentence of the results section implied (incorrectly) that ALL neurogenesis is not possible in the absence of Foxg1 We have modified this (and the discussion) to reflect that this applies to E14/15 progenitors and late-born cortical neurons.

Recommendations for the authors (Reviewer 2):

(c) We thank the reviewer for this suggestion. We will modify the schematic (Figure 7) to remove any ambiguity regarding Foxg1 expression.

eLife Assessment

The work by Han and collaborators describes valuable findings on the role of Akkermansia muciniphila during ETEC infection. If confirmed, these findings will add to a growing list of beneficial properties of this organism. The strength of the evidence used to justify the conclusions in the manuscript is solid, as the analyses broadly support the claims with only minor weaknesses.

Reviewer #3 (Public review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

As in previous revisions, there remains concerning ambiguity in the methodology used for microbiota sequence analysis and it would be difficult to replicate the analysis in any meaningful way. In this revision, concerns about the rigor and reproducibility of this component of the manuscript have been increased. Readers should be cautious with interpretation of this data.

(1) In previous versions of the manuscript it would appear the correct bioproject accession was listed but, the actual link went to an unrelated project. The updated accession link appears to contain raw data; however, the authors state they used an Illumina HiSeq 2500. This would be an unusual choice for V3-V4 as it would not have read lengths long enough to overlap. Inspection of the first sample (SRR19164796) demonstrates that this is absolutely not the raw data, as there is a ~400 nt forward read, and a 0 length reverse read. All quality scores are set to 30. There is no logical way to go from HiSeq 2500 raw data and read lengths to what was uploaded to the SRA and it was certainly not described in the manuscript.

(2) No multiple testing correction was applied to the microbiome data.

Author response:

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

Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval.

Reviewer #3 (Public review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

As in previous revisions, there remains concerning ambiguity in the methodology used for microbiota sequence analysis and it would be difficult to replicate the analysis in any meaningful way. In this revision, concerns about the rigor and reproducibility of this component of the manuscript have been increased. Readers should be cautious with interpretation of this data.

(1) In previous versions of the manuscript it would appear the correct bioproject accession was listed but, the actual link went to an unrelated project. The updated accession link appears to contain raw data; however, the authors state they used an Illumina HiSeq 2500. This would be an unusual choice for V3-V4 as it would not have read lengths long enough to overlap. Inspection of the first sample (SRR19164796) demonstrates that this is absolutely not the raw data, as there is a ~400 nt forward read, and a 0 length reverse read. All quality scores are set to 30. There is no logical way to go from HiSeq 2500 raw data and read lengths to what was uploaded to the SRA and it was certainly not described in the manuscript.

What we uploaded to the SRA was Contigs files for sample, we have modified the description on line 694.

(2) No multiple testing correction was applied to the microbiome data.

The alpha diversity indexes were tested using T-test and wilcox test, and we showed the result of T-test in FigureS1B. The p-values were corrected for multiple testing using the Benjamini-Hochberg method, we have modified the description on line 322.

---------

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

Public Reviews:

Reviewer #2 (Public Review):

Ma X. et al proposed that A. muciniphila was a key strain that promotes the proliferation and differentiation of intestinal stem cells through acting on the Wnt/β-catenin signaling pathway. They used various models, such as piglet model, mouse model and intestinal organoids to address how A. muciniphila and B. fragilis offer the protection against ETEC infection. They showed that FMT with fecal samples, A. muciniphila or B. fragilis protected piglets and/or mice from ETEC infection, and this protection is manifested as reduced intestinal inflammation/bacterial colonization, increased tight junction/Muc2 proteins, as well as proper Treg/Th17 cells. Additionally, they demonstrated that A. muciniphila protected basal-out and/or apical-out intestinal organoids against ETEC infection via Wnt signaling.

Comments on revised version:

Please add proper references to indicate the invasion of ETEC into organoids after 1 h of infection.

We have added references on line 211.

References:

Xiao K, Yang Y, Zhang Y, Lv QQ, Huang FF, Wang D, Zhao JC, Liu YL. 2022. Long-chain PUFA ameliorate enterotoxigenic Escherichia coli-induced intestinal inflammation and cell injury by modulating pyroptosis and necroptosis signaling pathways in porcine intestinal epithelial cells. Br. J. Nutr. 128(5):835-850.

Qian MQ, Zhou XC, Xu TT, Li M, Yang ZR, Han XY. 2023. Evaluation of Potential Probiotic Properties of Limosilactobacillus fermentum Derived from Piglet Feces and Influence on the Healthy and E. coli-Challenged Porcine Intestine. Microorganisms. 11(4).

Reviewer #3 (Public Review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

After an additional revision, the bioinformatics section of the methods has changed significantly from previous versions and now indicates a third sequencer was used instead: Ion S5 XL. Important parameters required to replicate analysis have still not been provided. Inspection of the SRA data indicates a mix of Illumina MiSeq and Illumina HiSeq 2500. It is now unclear which sequencing technology was used as authors have variably reported 4 different sequencers for these samples. Appropriate metadata was not provided in the SRA, although some groups may be inferred from sample names. These changing descriptions of the methodologies and ambiguity in making the data available create concerns about rigor of study and results.

Due to confusing the sequencing method of this experiment with other experiment samples, we apologize for the multiple incorrect modifications of the method description. We have modified the method for microbiome sequencing technology on line 304. The sequencing technology is Illumina HiSeq 2500. The SRA metadata can be viewed at https://www.ncbi.nlm.nih.gov/sra/PRJNA837047. The sample names ep1-6 and ef1-6 were correspond to the EP and EF groups, respectively.

Recommendations For the Authors:

As in the previous revision:

-provide important parameters required to replicate analysis

-ensure that reporting of sequencing technology is correct as data listed on SRA appears to be derived from Illumina sequencers, and was deposited indicating as such.

-update SRA metadata such that experimental groups are clear and match the nomenclature used in the manuscript (Particularly for samples which are labelled [A-Z][0-9]

- The multiple testing correction wasn’t applied.

-Due to confusing the sequencing method of this experiment with other experiment samples, we apologize for the multiple incorrect modifications of the method description. We have modified the method for microbiome sequencing technology on line 304. The sequencing technology is Illumina HiSeq 2500.

- The SRA metadata can be viewed at https://www.ncbi.nlm.nih.gov/sra/PRJNA837047. The sample names ep1-6 and ef1-6 were correspond to the EP and EF groups, respectively.

eLife Assessment

This study tested the specific hypothesis that age-related changes to hearing involve a partial loss of synapse connections between sensory cells in the ear and the nerve fibers that carry information about sounds to the brain, and that this interferes with the ability to discriminate rapid temporal fluctuations in sounds. Physiological, behavioral, and histological analyses provide a powerful combination to test this hypothesis in gerbils. Contrary to previous suggestions, it was found that chemically-induced isolated synaptopathy (at similar levels as observed in aged gerbils) did not result in worse performance on a behavioral task measuring sensitivity to fine-structure. Further, altered neural coding of rapid fluctuations produced no perceptual deficits in either these gerbils or in aged gerbils. These findings are important for understanding age-related changes to hearing; however, the evidence provided is incomplete due to problems in interpretation and the discussion of possible confounds and/or limitations of these data that currently limits mechanistic insight.

Reviewer #1 (Public review):

Summary:

The authors investigate the effects of aging on auditory system performance in understanding temporal fine structure (TFS), using both behavioral assessments and physiological recordings from the auditory periphery, specifically at the level of the auditory nerve. This dual approach aims to enhance understanding of the mechanisms underlying observed behavioral outcomes. The results indicate that aged animals exhibit deficits in behavioral tasks for distinguishing between harmonic and inharmonic sounds, which is a standard test for TFS coding. However, neural responses at the auditory nerve level do not show significant differences when compared to those in young, normal-hearing animals. The authors suggest that these behavioral deficits in aged animals are likely attributable to dysfunctions in the central auditory system, potentially as a consequence of aging. To further investigate this hypothesis, the study includes an animal group with selective synaptic loss between inner hair cells and auditory nerve fibers, a condition known as cochlear synaptopathy (CS). CS is a pathology associated with aging and is thought to be an early indicator of hearing impairment. Interestingly, animals with selective CS showed physiological and behavioral TFS coding similar to that of the young normal-hearing group, contrasting with the aged group's deficits. Despite histological evidence of significant synaptic loss in the CS group, the study concludes that CS does not appear to affect TFS coding, either behaviorally or physiologically.

Strengths:

This study addresses a critical health concern, enhancing our understanding of mechanisms underlying age-related difficulties in speech intelligibility, even when audiometric thresholds are within normal limits. A major strength of this work is the comprehensive approach, integrating behavioral assessments, auditory nerve (AN) physiology, and histology within the same animal subjects. This approach enhances understanding of the mechanisms underlying the behavioral outcomes and provides confidence in the actual occurrence of synapse loss and its effects. The study carefully manages controlled conditions by including five distinct groups: young normal-hearing animals, aged animals, animals with CS induced through low and high doses, and a sham surgery group. This careful setup strengthens the study's reliability and allows for meaningful comparisons across conditions. Overall, the manuscript is well-structured, with clear and accessible writing that facilitates comprehension of complex concepts.

Weaknesses:

The stimulus and task employed in this study are very helpful for behavioral research, and using the same stimulus setup for physiology is advantageous for mechanistic comparisons. However, I have some concerns about the limitations in auditory nerve (AN) physiology. Due to practical constraints, it is not feasible to record from a large enough population of fibers that covers a full range of best frequencies (BFs) and spontaneous rates (SRs) within each animal. This raises questions about how representative the physiological data are for understanding the mechanism in behavioral data. I am curious about the authors' interpretation of how this stimulus setup might influence results compared to methods used by Kale and Heinz (2010), who adjusted harmonic frequencies based on the characteristic frequency (CF) of recorded units. While, the harmonic frequencies in this study are fixed across all CFs, meaning that many AN fibers may not be tuned closely to the stimulus frequencies. If units are not responsive to the stimulus further clarification on detecting mistuning and phase locking to TFS effects within this setup would be valuable. Given the limited number of units per condition-sometimes as few as three for certain conditions - I wonder if CF-dependent variability might impact the results of the AN data in this study and discussing this factor can help with better understanding the results. While the use of the same stimuli for both behavioral and physiological recordings is understandable, a discussion on how this choice affects interpretation would be beneficial. In addition a 60 dB stimulus could saturate high spontaneous rate (HSR) AN fibers, influencing neural coding and phase-locking to TFS. Potentially separating SR groups, could help address these issues and improve interpretive clarity.

A deeper discussion on the role of fiber spontaneous rate could also enhance the study. How might considering SR groups affect AN results related to TFS coding? While some statistical measures are included in the supplement, a more detailed discussion in the main text could help in interpretation.

Although Figure S2 indicates no change in median SR, the high-dose treatment group lacks LSR fibers, suggesting a different distribution based on SR for different animal groups, as seen in similar studies on other species. A histogram of these results would be informative, as LSR fiber loss with CS-whether induced by ouabain in gerbils or noise in other animals-is well documented (e.g., Furman et al., 2013).

Although ouabain effects on gerbils have been explored in previous studies, since these data already seems to be recorded for the animal in this study, a brief description of changes in auditory brainstem response (ABR) thresholds, wave 1 amplitudes, and tuning curves for animals with cochlear synaptopathy (CS) in this study would be beneficial. This would confirm that ouabain selectively affects synapses without impacting outer hair cells (OHCs). For aged animals, since ABR measurements were taken, comparing hearing differences between normal and aged groups could provide insights into the pathologies besides CS in aged animals. Additionally, examining subject variability in treatment effects on hearing and how this correlates with behavior and physiology would yield valuable insights. If limited space maybe a brief clarification or inclusion in supplementary could be good enough.

Another suggestion is to discuss the potential role of MOC efferent system and effect of anesthesia in reducing efferent effects in AN recordings. This is particularly relevant for aged animals, as CS might affect LSR fibers, potentially disrupting the medial olivocochlear (MOC) efferent pathway. Anesthesia could lessen MOC activity in both young and aged animals, potentially masking efferent effects that might be present in behavioral tasks. Young gerbils with functional efferent systems might perform better behaviorally, while aged gerbils with impaired MOC function due to CS might lack this advantage. A brief discussion on this aspect could potentially enhance mechanistic insights.

Lastly, although synapse counts did not differ between the low-dose treatment and NH I sham groups, separating these groups rather than combining them with the sham might reveal differences in behavior or AN results, particularly regarding the significance of differences between aged/treatment groups and the young normal-hearing group.

Reviewer #2 (Public review):

Summary:

Using a gerbil model, the authors tested the hypothesis that loss of synapses between sensory hair cells and auditory nerve fibers (which may occur due to noise exposure or aging) affects behavioral discrimination of the rapid temporal fluctuations of sounds. In contrast to previous suggestions in the literature, their results do not support this hypothesis; young animals treated with a compound that reduces the number of synapses did not show impaired discrimination compared to controls. Additionally, their results from older animals showing impaired discrimination suggest that age-related changes aside from synaptopathy are responsible for the age-related decline in discrimination.

Strengths:

(1) The rationale and hypothesis are well-motivated and clearly presented.

(2) The study was well conducted with strong methodology for the most part, and good experimental control. The combination of physiological and behavioral techniques is powerful and informative. Reducing synapse counts fairly directly using ouabain is a cleaner design than using noise exposure or age (as in other studies), since these latter modifiers have additional effects on auditory function.

(3) The study may have a considerable impact on the field. The findings could have important implications for our understanding of cochlear synaptopathy, one of the most highly researched and potentially impactful developments in hearing science in the past fifteen years.

Weaknesses:

(1) My main concern is that the stimuli may not have been appropriate for assessing neural temporal coding behaviorally. Human studies using the same task employed a filter center frequency that was (at least) 11 times the fundamental frequency (Marmel et al., 2015; Moore and Sek, 2009). Moore and Sek wrote: "the default (recommended) value of the centre frequency is 11F0." Here, the center frequency was only 4 or 8 times the fundamental frequency (4F0 or 8F0). Hence, relative to harmonic frequency, the harmonic spacing was considerably greater in the present study. By my calculations, the masking noise used in the present study was also considerably lower in level relative to the harmonic complex than that used in the human studies. These factors may have allowed the animals to perform the task using cues based on the pattern of activity across the neural array (excitation pattern cues), rather than cues related to temporal neural coding. The authors show that mean neural driven rate did not change with frequency shift, but I don't understand the relevance of this. It is the change in response of individual fibers with characteristic frequencies near the lowest audible harmonic that is important here.

The case against excitation pattern cues needs to be better made in the Discussion. It could be that gerbil frequency selectivity is broad enough for this not to be an issue, but more detail needs to be provided to make this argument. The authors should consider what is the lowest audible harmonic in each case for their stimuli, given the level of each harmonic and the level of the pink noise. Even for the 8F0 center frequency, the lowest audible harmonic may be as low as the 4th (possibly even the 3rd). In human, harmonics are thought to be resolvable by the cochlea up to at least the 8th.

(2) The synapse reductions in the high ouabain and old groups were relatively small (mean of 19 synapses per hair cell compared to 23 in the young untreated group). In contrast, in some mouse models of the effects of noise exposure or age, a 50% reduction in synapses is observed, and in the human temporal bone study of Wu et al. (2021, https://doi.org/10.1523/JNEUROSCI.3238-20.2021) the age-related reduction in auditory nerve fibres was ~50% or greater for the highest age group across cochlear location. It could be simply that the synapse loss in the present study was too small to produce significant behavioral effects. Hence, although the authors provide evidence that in the gerbil model the age-related behavioral effects are not due to synaptopathy, this may not translate to other species (including human). This should be discussed in the manuscript.

It would be informative to provide synapse counts separately for the animals who were tested behaviorally, to confirm that the pattern of loss across the group was the same as for the larger sample.

(3) The study was not pre-registered, and there was no a priori power calculation, so there is less confidence in replicability than could have been the case. Only three old animals were used in the behavioral study, which raises concerns about the reliability of comparisons involving this group.

Reviewer #3 (Public review):

This study is a part of the ongoing series of rigorous work from this group exploring neural coding deficits in the auditory nerve, and dissociating the effects of cochlear synaptopathy from other age-related deficits. They have previously shown no evidence of phase-locking deficits in the remaining auditory nerve fibers in quiet-aged gerbils. Here, they study the effects of aging on the perception and neural coding of temporal fine structure cues in the same Mongolian gerbil model.

They measure TFS coding in the auditory nerve using the TFS1 task which uses a combination of harmonic and tone-shifted inharmonic tones which differ primarily in their TFS cues (and not the envelope). They then follow this up with a behavioral paradigm using the TFS1 task in these gerbils. They test young normal hearing gerbils, aged gerbils, and young gerbils with cochlear synaptopathy induced using the neurotoxin ouabain to mimic synapse losses seen with age.

In the behavioral paradigm, they find that aging is associated with decreased performance compared to the young gerbils, whereas young gerbils with similar levels of synapse loss do not show these deficits. When looking at the auditory nerve responses, they find no differences in neural coding of TFS cues across any of the groups. However, aged gerbils show an increase in the representation of periodicity envelope cues (around f0) compared to young gerbils or those with induced synapse loss. The authors hence conclude that synapse loss by itself doesn't seem to be important for distinguishing TFS cues, and rather the behavioral deficits with age are likely having to do with the misrepresented envelope cues instead.

The manuscript is well written, and the data presented are robust. Some of the points below will need to be considered while interpreting the results of the study, in its current form. These considerations are addressable if deemed necessary, with some additional analysis in future versions of the manuscript.

Spontaneous rates - Figure S2 shows no differences in median spontaneous rates across groups. But taking the median glosses over some of the nuances there. Ouabain (in the Bourien study) famously affects low spont rates first, and at a higher degree than median or high spont rates. It seems to be the case (qualitatively) in Figure S2 as well, with almost no units in the low spont region in the ouabain group, compared to the other groups. Looking at distributions within each spont rate category and comparing differences across the groups might reveal some of the underlying causes for these changes. Given that overall, the study reports that low-SR fibers had a higher ENV/TFS log-z-ratio, the distribution of these fibers across groups may reveal specific effects of TFS coding by group.

Threshold shifts - It is unclear from the current version if the older gerbils have changes in hearing thresholds, and whether those changes may be affecting behavioral thresholds. The behavioral stimuli appear to have been presented at a fixed sound level for both young and aged gerbils, similar to the single unit recordings. Hence, age-related differences in behavior may have been due to changes in relative sensation level. Approaches such as using hearing thresholds as covariates in the analysis will help explore if older gerbils still show behavioral deficits.

Task learning in aged gerbils - It is unclear if the aged gerbils really learn the task well in two of the three TFS1 test conditions. The d' of 1 which is usually used as the criterion for learning was not reached in even the easiest condition for aged gerbils in all but one condition for the aged gerbils (Fig. 5H) and in that condition, there doesn't seem to be any age-related deficits in behavioral performance (Fig. 6B). Hence dissociating the inability to learn the task from the inability to perceive TFS 1 cues in those animals becomes challenging.

Increased representation of periodicity envelope in the AN - the mechanisms for increased representation of periodicity envelope cues is unclear. The authors point to some potential central mechanisms but given that these are recordings from the auditory nerve what central mechanisms these may be is unclear. If the authors are suggesting some form of efferent modulation only at the f0 frequency, no evidence for this is presented. It appears more likely that the enhancement may be due to outer hair cell dysfunction (widened tuning, distorted tonotopy). Given this increased envelope coding, the potential change in sensation level for the behavior (from the comment above), and no change in neural coding of TFS cues across any of the groups, a simpler interpretation may be -TFS coding is not affected in remaining auditory nerve fibers after age-related or ouabain induced synapse loss, but behavioral performance is affected by altered outer hair cell dysfunction with age.

Emerging evidence seems to suggest that cochlear synaptopathy and/or TFS encoding abilities might be reflected in listening effort rather than behavioral performance. Measuring some proxy of listening effort in these gerbils (like reaction time) to see if that has changed with synapse loss, especially in the young animals with induced synaptopathy, would make an interesting addition to explore perceptual deficits of TFS coding with synapse loss.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors investigate the effects of aging on auditory system performance in understanding temporal fine structure (TFS), using both behavioral assessments and physiological recordings from the auditory periphery, specifically at the level of the auditory nerve. This dual approach aims to enhance understanding of the mechanisms underlying observed behavioral outcomes. The results indicate that aged animals exhibit deficits in behavioral tasks for distinguishing between harmonic and inharmonic sounds, which is a standard test for TFS coding. However, neural responses at the auditory nerve level do not show significant differences when compared to those in young, normal-hearing animals. The authors suggest that these behavioral deficits in aged animals are likely attributable to dysfunctions in the central auditory system, potentially as a consequence of aging. To further investigate this hypothesis, the study includes an animal group with selective synaptic loss between inner hair cells and auditory nerve fibers, a condition known as cochlear synaptopathy (CS). CS is a pathology associated with aging and is thought to be an early indicator of hearing impairment. Interestingly, animals with selective CS showed physiological and behavioral TFS coding similar to that of the young normal-hearing group, contrasting with the aged group's deficits. Despite histological evidence of significant synaptic loss in the CS group, the study concludes that CS does not appear to affect TFS coding, either behaviorally or physiologically.

We agree with the reviewer’s summary.

Strengths:

This study addresses a critical health concern, enhancing our understanding of mechanisms underlying age-related difficulties in speech intelligibility, even when audiometric thresholds are within normal limits. A major strength of this work is the comprehensive approach, integrating behavioral assessments, auditory nerve (AN) physiology, and histology within the same animal subjects. This approach enhances understanding of the mechanisms underlying the behavioral outcomes and provides confidence in the actual occurrence of synapse loss and its effects. The study carefully manages controlled conditions by including five distinct groups: young normal-hearing animals, aged animals, animals with CS induced through low and high doses, and a sham surgery group. This careful setup strengthens the study's reliability and allows for meaningful comparisons across conditions. Overall, the manuscript is well-structured, with clear and accessible writing that facilitates comprehension of complex concepts.

Weaknesses:

The stimulus and task employed in this study are very helpful for behavioral research, and using the same stimulus setup for physiology is advantageous for mechanistic comparisons. However, I have some concerns about the limitations in auditory nerve (AN) physiology. Due to practical constraints, it is not feasible to record from a large enough population of fibers that covers a full range of best frequencies (BFs) and spontaneous rates (SRs) within each animal. This raises questions about how representative the physiological data are for understanding the mechanism in behavioral data. I am curious about the authors' interpretation of how this stimulus setup might influence results compared to methods used by Kale and Heinz (2010), who adjusted harmonic frequencies based on the characteristic frequency (CF) of recorded units. While, the harmonic frequencies in this study are fixed across all CFs, meaning that many AN fibers may not be tuned closely to the stimulus frequencies.

We chose the stimuli for the AN recordings to be identical to the stimuli used in the behavioral evaluation of the perceptual sensitivity. Only with this approach can we directly compare the response of the population of AN fibres with perception measured in behaviour. We will address this more clearly in the revision.

If units are not responsive to the stimulus further clarification on detecting mistuning and phase locking to TFS effects within this setup would be valuable.

It is unclear to us what the reviewer alludes to. We ask to rephrase the question.

Given the limited number of units per condition-sometimes as few as three for certain conditions - I wonder if CF-dependent variability might impact the results of the AN data in this study and discussing this factor can help with better understanding the results. While the use of the same stimuli for both behavioral and physiological recordings is understandable, a discussion on how this choice affects interpretation would be beneficial. In addition a 60 dB stimulus could saturate high spontaneous rate (HSR) AN fibers, influencing neural coding and phase-locking to TFS. Potentially separating SR groups, could help address these issues and improve interpretive clarity.

In the discussion of a revised version of the manuscript, we will point out the pros and cons of using fixed-level stimuli that were not adjusted in frequency to the BF.

A deeper discussion on the role of fiber spontaneous rate could also enhance the study. How might considering SR groups affect AN results related to TFS coding? While some statistical measures are included in the supplement, a more detailed discussion in the main text could help in interpretation. We do not think that it will be necessary to conduct any statistical analysis in addition to that already reported in the supplement.

We will consider moving some supplementary information back into the main manuscript when revising.

Although Figure S2 indicates no change in median SR, the high-dose treatment group lacks LSR fibers, suggesting a different distribution based on SR for different animal groups, as seen in similar studies on other species. A histogram of these results would be informative, as LSR fiber loss with CS-whether induced by ouabain in gerbils or noise in other animals-is well documented (e.g., Furman et al., 2013).

We will add information on the distribution when revising.

Although ouabain effects on gerbils have been explored in previous studies, since these data already seems to be recorded for the animal in this study, a brief description of changes in auditory brainstem response (ABR) thresholds, wave 1 amplitudes, and tuning curves for animals with cochlear synaptopathy (CS) in this study would be beneficial. This would confirm that ouabain selectively affects synapses without impacting outer hair cells (OHCs). For aged animals, since ABR measurements were taken, comparing hearing differences between normal and aged groups could provide insights into the pathologies besides CS in aged animals. Additionally, examining subject variability in treatment effects on hearing and how this correlates with behavior and physiology would yield valuable insights. If limited space maybe a brief clarification or inclusion in supplementary could be good enough.

We do indeed have data on ABR amplitudes and the wave 1 growth functions but only in response to broadband clicks. For more frequency-specific information, mass-potential recordings are available, obtained before and after ouabain treatment. Regarding neural tuning, we did not obtain full frequency-threshold curves but do have bandwidths for response curves recorded close to threshold. We are in the process of analyzing all these data further and will consider how to best incorporate them into the manuscript, to address the reviewer’s concerns.

Another suggestion is to discuss the potential role of MOC efferent system and effect of anesthesia in reducing efferent effects in AN recordings. This is particularly relevant for aged animals, as CS might affect LSR fibers, potentially disrupting the medial olivocochlear (MOC) efferent pathway. Anesthesia could lessen MOC activity in both young and aged animals, potentially masking efferent effects that might be present in behavioral tasks. Young gerbils with functional efferent systems might perform better behaviorally, while aged gerbils with impaired MOC function due to CS might lack this advantage. A brief discussion on this aspect could potentially enhance mechanistic insights.

Our provisional response below will be integrated in similar form into the Discussion.

Olivocochlear efferent activity is a potential modulator of OHC gain (by medial olivocochlear neurons, MOC) and afferent activity (by lateral olivocochlear neurons, LOC). Beyond this general observation it is, however, difficult to speculate about its specific role in the TFS1 test, as almost nothing is known about efferent activity under naturalistic conditions in a behaving animal (reviewed by Lauer et al., 2022). We note, however, that efferent activity is believed to be reduced under general anesthesia (reviewed by Guinan, 2011, DOI 10.1007/978-1-4419-7070-1_3) and possibly abnormal in other ways, considering the potential top-down inputs to the efferent neurons from extensive brain networks (reviewed by Schofield, 2011, DOI 10.1007/978-1-4419-7070-1_9; Romero and Trussell, 2022, DOI: 10.1016/j.heares.2022.108516). Thus, it is reasonable to assume a reduced efferent influence in our auditory-nerve data, compared to the behavioral test situation. In contrast, we assume more comparable efferent influences in young-adult and old gerbils. It was recently shown that, despite age-related losses in both MOC and LOC cochlear innervation, this basically reflected the loss of efferent target structures (OHC and type-I afferents), with the surviving cochlear circuitry remaining largely normal (Steenken et al., 2024, DOI: 10.3389/fnsyn.2024.1422330). The main difference was an increased proportion of OHC without any efferent innervation, predominantly in low-frequency cochlear regions (Steenken et al., 2024). Such OHC are thus not under efferent control, and they are more numerous (about 10 – 30%) in old gerbils.

Lastly, although synapse counts did not differ between the low-dose treatment and NH I sham groups, separating these groups rather than combining them with the sham might reveal differences in behavior or AN results, particularly regarding the significance of differences between aged/treatment groups and the young normal-hearing group. For maximizing statistical power, we combined those groups in the statistical analysis. These two groups did not differ in synapse number and had quite similar ABR wave 1 growth functions.

Reviewer #2 (Public review):

Summary:

Using a gerbil model, the authors tested the hypothesis that loss of synapses between sensory hair cells and auditory nerve fibers (which may occur due to noise exposure or aging) affects behavioral discrimination of the rapid temporal fluctuations of sounds. In contrast to previous suggestions in the literature, their results do not support this hypothesis; young animals treated with a compound that reduces the number of synapses did not show impaired discrimination compared to controls. Additionally, their results from older animals showing impaired discrimination suggest that age-related changes aside from synaptopathy are responsible for the age-related decline in discrimination.

We agree with the reviewer’s summary.

Strengths:

(1) The rationale and hypothesis are well-motivated and clearly presented.

(2) The study was well conducted with strong methodology for the most part, and good experimental control. The combination of physiological and behavioral techniques is powerful and informative. Reducing synapse counts fairly directly using ouabain is a cleaner design than using noise exposure or age (as in other studies), since these latter modifiers have additional effects on auditory function.

(3) The study may have a considerable impact on the field. The findings could have important implications for our understanding of cochlear synaptopathy, one of the most highly researched and potentially impactful developments in hearing science in the past fifteen years.

Weaknesses:

(1) My main concern is that the stimuli may not have been appropriate for assessing neural temporal coding behaviorally. Human studies using the same task employed a filter center frequency that was (at least) 11 times the fundamental frequency (Marmel et al., 2015; Moore and Sek, 2009). Moore and Sek wrote: "the default (recommended) value of the centre frequency is 11F0." Here, the center frequency was only 4 or 8 times the fundamental frequency (4F0 or 8F0). Hence, relative to harmonic frequency, the harmonic spacing was considerably greater in the present study. By my calculations, the masking noise used in the present study was also considerably lower in level relative to the harmonic complex than that used in the human studies. These factors may have allowed the animals to perform the task using cues based on the pattern of activity across the neural array (excitation pattern cues), rather than cues related to temporal neural coding. The authors show that mean neural driven rate did not change with frequency shift, but I don't understand the relevance of this. It is the change in response of individual fibers with characteristic frequencies near the lowest audible harmonic that is important here.

The auditory filter bandwidth of the gerbil is about double that of human subjects. Because of this, the masking noise has a larger overall level than in the human studies in the filter. This precludes that the gerbils can use excitation patterns, especially in the condition with a center frequency of 1600 Hz and a fundamental of 200 Hz and in the condition with a center frequency of 3200 Hz and a fundamental of 400 Hz.

The case against excitation pattern cues needs to be better made in the Discussion. It could be that gerbil frequency selectivity is broad enough for this not to be an issue, but more detail needs to be provided to make this argument. The authors should consider what is the lowest audible harmonic in each case for their stimuli, given the level of each harmonic and the level of the pink noise. Even for the 8F0 center frequency, the lowest audible harmonic may be as low as the 4th (possibly even the 3rd). In human, harmonics are thought to be resolvable by the cochlea up to at least the 8th.

Because of the gerbil’s broader auditory filters, with the exception of the condition with center frequency of 1600 Hz and fundamental of 400 Hz harmonics are are not resolved. We will expand the topic of potential excitation pattern cues in the discussion of the revised version and add results on modeled excitation patterns to the supplement.

(2) The synapse reductions in the high ouabain and old groups were relatively small (mean of 19 synapses per hair cell compared to 23 in the young untreated group). In contrast, in some mouse models of the effects of noise exposure or age, a 50% reduction in synapses is observed, and in the human temporal bone study of Wu et al. (2021, https://doi.org/10.1523/JNEUROSCI.3238-20.2021) the age-related reduction in auditory nerve fibres was ~50% or greater for the highest age group across cochlear location. It could be simply that the synapse loss in the present study was too small to produce significant behavioral effects. Hence, although the authors provide evidence that in the gerbil model the age-related behavioral effects are not due to synaptopathy, this may not translate to other species (including human). This should be discussed in the manuscript.

Our provisional response below will be integrated in similar form into the Discussion.

The observed extent of age-related or noise-induced loss of type-I afferent synapses on IHC varies widely between species and studies. For example, in ageing CBA/CaJ mice, mean losses of between 20 and 50% of afferent synapses (depending on cochlear location and precise age) were reported (Sergeyenko et al., 2013, DOI: 10.1523/JNEUROSCI.1783-13.2013; Kobrina et al., 2020, DOI: 10.1016/j.neurobiolaging.2020.08.012). Humans showed more pronounced losses of peripheral axons, of 40–100%, again depending on cochlear location, precise age, and noise history (Wu et al., 2019, DOI: 10.1016/j.neuroscience.2018.07.053; 2021, DOI: 10.1523/JNEUROSCI.3238-20.2021). The age-related and induced synapse losses in our gerbils were in a more moderate range, around 20% (Steenken et al., 2021, DOI: 10.1016/j.neurobiolaging.2021.08.019; this study). Thus, it is possible that a more severe, induced synaptopathy would have resulted in behavioral deficits in young-adult gerbils. However, in the absence of additional noise or pharmacologically induced damage, our study provides strong evidence for other factors causing temporal processing problems with advancing age. Our 3-year-old gerbils are approximately comparable to a 60-year-old human (Castano-Gonzalez et al., 2024, DOI: 10.1016/j.heares.2024.108989) with beginning but not yet clinically relevant hearing loss (Hamann et al., 2002, DOI: 10.1016/S0378-5955(02)00454-9).

It would be informative to provide synapse counts separately for the animals who were tested behaviorally, to confirm that the pattern of loss across the group was the same as for the larger sample.

Yes, the pattern was the same for the subgroup of behaviorally tested animals. We will add this information to the revised version of the manuscript.

(3) The study was not pre-registered, and there was no a priori power calculation, so there is less confidence in replicability than could have been the case. Only three old animals were used in the behavioral study, which raises concerns about the reliability of comparisons involving this group.

The results for the three old subjects differed significantly from those of young subjects and young ouabain-treated subjects. This indicates a sufficient statistical power, since otherwise no significant differences would be observed.

Reviewer #3 (Public review):

This study is a part of the ongoing series of rigorous work from this group exploring neural coding deficits in the auditory nerve, and dissociating the effects of cochlear synaptopathy from other age-related deficits. They have previously shown no evidence of phase-locking deficits in the remaining auditory nerve fibers in quiet-aged gerbils. Here, they study the effects of aging on the perception and neural coding of temporal fine structure cues in the same Mongolian gerbil model.

They measure TFS coding in the auditory nerve using the TFS1 task which uses a combination of harmonic and tone-shifted inharmonic tones which differ primarily in their TFS cues (and not the envelope). They then follow this up with a behavioral paradigm using the TFS1 task in these gerbils. They test young normal hearing gerbils, aged gerbils, and young gerbils with cochlear synaptopathy induced using the neurotoxin ouabain to mimic synapse losses seen with age. In the behavioral paradigm, they find that aging is associated with decreased performance compared to the young gerbils, whereas young gerbils with similar levels of synapse loss do not show these deficits. When looking at the auditory nerve responses, they find no differences in neural coding of TFS cues across any of the groups.

However, aged gerbils show an increase in the representation of periodicity envelope cues (around f0) compared to young gerbils or those with induced synapse loss. The authors hence conclude that synapse loss by itself doesn't seem to be important for distinguishing TFS cues, and rather the behavioral deficits with age are likely having to do with the misrepresented envelope cues instead.

We agree with the reviewer’s summary.

The manuscript is well written, and the data presented are robust. Some of the points below will need to be considered while interpreting the results of the study, in its current form. These considerations are addressable if deemed necessary, with some additional analysis in future versions of the manuscript.

Spontaneous rates - Figure S2 shows no differences in median spontaneous rates across groups. But taking the median glosses over some of the nuances there. Ouabain (in the Bourien study) famously affects low spont rates first, and at a higher degree than median or high spont rates. It seems to be the case (qualitatively) in Figure S2 as well, with almost no units in the low spont region in the ouabain group, compared to the other groups. Looking at distributions within each spont rate category and comparing differences across the groups might reveal some of the underlying causes for these changes. Given that overall, the study reports that low-SR fibers had a higher ENV/TFS log-z-ratio, the distribution of these fibers across groups may reveal specific effects of TFS coding by group.

As the reviewer points out, our sample from the group treated with a high concentration of ouabain showed very few low-spontaneous-rate auditory-nerve fibers, as expected from previous work. However, this was also true, e.g., for our sample from sham-operated animals, and may thus well reflect a sampling bias. We are therefore reluctant to attach much significance to these data distributions. We will consider moving some supplementary information back into the main manuscript when revising.

Threshold shifts - It is unclear from the current version if the older gerbils have changes in hearing thresholds, and whether those changes may be affecting behavioral thresholds. The behavioral stimuli appear to have been presented at a fixed sound level for both young and aged gerbils, similar to the single unit recordings. Hence, age-related differences in behavior may have been due to changes in relative sensation level. Approaches such as using hearing thresholds as covariates in the analysis will help explore if older gerbils still show behavioral deficits.

Unfortunately, we did not obtain behavioral thresholds that could be used here. The ABR thresholds, although not directly comparable to behavioral thresholds, suggest that our old animals had at most a moderate threshold increase in quiet. Furthermore, we want to point out that the TFS 1 stimuli had an overall level of 68 dB SPL, and the pink noise masker would have increased the threshold more than expected from the moderate, age-related hearing loss in quiet. Thus, the masked thresholds for all gerbil groups are likely similar and should have no effect on the behavioral results.

Task learning in aged gerbils - It is unclear if the aged gerbils really learn the task well in two of the three TFS1 test conditions. The d' of 1 which is usually used as the criterion for learning was not reached in even the easiest condition for aged gerbils in all but one condition for the aged gerbils (Fig. 5H) and in that condition, there doesn't seem to be any age-related deficits in behavioral performance (Fig. 6B). Hence dissociating the inability to learn the task from the inability to perceive TFS 1 cues in those animals becomes challenging.

Even in the group of gerbils with the lowest sensitivity, for the condition 400/1600 the animals achieved a d’ of on average above 1. Furthermore, stimuli were well above threshold and audible, even when no discrimination could be observed. Finally, as explained in the methods, different stimulus conditions were interleaved in each session, providing stimuli that were easy to discriminate together with those being difficult to discriminate. This approach ensures that the gerbils were under stimulus control, meaning properly trained to perform the task. Thus, an inability to discriminate does not indicate a lack of proper training.

Increased representation of periodicity envelope in the AN - the mechanisms for increased representation of periodicity envelope cues is unclear. The authors point to some potential central mechanisms but given that these are recordings from the auditory nerve what central mechanisms these may be is unclear. If the authors are suggesting some form of efferent modulation only at the f0 frequency, no evidence for this is presented. It appears more likely that the enhancement may be due to outer hair cell dysfunction (widened tuning, distorted tonotopy). Given this increased envelope coding, the potential change in sensation level for the behavior (from the comment above), and no change in neural coding of TFS cues across any of the groups, a simpler interpretation may be -TFS coding is not affected in remaining auditory nerve fibers after age-related or ouabain induced synapse loss, but behavioral performance is affected by altered outer hair cell dysfunction with age.

A similar point is made by Reviewer #1. As indicated above, we do have limited data on neural bandwidths and will explore if these are sufficient to address the reviewers’ questions about potential, age-related changes in neural tuning in our sample. Previous work found no substantial OHC losses (Tarnowski et al., 1991, DOI: 10.1016/0378-5955(91)90142-V; Adams and Schulte, 1997, DOI: 10.1016/S0378-5955(96)00184-0; Steenken et al., 2024, DOI: 10.3389/fnsyn.2024.1422330) nor any deterioration in neural frequency tuning (Heeringa et al., 2020, DOI: 10.1523/JNEUROSCI.2784-18.2019), in quiet-aged gerbils of similar age as the ones used here.

Emerging evidence seems to suggest that cochlear synaptopathy and/or TFS encoding abilities might be reflected in listening effort rather than behavioral performance. Measuring some proxy of listening effort in these gerbils (like reaction time) to see if that has changed with synapse loss, especially in the young animals with induced synaptopathy, would make an interesting addition to explore perceptual deficits of TFS coding with synapse loss.

This is an interesting suggestion that we will explore in the revision of the manuscript. Reaction times were recorded for responses that can be used as a proxy for listening effort.

eLife Assessment

This valuable study shows that a very slow (infraslow) oscillation occurs in voltage recordings from the dentate gyrus of the adult mouse. The authors suggest that it is related to sleep stage and serotonin acting at one type of serotonin receptor in the dentate gyrus. The ideas are potentially significant because they suggest that this oscillation affects memory through serotonin receptors in the dentate gyrus. Solid evidence is provided to broadly support the main claims, although analytical weaknesses remain that could be improved by clarification of methods, analyses, and data shown in the figures.

Reviewer #1 (Public review):

Turi, Teng and the team used state-of-the-art techniques to provide convincing evidence on the infraslow oscillation of DG cells during NREM sleep, and how serotonergic innervation modulates hippocampal activity pattern during sleep and memory. First, they showed that the glutamatergic DG cells become activated following an infraslow rhythm during NREM sleep. In addition, the infraslow oscillation in the DG is correlated with rhythmic serotonin release during sleep. Finally, they found that specific knockdown of 5-HT receptors in the DG impairs the infraslow rhythm and memory, suggesting that serotonergic signaling is crucial for regulating DG activity during sleep. Given that the functional role of infraslow rhythm still remains to be studied, their findings deepen our understanding on the role of DG cells and serotonergic signaling in regulating infraslow rhythm, sleep microarchitecture and memory.

Reviewer #2 (Public review):

Summary:

The authors investigated DG neuronal activity at the population and single cell level across sleep/wake periods. They found an infraslow oscillation (0.01-0.03 Hz) in both granule cells (GC) and mossy cells (MC) during NREM sleep. The important findings are 1) the antiparallel temporal dynamics of DG neuron activities and serotonin neuron activities/extracellular serotonin levels during NREM sleep, and 2) the GC Htr1a-mediated GC infraslow oscillation.

Strengths:

(1) The combination of polysomnography, Ca-fiber photometry, two-photon microscopy and gene depletion is technically sound. The coincidence of microarousals and dips in DG population activity is convincing. The dip in activity in upregulated cells is responsible for the dip at the population level.<br /> (2) DG GCs express excitatory Htr4 and Htr7 in addition to inhibitory Htr1a, but deletion of Htr1a is sufficient to disrupt DG GC infraslow oscillation, supporting the importance of Htr1a in DG activity during NREM sleep.

Weaknesses:

(1) The current data set and analysis are insufficient to interpret the observation correctly.<br /> a. In Fig 1A, during NREM, the peaks and troughs of GC population activities seem to gradually decrease over time. Please address this point.<br /> b. In Fig 1F, about 30% of Ca dips coincided with MA (EMG increase) and 60% of Ca dips did not coincide with EMG increase. If this is true, the readers can find 8 Ca dips which are not associated with MAs from Fig 1E. If MAs were clustered, please describe this properly.<br /> c. In Fig 1F, the legend stated the percentage during NREM. If the authors want to include the percentage of wake and REM, please show the traces with Ca dips during wake and REM. This concern applies to all pie charts provided by the authors.<br /> d. In Fig 1C, please provide line plots connecting the same session. This request applies to all related figures.<br /> e. In Fig 2C, the significant increase during REM and the same level during NREM are not convincing. In Fig 2A, the several EMG increasing bouts do not appear to be MA, but rather wakefulness, because the duration of the EMG increase is greater than 15 seconds. Therefore, it is possible that the wake bouts were mixed with NREM bouts, leading to the decrease of Ca activity during NREM. In fact, In Fig 2E, the 4th MA bout seems to be the wake bout because the EMG increase lasts more than 15 seconds.<br /> f. Fig 5D REM data are interesting because the DRN activity is stably silenced during REM. The varied correlation means the varied DG activity during REM. The authors need to address it.<br /> g. In Fig 6, the authors should show the impact of DG Htr1a knockdown on sleep/wake structure including the frequency of MAs. I agree with the impact of Htr1a on DG ISO, but possible changes in sleep bout may induce the DG ISO disturbance.

(2) It is acceptable that DG Htr1a KO induces the reduced freezing in the CFC test (Fig. 6E, F), but it is too much of a stretch that the disruption of DG ISO causes impaired fear memory. There should be a correlation.

(3) It is necessary to describe the extent of AAV-Cre infection. The authors injected AAV into the dorsal DG (AP -1.9 mm), but the histology shows the ventral DG (Supplementary Fig. 4), which reduces the reliability of this study.

Comments on revisions:

In the first revision, I pointed out the inappropriate analysis of the EEG/EMG/photometry data and gave examples. The authors responded only to the points raised and did not seem to see the need to improve the overall analysis and description. In this second revision, I would like to ask the authors to improve them. The biggest problem is that the detection criteria and the quantification of the specific event are not described at all in Methods and it is extremely difficult to follow the statement. All interpretations are made by the inappropriate data analysis; therefore, I have to say that the statement is not supported by the data.

Please read my following concerns carefully and improve them.

(1) The definition of the event is critical to the detection of the event and the subsequent analysis. In particular, the authors explicitly describe the definition of MA (microarousal), the trough and peak of the population level of intracellular Ca concentrations, or the onset of the decline and surge of Ca levels.

(1-1) The authors categorized wake bouts of <15 seconds with high EMG activity as MA (in Methods). What degree of high EMG is relevant to MA and what is the lower limit of high EMG? In Fig 1E, there are some EMG spikes, but it was unclear which spike/wave (amplitude/duration) was detected as MA-relevant spike and which spike was not detected. In Fig 2E, the 3rd MA coincides with the EMG spike, but other EMG spikes have comparable amplitude to the 3rd MA-relevant EMG spike. Correct counting of MA events is critical in Fig 1F, 2F, 4C.

(1-2) Please describe the definition of Ca trough in your experiments. In Fig 1G, the averaged trough time is clear (~2.5 s), so I can acknowledge that MA is followed by Ca trough. However, the authors state on page 4 that "30% of the calcium troughs during NREM sleep were followed by an MA epoch". This discrepancy should be corrected.

(1-3) Relating comment 1-2, I agree that the latency is between MA and Ca through in page 4, as the authors explain in the methods, but, in Fig 1G, t (latency) is labeled at incorrect position. Please correct this.

(1-4) The authors may want to determine the onset of the decline in population Ca activity and the latency between onset and trough (Fig 1G, latency t). If so, please describe how the onset of the decline is determined. In Fig 1G, 2G, S6, I can find the horizontal dashed line and infer that the intersection of the horizontal line and the Ca curve is considered the onset. However, I have to say that the placement of this horizontal line is super arbitrary. The results (t and Drop) are highly dependent on the position of horizontal line, so the authors need to describe how to set the horizontal line.

(1-5) In order to follow Fig 1F correctly, the authors need to indicate the detection criteria of "Ca dip (in legend)". Please indicate "each Ca dip" in Fig 1E. As a reader, I would like to agree with the Ca dip detection of this Ca curve based on the criteria. Please also indicate "each Ca dip" in Fig 2E and 2F. In the case of the 2nd and 3rd MAs, do they follow a single Ca dip or does each MA follow each Ca dip? This chart is highly dependent on the detection criteria of Ca dip.

As I mentioned above, most of the quantifications are not based on the clear detection criteria. The authors need to re-analyze the data and fix the quantification. Please interpret data and discuss the cellular mechanism of ISO based on the re-analyzed quantification.

Reviewer #3 (Public review):

Summary:

The authors employ a series of well-conceived and well-executed experiments involving photometric imaging of the dentate gyrus and raphe nucleus, as well as cell-type specific genetic manipulations of serotonergic receptors that together serve to directly implicate serotonergic regulation of dentate gyrus (DG) granule (GC) and mossy cell (MC) activity in association with an infra slow oscillation (ISO) of neural activity has been previously linked to general cortical regulation during NREM sleep and microarousals.

Strengths:

There are a number of novel and important results, including the modulation of dentage granule cell activity by the infraslow oscillation during NREM sleep, the selective association of different subpopulations of granule cells to microarousals (MA), the anticorrelation of raphe activity with infraslow dentate activity.

The discussion includes a general survey of ISOs and recent work relating to their expression in other brain areas and other potential neuromodulatory system involvement, as well as possible connections with infraslow oscillations, micro arousals, and sensory sensitivity.

Weaknesses:

- The behavioral results showing contextual memory impairment resulting from 5-HT1a knockdown are fine, but are over-interpreted. The term memory consolidation is used several times, as well as references to sleep-dependence. This is not what was tested. The receptor was knocked down, and then 2 weeks later animals were found to have fear conditioning deficits. They can certainly describe this result as indicating a connection between 5-HT1a receptor function and memory performance, but the connection to sleep and consolidation would just be speculation. The fact that 5-HT1a knockdown also impacted DG ISOs does not establish dependency. Some examples of this are:<br /> o The final conclusion asserts "Together, our study highlights the role of neuromodulation in organizing neuronal activity during sleep and sleep-dependent brain functions, such as memory.", but the reported memory effects (impairment of fear conditioning) were not shown to be explicitly sleep-dependent.<br /> o Earlier in the discussion it mentions "Finally, we showed that local genetic ablation of 5-HT1a receptors in GCs impaired the ISO and memory consolidation". The effect shown was on general memory performance - consolidation was not specifically implicated.

- The assertion on page 9 that the results demonstrate "that the 5-HT is directly acting in the DG to gate the oscillations" is a bit strong given the magnitude of effect shown in Fig. 6D, and the absence of demonstration of negative effect on cortical areas that also show ISO activity and could impact DG activity (see requested cortical sigma power analysis).

- Recent work has shown that abnormal DG GC activity can result from the use of the specific Ca indicator being used (GCaMP6s). (Teng, S., Wang, W., Wen, J.J.J. et al. Expression of GCaMP6s in the dentate gyrus induces tonic-clonic seizures. Sci Rep 14, 8104 (2024). https://doi.org/10.1038/s41598-024-58819-9). The authors of that study found that the effect seemed to be specific to GCaMP6s and that GCaMP6f did not lead to abnormal excitability. Note this is of particular concern given similar infraslow variation of cortical excitability in epilepsy (cf Vanhatalo et al. PNAS 2004). While I don't think that the experiments need to be repeated with a different indicator to address this concern, you should be able to use the 2p GCaMP7 experiments that have already been done to provide additional validation by repeating the analyses done for the GCaMP6s photometry experiments. This should be done anyway to allow appropriate comparison of the 2p and photometry results.

- While the discussion mentions previous work that has linked ISOs during sleep with regulation of cortical oscillations in the sigma band, oddly no such analysis is performed in the current work even though it is presumably available and would be highly relevant to the interpretation of a number of primary results including the relationship between the ISOs and MAs observed in the DG and similar results reported in other areas, as well as the selective impact of DG 5-HT1a knockdown on DG ISOs. For example, in the initial results describing the cross correlation of calcium activity and EMG/EEG with MA episodes (paragraph 1, page 4), similar results relating brief arousals to the infraslow fluctuation in sleep spindles (sigma band) have been reported also at .02 Hz associated with variation in sensory arousability (cf. Cardis et al., "Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain", eLife 2021). It would be important to know whether the current results show similar cortical sigma band correlations. Also, in the results on ISO attenuation following 5-HT1 knockdown on page 7 (fig. 6), how is cortical EEG affected? is ISO still seen in EEG but attenuated in DG?

- The illustrations of the effect of 5-HT1a knockdown shown in Figure 6 are somewhat misleading. The examples in panels B and C show an effect that is much more dramatic than the overall effect shown in panel D. Panels B and C do not appear to be representative examples. Which of the sample points in panel D are illustrated in panels B, C? it is not appropriate to arbitrarily select two points from different animals for comparison, or worse, to take points from the extremes of the distributions. If the intent is to illustrate what the effect shown in D looks like in the raw data, then you need to select examples that reflect the means shown in panel D. It is also important to show the effect on cortical EEG, particularly in sigma band to see if the effects are restricted to the DG ISOs. It would also be helpful to show that MAs and their correlations as shown in Fig 1 or G as well as broader sleep architecture are not affected.

- On page 9 of the results it states that GCs and MCs are upregulated during NREM and their activity is abruptly terminated by MAs through a 5-HT mediated mechanism. I didn't see anything showing the 5-HT dependence of the MA activity correlation. The results indicate a reduction in ISO modulation of GC activity but not the MA correlated activity. I would like to see the equivalent of Fig 1,2 G panels with the 5-HT1a manipulation.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study provides convincing evidence on the infraslow oscillation of DG cells during NREM sleep, and how serotonergic innervation modulates hippocampal activity pattern during sleep and memory.

Strengths and Weaknesses:

The authors used state-of-the-art techniques to carry out these experiments. Given that the functional role of infraslow rhythm still remains to be studied, this study provides convincing evidence of the role of DG cells in regulating infraslow rhythm, sleep microarchitecture, and memory.

I have a few minor comments.

(1) Decreased infraslow rhythm during NREMs in the 5ht1a KO mice is striking. It would be helpful to know whether sleep-wake states, MAs, and transitions to REMs are changed.

We agree with the reviewer that serotonin receptors may be involved in sleep regulation therefore it is important to analyze the effect of their manipulation. We would also like to bring to the attention of the reviewer that in this case we restricted the 5ht1a manipulation to the hippocampus which does not have a known impact on sleep-wake regulation. The analysis of our recorded dataset from these mice confirmed this notion, because we did not see any changes in sleep metrics (see: supplementary figure 6A).

(2) It would be interesting to discuss whether the magnitude in changes of infraslow rhythm strength is correlated with memory performance (Figure 6).

We agree with the reviewer that this could be an interesting point. In our experiments we wanted to minimize the impact of the surgical procedures on the behavior, thus we used separate cohorts to record the photometry and to carry out the behavior experiments, therefore we are unable to correlate behavior and infraslow oscillatory amplitudes in our dataset.

However, a similar experiment was carried out in a recent paper where the authors discovered that the norepinephrine system also displays infraslow oscillatory cycles during NREM sleep (Kjaerby et al 2022). The authors of that paper gradually decreased the magnitude of the NE pulses during NREM by optogenetic manipulation of the locus coeruleus which led to a fragmented sleep phenotype characterized by increased micro arousal occurrence, decreased REM and reduced spindle activity. They also tested the memory performance of the mice in a novel object recognition task and found diminished performance level in the opto group. Serotonin has multiple roles in the brain, many of them show overlap with proposed functions of the noradrenergic system including regulation of plasticity, signaling reward or fearful stimuli. Therefore, we speculate that the modification of serotonin dynamics during sleep will most likely interfere with memory performance.

We inserted this paragraph in the discussion part of our paper.

(3) The authors should cite the Oikonomou Neuron paper that describes slow oscillatory activity of DRN SERT neurons during NREM sleep.

Thank you for the suggestion, we inserted this paper in the manuscript.

(4) The authors should clarify how they define the phasic pattern of the photometry signal.

We have added the details in the Methods.

Reviewer #2 (Public review):

Summary:

The authors investigated DG neuronal activity at the population and single-cell level across sleep/wake periods. They found an infraslow oscillation (0.01-0.03 Hz) in both granule cells (GC) and mossy cells (MC) during NREM sleep.

The important findings are:

(1) The antiparallel temporal dynamics of DG neuron activities and serotonin neuron activities/extracellular serotonin levels during NREM sleep, and

(2) The GC Htr1a-mediated GC infraslow oscillation.

Strengths:

(1) The combination of polysomnography, Ca-fiber photometry, two-photon microscopy, and gene depletion is technically sound. The coincidence of microarousals and dips in DG population activity is convincing. The dip in activity in upregulated cells is responsible for the dip at the population level.

(2) DG GCs express excitatory Htr4 and Htr7 in addition to inhibitory Htr1a, but deletion of Htr1a is sufficient to disrupt DG GC infraslow oscillation, supporting the importance of Htr1a in DG activity during NREM sleep.

Weaknesses:

(1) The current data set and analysis are insufficient to interpret the observation correctly.

a. In Figure 1A, during NREM, the peaks and troughs of GC population activities seem to gradually decrease over time. Please address this point.

Thank you for the suggestion. We have analyzed and compared the magnitude of the oscillatory signals in the first and last minute of the NREM sleep epochs in Dock10-Cre mice and found no significant difference. However, we did observe that the ISO amplitude is smaller in the early stage of the first NREM epochs, defined as those with the prior wakefulness longer than 5 minutes (new supplementary figure 1).

b. In Figure 1F, about 30% of Ca dips coincided with MA (EMG increase) and 60% of Ca dips did not coincide with EMG increase. If this is true, the readers can find 8 Ca dips which are not associated with MAs from Figure 1E. If MAs were clustered, please describe this properly.

We did not find evidence that MAs were clustered in our dataset (see a representative example in supplementary figure 1A). We replaced the example trace with a new one which shows calcium dips with and without MAs. We believe this new trace better represents the data.

c. In Figure 1F, the legend stated the percentage during NREM. If the authors want to include the percentage of wake and REM, please show the traces with Ca dips during wake and REM. This concern applies to all pie charts provided by the authors.

Figure 1F (and all other pie charts) shows the outcome of brain states following a calcium-dip episode. That is, we found that the Ca-dips during NREM were followed by MAs in 30% of the cases, 59% of the Ca-dips led to the maintenance of NREM (no MAs) while in 2% and 9% of the cases we detected either REM state or wakening of the animal. These numbers correspond very well with similar analysis done in a recent paper which looked at the infraslow oscillatory behavior of the norepinephrine system (Kjaerby et al 2022) during NREM sleep. We apologize if the wording in the manuscript was misleading, we modified the figure legends to clarify what the pie charts represent. 

d. In Figure 1C, please provide line plots connecting the same session. This request applies to all related figures.

We have replaced the dot plots in all related figures with the line plots. 

e. In Figure 2C, the significant increase during REM and the same level during NREM are not convincing. In Figure 2A, the several EMG increasing bouts do not appear to be MA, but rather wakefulness, because the duration of the EMG increase is greater than 15 seconds. Therefore, it is possible that the wake bouts were mixed with NREM bouts, leading to the decrease of Ca activity during NREM. In fact, In Figure 2E, the 4th MA bout seems to be the wake bout because the EMG increase lasts more than 15 seconds.

We have replaced the Figure 2C with line plots as suggested above. It is clear that MC activity during REM sleep is higher, compared to that in NREM sleep, whereas the overall difference between wake and NREM is not significant (some increased, some decreased). Regarding the MAs, we have added a trace of averaged EMG signals in Figure 2G, showing that the averaged EMG bursts during MA are shorter than 5 seconds.

f. Figure 5D REM data are interesting because the DRN activity is stably silenced during REM. The varied correlation means the varied DG activity during REM. The authors need to address it.

We thank the reviewer for this suggestion. We have added this point to the discussion. We speculate that inputs from the supramammillary nucleus or entorhinal cortex to the DG during REM sleep may both contribute to this variability.

g. In Figure 6, the authors should show the impact of DG Htr1a knockdown on sleep/wake structure including the frequency of MAs. I agree with the impact of Htr1a on DG ISO, but possible changes in sleep bout may induce the DG ISO disturbance.

As suggested, we have performed sleep analysis in the Htr1a knockdown experiments including MA quantification. We have found no significant difference between Hrt1-knockdown and control mice in any of the sleep metrics (see: supplemental figure 6). Our interpretation is that the lack of changes in sleep/wake cycles is likely due to the hippocampus not being directly involved in regulating these brain states.

(2) It is acceptable that DG Htr1a KO induces the reduced freezing in the CFC test (Figure 6E, F), but it is too much of a stretch that the disruption of DG ISO causes impaired fear memory. There should be a correlation.

We have modified the discussion accordingly.

(3) It is necessary to describe the extent of AAV-Cre infection. The authors injected AAV into the dorsal DG (AP -1.9 mm), but the histology shows the ventral DG (Supplementary Figure 4), which reduces the reliability of this study.

The histology image shown in the manuscript was taken from the -2.5 mm anteroposterior level, which we still consider to be part of the dorsal DG. For additional clarity, we have replaced the figure with new histology images slightly more anterior position (AP~2.0mm). 

Reviewer #3 (Public review):

Summary:

The authors employ a series of well-conceived and well-executed experiments involving photometric imaging of the dentate gyrus and raphe nucleus, as well as cell-type specific genetic manipulations of serotonergic receptors that together serve to directly implicate serotonergic regulation of dentate gyrus (DG) granule (GC) and mossy cell (MC) activity in association with an infra slow oscillation (ISO) of neural activity has been previously linked to general cortical regulation during NREM sleep and microarousals.

Strengths:

There are a number of novel and important results, including the modulation of dentage granule cell activity by the infraslow oscillation during NREM sleep, the selective association of different subpopulations of granule cells to microarousals (MA), the anticorrelation of raphe activity with infraslow dentate activity.

The discussion includes a general survey of ISOs and recent work relating to their expression in other brain areas and other potential neuromodulatory system involvement, as well as possible connections with infraslow oscillations, micro-arousals, and sensory sensitivity.

Weaknesses:

(1) The behavioral results showing contextual memory impairment resulting from 5-HT1a knockdown are fine but are over-interpreted. The term memory consolidation is used several times, as well as references to sleep-dependence. This is not what was tested. The receptor was knocked down, and then 2 weeks later animals were found to have fear conditioning deficits. They can certainly describe this result as indicating a connection between 5-HT1a receptor function and memory performance, but the connection to sleep and consolidation would just be speculation. The fact that 5-HT1a knockdown also impacted DG ISOs does not establish dependency. Some examples of this are:

a. The final conclusion asserts "Together, our study highlights the role of neuromodulation in organizing neuronal activity during sleep and sleep-dependent brain functions, such as memory.". However, the reported memory effects (impairment of fear conditioning) were not shown to be explicitly sleep-dependent.

We thank the reviewer for this comment. We have revised the sentence.

b. Earlier in the discussion it mentions "Finally, we showed that local genetic ablation of 5-HT1a receptors in GCs impaired the ISO and memory consolidation". The effect shown was on general memory performance - consolidation was not specifically implicated.

We have revised the sentence.

(2) The assertion on page 9 that the results demonstrate "that the 5-HT is directly acting in the DG to gate the oscillations" is a bit strong given the magnitude of effect shown in Figure 6D, and the absence of demonstration of negative effect on cortical areas that also show ISO activity and could impact DG activity (see requested cortical sigma power analysis).

We have revised the sentence.

(3) Recent work has shown that abnormal DG GC activity can result from the use of the specific Ca indicator being used (GCaMP6s). (Teng, S., Wang, W., Wen, J.J.J. et al. Expression of GCaMP6s in the dentate gyrus induces tonic-clonic seizures. Sci Rep 14, 8104 (2024). https://doi.org/10.1038/s41598-024-58819-9). The authors of that study found that the effect seemed to be specific to GCaMP6s and that GCaMP6f did not lead to abnormal excitability. Note this is of particular concern given similar infraslow variation of cortical excitability in epilepsy (cf Vanhatalo et al. PNAS 2004). While I don't think that the experiments need to be repeated with a different indicator to address this concern, you should be able to use the 2p GCaMP7 experiments that have already been done to provide additional validation by repeating the analyses done for the GCaMP6s photometry experiments. This should be done anyway to allow appropriate comparison of the 2p and photometry results.

We would like to thank the reviewer for this comment. We also analyzed the two-photon data in the same manner as the photometry data. However, the only supportive evidence that might be related to ISO in the two-photon data, recorded at the somatic level, was decreased fluorescence during MAs in the NREM-upregulated cell group (see Figure 3 D, E). We are unsure why this discrepancy exists, but we have discussed it in the manuscript and offered some alternative explanations. One hypothesis we are currently exploring relates to the different subcellular compartments sampled by the two imaging techniques. The photometry probe was implanted above the dentate gyrus, and since light collection efficiency declines sharply with distance from the probe tip (Pisano et al., 2019), we hypothesize that ISO is stronger at the dendritic level which directly receive the inputs from entorhinal cortex, and which is closest to the probe's tip. We are now conducting multiplane two-photon imaging experiments in our labs to test this hypothesis.

(4) While the discussion mentions previous work that has linked ISOs during sleep with regulation of cortical oscillations in the sigma band, oddly no such analysis is performed in the current work even though it is presumably available and would be highly relevant to the interpretation of a number of primary results including the relationship between the ISOs and MAs observed in the DG and similar results reported in other areas, as well as the selective impact of DG 5-HT1a knockdown on DG ISOs. For example, in the initial results describing the cross-correlation of calcium activity and EMG/EEG with MA episodes (paragraph 1, page 4), similar results relating brief arousals to the infraslow fluctuation in sleep spindles (sigma band) have been reported also at .02 Hz associated with variation in sensory arousability (cf. Cardis et al., "Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain", eLife 2021). It would be important to know whether the current results show similar cortical sigma band correlations. Also, in the results on ISO attenuation following 5-HT1 knockdown on page 7 (Figure 6), how is cortical EEG affected? Is ISO still seen in EEG but attenuated in DG?

Thank you for this valuable comment. We performed the analysis and found a positive correlation between cortical sigma band activity and DG activity during NREM sleep (see supplementary figure 1C-1E). Additionally, we conducted further analyses using the local 5-HT1a KO mouse model but did not observe significant changes in sleep architecture or MA frequency (see supplementary figure 6A). It is also important to note that ISO was only analyzed using calcium signals, not EEG signals. The standard filtering settings in our EEG data collection (0.5-500 Hz) do not allow us to analyze signals in such a low-frequency range.

(5) The illustrations of the effect of 5-HT1a knockdown shown in Figure 6 are somewhat misleading. The examples in panels B and C show an effect that is much more dramatic than the overall effect shown in panel D. Panels B and C do not appear to be representative examples. Which of the sample points in panel D are illustrated in panels B and C? It is not appropriate to arbitrarily select two points from different animals for comparison, or worse, to take points from the extremes of the distributions. If the intent is to illustrate what the effect shown in D looks like in the raw data, then you need to select examples that reflect the means shown in panel D. It is also important to show the effect on cortical EEG, particularly in sigma band to see if the effects are restricted to the DG ISOs. It would also be helpful to show that MAs and their correlations as shown in Figure 1 or G as well as broader sleep architecture are not affected.

We agree with the reviewer that the chosen example may appear somewhat exaggerated. However, we must point out that visually assessing missing or downregulated frequency components can be challenging. To provide a more objective presentation, we included Supplementary Figure 6B-C, in which we performed analysis similar to that in Fig1G in 5HT1a mice. These figures show a significant decrease in ISO amplitude, though the blockade is not complete, due to the incomplete nature of genetic manipulation with viral injection (see Suppl Fig 5). Furthermore, recent studies (Dong et al., 2023; Zhang et al., 2024; Kjaerby et al., 2022) have identified several other neuromodulatory and peptidergic systems that might affect DG activity during MAs.

To explore this further, we conducted pharmacological experiments. We administered 8-hydroxy-DPAT, a 5-HT1a agonist (i.p. 1 mg/kg) in Dock10-Cre mice injected with AAV-FLEX-GcaMP6s in the DG. Since 5-HT1a receptors act as autoreceptors on raphe 5-HT neurons, this treatment effectively silences the serotonergic system, thereby “removing” 5-HT signaling from the brain. The results, shown in Author response image 1, indicate that pharmacological suppression of 5-HT dampens the ISO in the DG during subsequent sleep intervals, with ISO recovering after the drug is washed out. These findings are consistent with the results obtained with the more specific local genetic manipulation. We have not included this result in the manuscript because we believe that the local downregulation is a cleaner experiment whose interpretation is more straightforward.

Author response image 1.

Finally, we also performed sleep analysis in 5-HT1a KO mice, showing that the local downregulation of 5-HT1a receptors had no significant effect on sleep metrics (Suppl Fig 6A). The hippocampus is not typically involved in regulating sleep-wake cycles, so we believe this result is consistent with that understanding.

(6) On page 9 of the results it states that GCs and MCs are upregulated during NREM and their activity is abruptly terminated by MAs through a 5-HT mediated mechanism. I didn't see anything showing the 5-HT dependence of the MA activity correlation. The results indicate a reduction in ISO modulation of GC activity but not the MA-correlated activity. I would like to see the equivalent of Figure 1,2 G panels with the 5-HT1a manipulation.

We agree with the reviewer on this point. We did not conduct any pharmacological or genetic manipulation in 2-photon calcium imaging experiments. We have removed that statement. As for the suggested analysis, please see our explanation above (Suppl Fig 6B-C).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Since the authors did not monitor DG neuronal activity with an electrophysiological tool, please rephrase the following sentence: "In this study, we investigated the neuronal activity of the dentate gyrus (DG) with electrophysiological and optical imaging tools during sleep-wake cycles." in the Abstract.

We have rephrased the sentence as suggested.

(2) Since the authors did not manipulate the serotonin release during sleep to investigate whether serotonin release modulates DG ISO, please edit the following sentence: "Further experiments revealed that the infraslow oscillation in the DG is modulated by rhythmic serotonin release during sleep" in the Abstract.

We have rephrased the sentence as suggested.

(3) Single-cell recording in DG with two-photon microscopy may address the issue raised in the 4th paragraph of the Discussion. In addition, in Fig 6C, the photometry has only captured the diminished oscillation in Htr1a KO, but cannot distinguish whether the activity levels of GC remain at high or low, which is a clear disadvantage of photometry.

We agree with the reviewer, and have added text to the discussion.

Reviewer #3 (Recommendations for the authors):

(1) Some of the figures are missing labels in the spectrogram panels (e.g. no freq units in Figures 4 and 6).

We have added information in those figures.

(2) Missing specific locations for EEG electrodes/screws. The text states "we predrilled 2 holes on the right side of the skull (1.5 mm posterior of the Bregma) for implanting recording electrodes". 2 holes on the right side of the skull are pretty vague.

We have added this information in the Methods.

(3) Some additional work that could be cited particularly when discussing the serotonergic impact on hippocampal function as it might relate to sleep and memory would include work linking mesopontine activity (both serotonergic and non-serotonergic) to memory-associated hippocampal sharp-wave ripple activity (e.g. Jelitai et al. Front. Neural Circ. 2021, Wang et al Nat. Neuro. 2015).

We have cited these papers.

(4) The work cited at the beginning of the Results describing higher population calcium activity during sleep states (15,18,30) is generally appropriate but not explicitly related to GCamP imaging. Pilz et al. "Functional Imaging of Dentate Granule Cells in the Adult Mouse Hippocampus", J.Neurosci. 2016 might be a more relevant citation.

We have added the citation.

eLife Assessment

This important study introduces a new cortical circuit model for predictive processing. Simulations effectively illustrate that, with appropriate synaptic plasticity, a canonical layer 2/3 cortical circuit - comprising two classes of interneurons providing subtractive and divisive inhibition - can generate uncertainty-modulated prediction errors by pyramidal neurons. The model is compelling; although it relies on many assumptions and has not yet been compared directly to data, the model does align with empirical observations and yields a range of testable predictions. The study is expected to be of great interest to those involved in cortical and predictive processing research.

Reviewer #2 (Public review):

Summary:

This computational modeling study addresses the observation that variable observations are interpreted differently depending on how much uncertainty an agent expects from its environment. That is, the same mismatch between a stimulus and an expected stimulus would be less significant, and specifically would represent a smaller prediction error, in an environment with a high degree of variability than in one where observations have historically been similar to each other. The authors show that if two different classes of inhibitory interneurons, the PV and SST cells, (1) encode different aspects of a stimulus distribution and (2) act in different (divisive vs. subtractive) ways, and if (3) synaptic weights evolve in a way that causes the impact of certain inputs to balance the firing rates of the targets of those inputs, then pyramidal neurons in layer 2/3 of canonical cortical circuits can indeed encode uncertainty-modulated prediction errors. To achieve this result, SST neurons learn to represent the mean of a stimulus distribution and PV neurons its variance.

The impact of uncertainty on prediction errors in an understudied topic, and this study provides an intriguing and elegant new framework for how this impact could be achieved and what effects it could produce. The ideas here differ from past proposals about how neuronal firing represents uncertainty. The developed theory is accompanied by several predictions for future experimental testing, including the existence of different forms of coding by different subclasses of PV interneurons, which target different sets of SST interneurons (as well as pyramidal cells). The authors are able to point to some experimental observations that are at least consistent with their computational results. The simulations shown demonstrate that if we accept its assumptions, then the authors' theory works very well: SSTs learn to represent the mean of a stimulus distribution, PVs learn to estimate its variance, firing rates of other model neurons scale as they should, and the level of uncertainty automatically tunes the learning rate, so that variable observations are less impactful in a high uncertainty setting.

Strengths:

The ideas in this work are novel and elegant, and they are instantiated in a progression of simulations that demonstrate the behavior of the circuit. The framework used by the authors is biologically plausible and matches some known biological data. The results attained, as well as the assumptions that go into the theory, provide several predictions for future experimental testing. The authors have taken into account earlier review comments to revise their paper in ways that enhance its clarity.

Weaknesses:

One weakness could be that the proposed theory does rely on a fairly large number of assumptions. However, there is at least some biological support for these. Importantly, the authors do lay out and discuss their key assumptions in the Discussion section, so readers can assess their validity and implications for themselves.

Comments on revisions:

I have no further suggestions for the authors.

Reviewer #4 (Public review):

Summary:

Wilmes and colleagues develop a model for the computation of uncertainty modulated prediction errors based on an experimentally inspired cortical circuit model for predictive processing. Predictive processing is a promising theory of cortical function. An essential aspect of the model is the idea of precision weighting of prediction errors. There is ample experimental evidence for prediction error responses in cortex. However, a central prediction of the theory is that these prediction error responses are regulated by the uncertainty of the input. Testing this idea experimentally has been difficult due to a lack of concrete models. This work provides one such model and makes experimentally testable predictions.

Strengths:

The model proposed is novel and well-implemented. It has sufficient biological accuracy to make useful and testable predictions.

Weaknesses:

One key idea the model hinges on is that stimulus uncertainty is encoded in the firing rate of parvalbumin positive interneurons. While this assumption is rather speculative, the model also here makes experimentally testable predictions.

Comments on revisions:

Congratulations on a very nice paper.

Author response:

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

Public Reviews:

Reviewer #2 (Public Review):

Summary:

This computational modeling study addresses the observation that variable observations are interpreted differently depending on how much uncertainty an agent expects from its environment. That is, the same mismatch between a stimulus and an expected stimulus would be less significant, and specifically would represent a smaller prediction error, in an environment with a high degree of variability than in one where observations have historically been similar to each other. The authors show that if two different classes of inhibitory interneurons, the PV and SST cells, (1) encode different aspects of a stimulus distribution and (2) act in different (divisive vs. subtractive) ways, and if (3) synaptic weights evolve in a way that causes the impact of certain inputs to balance the firing rates of the targets of those inputs, then pyramidal neurons in layer 2/3 of canonical cortical circuits can indeed encode uncertainty-modulated prediction errors. To achieve this result, SST neurons learn to represent the mean of a stimulus distribution and PV neurons its variance.

The impact of uncertainty on prediction errors in an understudied topic, and this study provides an intriguing and elegant new framework for how this impact could be achieved and what effects it could produce. The ideas here differ from past proposals about how neuronal firing represents uncertainty. The developed theory is accompanied by several predictions for future experimental testing, including the existence of different forms of coding by different subclasses of PV interneurons, which target different sets of SST interneurons (as well as pyramidal cells). The authors are able to point to some experimental observations that are at least consistent with their computational results. The simulations shown demonstrate that if we accept its assumptions, then the authors’ theory works very well: SSTs learn to represent the mean of a stimulus distribution, PVs learn to estimate its variance, firing rates of other model neurons scale as they should, and the level of uncertainty automatically tunes the learning rate, so that variable observations are less impactful in a high uncertainty setting.

Strengths:

The ideas in this work are novel and elegant, and they are instantiated in a progression of simulations that demonstrate the behavior of the circuit. The framework used by the authors is biologically plausible and matches some known biological data. The results attained, as well as the assumptions that go into the theory, provide several predictions for future experimental testing. The authors have taken into account earlier review comments to revise their paper in ways that enhance its clarity.

Weaknesses:

One weakness could be that the proposed theory does rely on a fairly large number of assumptions. However, there is at least some biological support for these. Importantly, the authors do lay out and discuss their key assumptions in the Discussion section, so readers can assess their validity and implications for themselves.

Thank you very much, we are very satisfied with this public review.

Reviewer #4 (Public Review):

Summary:

Wilmes and colleagues develop a model for the computation of uncertainty modulated prediction errors based on an experimentally inspired cortical circuit model for predictive processing. Predictive processing is a promising theory of cortical function. An essential aspect of the model is the idea of precision weighting of prediction errors. There is ample experimental evidence for prediction error responses in cortex. However, a central prediction of the theory is that these prediction error responses are regulated by the uncertainty of the input. Testing this idea experimentally has been difficult due to a lack of concrete models. This work provides one such model and makes experimentally testable predictions.

Strengths:

The model proposed is novel and well-implemented. It has sufficient biological accuracy to make useful and testable predictions.

Weaknesses:

One key idea the model hinges on is that stimulus uncertainty is encoded in the firing rate of parvalbumin positive interneurons. This assumption, however, is rather speculative and there is no direct evidence for this.

Thank you very much for this nice description. With regard to the weakness: it is true that the key idea hinges on uncertainty being encoded in the firing of inhibitory neurons. If it turns out that these inhibitory neurons are not PV neurons, however, the theory does not break down. The suggestion of PV neurons is fueled by the observation that PV neurons implement shunting and hence divisive inhibition and by the connectivity of PVs in the circuit. We discuss this in the discussion section: "To provide experimental predictions that are immediately testable, we suggested specific roles for SSTs and PVs, as they can subtractively and divisively modulate pyramidal cell activity, respectively. In principle, our theory more generally posits that any subtractive or divisive inhibition could implement the suggested computations. With the emerging data on inhibitory cell types, subtypes of SSTs and PVs or other cell types may turn out to play the proposed role."

Recommendations for the authors:

Reviewer #4 (Recommendations For The Authors):

(1) Line numbers would simplify reviewing.

We will add line numbers to our next submission.

(2) The existence of positive and negative PE was already suggested by Rao & Ballard.

We added the citation to the sentence "Because baseline firing rates are low in layer 2/3 pyramidal cells () positive and negative prediction errors were suggested to be represented by distinct neuronal populations [44,66],[...]" in the section "Computation of UPEs in cortical microcircuits".

(3) wekk should probably read well.

Indeed, thank you. We fixed it.

(4) Figure 4. legends A-C are mixed up. What are the two values of ¦s-u¦ in F and I - the same as in D and F.

Thank you, we fixed this.

(5) "representation neurons, the activity of which reflects the internal model". For consistency with the original definitions this should read "the activity of which reflects the internal representation". The internal "model" is the synaptic weights (or transformation between areas) - the activity of representation neurons (as the name implies) is the internal "representation".

Thank you, we changed it.

(6) "Mice trained in a predictable environment [...] [4]." This should read "reared" in an unpredictable environment, etc. Relatedly, the problem with this argument is that, the referenced paper argues that the mice never learned to predict and the reduced PE responses are a consequence of a reduction in prediction strength (these mice never - in life - had experience of visuomotor coupling). Better evidence might be the acute changes observed in normal mice (see e.g. Figure 3B in https://pubmed.ncbi.nlm.nih.gov/22681686/ However, another finding from the paper referenced is that in mice reared without visuomotor coupling, MM responses of SST interneurons are unchanged, while those in PV interneurons are completely absent. Would the authors model come to similar results if trained in an environment with (very) high uncertainty and then tested in a low uncertainty environment?

Thank you for pointing us to Figure 3B of Keller et al. 2012. We are now citing this result as it is indeed better evidence.

Thank you very much for your illuminating question and for pointing out that a mouse that never experienced a predictable visual flow may not have formed a model of the visual flow, and hence may not have any prediction about its visual experience. We haven’t considered this scenario in our paper before. So far, we only considered scenarios, in which it is possible to learn a prediction, i.e. to infer the mean from the sensory input. We now consider this other scenario in which the mouse that was reared in an unpredictable environment did not form a prediction and compare SST (1) and PV (2) activity in this mouse to one that learned to form a prediction, and added it to the section "Predictions for different cell types":

"Second, prediction error activity seems to decrease in less predictable, and hence more uncertain, contexts: in mice reared in a predictable environment [where locomotion and visual flow match, 42], error neuron responses to mismatches in locomotion and visual flow decreased with each day of experiencing these unpredictable mismatches. Third, the responses of SSTs and PVs to mismatches between locomotion and visual flow [4] are in line with our model (note that in this experiment the mismatches are negative prediction errors as visual flow was halted despite ongoing locomotion): In this study, SST responses decreased during mismatch, i.e. when the visual flow was halted, and there was no difference between mice reared in a predictable or unpredictable environment. In line with these observations, the authors concluded that SST responses reflected the actual visual input. In our model negative PE circuit, SSTs also reflect the actual stimulus input, which in our case was a whisker stimulus (SST rates in Fig. 6C and I reflect the stimuli (black and grey bar) in A and G, respectively) and SST rates are the same for high and low uncertainty (corresponding to mice reared in a predictable or unpredictable environment). In the same study, PV responses were absent towards mismatches in animals reared in an unpredictable environment [4]. The authors argued that mice reared in an unpredictable environment did not learn to form a prediction. In our model, the missing prediction corresponds to missing predictive input from the auditory domain (e.g. due to undeveloped synapses from the predictive auditory input). If we removed the predictive input in our model, PVs in the negative PE circuit would also be silent as they would not receive any of the excitatory predictive inputs."

(7) "Our model further posits the existence of two distinct subtypes of SSTs in positive and negative error circuits." There is some evidence for this: Figure 5a in https://pubmed.ncbi.nlm.nih.gov/36747710/

Thank you, we added this citation to the corresponding section.

eLife Assessment

This valuable study reports the induction of supernumerary inner hair cells in the mouse cochlea upon reducing the expression level of a tight-junction protein (claudin-9) at developmental stages. Although these ectopic hair cells are functional and persists through adulthood, the evidence supporting some of the claims is incomplete, particularly regarding the underlying mechanisms of cell differentiation and the potential of the approach for hair-cell regeneration. The work will be of interest to scientists working in the development and regeneration of hair cells in the inner ear.

Reviewer #1 (Public review):

The focus of this manuscript was to investigate the role of Cldn9 in the development of the mammalian cochlea. The main rationale of the study is the fact that cochlear hair cells do not regenerate, so when damaged they are lost forever, causing irreparable hearing loss. The authors have attempted to address this problem by inducing the ectopic production of additional hair cells and test whether they acquire the morphological and functional characteristics of native hair cells. They show that downregulation of Cldn9 using a well-established genetic manipulation of transgenic mice led to the production of extra numerary inner hair cells, which were able to survive for several months. By performing a large battery of experiments, the authors were able to determine that the native and ectopic inner hair cells have comparable morphological and physiological characteristics. There are several conclusions highlighted by the authors in different parts of the manuscript, including the key role of Cldn9 in coordinating embryonic and postnatal development, the differentiation of supporting cells into inner hair cells, and the possible use of Cldn9 to induce inner hair cell differentiation following deafness induced by hair cell loss.

Comments on revised version:

The authors have addressed the following points raised during the first submission: statistical analysis and wave 1 analysis. However, very little was done to address the other key aspects of my report, which are essential for the interpretation of the results. As mentioned in my previous report, some aspects of the work are not justified by the current data and will require either a tone-down of the claims or further experiments.

For example, one puzzling finding that is not addressed in the manuscript is the lack of functional benefit from these additional inner hair cells. In fact, it appears to be detrimental based on the increased ABR thresholds and EP. So, it is not clear to this reviewer the advantage of this approach.

It is not clear what direct evidence there is, apart from some immunostaining, indicating that the ectopic inner hair cells derive from the supporting cells. This part would benefit from a more careful consideration and maybe an attempt at a more direct experimental approach. Alternatively, the text should be modified accordingly.

One point that should be made clear throughout the manuscript is that the ectopic inner hair cells are generated in a cochlea that is undergoing normal maturation. Thus, there is no guarantee that modulating the expression levels of Cldn9 in a deaf mouse lacking hair cells would produce the same result as that shown in this study. This point should be at least discussed.

Reviewer #3 (Public review):

The study by Chen et al reports an interesting and previously unknown phenomenon of generation of supernumerary inner hair cells (IHCs) in response to downregulation of Cldn9 during embryonic or postnatal development. The authors developed an inducible doxycycline (dox)-tet-OFF-Cldn9 transgenic mice to regulate expression levels of Cldn9 and show that downregulation of Cldn9 resulted in additional, although incomplete row of IHCs immediately adjacent to the original IHC row. These induced extra IHCs had similar well-developed hair bundles, able to mechanotransduce and were innervated by auditory neurons, resembling wild-type IHCs. In addition, the authors knock down Cldn9 postnatally using shRNA injections in P1-7 mice with similar induction of extranumerary IHCs next to the original row of IHCs. The conclusions of this paper are mostly well supported by the data. However, some data analyses are limited, and some important controls are not shown.<br /> The data from this study are important and promising for future gene therapy applications. The generation of extra IHCs postnatally using downregulation of Cldn9 by shRNA could potentially be used as a replacement of IHCs lost after noise-induced trauma, ototoxic agents, or other environmental trauma. However, it is not clear if downregulation of CLDN9 in adult mice would lead to extranumerary IHCs. On the other hand, the replacement of lost inner hair cells due to various genetic mutations by inducing supernumerary mutant IHCs with the same abnormalities would not be reasonable.

The authors show that postnatally generated ectopic IHCs are viable and mechanotransducive, but the hearing function of the mice with ectopic hair cells is not improved. However, the ectopic hair cells seems to be generated from supporting cell trans-differentiation, and the intricate mosaic of the organ of Corti is altered (the extra row of IHCs seems to be positioned immediately adjacent to the original IHC row), which could by itself lead to hearing issues. It is not clear if the newly formed unusual junctions between the ectopic and original IHCs are sufficiently tight to prevent leakage of the endolymph to the basolateral surface of IHCs. Also, it is not clear if the other organ of Corti tight junctions could lose their tightness due to the downregulation of Cldn9, which could over time affect the endocochlear potential and hearing abilities as shown by this study.

Overall, the manuscript could be of interest to scientists working in the inner ear development and regeneration field, and to the hearing researchers in general and perhaps developmental biologists and cell biologists interested in tight junction proteins and their function.

Strength

The methodologies used are solid and convincing. There is a great potential for practical use of these valuable findings and new knowledge on IHC developmental regulation by Cldn9 expression.

Weakness

Some of the data in this study would benefit from showing corresponding negative controls and higher-resolution images of CLDN9 localization, which the authors chose not to show in the revised manuscript. Importantly, CLDN9 immunofluorescence staining data look different from previously published observations and show cytoplasmic staining of supporting cells only and did not show the staining of tight junctions between the OHCs and supporting cells as well as between the IHCs and supporting cells as reported previously (Kitajiri et al., 2004; Nakano et al., 2009, Ramzan et al., 2021). The organ of Corti schematics showing CLDN9 expression reflects the authors' immunostaining data but is unusual considering that CLDN9 localizes to the tight junctions of the reticular lamina as was shown by immuno-EM in this study and described in previous publications (Kitajiri et al., 2004; Nakano et al., 2009, Ramzan et al., 2021). However, the authors did not provide an explanation for these discrepancies in the Discussion of the manuscript.

Also, more detailed investigations would in some instances clarify the data. For example, it is not clear if the downregulation of Cldn9 affects the other genes known to participate in cell fate determination, and why downregulation of Cldn9 expression resulted in production of extranumerary inner hair cells only and not the other cell types, like OHCs, for example.

Reviewer #4 (Public review):

The work by Yingying Chen, Jeong Han Lee, and co-authors summarizes the morphological and functional outcomes of Cldn9 loss in the inner ear, particularly in the organ of Corti. While the study does not provide mechanistic insights into how the developmental loss of Cldn9 leads to ectopic hair cell formation, the phenomenon itself is curious. The work primarily focuses on a detailed characterization of the ectopic hair cells, which is well done. Despite the lack of mechanistic insights, the study will be of interest to the inner ear field if several major issues with the manuscript are addressed.

(1) The title, "Genetic and pharmacologic alterations of claudin9 levels suffice to induce functional and mature inner hair cells," is misleading. First, both manipulations (knockout and knockdown) are genetic, and no pharmacology is involved. Second, both manipulations are carried out during the embryonic and neonatal periods, and there is no evidence of mature hair cell regeneration in this study. The title should be revised to reflect this. A more accurate title could be: "Developmental loss of Cldn9 results in functional ectopic inner hair cells that persist through adulthood."<br /> (2) Contact-mediated lateral inhibition in hair cell fate determination is one of the most well-studied phenomena in the inner ear field, and numerous groups have shown that it is mediated by Notch signaling. This must be added to the introduction.<br /> (3) A large body of literature has demonstrated that Notch inhibition alone is not sufficient to regenerate hair cells in adult mice. Therefore, if the loss of claudins disrupts Notch signaling-the proposed mechanisms in the discussion - it is unlikely to be a viable therapeutic strategy for hair cell regeneration in the adult ear. Furthermore, no hair cell ablation experiments were conducted to demonstrate what could be considered true regeneration. These speculative statements should be removed or revised accordingly.<br /> (4) Cldn9 is a tight junction protein and should localize to the membrane. Yet, the data presented show what appears to be diffuse cytoplasmic staining, which is concerning.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The focus of this manuscript was to investigate the role of Cldn9 in the development of the mammalian cochlea. The main rationale of the study is the fact that cochlear hair cells do not regenerate, so when damaged they are lost forever, causing irreparable hearing loss. The authors have attempted to address this problem by inducing the ectopic production of additional hair cells and testing whether they acquire the morphological and functional characteristics of native hair cells. They show that downregulation of Cldn9 using a well-established genetic manipulation of transgenic mice led to the production of extra numerary inner hair cells, which were able to survive for several months. By performing a large battery of experiments, the authors were able to determine that the native and ectopic inner hair cells have comparable morphological and physiological characteristics. There are several conclusions highlighted by the authors in different parts of the manuscript, including the key role of Cldn9 in coordinating embryonic and postnatal development, the differentiation of supporting cells into inner hair cells, and the possible use of Cldn9 to induce inner hair cell differentiation following deafness induced by hair cell loss.

Strengths:

Several of the conclusions in this study are well supported by the experimental work.

Weaknesses:

Some aspects of the data and its interpretation needs better explanation and requires further investigation.

(1) The Results section is the most difficult part to read and understand. It contains a very limited, and in some places confusing and repetitive, description of the data. Statistical analysis is missing for some of the key data (e.g., ABRs), and in some places the text contradicts the data presented in the figures (e.g., Figure 8). I am sure carefully revising the text would clarify some of these issues.

We thank the reviewer for the suggestion. We revised parts of the results section and added the statistical analysis to the ABRs and DPOAE (lines 151-159; Page 29, lines 846-880). 

(2) One puzzling finding that is not addressed in the manuscript is the lack of functional benefit from these additional inner hair cells. In fact, it appears to be detrimental based on the increased ABR thresholds. Maybe it would be useful to analyze the wave 1 characteristics.

We thank the reviewer for the suggestion. We added the wave 1 characteristics as S8.

(3) It is not clear what direct evidence there is, apart from some immunostaining, indicating that the ectopic inner hair cells derive from the supporting cells. This part would benefit from a more careful consideration and maybe an attempt at a more direct experimental approach.

We thank the reviewer for the suggestion. We intend to investigate the origin of the ectopic inner hair cells using (for example, a qRT-PCR, sm FISH, etc.) in our future study.

(4) One point that should be made clear throughout the manuscript is that the ectopic inner hair cells are generated in a cochlea that is undergoing normal maturation. Thus, there is no guarantee that modulating the expression levels of Cldn9 in a deaf mouse lacking hair cells would produce the same result as that shown in this study. My guess is that it probably won't, but I am sure this could be tested (maybe in the future) using the excellent experimental approach applied in this study.

That is a great point. We will explore it in our future experiments.

Reviewer #2 (Public Review):

Summary:

The generation of functional extranumerary inner hair cells (IHCs) in postnatal mice, particularly with virus-mediated knockdown of Cldn9 mRNA expression in the neonatal cochlear duct, is an important observation. It is significant because not many studies exist that report molecular manipulations of the neonatal organ of Corti that result in the generation of new hair cells that remain functional and appear to be intact for an extended time, here more than one year. Overall, this is a carefully conducted study; the observations are clear, and the methods are solid. Two independent methods for reducing the expression of Cldn9 mRNA were used: a conditional transgenic model and AAV-mediated knockdown with shRNA. The lack of a functional explanation of how the reduced expression of Cldn9 specifically leads to the formation of extranumerary IHCs leaves open questions. For example, it is not clear whether there is indeed a fate change happening and whether Cldn9 reduction affects developmental processes. The discussion of how Cldn9 reduction potentially affects Notch signaling, without hard evidence, is handwaving.

Strengths:

It is a very interesting observation and somewhat unexpected in its specificity for inner hair cells. Using two different approaches to manipulate Cldn9 expression provides a strong experimental foundation. The study is conducted quantitatively and with care.

Weaknesses:

The lack of mechanistic insight results in an open-ended story where at least the potential interaction of Cldn9 reduction with known and well-characterized signaling pathway components should have been investigated. This missed opportunity limits the scope of the study and should be addressed: How does Cldn9 downregulation affect the expression levels of other known genes linked to hair cell production and cell fate decisions? Quantitative RT-PCR works well for the authors, and comparing the expression of Notch or other known pathway components could provide mechanistic insight.

We thank the reviewer for the suggestion. We did quantitative RT-PCR to compare the expression of Notch or other known pathway components in our future work. Besides, we used smFISH with ccnd1 probe and cdkn1b probe to detect cyclin D1 and cyclin-dependent kinase inhibitor 1B (p27) separately in the mouse cochlea. GAPDH was selected as a reference gene. The quantification results showed no significant difference between Cldn9^+/T mice and Cldn9^+/+ mice at P2, P7, and P14.

It is unclear how P21 inner hair cells were identified for the patch-clamp experiments shown in Fig 4E-H. This is a challenging endeavor without the possibility of using specific markers.

We did not have a specific marker for IHCs. However, one with experience in hair bundle morphology and knowledge of their location in the epithelia can identify IHCs from the upright microscope.

Please also address the numerous minor points outlined below; it will improve the paper's readability.

Thanks. Please find the point-to-point answers below.

Please include page numbers and line numbers in a revised manuscript.

We include page numbers and line numbers in a revised manuscript.

Reviewer #3 (Public Review):

This important study by Chen et al help in advancing our knowledge about the regulation of inner hair cell (IHC) development and revealed the role of Cldn9 in IHC embryonic and postnatal induction by transdifferentiation from the supporting cells. The authors developed an inducible doxycycline (dox)-tet-OFF-Cldn9 transgenic mice to regulate expression levels of Cldn9 and show that downregulation of Cldn9 resulted in additional, although incomplete row of IHCs immediately adjacent to the original IHC row. These induced extra IHCs had similar well developed hair bundles, able to mechanotransduce and were innervated by auditory neurons resembling wild-type IHCs. In addition, the authors knock down Cldn9 postnatally using shRNA injections in P1-7 mice with similar induction of extranumerary IHC next to the original row of IHCs. The conclusions of this paper are mostly well supported by the data, but some data analysis needed to be clarified and some crucial controls should be provided to improve the confidence in the presented results. There is a great potential for practical use of these valuable findings and new knowledge on IHC developmental regulation to design Cldn9 gene therapy in the future.

The described by Chen et al mechanisms of extra hair cell generation by suppression of the tight junction protein Cldn9 expression level are very interesting and previously unknown. In particular, the generation of extra IHCs postnatally using downregulation of Cldn9 by shRNA could potentially be very useful as a replacement of HCs lost after noise-induced trauma, ototoxic agents, or other environmental trauma. On the other hand, the replacement of lost hair cells due to various genetic mutations by inducing a supernumerary IHCs with the same abnormalities would not be reasonable.

The authors show that postnatally generated ectopic IHCs are viable and mechanotransducive, but it would be nice to show the maturation steps of ectopic IHC during this postnatal period. For example, stereocilia bundles of the ectopic hair cells should mature later than the original IHCs. A few days after viral delivery of shRNA, you should be able to observe immature IHC bundles that unequivocally will define newly generated IHCs. Unfortunately, the authors show only examples of already mature ectopic IHCs at P21 and in 5-6 weeks old mice and at relatively low resolution. Also, during maturation, IHCs usually have transient axo-somatic synapses that are not present in mature IHCs. It would be great to see if, in 5-6 weeks old mouse, the ectopic IHCs still have axo-somatic synapses or not, and if the majority of the ectopic IHCs have innervation. Some of the data in this study would benefit from showing corresponding controls and some - from higher resolution imaging.

We appreciate the reviewer's suggestion. The objective of the paper is to report the phenomenon and present the coarse features of the Cldn9-mediated induced ectopic hair cells. The systematic details are for future studies, which are ongoing and out of the current scope.

In the mammalian cochlea, each HC is separated from the next by intervening supporting cells, forming an invariant and alternating mosaic along the cochlea's length. Cochlear supporting cells in some conditions can divide and trans-differentiate into HCs, serving as a potential resource for HC differentiation, using transcription and other developmental signaling factors.

However, when ectopic hair cells are generated from supporting cell trans-differentiation, the intricate mosaic of the organ of Corti is altered, which could by itself lead to hearing issues. In case of downregulation of Cldn9, the extra row of IHCs seems to be positioned immediately adjacent to the original IHC row. It is not clear if the newly formed unusual junctions between the ectopic and original IHCs are sufficiently tight to prevent leakage of the endolymph to the basolateral surface of IHCs. Also, it is not clear if the other organ of Corti tight junctions could lose their tightness due to the downregulation of Cldn9, which could over time affect the endocochlear potential as shown by this study and hearing abilities.

There was a slightly increased ABR threshold (5 dB -15 dB) (Fig. 4A) and a decrease in the magnitude of the EP and the rise in the K⁺ concentration in the endolymph and perilymph of Cldn9+/T mice compared to from age-matched littermates (S10) indicated there might be a compromised epithelium tight junction. The downregulation of Cldn9 affected the endocochlear potential and hearing abilities ((Fig. 4A, S10) after 2m, suggesting an age-dependent effect. The effective downregulation of Cldn9 would require proper titration of Cldn9 levels to induce extra hair cells with intact epithelial integrity; work may require additional studies.

Importantly, CLDN9 immunofluorescence staining data that show cytoplasmic staining of supporting cells should be revisited and the organ of Corti schematics showing CLDN9 expression should be corrected, considering that CLDN9 localizes to the tight junctions of the reticular lamina as was shown by immunoEM in this study and described in previous publications (Kitajiri et al., 2004; Nakano et al., 2009, Ramzan et al., 2021). While the current version of the manuscript will interest scientists working in the inner ear development and regeneration field, it could be more valuable to hearing researchers outside this immediate field and perhaps developmental biologists and cell biologists after proper revision.

We appreciate the reviewer's comments. We were concerned about the observation, but the results were consistent. Indeed, that was the motivation for performing the immunoEM (S3). A follow-up report may address it further.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Please address the points I made about the presentation (word choice, inconsistencies in labeling, etc). It ultimately helps a reader to understand and to follow your logic. This is an important observation.

We corrected the inconsistencies in labeling and addressed the points you suggested.

Making the extra effort to investigate a possible interaction between Cldn9 and Notch signaling would substantially increase the significance of the work.

Thanks for the suggestions. We will explore it in our future work.

Minor points:

Some sentences would benefit from revision:

- The abstract argues that hearing loss is incurable because mammalian hair cells are terminally differentiated (3rd sentence). This is not accurate.

Mammalian HCs are terminally differentiated by birth, making HC loss challenging to replace.

- The second sentence of the second paragraph of the introduction, "Cochlear SCs can divide and trans-differentiate into HCs, serving as a potential resource for HC differentiation, using transcription and developmental signaling factors (White et al., 2006)," should be referenced in the context of the animal's age. This feature of supporting cells is transient and only observed in neonatal mice. The following sentences in the same paragraph would also benefit from being placed into the same context when appropriate.

We thank the reviewer for the suggestion. These sentences have been corrected.

- Introduction: "But functional features of the newly developed HC are circumspect." The authors probably meant "circumspect," but is this the appropriate word? Also, please use the plural of HC = HCs.

The sentence has been corrected to “but the functional features of the newly developed HCs are circumspect”.

- Introduction: Isn't an essential function of tight junctions in the organ of Corti the separation of fluid-filled spaces? Perhaps additional functions of tight junction proteins are unclear, but at least this one function appears clear.

We thank the reviewer for the suggestion. We added the “additional” before the “function” in this sentence.

- Introduction: "using shRNA injection in postnatal (P) days (P1-7) mice." This is a rather vague statement that could be better defined. Perhaps mention that the injections targeted the round window and that an AAV-based method was used. Also, it is not clear from the methods whether the injection needle pierced the round window. Please clarify. Likewise, the methods state that these experiments were conducted in P1-P15 mice, but the main text says P1-P7. Later, in the results section and in the figure legend for Fig 7, the mice are between P1-P7 and P14; the figure itself is labeled with P1 and P14. However, data is presented (Fig 6) for injections at P2, P4, P7, and P14. In the text referring to Fig 6B in the results section, it is stated, "By contrast, the P14-21 inner ear transfected with Cldn9-shRNA produced no detectable increase..." Only data for P2, P4, P7, and P14 injections are presented. These are minor issues, but please check the inconsistencies because they make it difficult to follow.

We corrected this sentence to “Analogous additional putative IHCs differentiation was observed when Cldn9-shRNA was injected through the round window to postnatal (P) days (P2-7, and P14) mice…”.  The label in Fig 7A has been changed to P2-7, and the text referring to Fig 6B in the result section has been changed to “the P14 inner ear transfected with Cldn9-shRNA produced no detectable increase...".

- Last statement of the Introduction: "making Cldn9 a viable target for generating transformed IHCs." It is not clear what transformed IHCs are.

We replaced the transformed with supernumerary.

- To understand the Southern Blot analysis in Fig 1E, the location of BstAPI and BamHI restriction sites and the probe need to be illustrated in Fig 1D.

The restriction sites BstAPI, (Bst), and BamHI (Bam) are indicated (Fig. 1D).

- Please define the purple arrows and arrowheads in Fig 1D. What do the different colors for the backbone mean? I see red and green, but also orange and yellow in the floxed allele. In Fig 1F, is "Knock-in" synonymous with homozygote? Would it be clearer to use the nomenclature Cldn9(T/T), Cldn9(T/+), and Cldn9(+/+), which is used later in the text?

We have made the changes as requested.

- Results, first paragraph: "Results of RT-PCR..." This refers to quantitative RT-PCR; please add the word "quantitative."

Thanks. We added “quantitative” to the sentence.

- Results and Fig S1. Is the strong upregulation of Cldn9 mRNA (S1A) also reflected in stronger Cldn9 immunoreactivity?

Yes, the strong upregulation of Cldn9 mRNA showed higher cldn9 immunoreactivity.

- Results, Fig 1. Please add a schematic drawing showing all elements of the inducible gene expression cassette in the final transgenic allele, and please illustrate how the system works. This helps the reader to understand the strong Cldn9 mRNA upregulation in Cldn9(T/T) mice, where expression is likely driven by the CMV promoter and reciprocally, in the presence of doxycycline, the suppression of transcription by binding of the tTA-dox protein to the TRE elements of the modified CMV promoter. Is this a correct assumption?

Yes, this is a correct assumption

- Results, about Fig S3. Why is it important to investigate Cldn6 and ILDR1 levels in the context of Cldn9 downregulation? Also, that is meant with "no comparative differences in others?". If a potential compensatory effect is suspected, why are the authors not systematically characterizing the expression of other tight junction proteins with quantitative RT-PCR? The results shown in S3 are anecdotal, without proper quantification, and lack context.

The goal is to examine the potential compensatory changes in other TJ proteins. It was not to examine all possible TJ proteins localized in the inner ear.

Results, section headed with "Downregulation of..." First sentence. Fig. 2A-C à Fig. 2A-E.

Thanks. We corrected the sentence “5-week-old mice Cldn9^+/T cochleae displayed a notable row of ectopic HCs (Fig. 2A-C).” to “5-week-old mice Cldn9^+/T cochleae displayed a notable row of ectopic HCs (Fig. 2A-E).”

The same section: "were negatively labeled with anti-prestin antibody." Consider "were not labeled with antibody to prestin." Likewise, a few sentences below, please consider rephrasing "the ectopic HCs ... reacted positively to otoferlin antibodies". Also, "...expressed multiple CtBP2 labeling..." - this reads like an incomplete sentence.

Thanks for the suggestions. We have corrected the three sentences mentioned.

The phrase "putative ectopic" lacks clarity because "putative" could refer to "ectopic" (like an adverb). Consider swapping the two words and writing "ectopic putative IHCs" or simply "ectopic IHCs."

Thanks for the suggestions. We replaced the “putative ectopic IHCs” with “ectopic IHCs” in all contexts.

Please use more precise figure labels when referring to a specific figure panel. For example, "Additionally, the ectopic HCs show IHC bundle features (Fig. 2)," - Bundles are shown in Fig 2D and Fig 2E. Please check all instances where a full figure is mentioned, but the specific reference is to a panel of the figure. Another example, "... using quantitative RT-PCR (S7)..." would be more specific if Fig S7A is referred to.

Thanks for the suggestions. We checked all instances and corrected the labels. Thanks!

"IHC counts at different ages (P2-P21) and the cochlear frequency segments (4-32 kHz) demonstrate..."- the figure shows data for 8 kHz and 32 kHz; please revise: "segments (8 kHz and 32 kHz) demonstrate."

This sentence has been revised based on your suggestion. Thanks!

Please add a legend to Fig. 3C (like the one shown in Fig. 2F).

Thanks for the reminder. The legend for Fig. 3C was modified.

Fig 4A and Fig 4B. It is impossible to distinguish the open/closed circles and the many lines. Please consider a different format or an extended supplemental figure. Also, drawing a line connection between the 32 kHz and click data points in 4A is inappropriate.

Instead of the open/closed circles, the dashed line means Cldn9^+/+ mice, and solid lines represent Cldn9^+/T mice. We added the line labels. The line connecting between 32 kHz and click data points was removed.

Fig 4, legend. Please define BHB and BHC levels.

BHB and BHC are defined.

The paragraph "Synaptic features of PE IHCs match original IHCs" is confusing because it states the following: "The synapses between the IHCs and auditory neurons at the apical, middle, and basal cochlear locations from 5-week-old Cldn9+/+ and Cldn9+/T mice show substantial differences." The meaning of the heading, therefore, does not match what is ultimately shown and discussed.

We have changed the title to “Synaptic features of ectopic IHCs and original IHCs”.

Moreover, no actual features of synapses are investigated; CtBP2/Homer pairs were used to identify afferent synapses, which this reviewer would argue provides a reasonable estimate of the number of synapses where pre- and post-synaptic markers are detected in close vicinity. It would be helpful to describe the method for counting juxtaposed CtBP2 and Homer-labeled puncta with more detail.

The method section now includes more information about the synapse count, which this reviewer would argue provides a reasonable estimate of the number of synapses where pre- and post-synaptic markers are detected in close proximity.

The final concluding sentence of the section also suggests that synaptic transmission from PE IHCs might be compromised because significant differences in synapse numbers were identified. It would be important to mention this.

Thanks for the reminder. We added this information to the final concluding sentence.

Fig. 5C, 5D; legend. Is "co-expressed" the right word choice? Consider "colocalized" or "juxtaposed".

The "co-expressed" has been replaced with "colocalized".

Voltage-clamp recordings of P21 inner hair cell mechanoelectrical transduction currents. This reviewer cannot identify a previous publication describing the details of this method on P21 cochlear inner hair cells; this seems like an excellent methodological advance.

Yes, we can record data from older mice. Thanks for pointing it out.

"Transfection in vivo of Cldn9 shRNA," the P14-21 inner ear transfected with Cldn9-shRNA." Plus, additional use of the word "transfection." Transfection generally means the introduction of plain nucleic acid into cells. The word refers to methods that do not use viruses. In contrast, "transduction" is the term used for virus-mediated gene transfer. The authors used AAVs. Please correct for appropriate scientific terminology.

Thanks for the clarification. This information has been corrected accordingly.

"A slight decline in the amplitude of the EP and a substantial rise in perilymph K+ was detected in 8-month-old Cldn9+/T (S7)." Probably Fig. S8A,B is meant.

Yes, it referred to Fig. S8 A, B. We corrected it in the result section. Thanks!

Heading "Discussions" -> "Discussion"

The focus of the second part of the discussion on potential interactions between Cldn9 suppression and known signaling pathways is essential. The logic that is presented with respect to Notch signaling, however, is not clear and misleading. For example, it is not obvious what is meant by "Cldn9 subserves the signaling catalyst to activate NICD cascades" and whether this statement is supported by any published data.

The statement was a suggestion and has been qualified with a “may” clause (line 299).

The authors might consider discussing whether the observed effect caused by Cldn9 elimination is a specific role of the Cldn9 protein itself or is an epiphenomenon resulting from cytomechanical changes in the developing and maturing organ of Corti. This would add a potential Notch-independent component for a possible interpretation of the observations.

We state lines 302-304 “Alternatively, Cldn9 levels disruption may alter the mechanical properties of the developing and maturing organ of Corti that may trigger ectopic IHC differentiation, an epiphenomenon independent of the Notch signaling“.

Methods:

"Deletion of the selection marker in the tTA cassette by crossing the F1 mouse with the embryonic Cre line (B6.129S4-Meox2tm1(cre)Sor/J)." This sentence seems to be incomplete.

Thanks for pointing it out. This sentence has been rewritten.

"Images were captured under a confocal microscope." Consider writing "with a confocal microscope".

This sentence has been corrected. Thanks!

RNA extraction and... How many mice were used per experiment? 10-15 or just 10?

The mice number for the RNA extraction is between 10 and 15. Thanks

Reviewer #3 (Recommendations For The Authors):

Below are my suggestions, questions, and criticisms.

(1) The red outline on Fig1A schematic does not correspond to the previously published expression pattern of CLDN9 in the organ of Corti reticular lamina tight junctions (Kitajiri et al, 2004, Nakano et al., 2009, Ramzan et al., 2021). Also, there are no tight junctions all around the pillar cells. The tight junctions are restricted to the sites of tight attachments between two cells. The immunofluorescence staining using CLDN9 antibody looks rather cytoplasmic (Fig 1 and Fig S1) than associated with the tight junctions as it was shown by immunoEM data here and reported previously (Kitajiri et al, 2004; Nakano et al, 2009; Ramzan et al, 2021). Please correct the schematic and explain your data.

We have redrawn the diagram (Fig. 7).

(2) The CLDN9 staining in Figure 1, B and C, highlights the cytoplasm of the supporting cells, and hair cells devoid of the staining. From the images in Fig. S1C, it also looks like CLDN9 is present only in supporting cells and not in hair cells? How would the authors reconcile their data with Cldn9 expression data from the gEAR database and Ramzan et al.'s 2021 RNAscope data? Please provide the validation of the antibody used in this study.

We recognize the reviewer’s concern but RNA and protein levels are not always in parallel.

(3) Figure 1D. The dash lines from the targeting vector to the wt allele seem to indicate a recombination event. Please do not show the recombination event, instead just show what part of the targeting vector was incorporated to replace wt Cldn9. There is no description in the figure 1 legend what purple arrows and arrowheads mean and what yellow and orange line segments in the floxed allele schematic indicate. Please also show where the BstAPI and BamHI restriction enzyme sites are.

We have provided supplement Fig 1., and have noted the BstAPI and BamHI restriction enzyme sites in Fig. 1D.

(4) What does the organ of Corti that has 40-to-55-fold increase in Cldn9 mRNA expression looks like before dox treatment? Any abnormalities at all? How is CLDN9 protein localization looks in the Cldn9+/T untreated mice? Do they have normal number of IHCs? Cldn9+/T untreated mice should be used as another control at least in Figure S1. What does the organ of Corti that has a 40-to-55-fold increase in Cldn9 mRNA expression look like before dox treatment? Are there any abnormalities at all?

The untreated Cldn9^+/T mice can grow normally but are not fertile. So, we used a very low concentration of dox water (0.1 mg/ml) instead of normal water to keep the breeding pairs. The protein level increased in the Cldn9^+/T mice compared with Cldn9^+/+mice. With 0.1 mg/ml dox water, they also showed ectopic IHCs.

(5) It is interesting that decline of 0.4-0.6-fold in mRNA level leads to about 8-fold decrease in protein level based on your immunoEM data on tight junctions of IHC with supporting cells. Do you observe the same effect in OHC-SC tight junctions, or the decrease was observed selectively around IHCs?

The reviewer is alluding to matching RNA and protein levels. It appears that for Clnd9 one cannot expect a closely matched relationship.

(6) The quality of the immunoEM data is great, but a control of secondary antibody alone staining in wt and Cldn9+/T dox treated should be shown and compared to the Cldn9+/T treated sample.

We thank the reviewer for raising the issue. Secondary antibodies are used as a control in all immunoEMs in the laboratory. We opted not to show negative results.

(7) The authors observed a decrease in Cldn6 expression albeit not quantitative in response to Cldn9 downregulation. How were the immunofluorescence signals compared and evaluated? Please provide a detailed description of the method used. Did the authors used the same image acquisition parameters? Was the Cldn9 and Cldn6 immunostaining done using same protocol with the same aliquot and dilution of the secondary antibodies, etc.? The staining for CLDN6 seems to be concentrated in the cytoplasm of supporting cells, and not in the tight junctions, similar to CLDN9 immunoreactivity shown in Fig. S1C and to the ILDR1 pattern of staining in Fig. S3. How can the authors explain this? How were the antibodies validated?

The Cldn9 and Cldn6 immunostaining were done using the same protocol with the same aliquot and dilution of the secondary antibodies.

(8) CLDN14 is also expressed in the organ of Corti tight junctions. What happened to this TJ protein during CLDN9 downregulation?

We detected Cldn14 with immunostaining in the Cldn9+/T mice and Cldn9+/+ mice fed with 0.25 mg/ml dox water, and the results showed increased expression of Cldn14 in Cldn9+/T mice. Detail alterations of other TJ proteins have been reserved for future studies. 

(9) When supernumerary IHCs were observed in Cldn9+/T mice, have the authors noticed a corresponding decrease in supporting cells surrounding IHCs? Quantification of the IHCs supporting cells would be useful. Do the ectopic IHCs have apical tight junctions with original IHCs or they are surrounded by supporting cells?

We quantified the SCs around the IHCs but did not detect significant differences among the groups.

(10) The authors indicated that viable PE IHCs were observed in 15 months old Cldn9+/T dox treated mice. How stereocilia bundles look in these ectopic hair cells? Are they preserved similar to the original IHCs or degenerated? It is hard to see this in Fig 3, phalloidin panel. High-resolution SEM would show this better.

For the remaining ectopic IHCs in 15 months, we did not detect apparent differences in hair bundles compared with the original IHCs.

(11) Interestingly, the authors indicate that the highest number of the ectopic IHCs were developed in the apical turn and the higher elevation of ABR threshold was also observed at low frequencies end. This may indicate that extra IHCs do not help hearing function.

The extra IHCs showed along the whole cochlea, even though it is more obvious in the apical turn. The declined hearing may have resulted from the leakage of the endolymph K+ to the perilymph and EP decline.

(12) No age-matched wt control is shown for decreased expression of Cldn9 after shRNA injection at P2 (Fig. 6A).

As indicated earlier, we opted to state but did not show negative results.

(13) Figure 6C. The better- quality SEM images showing a longer stretch of IHCs are needed to convince readers that there are ectopic IHCs that are well preserved in 5-6 weeks old mice in all cochlear turns after GFP-Cldn9 shRNA treatment at P2-P7.

In S4, we showed that there are ectopic IHCs along the cochlear axis.

(14) Do scrambled shRNA control samples had some ectopic IHCs? This control is missing in Fig.6D.

No scrambled shRNA controls did not show ectopic IHCs. We have stated it.

(15) Figure 7B, lower schematic. There are no known continuous tight junctions and CLDN9 expression around the OHCs and IHCs. CLDN9 is known to be concentrated at the reticular lamina tight junctions which separate the endolymph from perilymph. Please, correct all schematics accordingly.

We have made the changes as requested.

Minor comments:

(1) Page 1, Abstract. I would not say "making HC loss incurable" since recent gene therapy results show some advances in this direction. Please rephrase more accurately.

We have made the changes as requested.

(2) Page 4, Results, line 5; please rephrase "PCR of tail tissue samples performed genotyping."

It has been corrected to “The genotyping was performed by the PCR with the tail tissue.”

(3) Fig. 1 legend, panel B, replace "showing IHC stained myosin7a" with "showing IHC stained by myosin7a". Also, in the same sentence, "phalloidin, actin (green) antibodies," Phalloidin is not an antibody; please change this.

Thanks. We have corrected this information.

(4) Fig 2C, IHC label obscures the view of IHCs, please move this label out and use an arrow to point to IHCs.

We have made the changes as requested.

(5) Figure 4, title. Replace "currents elicited original" with "current elicited from original".

This sentence has been corrected. Thanks.

(6) Figure 4, panel A. It is hard to see the open symbols on the graph. Are they associated with the dash lines? Please make them more visible or indicate what dash lines are. "ABR threshold for (n=12)" should be "ABR threshold for Cldn9+/+(n=12)"?

Yes, they are associated with the dash lines. We added the labels for the solid lines and dash lines. "ABR threshold for (n=12)" was corrected to "ABR threshold for Cldn9+/+(n=12)."

(7) Figure 4, legend. "Within each wt and heterozygote mice, there was no significant shift...". Do you mean within each group of mice? Also "Mean DPOAE threshold for 2-8 mos (n=9) was tested,..." Do you mean (n=9) for each group or what group?

Yes, "Within each wt and heterozygote mice, there was no significant shift..." has been revised. The number of mice in each group for the DPOAE test was clarified in the Fig. 4B legend. Thanks.

(8) Please label the X axis in Figure 4D.

The X-axis has been labeled (Time (s))

(9) Figure 4 B, do the colors of the lines indicate the same age groups as in Fig 4A? Do the dash lines associate with open symbols? Please state this clearly in the figure's legend.

Yes. We added this information in Fig. 4B legend.

(10) Figure 4D. Please label the X axis of the fluorescence intensity graph.

The X-axis has been labeled (Time (s))

(11) Figure 4G, legend. Replace "(mean +std)" with "(mean +SD)" for consistency here and in Figure 5 legend.

Thanks. We replaced "(mean +std)" with "(mean +SD) in the legend of Fig. 4G and Fig.5 and Fig.6.

(12) Figure 5B, legend. Replace "makers" with "markers".

Thanks. This information was corrected.

(13) Figure 6A, legend. There is no downregulation of Cldn9 by shRNA shown in "S5". Do the authors mean Figure S7? Please, correct "S5" to "Fig. S7".

This information was corrected. Thanks.

(14) Figure 6A, legend. There is no reduced CLDN9 protein expression shown in Fig. 1C. Do the authors mean Fig. 6A, third panel? Please correct the phrase "reduced protein expression (Fig. 1C) is shown in the 3rd Panel (Cldn9, red)" accordingly, and do not capitalize "p" in the "3rd Panel".

This information was corrected. Thanks (line 917-918).

(15) Also there, replace "The right Panel shows two rows of IHCs (marked HC marker, Myo7a (cyan), and the merged photomicrograph" with "The right panel shows the merged image with two rows of IHCs stained with HC marker Myo7a (cyan) and the expression of Ad-GFP-mCldn9 shRNA (green) in the adjacent row of supporting cells". Please indicate in what cells Ad-GFP-mCldn9 shRNA (green) is expressed. It looks like only one row of supporting cells has this green signal.

This information was corrected.

(16) Figure 6B, legend. Replace "Examples of photomicrographs of sections of the whole-mount cochlea of P2, P4, P7, and P14 Cldn9 shRNA injected mice" with "Examples of phalloidin stained whole-mount organ of Corti samples from cochleae of the wild-type mice injected at P2, P4, P7 and P14 with Cldn9 shRNA"

This sentence has been modified based on your suggestions. Thanks!

(17) Replace "action labeling" with "actin labeled."

Thanks!  The "action labeling" has been replaced with "actin labeled." Line 924

(18) Figure 6C. Insert "C" before SEM images description in the legend. The authors stated that SEM images of "5-6-wks-old mice" are shown. Please indicate the exact age of mice shown on each image and at what age these mice received the virus injection.

Thanks!  The “C” has been added. We have noted that the SEM images are from 5-week-old mice" in the legend, and the virus was injected at P2.

(19) Figure 6D, legend. Last sentence: move "are significantly different" and insert this between "IHCs" and "at P2 apex".

This information was corrected.

(20) Figure S7, legend. Replace "(sram)" with "(scram)" as in the figure itself. Also, Indicate the age of samples at the harvesting time for imaging and the age at injection of Cldn9 shRNA.

"(sram)" has been replaced with "(scram)". The age of samples at the harvesting time for imaging and the age at injection of Cldn9 shRNA are indicated.

(21) Figure S8. Replace "4 mos-old" and "8 mos-old" with "4 months-old" and "8 months-old" everywhere in the legend and in the figure labels.

We have made the changes as suggested.

(22) Page 8, 5th lane from the bottom. Change "EP and K+ concentration endolymph" to "EP and K+ concentration of the endolymph".

It has been corrected. Thanks.

(23) Page 8, next to the last sentence before the Discussion. Wrong figure number, please replace "(S7)" with "Fig. S8".

It has been corrected. Thanks.

eLife Assessment

What makes one member of the species behave differently from another? This is a core problem in behavioral neuroscience. This valuable study seeks an answer for the specific case of the fruit fly expressing preferences for one odor over another. By a combination of behavioral measurements, neurophysiology, and network modeling, the authors find solid evidence for at least one locus of individuality in the peripheral olfactory system.

Joint Public Review:

Summary:

The authors aimed to identify the neural sources of behavioral variation in fruit flies deciding between odor and air, or between two odors.

Strengths:

- The question is of fundamental importance.<br /> - The behavioral studies are automated, and high-throughput.<br /> - The data analyses are sophisticated and appropriate.<br /> - The paper is clear and well-written aside from some initially strong wording.<br /> - The figures beautifully illustrate their results.<br /> - The modeling efforts mechanistically ground observed data correlations.

Weaknesses:

-The correlations between behavioral variations and neural activity/synapse morphology are statistically significant but relatively weak.

Author response:

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

Joint Public Review:

Summary:

The authors aimed to identify the neural sources of behavioral variation in fruit flies deciding between odor and air, or between two odors.

Strengths:

- The question is of fundamental importance.

- The behavioral studies are automated, and high-throughput.

- The data analyses are sophisticated and appropriate.

- The paper is clear and well-written aside from some initially strong wording.

- The figures beautifully illustrate their results.

- The modeling efforts mechanistically ground observed data correlations.

Weaknesses:

- The correlations between behavioral variations and neural activity/synapse morphology are relatively weak, and sometimes overstated in the wording that describes them.

We sincerely thank the reviewers for these evaluations.

Recommendations for the authors:

Line 56: "We hypothesize that as sensory cues are encoded and transformed to produce motor outputs, their representation in the nervous system becomes increasingly idiosyncratic and predictive of individual behavioral responses". This seems obvious a priori. The sensory stimuli are the same, but the motor responses are different. Along the way there has to be a progression from same to different. Is there an alternative hypothesis? If so, perhaps state the alternative.

We added text to the first paragraph of the introduction (lines 58-60) laying out an alternative hypothesis that individuality emerges through biomechanical differences and environmental interactions, and we have altered our motivating question to assess whether circuit elements in which activity is predictive of individual behavior exist, and if so, where (lines 60-62).

Line 157: typo "remaining"

We changed “remaining” to “remain” (line 160).

Line 163: why report r sometimes and R^2 other times? Better to use R^2 throughout.

We changed all instances of r to R², notably when reporting combined train/test statistics for calcium - behavior models (line 162). We also reframed the outputs (medians + 90% confidence intervals) of the supplemental analysis inferring the strength of the latent calcium-behavior relationship to be in terms of R² (lines 166, 173-175, 241, 252; modified text in Inference of correlation between latent calcium and behavior states in Materials and Methods; adjusted figure and caption for Figure 1 – figure supplement 9).

Line 182: "odorant". Should be "odorant receptors"?

We respectfully disagree – our ORN and PN calcium data are responses to odorants in 5 glomerulus/odorant receptor types. When we group PCA loadings by glomerulus for both ORN and PN calcium, the consistency within groups is much stronger than when we group the loadings by odorant (Figure 1 – figure supplement 8). Additionally, “odorant receptor organization” would mean the same thing as “glomerular organization,” since all ORNs expressing the same odorant receptor project to a single glomerulus.

Line 331: "harbor". Maybe more modestly "contribute to"?

We changed “harbor” to “contribute to” (line 334) and added additional moderating language that the difference in DC2 and DM2 activations in PNs explains a large portion of the individuality signal (lines 337-339).

Line 403: typo "is"

We retained “is” as the corresponding verb for “the net effect,” but we adjusted the position of the reference to Gomez-Marin and Ghazanfar, 2019 for more clarity (lines 406-408).

eLife Assessment

The study evaluates the feasibility, safety, and tolerability of neoadjuvant radiotherapy followed by a CDK4/6 inhibitor (dalpiciclib) and hormonal therapy in treatment-naive patients with unilateral early-stage HR+/HER2- breast cancer. The findings are convincing, with a strong scientific rationale supported by integrated correlative studies. The trial is considered to be important as the outcomes could inform the design of larger, future studies. The strength of the conclusions should be tempered as the study included only a small cohort of patients (n=12) and was not adequately powered to definitively assess the efficacy or safety of this combinatorial treatment approach.

Reviewer #1 (Public review):

Summary:

This manuscript details the results of a small pilot study of neoadjuvant radiotherapy followed by combination treatment with hormone therapy and dalpiciclib for early-stage HR+/HER2-negative breast cancer.

Strengths:

The strengths of the manuscript include the scientific rationale behind the approach and the inclusion of some simple translational studies.

Weaknesses:

The main weakness of the manuscript is that overly strong conclusions are made by the authors based on a very small study of twelve patients. A study this small is not powered to fully characterize the efficacy or safety of a treatment approach, and can, at best, demonstrate feasibility. These data need validation in a larger cohort before they can have any implications for clinical practice, and the treatment approach outlined should not yet be considered a true alternative to standard evidence-based approaches.

I would urge the authors and readers to exercise caution when comparing results of this 12-patient pilot study to historical studies, many of which were much larger, and had different treatment protocols and baseline patient characteristics. Cross-trial comparisons like this are prone to mislead, even when comparing well powered studies. With such a small sample size, the risk of statistical error is very high, and comparisons like this have little meaning.

Reviewer #2 (Public review):

The author and his team explored a novel neoadjuvant strategy of radiotherapy followed by CDK4/6 inhibitor and exemestane for HR+/HER2- breast cancer. This strategy interestingly reached an ORR of 91.7% and RCB 0-I of 16.7%, with satisfying tolerance.

There are several questions for your further consideration.

Firstly, as this is a single-arm preliminary study, we are curious about the order of radiotherapy and the endocrine therapy. Besides, considering the radiotherapy, we also concern about the recovery of the wound after the surgery and whether related data were collected.

Secondly, in the methodology, please describe the sample size estimation of this study and follow up details.

Thirdly, in Table 1, the item HER2 expression, it's better to categorise HER2 into 0, 1+, 2+ and FISH-.

Author response:

Reviewer #1(Public review):

Summary:

This manuscript details the results of a small pilot study of neoadjuvant radiotherapy followed by combination treatment with hormone therapy and dalpiciclib for early-stage HR+/HER2-negative breast cancer.

Strengths:

The strengths of the manuscript include the scientific rationale behind the approach and the inclusion of some simple translational studies.

Weaknesses:

The main weakness of the manuscript is that overly strong conclusions are made by the authors based on a very small study of twelve patients. A study this small is not powered to fully characterize the efficacy or safety of a treatment approach, and can, at best, demonstrate feasibility. These data need validation in a larger cohort before they can have any implications for clinical practice, and the treatment approach outlined should not yet be considered a true alternative to standard evidence-based approaches.

I would urge the authors and readers to exercise caution when comparing results of this 12-patient pilot study to historical studies, many of which were much larger, and had different treatment protocols and baseline patient characteristics. Cross-trial comparisons like this are prone to mislead, even when comparing well powered studies. With such a small sample size, the risk of statistical error is very high, and comparisons like this have little meaning.

We greatly appreciate your evaluation of our study and fully agree with the limitations you have pointed out. We have clearly stated the limitations of the small sample size and emphasized the need for a larger population to validate our preliminary findings in the discussion section (Lines 311-316).

We acknowledge that this small sample size is not powered to characterize this regimen as a promising alternative regimen in the treatment of patients with HR-positive, HER2-negative breast cancer. Therefore, we have revised the description of this regimen to serve as a feasible option for neoadjuvant therapy in HR-positive, HER2-negative breast cancers both in the discussion (Lines 317-320) and the abstract (Lines 71-72).

We agree with you that cross-trial comparisons should be approached with caution due to differences in study designs and patient populations. In our discussion section, we acknowledge that small sample size limited the comparison of our data with historical data in the literature due to the potential bias (Lines 312-313). We clearly state that such comparisons hold limited significance (Lines 313-314) and suggest a larger population to validate our preliminary findings.

• Why was dalpiciclib chosen, as opposed to another CDK4/6 inhibitor?

Thank you for your comments. The rationale for selecting dalpiciclib over other CDK4/6 inhibitors in our study is primarily based on the following considerations:

(1) Clinical Efficacy: In several clinical trials, including DAWNA-1 and DAWNA-2, the combination of dalpiciclib with endocrine therapies such as fulvestrant, letrozole, or anastrozole has been shown to significantly extend the progression-free survival (PFS) in patients with hormone receptor-positive, HER2-negative advanced breast cancer (1-2).

(2) Tolerability and Management of Adverse Reactions: The primary adverse reactions associated with dalpiciclib are neutropenia, leukopenia, and anemia. Despite these potential side effects, the majority of patients are able to tolerate them, and with proper monitoring and management, these reactions can be effectively mitigated (1-2).

(3) Comparable pharmacodynamic with other CDK4/6 inhibitors: The combination of CDK4/6 inhibitors, including palbociclib, ribociclib, and abemaciclib, with aromatase inhibitors has demonstrated an enhanced ability to suppress tumor proliferation and increase the rate of clinical response in neoadjuvant therapy for HR-positive, HER2-negative breast cancer (3-5). Furthermore, preclinical studies have shown that dalpiciclib has comparable in vivo and in vitro pharmacodynamic activity to palbociclib, suggesting its potential effectiveness in similar treatment regimens (6).

(4) Accessibility and Regulatory Approval: Dalpiciclib has gained marketing approval in China on December 31, 2021, which facilitates the accessibility of this medication, making it a more convenient option when considering treatment plans.

References:

(1) Zhang P, Zhang Q, Tong Z, et al. Dalpiciclib plus letrozole or anastrozole versus placebo plus letrozole or anastrozole as first-line treatment in patients with hormone receptor-positive, HER2-negative advanced breast cancer (DAWNA-2): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial(J). The Lancet Oncology, 2023, 24(6): 646-657.

(2) Xu B, Zhang Q, Zhang P, et al. Dalpiciclib or placebo plus fulvestrant in hormone receptor-positive and HER2-negative advanced breast cancer: a randomized, phase 3 trial(J). Nature medicine, 2021, 27(11): 1904-1909.

(3) Hurvitz S A, Martin M, Press M F, et al. Potent cell-cycle inhibition and upregulation of immune response with abemaciclib and anastrozole in neoMONARCH, phase II neoadjuvant study in HR+/HER2− breast cancer(J). Clinical Cancer Research, 2020, 26(3): 566-580.

(4) Prat A, Saura C, Pascual T, et al. Ribociclib plus letrozole versus chemotherapy for postmenopausal women with hormone receptor-positive, HER2-negative, luminal B breast cancer (CORALLEEN): an open-label, multicentre, randomised, phase 2 trial(J). The lancet oncology, 2020, 21(1): 33-43.

(5) Ma C X, Gao F, Luo J, et al. NeoPalAna: neoadjuvant palbociclib, a cyclin-dependent kinase 4/6 inhibitor, and anastrozole for clinical stage 2 or 3 estrogen receptor–positive breast cancer(J). Clinical Cancer Research, 2017, 23(15): 4055-4065.

(6) Long F, He Y, Fu H, et al. Preclinical characterization of SHR6390, a novel CDK 4/6 inhibitor, in vitro and in human tumor xenograft models(J). Cancer science, 2019, 110(4): 1420-1430.

• The eligibility criteria are not consistent throughout the manuscript, sometimes saying early breast cancer, other times saying stage II/III by MRI criteria.

criteria in our manuscript. We deeply apologize for any confusion caused by these inconsistencies. We have revised the term from “early-stage HR-positive, HER2-negative breast cancer” to “early or locally advanced HR-positive, HER2-negative breast cancer” (Lines 128 and 150). The term “early or locally advanced” encompasses two different stages of breast cancer, whereas “Stage II/III by MRI criteria” refers to specific stages within the TNM staging system.

• The authors should emphasize the 25% rate of conversion from mastectomy to breast conservation and also report the type and nature of axillary lymph node surgery performed. As the authors note in the discussion section, rates of pathologic complete response/RCB scores are less prognostic for hormone-receptor-positive breast cancer than other subtypes, so one of the main rationales for neoadjuvant medical therapy is for surgical downstaging. This is a clinically relevant outcome.

We appreciate your constructive comments. Based on your suggestions, we have made the following revisions and additions to the article.

The breast conservation rate serves as a secondary endpoint in our study (Line 62 and 179). We have highlighted the significant 25% conversion rate from mastectomy to breast conservation in both the results (Lines 229-230) and discussion sections (Lines 290-292).

In our study, all patients underwent lymph node surgery, including sentinel lymph node biopsy or axillary lymph node dissection. Among them, 58.3% of patients (7/12) underwent sentinel lymph node biopsies.

We agree with your point that the prognostic value of pathologic complete response/RCB score is lower for hormone receptor-positive breast cancer compared to other subtypes, we have revised the discussion section to clarify that one of the principal objectives for neoadjuvant therapy in this patient population is to facilitate downstaging and enhance the rate of breast conservation (Lines 289-290). And also emphasized that this neoadjuvant therapeutic regiment appeared to improve the likelihood of pathological downstaging and achieve a margin-free resection, particularly for those with locally advanced and high-risk breast cancer (Lines 293-295).

Reviewer #2 (Public review):

Firstly, as this is a single-arm preliminary study, we are curious about the order of radiotherapy and the endocrine therapy. Besides, considering the radiotherapy, we also concern about the recovery of the wound after the surgery and whether related data were collected.

Thanks for the comments. The treatment sequence in this study is to first administer radiotherapy, followed by endocrine therapy. A meta-analysis has indicated that concurrent radiotherapy with endocrine therapy does not significantly impact the incidence of radiation-induced toxicity or survival rates compared to a sequential approach (1). In light of preclinical research suggesting enhanced therapeutic efficacy when radiotherapy is delivered prior to CDK4/6 inhibitors, we have opted to administer radiotherapy before the combination therapy of CDK4/6 inhibitors and hormone therapy (2).

In our study, we collected data on surgical wound recovery. All 12 patients had Class I incisions, which healed by primary intention. The wounds exhibited no signs of redness, swelling, exudate, or fat necrosis.

References:

(1) Li Y F, Chang L, Li W H, et al. Radiotherapy concurrent versus sequential with endocrine therapy in breast cancer: A meta-analysis(J). The Breast, 2016, 27: 93-98.

(2) Petroni G, Buqué A, Yamazaki T, et al. Radiotherapy delivered before CDK4/6 inhibitors mediates superior therapeutic effects in ER+ breast cancer(J). Clinical Cancer Research, 2021, 27(7): 1855-1863.

Secondly, in the methodology, please describe the sample size estimation of this study and follow up details.

Thanks for pointing out this crucial omission. Sample size estimation for this study and follow-up details have been added in the methodology section. The section on sample size estimation has been revised to state in Statistical analysis: “This exploratory study involves 12 patients, with the sample size determined based on clinical considerations, not statistical factors (Lines 210-211).” The section on follow up has been revised to state in Procedures section “A 5-year follow-up is conducted every 3 months during the first 2 years, and every 6 months for the subsequent 3 years. Additionally, safety data are collected within 90 days after surgery for subjects who discontinue study treatment (Lines 169-172).”

Thirdly, in Table 1, the item HER2 expression, it's better to categorise HER2 into 0, 1+, 2+ and FISH-.

Thank you very much for pointing out this issue. The item HER2 expression in Table 1 has been revised from “negative, 1+, 2+ and FISH-” to “0, 1+, 2+ and FISH-”.

eLife Assessment

This valuable study uses zebrafish as a model to reveal a role for the cell cycle protein kinase CDK2 as a negative regulator of type I interferon signaling. The evidence supporting the authors' claims is convincing, including both in vivo and in vitro investigative approaches that corroborate a role for CDK2 in regulating TBK1 degradation. In this latest version, the authors included data addressing concerns raised by the reviewers at the first peer review round that strengthens the conclusions. This work will interest cell biologists, immunologists, and virologists.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate the regulation of interferon (IFN) gene expression in fish, using mainly zebrafish as a model system. Similar to more widely characterized mammalian systems, fish IFN is induced during viral infection through the action of the transcription factor IRF3 which is activated by phosphorylation by the kinase TBK1. It has been previously shown in many systems that TBK1 is subjected to both positive and negative regulation to control IFN production. In this work, the authors find that the cell cycle kinase CDK2 functions as a TBK1 inhibitor by decreasing its abundance through recruitment of the ubiquitinylation ligase, Dtx4, which has been similarly implicated in the regulation of mammalian TBK1. Experimental data are presented showing that CDK2 interacts with both TBK1 and Dtx4, leading to TBK1 K48 ubiqutinylation on K567 and its subsequent degradation by the proteasome.

Strengths:

The strengths of this manuscript are its novel demonstration of the involvement of CDK2 in a process in fish that is controlled by different factors in other vertebrates and its clear and supportive experimental data.

Weaknesses:

The weaknesses of the study include the following. 1) It remains unclear how CDK is regulated during viral infection and how it specifically recruits E3 ligase to TBK1. 2) The implications and mechanisms for a relationship between the cell cycle and IFN production will be a fascinating topic for future studies.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate the regulation of interferon (IFN) gene expression in fish, using mainly zebrafish as a model system. Similar to more widely characterized mammalian systems, fish IFN is induced during viral infection through the action of the transcription factor IRF3 which is activated by phosphorylation by the kinase TBK1. It has been previously shown in many systems that TBK1 is subjected to both positive and negative regulation to control IFN production. In this work, the authors find that the cell cycle kinase CDK2 functions as a TBK1 inhibitor by decreasing its abundance through recruitment of the ubiquitinylation ligase, Dtx4, which has been similarly implicated in the regulation of mammalian TBK1. Experimental data are presented showing that CDK2 interacts with both TBK1 and Dtx4, leading to TBK1 K48 ubiqutinylation on K567 and its subsequent degradation by the proteasome.

Strengths:

The strengths of this manuscript are its novel demonstration of the involvement of CDK2 in a process in fish that is controlled by different factors in other vertebrates and its clear and supportive experimental data.

Weaknesses:

The weaknesses of the study include the following. 1) It remains unclear how CDK is regulated during viral infection and how it specifically recruits E3 ligase to TBK1. 2) The implications and mechanisms for a relationship between the cell cycle and IFN production will be a fascinating topic for future studies.

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

The weaknesses of the study include the following. 

(1) It remains unclear whether the function described for CDK2 is regulatory, that is, it affects TBK1 levels during physiological responses such as viral infection or cell cycle progression, or if it is homeostatic, governing the basal abundance of TBK1 but not responding to signaling.

The regulation of TBK1 by CDK2 described in this article occurs during viral infection. Simultaneously, we also investigated the effects of CDK2 overexpression and knockdown on TBK1 levels under non-infected state and observed a slight reduction, as shown in Figure 4K and 4L. Thus, we speculate that the regulation of TBK1 by CDK2 serves, on one hand, to maintain cellular homeostasis and, on the other hand, to respond to signaling triggered by viral infection.

(2) The authors have not explored whether the catalytic activity of CDK2 is required for TBK1 ubiquitinoylation and, if so, what its target specificity is.

We found that the ubiquitination modification of TBK1 was not affected by treatment with a CDK2 kinase activity inhibitor (SNS-032), as demonstrated in the results below (Author response image 1).

Author response image 1.

(3) Given the multitude of CDK isoforms in fish, it remains unexplored whether the identified fish CDK2 homolog is a requisite cell cycle regulator or if its action in the cell cycle is redundant with other CDKs.

A comparison of the protein sequences of fish CDK2 and human CDK2 revealed a 90% similarity (Author response image 2). It has also been reported that the kinase activity of goldfish CDK2 significantly increases during oocyte maturation (ref. 1). Furthermore, UHRF1 phosphorylation by cyclin A2/CDK2 is crucial for zebrafish embryogenesis (ref. 2). Additionally, Red grouper nervous necrosis virus (RGNNV) infection activated the p53 pathway, leading to the upregulation of p21 and downregulation of cyclin E and CDK2, which forces infected cells to remain in the G1/S replicative phase (ref. 3). All these evidences suggest that fish CDK2 plays a vital role in cell cycle regulation, and there have been no reports of other CDKs demonstrating CDK2-like functions.

References:

(1) Hirai T, et al. (1992) Isolation and Characterization of Goldfish Cdk2, a Cognate Variant of the Cell-Cycle Regulator Cdc2. Developmental biology 152(1):113-120.

(2) Chu J, et al. (2012) UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Molecular biology of the cell 23(1):59-70. 

(3) Mai WJ, Liu HX, Chen HQ, Zhou YJ, & Chen Y (2018) RGNNV-induced cell cycle arrest at G1/S phase enhanced viral replication via p53-dependent pathway in GS cells. Virus Res 256:142-152.

Author response image 2.

Reviewer #2 (Public Review):

Weaknesses:

(1) While the study focuses on fish, the broader implications for other lower vertebrates and higher vertebrates are not extensively discussed.

Thanks to your comment, we have added a paragraph to the Discussion section of the manuscript regarding the implications of the negative regulation of IFN expression by fish CDK2 for other vertebrates (lines 398-403). The details are as follows: first, we selected representative species from each of the six major vertebrate groups and compared their CDK2 protein sequences, finding that they are over 90% similar to one another (Author response image 3). This suggests that the function of CDK2 may be conserved to some extent across vertebrates. Additionally, CDK2 inhibition has been shown to enhance anti-tumor immunity by increasing the IFN response to endogenous retroviruses (ref. 1). Our studies provide evidence that fish CDK2 inhibits the IFN response by promoting the ubiquitination and degradation of TBK1, strongly supporting the role of CDK2 in the regulation of the immune response.

Reference:

(1) Chen Y, et al. (2022) CDK2 Inhibition Enhances Antitumor Immunity by Increasing IFN Response to Endogenous Retroviruses. Cancer Immunol Res 10(4):525-539.

Author response image 3.

(2) The study heavily relies on specific fish models, which may limit the generalizability of the findings across different species.

Thank you for your comment. First, we compared the amino acid sequences of CDK2 proteins from fish and other vertebrates, which show over 90% similarity. Moreover, the small size, low cost, and external development of zebrafish make it an excellent model for vertebrate developmental biology. It has been reported that due to the high genomic and molecular similarities between zebrafish and other vertebrates, including humans, many significant discoveries in zebrafish development are relevant to humans (ref. 2). Our study concentrated on CDK2 in zebrafish, and the findings should be valuable for other vertebrates.

Reference:

(2) Veldman MB & Lin S (2008) Zebrafish as a Developmental Model Organism for Pediatric Research. Pediatr Res 64(5):470-476.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The following additional data/discussion could improve the manuscript.

(1) Investigate whether the catalytic activity of CDK2 is required to regulate TBK1 abundance. It is common for E3 ligases to be directed towards phosphorylated substrates, so it would be of interest to know if CDK2 phosphorylates TBK1 to facilitate its recognition for ubiquitinylation.

We examined the effect of CDK2 on the TBK1 protein after inhibiting its kinase activity with SNS-032 treatment and found that it could still affect TBK1 expression, as shown in the results below (Figure R4). Our previous experiments investigating the effect of CDK2 on TBK1 did not show that CDK2 caused the migration of TBK1 bands (typically, proteins that undergo phosphorylation exhibit band migration). Furthermore, in this study, CDK2 did not function as an E3 ligase; instead, it recruited the E3 ligase Dtx4 to ubiquitinate TBK1.

Author response image 4.

(2) Investigate how CDK2 abundance is regulated by viral infection and whether viral infection impacts cell cycle progression in a CDK2-dependent manner.

In fact, as illustrated in Figure 1, we investigated the changes in CDK2 at both the mRNA and protein levels following viral infection. Our findings revealed that SVCV infection resulted in an increase in CDK2 mRNA and protein expression. Additionally, our earlier reports have indicated that SVCV infection can induce alterations in the cell cycle, resulting in a notable increase in the S phase (Figure 1 of ref. 1). However, whether SVCV infection impacts cell cycle progression in a CDK2dependent manner will be explored in our upcoming study.

Reference:

(1) Li S, et al. Spring viraemia of carp virus modulates p53 expression using two distinct mechanisms. PLoS Pathog 15, e1007695 (2019).

(3) Provide data/discussion concerning the role of fish CDK2 in the regulation of cell cycle progression and whether this process is impacted by viral infection (part 1). Are TBK1 abundance and interferon production differentially regulated across the cell cycle due to the action of CDK2 (part 2).

Thank you for your advice. This concern is addressed in two parts, as follows: 

For part 1: To date, there has been limited research conducted on fish CDK2 in the regulation of cell cycle progression. The details are as follows: It has been reported that the kinase activity of goldfish CDK2 significantly increases during oocyte maturation (ref. 1). Furthermore, UHRF1 phosphorylation by cyclin A2/CDK2 is crucial for zebrafish embryogenesis (ref. 2). Additionally, a novel CDK2 homolog has been identified in Japanese lamprey, which plays a crucial role in apoptosis (ref. 3). Red grouper nervous necrosis virus (RGNNV) infection activates the p53 pathway, leading to the upregulation of p21 and downregulation of cyclin E and CDK2, which forces infected cells to remain in the G1/S replicative phase (ref. 4). All this evidence suggests that fish CDK2 plays a vital role in cell cycle regulation, and this process is also impacted by viral infection. Relevant content has been added to the Discussion section in the revised manuscript (lines 389-398).

References:

(1) Hirai T, et al. (1992) Isolation and Characterization of Goldfish Cdk2, a Cognate Variant of the Cell-Cycle Regulator Cdc2. Developmental biology 152(1):113-120.

(2) Chu J, et al. (2012) UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Molecular biology of the cell 23(1):5970.

(3) Xu Y, Tian Y, Zhao H, Zheng N, Ren KX, Li QW. A novel CDK-2 homolog identified in lamprey, with roles in apoptosis. Fish Physiol Biochem 47, 189-189 (2021). 

(4) Mai WJ, Liu HX, Chen HQ, Zhou YJ, & Chen Y (2018) RGNNV-induced cell cycle arrest at G1/S phase enhanced viral replication via p53-dependent pathway in GS cells. Virus Res 256:142-152.

For part 2: TBK1 plays a crucial role in regulating IFN production. Variations in CDK2 activity during different phases of the cell cycle may lead to changes in the expression and function of TBK1. Our findings suggest that heightened CDK2 activity may suppress TBK1 expression, thereby hindering the cell's capacity to produce IFN. Conversely, during the late phase of the cell cycle or in an inhibited state, TBK1 expression may rise, enhancing IFN synthesis and release. In summary, CDK2 is involved in intracellular signaling by modulating TBK1 levels and IFN production, affecting the cellular immune response and cycle regulation—two processes that are notably distinct at various stages of the cell cycle. Relevant content has been added to the Discussion section in the revised manuscript (lines 377-384).

Minor suggestions:

(1) The authors introduce their study with the consideration that knowledge of fish signaling pathways can inform mammalian biology because mammals evolved from fish. This is not strictly true, since mammals and fish both evolved from an ancient common ancestor and the diversification of signaling in each species likely occurred in response to distinct evolutionary selective pressures.

Thank you for your suggestion. We have revised the statement in the manuscript to eliminate the notion that mammals evolved from fish (lines 98-99). The immune systems of higher vertebrates (e.g., humans) and lower vertebrates (e.g., fish) generally exhibit some consistency, although there are notable differences.

(2) On line 210 and line 276, the authors appear to have misstated the data. CDK2 knockout increases not decreases TBK1 and Dtx4 knockdown abrogated rather than restored CDK2 suppression of TBK1.

Thanks for your reminder, I jumped to the wrong conclusions in these two places (line 204 and line 267) and have changed them as you suggested.

Reviewer #2 (Recommendations For The Authors):

The manuscript has some shortcomings that, if addressed, could improve the overall quality of the article.

(1) Line 63-72, line 77-79, line 88-90- please add additional references for these sentences.

Thanks to your comment, we have added references for these sentences (Line 63-72, line 77-79, line 88-90).

(2) It is of the utmost importance to quantify the data presented in Figures 4J and 5D, as this will facilitate the visualization of the immunoblot.

Thank you for your comment. We have quantified the data presented in Figures 4J and 5D to enhance the clarity of the immunoblot.

(3) The scale in Figure 4E is difficult to discern.

Thanks for your comment. To improve the visual clarity of the image, we have enlarged the scale label in Figure 4E.

(4) In Figure 3B, shCDK2 is shown in italics, preferably in line with other standards such as Figures 3C and 3F.

Thank you for your comment. We have revised the shCDK2 in Figure 3B.

(5) The functions of CDK family members in immunity are hoped to be discussed.

Thanks for your suggestion. We have discussed the functions of CDK family members in immunity (lines 363-387). The details are as follows: Recent studies have demonstrated that CDK activity is crucial for virus-induced innate immune responses. Reports indicate that CDKs are involved in the Toll-like receptor (TLR) signaling pathway, the nuclear factor-κB (NF-κB) signaling pathway, and the JAK-STAT signaling pathway. For instance, CDK8 and/or CDK19 enhanced the transcription of inflammatory genes, such as IL-8 and IL-10, in cells following TLR9 stimulation. CDKs and NF-κB establish a remarkable paradigm where CDKs can act directly on substrate proteins rather than depending solely on transcriptional control. It has been reported that CDK1 serves as a positive regulator of the IFN-I signaling pathway, facilitating STAT1 phosphorylation, which subsequently boosts the expression of ISGs. Furthermore, inhibiting CDK activity has been shown to obstruct STAT phosphorylation, proinflammatory gene activation, and ISG mRNA induction in response to SeV infection. It is important to note that no evidence suggests the involvement of CDKs in RLR signaling pathways. This study has shown that fish CDK2 functions as a negative regulator of the key kinase TBK1, which is involved in the RLR signaling pathway. A better understanding of the relationship between CDK2 and RLR signaling pathways will enhance our grasp of the regulatory mechanisms of CDKs in antiviral innate immunity.

eLife Assessment

In this important work, Lodhiya et al. provide evidence that excessive ATP underlies the killing of the model organism Mycobacterium smegmatis by two mechanistically-distinct antibiotics. The data are generally solid as the authors deploy multiple, orthogonal readouts and methods for manipulating reactive oxygen species and ATP. The work will be of interest to those studying antibiotic mechanisms of action.

Reviewer #1 (Public review):

Summary:

Lodhiya et al. demonstrate that antibiotics with distinct mechanisms of action, norfloxacin and streptomycin, cause similar metabolic dysfunction in the model organism Mycobacterium smegmatis. This includes enhanced flux through the TCA cycle and respiration as well as a build-up of reactive oxygen species (ROS) and ATP. Genetic and/or pharmacologic depression of ROS or ATP levels protect M. smegmatis from norfloxacin and streptomycin killing. Because ATP depression is protective, but in some cases does not depress ROS, the authors surmise that excessive ATP is the primary mechanism by which norfloxacin and streptomycin kill M. smegmatis. In general, the experiments are carefully executed; alternative hypotheses are discussed and considered; the data are contextualized within the existing literature.

Strengths:

The authors tackle a problem that is both biologically interesting and medically impactful, namely, the mechanism of antibiotic-induced cell death.

Experiments are carefully executed, for example, numerous dose- and time-dependency studies; multiple, orthogonal readouts for ROS; and several methods for pharmacological and genetic depletion of ATP.

There has been a lot of excitement and controversy in the field, and the authors do a nice job of situating their work in this larger context.

Inherent limitations to some of their approaches are acknowledged and discussed e.g., normalizing ATP levels to viable counts of bacteria.

Weaknesses:

All of the experiments performed here were in the model organism M. smegmatis. As the authors point out, the extent to which these findings apply to other organisms (most notably, slow-growing pathogens like M. tuberculosis) is to be determined.

At first glance, some of the results in the manuscript seem to conflict with what has been previously reported in the (referenced) literature. In their response to reviewers, the authors addressed these concerns. Ideally they would have addressed them in the main manuscript too.

Figs. 9 and 10A-B and associated text make the manuscript significantly longer and more descriptive. They are more appropriate to the beginning of a new story rather than the end of the current one.

Reviewer #2 (Public review):

Summary:

The authors are trying to test the hypothesis that ATP bursts are the predominant driver of antibiotic lethality of Mycobacteria

Strengths:

No significant strengths in the current state as it is written.

Weaknesses:

A major weakness is that M. smegmatis has a doubling time of three hours and the authors are trying to conclude that their data would reflect the physiology of M. tuberculossi which has a doubling time of 24 hours. Moreover, the authors try to compare OD measurements with CFU counts and thus observe great variabilities.

Comments on revisions:

The authors confirm they are using CFU counts, but then Figure 1 has 0 as the first data point on the Y-axis. This should be somewhere between 10e5 or 10e6. CFU would not start at 0, your initial inoculum has to be more than 0 to have something to challenge.

Author response:

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

Reviewer #1 (Public review):

Summary:

Lodhiya et al. demonstrate that antibiotics with distinct mechanisms of action, norfloxacin and streptomycin, cause similar metabolic dysfunction in the model organism Mycobacterium smegmatis. This includes enhanced flux through the TCA cycle and respiration as well as a build-up of reactive oxygen species (ROS) and ATP. Genetic and/or pharmacologic depression of ROS or ATP levels protect M. smegmatis from norfloxacin and streptomycin killing. Because ATP depression is protective, but in some cases does not depress ROS, the authors surmise that excessive ATP is the primary mechanism by which norfloxacin and streptomycin kill M. smegmatis. In general, the experiments are carefully executed; alternative hypotheses are discussed and considered; the data are contextualized within the existing literature.

We thank the reviewer for the very comprehensive summary of the study.

Strengths:

The authors tackle a problem that is both biologically interesting and medically impactful, namely, the mechanism of antibiotic-induced cell death.

Experiments are carefully executed, for example, numerous dose- and time-dependency studies; multiple, orthogonal readouts for ROS; and several methods for pharmacological and genetic depletion of ATP.

There has been a lot of excitement and controversy in the field, and the authors do a nice job of situating their work in this larger context.

Inherent limitations to some of their approaches are acknowledged and discussed e.g., normalizing ATP levels to viable counts of bacteria.

We thank the reviewer for the encouraging comments.

Weaknesses:

All of the experiments performed here were in the model organism M. smegmatis. As the authors point out, the extent to which these findings apply to other organisms (most notably, slow-growing pathogens like M. tuberculosis) is to be determined. To avoid the perception of overreach, I would recommend substituting "M. smegmatis" for Mycobacteria (especially in the title and abstract).

At first glance, a few of the results in the manuscript seem to conflict with what has been previously reported in the (referenced) literature. In their response to reviewers, the authors addressed my concerns. It would also be ideal to include a few lines in the manuscript briefly addressing these points. (Other readers may have similar concerns).

In the first round of review, I suggested that the authors consider removing Figs. 9 and 10A-B as I believe they distract from the main point of the paper and appear to be the beginning of a new story rather than the end of the current one. I still hold this opinion. However, one of the strengths of the eLife model is that we can agree to disagree.

We acknowledge the reviewer’s concern and have changed title of the manuscript by including Mycobacterium smegmatis instead of Mycobacteria. The abstract already mentioned the same.

In the discussion section of the revised manuscript, we have already addressed and analysed our results extensively within the context of the available literature, regardless of whether our findings aligned with or differed from previous studies. We still believe that the mentioned discussion will help suffice to explain our results to the readers.

In this manuscript we also sought to assess the bacteria's ability to counteract drug induced stresses, contributing to our understanding of how antibiotic tolerance develop in Mycobacterium smegmatis. Results presented in Figure 9 clearly demonstrate that M.smegmatis attempt to reduce respiration by decreasing flux through the complete TCA cycle, thereby mitigating ROS and ATP production in response to antibiotics.  Additionally, the bacterial response also included increased expression of the protein Eis, which is exemplar for intrinsic drug resistance, with a concomitant increase in mutation frequency, thereby hinting at the development of antibiotic tolerance followed by resistance. We still believe that these data should be included to support our observations and they make the study more comprehensive.

Reviewer #2 (Public review):

Summary:

The authors are trying to test the hypothesis that ATP bursts are the predominant driver of antibiotic lethality of Mycobacteria

Strengths:

No significant strengths in the current state as it is written.

Weaknesses:

A major weakness is that M. smegmatis has a doubling time of three hours and the authors are trying to conclude that their data would reflect the physiology of M. tuberculossi that has a doubling time of 24 hours. Moreover, the authors try to compare OD measurements with CFU counts and thus observe great variabilities.

Comments on revisions:

I am surprised that the authors simply did not repeat the study in figure one with CFU counts and repeated in triplicate. Since this is M. smegmatis, it would take no longer than two weeks to repeat this experiment and replace the figure. I understand that obtaining CFU counts is much more laborious than OD measurements but it is necessary. Your graph still says that there is 0 bacteria at time 0, yet in your legend it says you started with 600,000 CFU/ml. I don't understand why this experiment was not repeated with CFU counts measured throughout. This is not a big ask since this is M. smegmatis but it appears that the authors do not want to repeat this experiment. Minimally, fix the graph to represent the CFU.

We acknowledge the reviewer’s concern and have changed title of the manuscript by specifying Mycobacterium smegmatis instead of Mycobacteria.

It is still not clear to the authors what the reviewer mean by OD measurements. All the data presented in the entire manuscript , including in Figure 1 are solely based on CFU measurements. So, as suggested by the reviewer, all experiments are already presented in terms of CFU.

eLife Assessment

This fundamental work extends our understanding of the role of TGFβ2 as a modulator of mechanosensing in the eye and identifies the TRPV4 ion channel as a common regulator of Trabecular Meshwork (TM) contractility and pathological OHT. The data and evidence are convincing, with some minor limitations. This work will clearly be of interest to researchers investigating the role of mechanosensors in the TM and may underpin future research into treatments that aim to lower intra ocular pressure. This work will additionally be of interest to the growing field of researchers investigating the regulation of force sensing via ion channels and their roles in health and disease, in particular the ion channel TRPV4.

Reviewer #1 (Public review):

Summary:

This comprehensive study employed molecular, optical, electrophysiological and tonometric strategies to establish the role of TGFβ2 in transcription and functional expression of mechanosensitive channel isoforms alongside studies of TM contractility in biomimetic hydrogels, and intraocular pressure regulation in a mouse model of TGFβ2 -induced ocular hypertension. TGFβ2 upregulated expression of TRPV4 and PIEZO1 transcripts and time-dependently augmented functional TRPV4 activation. TRPV4 activation induced TM contractility whereas pharmacological inhibition suppressed TGFβ2-induced hypercontractility and abrogated ocular hypertension in eyes overexpressing TGFβ2. Trpv4-/- mice resisted TGFβ2-driven increases in IOP. These data establish a fundamental role of TGFβ as a modulator of mechanosensing and identifies TRPV4 channel as a common mechanism for TM contractility and pathological ocular hypertension.

Strengths:

The manuscript is very well written and details the important function of TRPV4 in TM cell function. These data provide novel therapeutic targets and potential for disease-altering therapeutics.

Weaknesses:

The experimental rigor and design of the noctural IOP experiments was weak with low n values and differing methods of IOP measurement (conscious versus anesthetized). The same method of IOP measurement needs to be used for all measurements to make any conclusions on the circadian patterns of IOP in each condition.

Reviewer #2 (Public review):

The manuscript by Christopher N. Rudzitis et al. describes the role of TGFβ2 in the transcription and functional expression of mechanosensitive channel isoforms, alongside studies on TM contractility in biomimetic hydrogels and intraocular pressure. Overall, it is a very interesting study, nicely designed, and will contribute to the available literature on TRPV4 sensitivity to mechanical forces.

I have the following comment for the authors to address.

Figure 1A-C.<br /> Often there is a difference between the massage and transcript data. I recommend the authors to confirm with qPCR data with another mode of protein measurements.<br /> Does direct TRPV4 activation also induce the expression of these markers? Does inhibition of TRPV4, after TGF-β treatment, prevent the expression of these markers? Is TRPV4 acting downstream of this response?

Figure 1D. Beta tubulin is not a membrane marker. Having staining of b tubulin in membrane fraction shows contamination from the cytoplasm.<br /> Does the overall expression also increase?

Figure 4A: it is not very clear. I recommend including a zoom image or better resolution image.

Figure 5B and 6B.<br /> Why there is a difference between groups in pre-injection panel. As Figure 5A, in pre-injection, there is no difference between LV-TGFβ and LV-control while in 5B there is a significant difference between these groups.<br /> Discussion section.

Line 279, . "TRPV4 channels in cells treated with TGFβ2 are likely to be constitutively active" ... needs to be discussed further.

Line 280: "The residual contractility in HC-06-treated cells may reflect TGFβ2-mediated contributions from Piezo1."<br /> Piezo1 has a low threshold for mechanosensitivity. How do the authors discuss the observation that, in the presence of Piezo1, TRPV4 has a more prominent mechanosensory function? Is this tied to TGFβ signalling?

Author response:

We thank the editors and reviewers for the constructive assessment. We plan to address the comments as follows:

Reviewer #1 (Public review):

We are generating a new cohort of Lv-TGFB2 overexpressing mice in which IOP will be compared under the anesthesia conditions that are identical for diurnal and nocturnal states. Parenthetically, we used the awake (diurnal) and isoflurane (nocturnal) anesthesia to mirror the conditions in the Patel et al (2021) PNAS study.

Reviewer #2 (Public review):

We are not sure what the Reviewer means by the “difference between the message and transcript data” and are not sure whether providing evidence about the TRPV4-dependence of the expression of fibrotic genes and canonical TGFb2 pathway genes fits within the scope of our study (which focuses on the TGFB2-dependence of TRPV4 expression and IOP regulation). We propose to address this by including new data about the TGFb2- and TRPV4 dependence of TRPV4 and Piezo1 expression. We could include information about the effect of TGFB2 on fibrosis-related genes from a (submitted study) in which we used RNASeq to investigate TGFB2 and TGFB2 + HC067047-dependence of gene expression in TM cells on a confidential basis but not include it in the revised manuscript.

- Re:  b-tubulin comment  [b-tubulin associates with the plasma membrane by binding to integral membrane proteins in the plasma and organellar membranes, through palmitoylation and attachment to linker proteins and as an integral component of exocytotic vesicles (Wolff, BBA 2009; Hogerheide et al., PNAS 2017). Together with b-actin and Gapdh it is often used as a loading control to assess cellular TRPV4 protein expression (e.g., https://www.cellsignal.com/products/primary-antibodies/trpv4-antibody/65893; Grove et al., Science Signaling 2019 and Moore et al., PNAS 2013).  Our qPCR and RNASeq studies show that TGFB2 does not affect b-tubulin expression]

- We will provide a higher resolution image for Fig. 4A

- Will address the Fig 5A and 6A comment [We thank the Reviewer for noticing the ambiguity and revised Figure Legends to clarify that “pre-injection” in Figures 5B and 6B refers to IOP measurements before the intracameral injection of HC-06  not pre-injection of lentiviral constructs].

-  We will address the issue of constitutive TRPV4 activity and Piezo1 involvement in the revised Discussion.

We hope this is sufficient information at this point but would be more than happy to provide more information if needed.

Thank you, we are very impressed by the eLife review protocols.

eLife Assessment

The findings are important and intriguing, with theoretical or practical implications beyond a single subfield. The computational methods employed are clever and sophisticated and the strength of evidence is convincing. However, all three reviewers also highlight a lack of focus and clarity, as well as missing information about model comparison, fit statistics and group comparison of parameters from different models.

Reviewer #1 (Public review):

Summary:

The authors use a sophisticated task design and Bayesian computational modeling to test their hypothesis that information generalization (operationalized as a combination of self-insertion and social contagion) in social situations is disrupted in Borderline Personality Disorder. Their main finding relates to the observation that two different models best fit the two tested groups: While the model assuming both self-insertion and social contagion to be present when estimating others' social value preferences fit the control group best, a model assuming neither of these processes provided the best fit to BPD participants.

Strengths:

The strengths of the presented work lie in the sophisticated task design and the thorough investigation of their theory by use of mechanistic computational models to elucidate social decision-making and learning processes in BPD.

Weaknesses:

The manuscript's primary weakness relates to the number of comparisons conducted and a lack of clarity in how those comparisons relate to the authors' hypotheses. The authors specify a primary prediction about disruption to information generalization in social decision making & learning processes, and it is clear from the text how their 4 main models are supposed to test this hypothesis. With regards to any further analyses however (such as the correlations between multiple clinical scales and eight different model parameters, but also individual parameter comparisons between groups), this is less clear. I recommend the authors clearly link each test to a hypothesis by specifying, for each analysis, what their specific expectations for conducted comparisons are, so a reader can assess whether the results are/aren't in line with predictions. The number of conducted tests relating to a specific hypothesis also determines whether multiple comparison corrections are warranted or not. If comparisons are exploratory in nature, this should be explicitly stated.

Furthermore, the authors present some measures for external validation of the models, including comparison between reaction times and belief shifts, and correlations between model predicted accuracy and behavioural accuracy/total scores. However it would be great to see some more formal external validation of how the model parameters relate to participant behaviour, e.g., the correlation between the number of pro-social choices and ß-values, or the correlation between the change in absolute number of pro-social choices and the change in ß. From comparing the behavioural and computational results it looks like they would correlate highly, but it would be nice to see this formally confirmed.

The statement in the abstract that 'Overall, the findings provide a clear explanation of how self-other generalisation constrains and assists learning, how childhood adversity disrupts this through separation of internalised beliefs' makes an unjustified claim of causality between childhood adversity and separation of self - and other beliefs, although the authors only present correlations. I recommend this should be rephrased to reflect the correlational nature of the results.

Currently, from the discussion the findings seem relevant in explaining certain aberrant social learning and -decision making processes in BPD. However, I would like to see a more thorough discussion about the practical relevance of their findings in light of their observation of comparable prediction accuracy between the two groups.

Relatedly, the authors mention that a primary focus of mentalization based therapy for BPD is 'restoring a stable sense of self' and 'differentiating the self from the other'. These goals are very reminiscent of the findings of the current study that individuals with BPD show lower uncertainty over their own and relative reward preferences, and that they are less susceptible to social contagion. Could the observed group differences therefore be a result of therapy rather than adverse early life experiences?

Regarding partner similarity: It was unclear to me why the authors chose partners that were 50% similar when it would be at least equally interesting to investigate self-insertion and social contagion with those that are more than 50% different to ourselves? Do the authors have any assumptions or even data that shows the results still hold for situations with lower than 50% similarity?

Reviewer #2 (Public review):

Summary:

The paper investigates social-decision making, and how this changes after observing the behaviour of other people, in borderline personality disorder. The paper employs a task including three phases, the first where participants make decision on how to allocate rewards to oneself and to a virtual partner, the second where they observe the same task performed by someone else, and a third phase equivalent to phase one, but with a new partner. Using sophisticated computational modelling to analyse choice data, the study reports that borderline participants (versus controls) are more certain about their preferences in phase one, used more neutral priors and are less flexible during phase two, and are less influenced by partners in phase three.

Strengths:

The topic is interesting and important, and the findings are potentially intriguing. The computational methods employed is clever and sophisticated, at the cutting edge of research in the field.

Weaknesses:

There are two major weaknesses. First, the paper lacks focus and clarity. The introduction is rather vague and, after reading it, I remained confused about the paper's aims. Rather than relying on specific predictions, the analysis is exploratory. This implies that it is hard to keep track, and to understand the significance, of the many findings that are reported. Second, although the computational approach employed is clever and sophisticated, there is important information missing about model comparison which ultimately makes some of the results hard to assess from the perspective of the reader.

Reviewer #3 (Public review):

In this paper, the authors use a three-phase economic game to examine the tendency to engage in prosocial versus competitive exchanges with three anonymous partners. In particular, they consider individual differences in the tendency to infer about others' tendencies based on one's preferences and to update one's preferences based on observations of others' behavior. The study includes a sample of individuals diagnosed with borderline personality disorder and a matched sample of psychiatrically healthy control participants.

On the whole, the experimental design is well-suited to the questions and the computational model analyses are thorough, including modern model-fitting procedures. I particularly appreciated the clear exposition regarding model parameterization and the descriptive Table 2 for qualitative model comparison. My broad question about the experiment (in terms of its clinical and cognitive process relevance): Does the task encourage competition or give participants a reason to take advantage of others? I don't think it does, so it would be useful to clarify the normative account for prosociality in the introduction (e.g., some of Robin Dunbar's work).

The finding that individuals with BPD do not engage in self-other generalization on this task of social intentions is novel and potentially clinically relevant. The authors find that BPD participants' tendency to be prosocial when splitting points with a partner does not transfer into their expectations of how a partner will treat them in a task where they are the passive recipient of points chosen by the partner. In the discussion, the authors reasonably focus on model differences between groups (Bayesian model comparison), yet I thought this finding -- BPD participants not assuming prosocial tendencies in phase 2 while CON participant did -- merited greater attention. Although the BPD group was close to 0 on the \beta prior in Phase 2, their difference from CON is still in the direction of being more mistrustful (or at least not assuming prosociality). This may line up with broader clinical literature on mistrustfulness and attributions of malevolence in the BPD literature (e.g., a 1992 paper by Nigg et al. in Journal of Abnormal Psychology). My broad point is to consider further the Phase 2 findings in terms of the clinical interpretation of the shift in \beta relative to controls.

On the conceptual level, I had two additional concerns. First, the authors note that they have "proposed a theory with testable predictions" (p. 4 but also elsewhere) but they do not state any clear predictions in the introduction, nor do they consider what sort of patterns will be observed in the BPD group in view of extant clinical and computational literature. Rather, the paper seems to be somewhat exploratory, largely looking at group differences (BPD vs. CON) on all of the shared computational parameters and additional indices such as belief updating and reaction times. Given this, I would suggest that the authors make stronger connections between extant research on intention representation in BPD and their framework (model and paradigm). In particular, the authors do not address related findings from Ereira (2020) and Story (2024) finding that in a false belief task that BPD participants *overgeneralize* from self to other. A critical comparison of this work to the present study, including an examination of the two tasks differ in the processes they measure, is important.

In addition, perhaps it is fairer to note more explicitly the exploratory nature of this work. Although the analyses are thorough, many of them are not argued for a priori (e.g., rate of belief updating in Figure 2C) and the reader amasses many individual findings that need to by synthesized.

Second, in the discussion, the authors are too quick to generalize to broad clinical phenomena in BPD that are not directly connected to the task at hand. For example, on p. 22: "Those with a diagnosis of BPD also show reduced permeability in generalising from other to self. While prior research has predominantly focused on how those with BPD use information to form impressions, it has not typically examined whether these impressions affect the self." Here, it's not self-representation per se (typically, identity or one's view of oneself), but instead cooperation and prosocial tendencies in an economic context. It is important to clarify what clinical phenomena may be closely related to the task and which are more distal and perhaps should not be approached here.

On a more technical level, I had two primary concerns. First, although the authors consider alternative models within a hierarchical Bayesian framework, some challenges arise when one analyzes parameter estimates fit separately to two groups, particularly when the best-fitting model is not shared. In particular, although the authors conduct a model confusion analysis, they do not as far I could tell (and apologies if I missed it) demonstrate that the dynamics of one model are nested within the other. Given that M4 has free parameters governing the expectations on the absolute and relative reward preferences in Phase 2, is it necessarily the case that the shared parameters between M1 and M4 can be interpreted on the same scale? Relatedly, group-specific model fitting has virtues when believes there to be two distinct populations, but there is also a risk of overfitting potentially irrelevant sample characteristics when parameters are fit group by group.

To resolve these issues, I saw one straightforward solution (though in modeling, my experience is that what seems straightforward on first glance may not be so upon further investigation). M1 assumes that participants' own preferences (posterior central tendency) in Phase 1 directly transfer to priors in Phase 2, but presumably the degree of transfer could vary somewhat without meriting an entirely new model (i.e., the authors currently place this question in terms of model selection, not within-model parameter variation). I would suggest that the authors consider a model parameterization fit to the full dataset (both groups) that contains free parameters capturing the *deviations* in the priors relative to the preceding phase's posterior. That is, the free parameters $\bar{\alpha}_{par}^m$ and $\bar{\beta}_{par}^m$ govern the central tendency of the Phase 2 prior parameter distributions directly, but could be reparametrized as deviations from Phase 1 $\theta^m_{ppt}$ parameters in an additive form. This allows for a single model to be fit all participants that encompasses the dynamics of interest such that between-group parameter comparisons are not biased by the strong assumptions imposed by M1 (that phase 1 preferences and phase 2 observations directly transfer to priors). In the case of controls, we would expect these deviation parameters to be centred on 0 insofar as the current M1 fit them best, whereas for BPD participants should have significant deviations from earlier-phase posteriors (e.g., the shift in \beta toward prior neutrality in phase 2 compared to one's own prosociality in phase 1). I think it's still valid for the authors to argue for stronger model constraints for Bayesian model comparison, as they do now, but inferences regarding parameter estimates should ideally be based on a model that can encompass the full dynamics of the entire sample, with simpler dynamics (like posterior -> prior transfer) being captured by near-zero parameter estimates.

My second concern pertains to the psychometric individual difference analyses. These were not clearly justified in the introduction, though I agree that they could offer potentially meaningful insight into which scales may be most related to model parameters of interest. So, perhaps these should be earmarked as exploratory and/or more clearly argued for. Crucially, however, these analyses appear to have been conducted on the full sample without considering the group structure. Indeed, many of the scales on which there are sizable group differences are also those that show correlations with psychometric scales. So, in essence, it is unclear whether most of these analyses are simply recapitulating the between-group tests reported earlier in the paper or offer additional insights. I think it's hard to have one's cake and eat it, too, in this regard and would suggest the authors review Preacher et al. 2005, Psychological Methods for additional detail. One solution might be to always include group as a binary covariate in the symptom dimension-parameter analyses, essentially partialing the correlations for group status. I remain skeptical regarding whether there is additional signal in these analyses, but such controls could convince the reader. Nevertheless, without such adjustments, I would caution against any transdiagnostic interpretations such as this one in the Highlights: "Higher reported childhood trauma, paranoia, and poorer trait mentalizing all diminish other-to-self information transfer irrespective of diagnosis." Since many of these analyses relate to scales on which the groups differ, the transdiagnostic relevance remains to be demonstrated.

eLife Assessment

This valuable study tests a methodology for the discovery of new honey bee-repellent odorants via machine learning. The conclusions of the study are supported by solid evidence, with predicted compounds validated in the lab and the field. This work will be of interest to researchers in ecology, pest control and olfactory neuroscience.

Reviewer #1 (Public review):

Summary:

This manuscript reports a very interesting, novel and important research angle to add to the now enormous interest in how pesticides can be toxic to beneficial insects like the honey bee. Many studies have reported on how pesticides in standard use formulations show both lethality as well as sublethal negative effects on behavior and reproduction. The authors propose to use machine learning algorithms to identify new volatile compounds that can be tested for repellency. They use as input chemical structures that are derived from chemicals that have known repellent effects as identified in their initial behavioral assays.

Strengths:

The conclusion is that such chemicals specific to repelling bees and not pest insects (using the fruit fly as a model for the latter) can be identified using the ML approach. Have a list of such chemicals that can be rotated among in any field application would be a benefit because of the honey bees' ability to learn its way around any kind of stimulus designed to keep it from nectar and pollen, even when they may be tainted by pesticide.

Weaknesses:

The use of machine learning seems well-executed and legitimate. But this is beyond my expertise. So other reviewers can maybe comment more on that.

The behavioral data report on the use of a two-choice assay for bees in small Petrie plates. Bess can feed from two small wells place of filter paper impregnated with control or the control containing a chemical. The primary behavior, for ex in Fig 2C, is the first choice by one of the five bees in the plate of which well to feed from. For some chemical compound, there seems to be a 50:50 choice, indicating no repellent effects. In other cases the first bee making the choice chose the control, indicating possible repellent effects of the test chemical. Choices in this assay were validated in a free flying assay.

Concerns with the choice assay:<br /> - 50-70 microliters amounts to what one hungry bee will drink. Did the first bee drink most of it, such that measures of bait consumed reflect a single bee or multiple bees?<br /> - How many bees were repelled to the control side? Was it just the one bee? Were other measures considered? E.g. time to first approach; the number of bees feeding at different time points; the total number of bees observed feeding per unit time.

Reviewer #2 (Public review):

Summary:

The search for new repellent odors for honey bees has significant practical implications. The authors developed an iterative pipeline through machine learning to predict honey bee-repellent odors based on molecular structures. By screening a large number of candidate compounds, they identified a series of novel repellents. Behavioral tests were then conducted to validate the effectiveness of these repellents. Both the discovery and the methodological approach hold value for related fields.

Strengths:

* The study demonstrates that using molecular structures and a relatively small training dataset, the model could predict repellents with a reasonably high success rate. If the iterative approach works as described, it could benefit a wide range of olfaction-related fields.<br /> * The effectiveness of the predicted repellents was validated through both laboratory and field behavioral tests.

Weaknesses:

The small size of the training dataset poses a common challenge for machine learning applications. However, the authors did not clearly explain how their iterative approach addresses this limitation in this study. Quantitative evidence demonstrating improvements achieved in the second round of training would strengthen their claims. For instance, details on whether the success rate of predictions or the identification of higher-affinity components would be helpful. Furthermore, given that only 15 new components were added for the second round of training, it is surprising that such a small dataset could result in significant improvements.

Reviewer #3 (Public review):

The manuscript of Kowalewski et al. titled "Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field" did machine learning to predict potential candidates for honeybee repellents, which may keep foraging bees from pesticides. This is a pilot research with strong significance in the research of olfactory behavior and in pest control. However, some major issues need to be addressed to enhance the manuscript's clarity, strength, and overall coherence.

(1) Drosophila melanogaster is not considered as a true agricultural pest. The manuscript would be more compelling if using true pests, for example, Drosophila suzukii or others.<br /> (2) For repellency test, the result relies on dosage. An attractant may become a repellent at high concentration. Test a range of concentrations for each chemicals and compare responses between honeybees and pests.<br /> (3) Be more clear about bee behavior data and their scores (as in Page 4 Results "184 training chemicals and later for 203 chemicals" and Page 10 Methods). I suggest that authors add a supplemental table with each chemical and its behavioral score, feature and reference - which ones were used for training, and which ones for testing. Also add your own behavioral test data (second input) to this table.<br /> (4) The AUC in the first validation was 0.88 (Page 4), and in Page 5, "As expected, the computational validation results based on the AUC values, show an improvement." However, there were no other AUC values to show improvement.<br /> (5) Show plots of ROC AUC curves from Round 1 and Round 2.<br /> (6) In the Discussion, the authors mentioned olfactory receptors in honeybees. It would be useful to provide a general review of the current understanding of these receptors and their (potential) functions.<br /> (7) I suggest combining Fig. 1 and Fig. 3A as one pipeline for this work.<br /> (8) Figure 2C, some sample sizes are very small, such as 2-piperidone: 1 first-choice control vs 0 first-choice repellent? Increase sample size and do statistical analysis.<br /> (9) In general, to assist reviewers, include line numbers to the manuscript.

eLife Assessment

This is a fundamental body of work reporting anatomical, molecular, and functional mapping of the central complex in Drosophila. The tools generated and the findings, which include characterization of neuromodulators used by different cells, will undoubtedly serve as a foundation for future studies of this brain region. Overall, the data are compelling and likely to have a major impact.

Reviewer #1 (Public review):

Summary:

This work is meant to help create a foundation for future studies of the Central Complex, which is a critical integrative center in the fly brain. The authors present a systematic description of cellular elements, cell type classifications, behavioral evaluations and genetic resources available to the Drosophila neuroscience community.

Strengths:

The work contributes new, useful and systematic technical information in compelling fashion to support future studies of the fly brain. It also continues to set a high and transparent standard by which large-scale resources can be defined and shared.

Weaknesses:

manuscript p. 1<br /> "The central complex (CX) of the adult Drosophila melanogaster brain consists of approximately 2,800 cells that have been divided into 257 cell types based on morphology and connectivity (Scheer et al., 2020; Hulse et al. 2021; Wolff et al., 2015)."<br /> The 257 accumulated cell types have informational names (e.g., PBG2‐9.s‐FBl2.b‐NO3A.b) in addition to their associations with specific Gal4 lines and specific EM Body IDs. All this is very useful. I have one suggestion to help a reader trying to get a "bird's eye view" of such a large amount of detailed and multi-layered information. Give each of the 257 CX cell types an arbitrary number: 1 to 257. In fact, Supplemental File 2 lists ~277 cell types each with a number in sequence, so perhaps in principle, it is there. This could expedite the search function when a reader is trying to cross-reference CX cell type information from the text, to the Figures and/or to the Supplemental Figures. Also, the use of (arbitrary) cell type numbers could expedite the explanation of which cell types are included in any compilation of information (e.g., which ones were tested for specific NT expression).

manuscript p 2<br /> "Figure 2 and Figure 2-figure supplements 1-4 show the expression of 52 new split-GAL4 lines with strong GAL4 expression that is largely limited to the cell type of interest. .... We also generated lines of lesser quality for other cell types that in total bring overall coverage to more than three quarters of CX cell types."<br /> This section describes the generation and identification of specific split Gal4 lines, and the presentation is generally excellent. It represents an outstanding compendium of information. My reading of the text suggests ~200 cell types have Gal4 lines that are of immediate use (having high specificity or v close-to-high). Use of an arbitrary number system (mentioned above) could augment that description for the reasons stated. For example, which of the 257 cell types are represented by split Gal4 lines that constitute the ~1/3 representing "high-quality lines "? A second comment relates to this study 's functional analysis of the contributions of CX cell types to sleep physiology. The recent literature contains renewed interest in the specific expression patterns of Gal4 lines that can promote sleep-like behaviors. In particular Gal4 line expression outside the brain (in the VNC and outside the CNS) have been raised as important elements that need be included for interpretation interpretation of sleep regulation. This present study offers useful information about a large number of expression patterns, as well as a basis with which to seek additional information., including mention of VNC expression in many cases However, perhaps I missed it, but I could not find a short description of the over-all strategy used to describe the expression patterns and feel that could be helpful. Were all Gal4 lines studied for expression in the VNC? and in the peripheral NS? It is probably published elsewhere, but even a short reprise would still be useful.

manuscript p 9<br /> Neurotransmitter expression in CX cell types<br /> "To determine what neurotransmitters are used by the CX cell types, we carried out fluorescent in situ hybridization using EASI-FISH (Eddison and Irkhe, 2022; Close et al., 2024) on brains that also expressed GFP driven from a cell-type-specific split GAL4 line. In this way, we could determine what neurotransmitters were expressed in over 100 different CX cell types based on ...."<br /> Reading this description, I was uncertain whether the >100 cell types mentioned were tested with all the NT markers by EASI-FISH? Also, assigning arbitrary numbers to the cell types (same suggestion as above) could help the reader more readily ascertain which were the ~100 cell types classified in this context.

manuscript p 10<br /> "Our full results are summarized below, together with our analysis of neuropeptide expression in the same cell types."<br /> I recommend specifying which Figures and Tables contain the "full results" indicated.

NP expression in CX cell types<br /> Similar to the comments regarding studies of NT expression: were all ~100 cell types tested with each of the 17 selected NPs? Arbitrary numerical identifies could be useful for the reader to determine which cell types/ lines were tested and which were not yet tested

manuscript p. 11<br /> "The neuropeptide expression patterns we observed fell into two broad categories."<br /> This section presents information that is extensive and extremely useful. It supports consideration of peptidergic cell signaling at a circuits level and in a systematic fashion that will promote future progress in this field. I have two comments. First, regarding the categorization of two NP expression patterns, discernible by differences in cell number: this idea mirrors one present in prior literature. Recently the classification of the transcription factor DIMM summarizes this same two-way categorization (e.g., doi: 10.1371/journal.pone.0001896). That included the fact that a single NP can be utilized by cell of either category.

Second, regarding this comment:<br /> "In contrast, neuropeptides like those shown in Figure 6 appear to be expressed in dozens to hundreds of cells and appear poised to function by local volume transmission in multiple distinct circuits."<br /> Signaling by NPs in this second category (many small cells) suggests more local diffusion, a smaller geographic expanse compared to "volume" signaling by the sparser larger peptidergic cells. Given this, I suggest re-consideration in using the term "volume" in this instance, perhaps in favor of "local" or "paracrine". This is only a suggestion and in fact rests almost entirely on speculation/ interpretation, as the field lacks a strong empirical basis to say how far NPs diffuse and act. A recent study in the fly brain of peptide co-transmitters (doi: 10.1016/j.cub.2020.04.025) provides an instructive example in which differences between the spatial extents of long-range (peptide 1) versus short-range (peptide 2) NP signaling may be inferred in vivo.

Spab was mentioned (Figure 6 legend) but discarded as a candidate NP to include based on a personal communication, as was Nplp1. The manuscript did not include reasons to do so, nor include a reference to spab peptide. I suggest including explicit reasons to discard candidate NPs.

In Fig 9-supplement 1, neurotransmitter biosynthetic enzymes were measured by RNA-seq for given CX cell types to augment the cell type classification. The same methods could be used to support cell type classification regarding putative peptidergic character (in Figure 9 supplement 2) by measuring expression levels of critical, canonical neuropeptide biosynthetic enzymes. These include the proprotein convertase dPC2 (amon); the carboxypeptidase dCPD/E (silver); and the amidating enzymes dPHM; dPal1; dPal2. PHM is most related to DBM (dopamine beta monooxygenase), the rate limiting enzyme for DA production, and greater than 90% of Drosophila neuropeptides are amidated. If the authors are correct in surmising widespread use of NPs by CX cell types (and I expect they are), there could be diagnostic value to report expression levels of this enzyme set across many/most CX cell types.

Comment #6<br /> Screen of effects on Sleep behavior<br /> This work is large in scope and as suggested likely presents excellent starting points for many follow-up studies. I again suggest assigning stable number identities to the elements described. In this case, not cell types, but split Gal4 lines. This would expedite the cross-referencing of results across the four Supplemental Files 3-6. For example, line SS00273 is entry line #27 in S Files 3 and 4, but line entry #18 in S Files 5 and 6.

manuscript p 26<br /> Clock to CX<br /> "Not surprisingly, the connectome reveals that many of the intrinsic CX cell types with sleep phenotypes are connected by wired pathways (Figure 12 and Figure 12-figure supplement 1)."<br /> Do intrinsic CX cells with sleep phenotypes also connect by wired pathways to CX cells that do not have sleep phenotypes?

"The connectome also suggested pathways from the circadian clock to the CX. Links between clock output DN1 neurons to the ExR1 have been described in Lamaze et al. (2018) and Guo et al. (2018), and Liang et al. (2019) described a connection from the clock to ExR2 (PPM3) dopaminergic neurons."<br /> The introduction to this section indicates a focus on connectome-defined synaptic contacts. Whereas the first two studies cited featured both physiological and anatomic evidence to support connectivity from clock cells to CX, the third did not describe any anatomical connections, and that connection may in fact be due to diffuse not synaptic signaling

I could not easily discern the difference between Figs 12 and 12-S1? These appear to be highly-related circuit models, wherein the second features more elements. Perhaps spell out the basis for the differences between the two models to avoid ambiguity.

"...the cellular targets of Dh31 released from ER5 are unknown, however previous work (Goda et al., 2017; Mertens et al., 2005) has shown that Dh31 can activate the PDF receptor raising the possibility of autocrine signaling."<br /> Regarding pharmacological evidence for Dh31 activation of Pdfr: strong in vivo evidence was developed in doi: 10.1016/j.neuron.2008.02.018: a strong pdfr mutation greatly reduces response to synthetic dh31 in neurons that normally express Pdfr

manuscript p 30<br /> "Unexpectedly, we found that all neuropeptide-expressing cell types also expressed a small neurotransmitter."<br /> Did this conclusion apply only to CX cell types? - or was it also true for large peptidergic neurons? Prior evidence suggests the latter may not express small transmitters (doi: 10.1016/j.cub.2009.11.065). The question pertains to the broader biology of peptidergic neurons, and is therefore outside the strict scope of the main focus area - the CX. However, the text did initially consider peptidergic neurons outside the CX, so the information may be pertinent to many readers.

Reviewer #2 (Public review):

Summary:

In this paper, Wolff et al. describe an impressive collection of newly created split-GAL4 lines targeting specific cell types within the central complex (CX) of Drosophila. The CX is an important area in the brain that has been involved in the regulation of many behaviors including navigation and sleep/wake. The authors advocate that to fully understand how the CX functions, cell-specific driver lines need to be created. In that respect, this manuscript will be of very important value to all neuroscientists trying to elucidate complex behaviors using the fly model. In addition, and providing a further very important finding, the authors went on to assess neurotransmitter/neuropeptides and their receptors expression in different cells of the CX. These findings will also be of great interest to many and will help further studies aimed at understanding the CX circuitries. The authors then investigated how different CX cell types influence sleep and wake. While the description of the new lines and their neurochemical identity is excellent, the behavioral screen seems to be limited.

Strengths:

(1) The description of dozens of cell-specific split-GAL4 lines is extremely valuable to the fly community. The strength of the fly system relies on the ability to manipulate specific neurons to investigate their involvement in a specific behavior. Recently, the need to use extremely specific tools has been highlighted by the identification of sleep-promoting neurons located in the VNC of the fly as part of the expression pattern of the most widely used dorsal-Fan Shaped Body (dFB) GAL4 driver. These findings should serve as a warning to every neurobiologist, make sure that your tool is clean. In that respect, the novel lines described in this manuscript are fantastic tools that will help the fly community.<br /> (2) The description of neurotransmitter/neuropeptides expression pattern in the CX is of remarkable importance and will help design experiments aimed at understanding how the CX functions.

Weaknesses:

(1) I find the behavioral (sleep) screen of this manuscript to be limited. It appears to me that this part of the paper is not as developed as it could be. The authors have performed neuronal activation using thermogenetic and/or optogenetic approaches. For some cell types, only thermogenetic activation is shown. There is no silencing data and/or assessment of sleep homeostasis or arousal threshold. The authors find that many CX cell types modulate sleep and wake but it's difficult to understand how these findings fit one with the other. It seems that each CX cell type is worthy of its own independent study and paper. I am fully aware that a thorough investigation of every CX neuronal type in sleep and wake regulation is a herculean task. So, altogether I think that this manuscript will pave the way for further studies on the role of CX neurons in sleep regulation.<br /> (2) Linked to point 1, it is possible that the activation protocols used in this study are insufficient for some neuronal types. The authors have used 29{degree sign} for thermogenetic activation (instead of the most widely used 31{degree sign}) and a 2Hz optogenetic activation protocol. The authors should comment on the fact that they may have missed some phenotypes by using these mild activation protocols.<br /> (3) There are multiple spelling errors in the manuscript that need to be addressed.

Reviewer #3 (Public review):

Summary:

The authors created and characterized genetic tools that allow for precise manipulation of individual or small subsets of central complex (CX) cell types in the Drosophila brain. They developed split-GAL4 driver lines and integrated this with a detailed survey of neurotransmitter and neuropeptide expression and receptor localization in the central brain. The manuscript also explores the functional relevance of CX cell types by evaluating their roles in sleep regulation and linking circadian clock signals to the CX. This work represents an ambitious and comprehensive effort to provide both molecular and functional insights into the CX, offering tools and data that will serve as a critical resource for researchers.

Strengths:

(1) The extensive collection of split-GAL4 lines targeting specific CX cell types fills a critical gap in the genetic toolkit for the Drosophila neuroscience community.<br /> (2) By combining anatomical, molecular, and functional analyses, the authors provide a holistic view of CX cell types that is both informative and immediately useful for researchers across diverse disciplines.<br /> (3) The identification of CX cell types involved in sleep regulation and their connection to circadian clock mechanisms highlights the functional importance of the CX and its integrative role in regulating behavior and physiological states.<br /> (4) The authors' decision to present this work as a single, comprehensive manuscript rather than fragmenting it into smaller publications each focusing on separate central complex components is commendable. This decision prioritizes accessibility and utility for the broader neuroscience community, which will enable researchers to approach CX-related questions with a ready-made toolkit.

Weaknesses:

While the manuscript is an outstanding resource, it leaves room for more detailed mechanistic exploration in some areas. Nonetheless, this does not diminish the immediate value of the tools and data provided.

Appraisal:

The authors have succeeded in achieving their aims of creating well-characterized genetic tools and providing a detailed survey of neurochemical and functional properties in the CX. The results strongly support their conclusions and open numerous avenues for future research. The work effectively bridges the gap between genetic manipulation, molecular characterization, and functional assessment, enabling a deeper understanding of the CX's diverse roles.

Impact and Utility

This manuscript will have a significant and lasting impact on the field, providing tools and data that facilitate new discoveries in the study of the CX, sleep regulation, circadian biology, and beyond. The genetic tools developed here are likely to become a standard resource for Drosophila researchers, and the comprehensive dataset on neurotransmitter and neuropeptide expression will inspire investigations into the interplay between neuromodulation and classical neurotransmission.

Additional Context

By delivering an integrated dataset that spans anatomy, molecular properties, and functional relevance, the authors have created a resource that will serve the neuroscience community for years to come.

eLife Assessment

This important study investigates how hummingbird hawkmoths integrate stimuli from across their visual field to guide flight behavior. Cue conflict experiments provide solid evidence for an integration hierarchy within the visual field: hawkmoths prioritize the avoidance of dorsal visual stimuli, potentially to avoid crashing into foliage, while they use ventrolateral optic flow to guide flight control. With a more systematic quantification of specific parameter combinations, this paper would be of broad interest to enthusiasts of visual neuroscience and ethology.

Reviewer #1 (Public review):

Summary:

Recent work has demonstrated that the hummingbird hawkmoth, Macroglossum stellatarum, like many other flying insects, use ventrolateral optic flow cues for flight control. However, unlike other flying insects, the same stimulus presented in the dorsal visual field elicits a directional response. Bigge et al., use behavioral flight experiments to set these two pathways in conflict in order to understand whether these two pathways (ventrolateral and dorsal) work together to direct flight and if so, how. The authors characterize the visual environment (the amount of contrast and translational optic flow) of the hawkmoth and find that different regions of the visual field are matched to relevant visual cues in their natural environment and that the integration of the two pathways reflects a priortiziation for generating behavior that supports hawkmoth safety rather than than the prevalence for a particular visual cue that is more prevalent in the environment.

Strengths:

This study creatively utilizes previous findings that the hawkmoth partitions their visual field as a way to examine parallel processing. The behavioral assay is well-established and the authors take the extra steps to characterize the visual ecology of the hawkmoth habitat to draw exciting conclusions about the hierarchy of each pathway as it contributes to flight control.

Weaknesses:

The work would be further clarified and strengthened by additional explanation included in the main text, figure legends, and methods that would permit the reader to draw their own conclusions more feasibly. It would be helpful to have all figure panels referenced in the text and referenced in order, as they are currently not. In addition, it seems that sometimes the incorrect figure panel is referenced in the text, Figure S2 is mislabeled with D-E instead of A-C and Table S1 is not referenced in the main text at all. Table S1 is extremely important for understanding the figures in the main text and eliminating acronyms here would support reader comprehension, especially as there is no legend provided for Table S1. For example, a reader that does not specialize in vision may not know that OF stands for optic flow. Further detail in figure legends would also support the reader in drawing their own conclusions. For example, dashed red lines in Figures 3 and 4 A and B are not described and the letters representing statistical significance could be further explained either in the figure legend or materials to help the reader draw their own conclusions.

Reviewer #2 (Public review):

Summary:

Bigge and colleagues use a sophisticated free-flight setup to study visuo-motor responses elicited in different parts of the visual field in the hummingbird hawkmoth. Hawkmoths have been previously shown to rely on translational optic flow information for flight control exclusively in the ventral and lateral parts of their visual field. Dorsally presented patterns, elicit a formerly completely unknown response - instead of using dorsal patterns to maintain straight flight paths, hawkmoths fly, more often, in a direction aligned with the main axis of the pattern presented (Bigge et al, 2021). Here, the authors go further and put ventral/lateral and dorsal visual cues into conflict. They found that the different visuomotor pathways act in parallel, and they identified a 'hierarchy': the avoidance of dorsal patterns had the strongest weight and optic flow-based speed regulation the lowest weight.

Strengths:

The data are very interesting, unique, and compelling. The manuscript provides a thorough analysis of free-flight behavior in a non-model organism that is extremely interesting for comparative reasons (and on its own). These data are both difficult to obtain and very valuable to the field.

Weaknesses:

While the present manuscript clearly goes beyond Bigge et al, 2021, the advance could have perhaps been even stronger with a more fine-grained investigation of the visual responses in the dorsal visual field. Do hawkmoths, for example, show optomotor responses to rotational optic flow in the dorsal visual field?

Reviewer #3 (Public review):

The central goal of this paper as I understand it is to extract the "integration hierarchy" of stimulus in the dorsal and ventrolateral visual fields. The segregation of these responses is different from what is thought to occur in bees and flies and was established in the authors' prior work. Showing how the stimuli combine and are prioritized goes beyond the authors' prior conclusions that separated the response into two visual regions. The data presented do indeed support the hierarchy reported in Figure 5 and that is a nice summary of the authors' work. The moths respond to combinations of dorsal and lateral cues in a mixed way but also seem to strongly prioritize avoiding dorsal optic flow which the authors interpret as a closed and potentially dangerous ecological context for these animals. The authors use clever combinations of stimuli to put cues into conflict to reveal the response hierarchy.

My most significant concern is that this hierarchy of stimulus responses might be limited to the specific parameters chosen in this study. Presumably, there are parameters of these stimuli that modulate the response (spatial frequency, different amounts of optic flow, contrast, color, etc). While I agree that the hierarchy in Figure 5 is consistent for the particular stimuli given, this may not extend to other parameter combinations of the same cues. For example, as the contrast of the dorsal stimuli is reduced, the inequality may shift. This does not preclude the authors' conclusions but it does mean that they may not generalize, even within this species. For example, other cue conflict studies have quantified the responses to ranges of the parameters (e.g. frequency) and shown that one cue might be prioritized or up-weighted in one frequency band but not in others. I could imagine ecological signatures of dorsal clutter and translational positioning cues could depend on the dynamic range of the optic flow, or even having spatial-temporal frequency-dependent integration independent of net optic flow.

The second part of this concern is that there seems to be a missed opportunity to quantify the integration, especially when the optic flow magnitude is already calculated. The discussion even highlights that an advantage of the conflict paradigm is that the weights of the integration hierarchy can be compared. But these weights, which I would interpret as stimulus-responses gains, are not reported. What is the ratio of moth response to optic flow in the different regions? When the moth balances responses in the dorsal and ventrolateral region, is it a simple weighted average of the two? When it prioritizes one over the other is the response gain unchanged? This plays into the first concern because such gain responses could strongly depend on the specific stimulus parameters rather than being constant.

The authors do explain the choice of specific stimuli in the context of their very nice natural scene analysis in Fig. 1 and there is an excellent discussion of the ecological context for the behaviors. However, I struggled to directly map the results from the natural scenes to the conclusions of the paper. How do they directly inform the methods and conclusions for the laboratory experiments? Most important is the discussion in the middle paragraph of page 12, which suggests a relationship with Figure 1B, but seems provocative but lacking a quantification with respect to the laboratory stimuli.

The central conclusion of the first section of the results is that there are likely two different pathways mediating the dorsal and the ventrolateral response. This seems reasonable given the data, however, this was also the message that I got from the authors' prior paper (ref 11). There are certainly more comparisons being done here than in that paper and it is perfectly reasonable to reinforce the conclusion from that study but I think what is new about these results needs to be highlighted in this section and differentiated from prior results. Perhaps one way to help would be to be more explicit with the open hypotheses that remain from that prior paper.

eLife Assessment

This important Research Advance presents convincing evidence on the neuroprotective effects of reserpine in a well-established model of retinitis pigmentosa (P23H-1). This study builds on previous work establishing reserpine as a neuroprotectant in models of Leber congenital amaurosis. Here authors show reserpine's disease gene-independent influence on photoreceptor survival and emphasizes the importance of considering biological sex in understanding inherited retinal degeneration and the impact of drug treatments on mutant retinas. The work will be of interest to vision researchers as well as a broad audience in translational research.

Reviewer #1 (Public review):

Summary:

The authors investigate the neuroprotective effect of reserpine in a retinitis pigmentosa (P23H-1) model, characterized by a mutation in the rhodopsin gene. Their results reveal that female rats show better preservation of both rod and cone photoreceptors following reserpine treatment compared to males.

Strengths:

This study effectively highlights the neuroprotective potential of reserpine and underscores the value of drug repositioning as a strategy for accelerating the development of effective treatments. The findings are significant for their clinical implications, particularly in demonstrating sex-specific differences in therapeutic response.

Weaknesses:

The main limitation is the lack of precise identification of the specific pathway through which reserpine prevents photoreceptor death.

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Sex-specific attenuation of photoreceptor degeneration by reserpine in a rhodopsin P23H rat model of autosomal dominant retinitis pigmentosa" by Beom Song et al., the authors explore the transcriptomic differences between male and female wild-type (WT) and P23H retinas, highlighting significant gene expression variations and sex-specific trends. The study emphasizes the importance of considering biological sex in understanding inherited retinal degeneration and the impact of drug treatments on mutant retinas.

Strengths:

(1) Relevance to Clinical Challenges: The study addresses a critical limitation in inherited retinal degeneration (IRD) therapies by exploring a gene-agnostic approach. It emphasizes sex-specific responses, which aligns with recent NIH mandates on sex as a biological variable.<br /> (2) Multi-dimensional Methodology: Combining electroretinography (ERG), optical coherence tomography (OCT), histology, and transcriptomics strengthens the study's findings.<br /> (3) Novel Insights: The transcriptomic analysis uncovers sex-specific pathways impacted by reserpine, laying the foundation for personalized approaches to retinal disease therapy.

Weaknesses:

Dose Optimization<br /> The study uses a fixed dose (40 µM), but no dose-response analysis is provided. Sex-specific differences in efficacy might be influenced by suboptimal dosing, particularly considering potential differences in metabolism or drug distribution.

Statistical Analysis

In my opinion, there is room for improvement. How were the animals injected? Was the contralateral eye used as control? (no information in the manuscript about it!, line 390 just mentions the volume and concentration of injections). If so, why not use parametric paired analysis? Why use a non-parametric test, as it is the Mann-Whitney U? The Mann-Whitney U test is usually employed for discontinuous count data; is that the case here?<br /> Therefore, please specify whether contralateral eyes or independent groups served as controls. If contralateral controls were used, paired parametric tests (e.g., paired t-tests) would be statistically appropriate. Alternatively, if independent cohorts were used, non-parametric Mann-Whitney U tests may suffice but require clear justification.

Sex-Specific Pathways

The authors do identify pathways enriched in female vs. male retinas but fail to explicitly connect these to the changes in phenotype analysed by ERG and OCT. The lack of mechanistic validation weakens the argument.

The study does not explore why female rats respond better to reserpine. Potential factors such as hormonal differences, retinal size, or differential drug uptake are not discussed.<br /> It remains open, whether observed transcriptomic trends (e.g., proteostasis network genes) correlate with sex-specific functional outcomes.

Author response:

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

eLife Assessment:

This study provides valuable insights, addressing the growing threat of multi-drug-resistant (MDR) pathogens by focusing on the enhanced efficacy of colistin when combined with artesunate and EDTA against colistin-resistant Salmonella strains. The evidence is solid, supported by comprehensive microbiological assays, molecular analyses, and in vivo experiments demonstrating the effectiveness of this synergic combination. However, the discussion on the clinical application challenges of this triple combination is incomplete, and it would benefit from addressing the high risk associated with using three potential nephrotoxic agents in vivo.

The development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use. The discussion of potential toxicity of AS, colistin, EDTA and the triple combination have been added in line 318 to 337.

Public Reviews:

Reviewer #1 (Public Review):

(1) The study focuses on a limited number of Salmonella strains, and broader testing on various MDR pathogens would strengthen the findings.

The number of COL-resistant clinical strains that actually used was larger than that mentioned in our original article, when evaluating the antimicrobial activities of AS, EDTA, COL alone or drug combinations. But, considering that there were superfluous results of mcr-1 positive Salmonella strains, we omitted these results (Table supplement 7 and 8 in revised supplement materials) to avoid redundant data presentation in the original article. Additionally, much more gram-negative and -positive MDR bacteria, such as Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus will be selected for the next study including the development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis et al.

(2) While the study elucidates several mechanisms, further molecular details could provide deeper insights into the interactions between these drugs and bacterial targets.

In our next study, further molecular details will be focused on the regulatory targets of CheA and SpvD-related pathways, as well as the precise inhibition targets of MCR protein by the triple combination, through the generation of deletion or point mutations, and analysis of intermolecular interactions.

(3) The time-kill experiment was conducted over 12 hours instead of the recommended 24 hours. To demonstrate a synergistic effect among the drugs, a reduction of at least 2 log10 in colony count should be shown in a 24-hour experiment. Additionally, clarifying the criteria for selecting drug concentrations is important to improve the interpretation of the results.

The time-kill experiment of 24 hours have been re-executed and could be used to replace the Figure 1 in the original paper. The New Figure 1 has been uploaded and the change do not affect our interpretation of the result.

Although in vitro studies have determined that with increasing dose of AS and EDTA, the antibacterial synergistic activity was gradually enhanced, and meanwhie, may also resulting in more toxic side effects. Thus, in our study, the 1/8 MICs of AS and EDTA were selected to ensure excellent antibacterial activity whereas minimize the potential toxicity. The instructions on the selection of drug concentration have been added in line 323 to 326.

(4) While the combination of EDTA, artesunate, and colistin shows promising in vitro results against Salmonella strains, the clinical application of this combination warrants careful consideration due to potential toxicity issues associated with these compounds.

The development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use.

Reviewer #2 (Public Review):

(1) The study by Zhai et al describes repurposing of artesunate, to be used in combination with EDTA to resensitize Salmonella spp. to colistin. The observed effect applied both to strains with and without mobile colistin resistance determinants (MCR). It was already known that EDTA in combination with colistin has an inhibitory effect on MCR-enzymes, but at the same time, both colistin and EDTA can contribute to nephrotoxicity, something which is also true for artesunate. Thus, the triple combination of three nephrotoxic agents has significant challenges in vivo, which is not particularly discussed in this paper.

The discussion of potential toxicity of triple combination has been added in line 318 to 337.

(2) The selection of strains is not very clear. Nothing is known about the sequence types of the strains or how representative they are for strains circulating in general. Thus, it is difficult to generalize from this limited number of isolates, although the studies done in these isolates are comprehensive.

The tested strains in this study were all COL-resistant clinical isolates, and the genome sequencing and comparative analysis of these strains have not been analyzed. The antibacterial activities of different antimicrobial drugs against the S16 and S30 strains have been measured and listed in the Table supplement 9 within revised supplement materials. Considering that the number of COL-resistant clinical strains that actually used was larger than that mentioned in our original article (see the NO.1 response to the Public Reviewer #1), we think that the results obtained in this study could be representative to some extent.

(3) Nothing is known about the susceptibility of the strains to other novel antimicrobial agents. Colistin has a limited role in the treatment of gram-negative infections, and although it can be used sometimes in combination, it is not clear why it would be combined with two other nephrotoxic agents and how this could have relevance in a clinical setting.

The antibacterial activities of different antimicrobial drugs against the S16 and S30 strains have been measured and listed in the Table supplement 9 within revised supplement materials. Additionally, the discussion of potential toxicity of triple combination has been added in line 318 to 337.

(4) It is not clear whether their transcriptomics analysis should at least be carried out in duplicate for reasons of being able to assess reproducibility. It is also not clear why the samples were incubated for 6 hours - no discussion is presented on the selection of a time point for this.

As it can be seen from the time kill curves that the survival number of bacteria started to decrease after 4 h incubation of drug combinations. If the incubation time is too short (for example less than 4 h), the differentially expressed genes can not be fully revealed, while too long incubation time (such as 8 h and 12 h) may lead to a significant CFU reduction of bacteria, and result in inaccurate sequencing results. Therefore, we selected the incubation time 6 h, at which point drugs exhibited  significant antibacterial effects and there were also enough survival bacteria in the sample for transcriptome analysis. Each sample had three replications to preserve the accuracy of results.

(5) Discussion is lacking on the reproducibility and selection of details for the methodology.

The results obtained in this paper have been repeated several times, which indicated that the detailed operation steps described in the materials and methods section were reproducibility. To avoid redundancy, we did not include too much details in the discussion section.

Reviewer #3 (Public Review):

(1) Number of strains tested.

The number of COL-resistant clinical strains that actually used was larger than that mentioned in our original article (see the NO.1 response to the Public Reviewer #1)

(2) Response to comment: Lack of data on cytotoxicity.

The pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

(1) Introduction:

The introduction should provide more context about the pathogen Salmonella, its significance in both human and veterinary medicine, and the impact of colistin resistance in these pathogens. Salmonella is a leading cause of foodborne illnesses worldwide, resulting in substantial morbidity and mortality. It can cause a range of diseases, from gastroenteritis to more severe systemic infections like typhoid fever and invasive non-typhoidal salmonellosis. In veterinary medicine, Salmonella infections can lead to significant economic losses in livestock industries due to illness and death among animals, as well as through the contamination of animal products.

The description has been added in the introduction section in line 47 to 53.

(2) Results and Discussion:

(1) While the combination of EDTA, artesunate, and colistin shows promising in vitro results against Salmonella, the clinical application of this combination warrants careful consideration due to potential toxicity issues associated with these compounds. Colistin is known for nephrotoxicity and neurotoxicity, limiting its use to severe cases where the benefits outweigh the risks. EDTA, as a chelating agent, can disrupt essential metal ions in the body, posing risks of metabolic imbalances. Although it has clinical applications, primarily in cases of heavy metal poisoning, its use as an adjuvant in antibiotics may present risks. Although generally well-tolerated for malaria, interactions of artesunate with other drugs and long-term safety in combined therapies require thorough investigation.

The discussion of potential toxicity of triple combination has been added in line 318 to 337.

(2) Table 1: The manuscript mentions that some strains used in the study are mcr-positive and mcr-negative. It is important to indicate in Table 1, in addition to the identification of Salmonella species, which strains are mcr-positive or mcr-negative.

The relevant information has been added in Table 1.

(3) Figure 2: What is the authors' hypothesis regarding the growth curves labeled "a" and "e" where strains JS and S16 resume growth 12 hours after treatment with AS? In the legend of Figure 2, describe what was used as the "positive control group."

The growth curves labeled “a” and “e” were in Figure 1. After incubated with AC for 8 h, the survival CFUs of JS and S16 strains showed a slightly reduction, but there were still living cells. Since the bactericidal activity of AC is not strong enough to exert sustained bactericidal activity, these remaining living cells will resume growth after treatment with AC for 12 h. The “positive control group” in the legend of Figure 2 has been indicated in line 724.

(4) What is the authors' hypothesis for the differences observed in the transcriptome and metabolome?

The changes in gene transcription level may cause corresponding changes in protein level, but these proteins are not all involved in the bacterial metabolic process. For example, MCR protein  is encoded by the COL resistance related gene mcr, which mediates the modification of lipid A, but are not involved in the cellular metabolic process. Therefore, the transcriptome change of mcr gene may affect the protein production of MCR, nor the bacterial metabolic processes, so there are differences observed in the transcriptome and metabolome.

(5) In some parts of the text, the authors state that artesunate and EDTA potentiate the action of colistin, which is a bacteriostatic drug. However, in other parts, the authors describe the effect of the AEC combination as bacteriostatic (Abstract: line 32; Results: line 179). How do the authors explain this inconsistency?

The artesunate and EDTA could be regarded as “adjuvants” for the bacteriostatic drug colistin. Adjuvants itself act no or weak antibacterial effect on bacteria. For antimicrobial drugs, the “adjuvants” are compounds that generally used in combination with antibacterial drugs to re-sensitizing bacteria that have developed drug resistance. Thus, in this paper the AEC combination could be regared as bacteriostatic.

(6) According to Brennan & Kirby (2019; doi: 10.1016/j.cll.2019.04.002), to evaluate the synergism between different drug combinations, bacterial growth curves need to be assessed over 24 hours. If the colony count is {greater than or equal to} 2 log10 lower than that of the most active antimicrobial alone, the combination is considered synergistic. Based on the growth curve results shown in Figure 1, the experiment was conducted for 12 hours, and in some cases, only a small reduction in growth was observed, even at the maximum concentration of colistin. Moreover, in some cases, the curve resumes rising between 8 and 12 hours. What is the authors' hypothesis in this case? It is important to conduct the assay over 24 hours to confirm the synergism between these drugs.

The time-kill experiment of 24 hours have been re-executed and could be used to replace the Figure 1 in the original paper. Additionally, the phenomenon that “the curve resumes rising between 8 and 12 hours” has been explained in the response to comment of “Reviewer #1 (Recommendations For The Authors), Results and Discussion, (3) Figure 2”.

(7) To prove that CheA and SpvD play a critical role in the effect of the AEC combination, deletion of these genes should be performed, and the mutant strains should be tested.

The deletion of cheA and spvD will be carried out in our next study.

(8) To demonstrate that the flagellum is no longer assembled, a transmission electron microscopy image using antibodies against flagellin should be performed, along with motility tests.

The motility assays have been performed and displayed as Figure supplement 5 in the revised supplement materials.

(9) Figure 7: In the X-axis legend, specify what "model" refers to.

The “model” refers to the PBS control group that mice were treated with PBS after the intraperitoneal injection of 100 µL bacterial solution (1.31 × 10⁵ CFU).

(10) Figure 8 Legend: In the legend of Figure 8 (line 717), are the authors referring to E. coli or Salmonella?

It referred to Salmonella, which has already been illustrated in the headline of Figure 8 in the revised manuscript.

(3) Materials and Methods:

(1) Bacterial Strains and Agents: It would be beneficial to include in the table the species of the strains used in the study, as well as the concentrations of colistin, artesunate, and EDTA utilized (lines 321 - 332).

We have ever tried to add the above information to Table 1, but the addition of this information would make the table too large and beyond the margins, which is not conducive to the layout design of the table, so we chose to display these information in the materials and methods section instead of the table.

(2) Antibacterial Activity In Vitro: Ensure clarity and well-defined ranges for the concentrations of colistin, EDTA, and artesunate used separately and in combinations (lines 335 - 344).

The drug concentrations have been listed in line 369 to 371.

(3) Time-Kill Assays: Clarify the criteria for selecting concentrations, whether based on MICs or peak and trough concentrations relevant to human and animal treatments with colistin (lines 345 - 351).

Although in vitro studies have determined that with increasing dose of AS and EDTA, the antibacterial synergistic activity was gradually enhanced, and meanwhie, may also resulting in more toxic side effects. Thus, in our study, the 1/8 MICs of AS and EDTA were selected to ensure excellent antibacterial activity whereas minimize the potential toxicity. The instructions on the selection of drug concentration have been added in line 323 to 326.

(4) General Corrections: Throughout the manuscript, correct typographical errors and consistently include the concentration values in mg/L alongside the MIC fractions. Specify the strains used for all experiments to ensure clarity. In the manuscript, the term "medication regimens" is used to describe the experimental setups involving different combinations of drugs tested in vitro. To improve accuracy and clarity, it is recommended to use the term "drug combination" instead. This term is more appropriate for in vitro experiments and will help avoid confusion with clinical treatment protocols.

The typographical errors have been checked and corrected throughout the manuscript, and the “medication regimens” have been replaced by “drug combinations”.

Reviewer #2 (Recommendations For The Authors):

Please see above for recommendations on what can be done to improve the manuscript.

While other omics analyses have been conducted herein, the authors do not comment on the genomic analysis of their own strains. It would have been a natural step to sequence all the strains used in the experiments.

Due to limited program funding, the genome sequencing and comparative analysis of these strains have not been analyzed. The antibacterial activities of different antimicrobial drugs against the S16 and S30 strains have been measured and listed in the Table supplement 9 within revised supplement materials.

Some minor comments:

(1) There are some spelling errors - e.g. "bacteria strains" instead of "bacterial strains".

The grammar and spelling errors have been corrected throughout the manuscript.

(2) I would avoid words like "unfortunately".

The word “unfortunately” has been changed.

(3) Some MIC-values in Table 1 seem incorrect - e.g. 24 mg/L. This is not a 2-log value - the value should be 32 mg/L if the dilution series has been carried out correctly.

We are so sorry for the mistake. The data has been corrected, and we also checked other data.

Reviewer #3 (Recommendations For The Authors):

Below are some suggestions.

(1) Sentences L47 & L48 "Infections with antibiotic-resistant pathogens, especially carbapenemase-producing Enterobacteriaceae, represent an impending catastrophe of a return to the pre-antibiotic era" - this is slightly exaggerated! I also wonder if we need to use Enterobacterales instead of Enterobacteriaceae.

The sentences in L47 & L48 have been changed. We googled the “carbapenemase-producing Enterobacteriaceae” and found it is a high-frequency word in numerous reports.

(2) L48. The drying up of the antibiotic discovery pipeline is NOT necessarily the reason to use colistin as a drug of last resort!

The sentence has been revised.

(3) The manuscript requires extensive English editing but has merit based on the strong compilation of data.

We have optimized and revised the writing of the whole article.

(4) I suggest the authors have some data on the cytotoxicity of AS alone, colistin alone, and both of them against eucaryotic cells (Caco-) and if possible determine IS (index selectivity). This additional experiment will strengthen the quality of the manuscript. The authors must also explain how to put such tri-therapy into practice.

The development of novel pharmaceutical dosage forms, pharmacokinetic, pharmacodynamic and safety analysis of the triple combination will be further conducted in our next study to provide a theoretical basis for the next clinical drug use. The discussion of potential toxicity of AS, colistin, EDTA and the triple combination have been added in line 318 to 337.

eLife Assessment

This valuable study addresses the growing threat of multi-drug-resistant (MDR) pathogens by focusing on the enhanced efficacy of colistin when combined with artesunate and EDTA against colistin-resistant Salmonella strains. The evidence is solid, supported by comprehensive microbiological assays, molecular analyses, and in vivo experiments demonstrating the effectiveness of this synergic combination.

Reviewer #1 (Public review):

Summary:

The study addresses the growing threat of multi-drug-resistant (MDR) pathogens, focusing on the efficacy of colistin (COL), a last-resort antibiotic, and its enhanced activity when combined with artesunate (AS) and ethylenediaminetetraacetic acid (EDTA) against colistin-resistant Salmonella strains. The researchers aim to explore whether these combinations can restore the effectiveness of colistin and understand the underlying mechanisms. The study used a combination of microbiological and molecular techniques to evaluate the antibacterial activity and mechanisms of action of COL, AS, and EDTA. Key methods included: (i) Antimicrobial Susceptibility Testing: Determining minimum inhibitory concentrations (MICs) of COL, AS, and EDTA, both alone and in combination, against various Salmonella strains; (ii) Time-Kill Assays: Measuring bacterial growth inhibition over time with different drug combinations; (iii) Fluorescent Probe-Permeability Assays: Assessing cell membrane integrity using fluorescent dyes; (iv) Proton Motive Force Assay: Evaluating the impact on the electrochemical proton gradient (PMF); (v) Reactive Oxygen Species (ROS) Measurement: Quantifying intracellular ROS levels; (vi) Scanning Electron Microscopy (SEM): Observing morphological changes in bacterial cells; and (vii) Omics Analysis: Transcriptome and metabolome profiling to identify differentially expressed genes (DEGs) and significant differential metabolites (SDMs). The combination of COL, AS, and EDTA (AEC) showed significant antibacterial activity against colistin-resistant Salmonella strains, reducing the MICs and enhancing bacterial killing compared to individual treatments. The AEC treatment caused extensive damage to both the outer and inner bacterial membranes, as evidenced by increased fluorescence of membrane-impermeant dyes and SEM images showing deformed cell membranes. AEC treatment selectively collapsed the Δψ component of PMF, indicating disruption of vital cellular processes. The combination therapy increased intracellular ROS levels, contributing to bacterial killing. Transcriptome data revealed changes in genes related to two-component systems, flagellar assembly, and ABC transporters. Metabolome analysis highlighted disruptions in pathways such as arachidonic acid metabolism. The findings suggest that AS and EDTA can potentiate the antibacterial effects of colistin by disrupting bacterial membranes, collapsing PMF, and increasing ROS levels. This combination therapy could serve as a promising approach to combat colistin-resistant Salmonella infections.

Strengths:

- The study employs a wide range of techniques to thoroughly investigate the antibacterial mechanisms and efficacy of the drug combinations.<br /> - The results are consistent across multiple assays and supported by both in vitro and in vivo data.<br /> - Combining AS and EDTA with COL represents a novel strategy to tackle antibiotic resistance.

Weaknesses:

- The methodology used for interpreting and reporting time-kill assay results.

Comments on revised version:

Overall, the authors have adequately addressed the suggestions provided.

Reviewer #2 (Public review):

The study by Zhai et al describes repurposing of artesunate, to be used in combination with EDTA to resensitize Salmonella spp. to colistin. The observed effect applied both to strains with and without mobile colistin resistance determinants (MCR). It is known since earlier that EDTA in combination with colistin has an inhibitory effect on MCR-enzymes, but at the same time both colistin and EDTA can contribute to nephrotoxicity, something which is also true for artesunate. Thus, the triple combination of three nephrotoxic agents has significant challenges in vivo, which is not particularly discussed in this paper.

The study is sound from a methodological point of view and has many interesting angles to address mechanistically how the three compounds can synergize.

Comments on revised version:

After having read the revised version, I have the following comments:

(1) The antimicrobials tested in Figure 9 are not really very relevant. I would want to see carbapenems and novel beta-lactam/beta-lactamase inhibitors rather than many old drugs with a debatable role in the treatment of Gram-negative infections. At least the authors should be able to test carbapenem resistance<br /> (2) The genomics analysis of the strains should be fairly quick - both in terms of characterizing the mobile resistome and the sequence types. There are publicly available databases for this purpose

The rest of my comments have been addressed in the revised version. There are still some remaining valid points from other reviewers that could be debatable whether they should be address. The authors refer to plans of studying these aspects in subsequent studies, but it could be discussed whether some of the data could be expected already in this study.

eLife Assessment

This study provides important insights into how IL-1 cytokines protect cells against SARS-CoV-2 infection. By inducing a non-canonical RhoA/ROCK signaling pathway, IL-1beta inhibits the ability of SARS-CoV-2 infected cells to fuse with uninfected cells and produce syncytia. Convincing evidence underlies the identification of the key signaling components required for this inhibitory phenotype, and suggests that this process may also function to inhibit SARS-CoV-2 infection in vivo.

Reviewer #1 (Public review):

Summary:

SARS-CoV-2 infection induces syncytia formation, which promotes viral transmission. In this paper, the authors aimed to understand how host-derived inflammatory cytokines IL-1α/β combat SARS-CoV-2 infection.

Strengths:

First, they used a cell-cell fusion assay developed previously to identify IL-1α/β as the cytokines that inhibit syncytia formation. They co-cultured cells expressing the spike protein and cells expressing ACE2 and found that IL-1β treatment decreased syncytia formation and S2 cleavage.

Second, they investigated the IL-1 signaling pathway in detail, using knockouts or pharmacological perturbation to understand the signaling proteins responsible for blocking cell fusion. They found that IL-1 prevents cell-cell fusion through MyD88/IRAK/TRAF6 but not TAK1/IKK/NF-κB, as only knocking out MyD88/IRAK/TRAF6 eliminates the inhibitory effect on cell-cell fusion in response to IL-1β. This revealed that the inhibition of cell fusion did not require a transcriptional response and was mediated by IL-1R proximal signaling effectors.

Third, the authors identified RhoA/ROCK activation by IL-1 as the basis for this inhibition of cell fusion. By visualizing a RhoA biosensor and actin, they found a redistribution of RhoA to the cell periphery and cell-cell junctions after IL-1 stimulation. This triggered the formation of actin bundles at cell-cell junctions, preventing fusion and syncytia formation. The authors confirmed this molecular mechanism by using constitutively active RhoA and an inhibitor of ROCK.<br /> Diverse Cell types and in vivo models were used, and consistent results were shown across diverse models. These results were convincing and well-presented.

In summary, the authors have provided compelling evidence regarding how IL-1 signaling induces a prophylactic response to viral infection. While the mechanistic details of how IL-1R and MyD88 induce RhoA/Rock pathway to mediate actin remodeling remain unclear, this manuscript serves as the basis for future studies.

Reviewer #2 (Public review):

Summary:

In this study, Zheng et al investigated the role of inflammatory cytokines in protecting cells against SARS-CoV-2 infection. They demonstrate that soluble factors in the supernatants of TLR stimulated THP1 cells reduce fusion events between HEK293 cells expressing SARS-CoV-2 S protein and the ACE2 receptor. Using qRT-PCR and ELISA, they demonstrate that IL-1 cytokines are (not surprisingly) upregulated by TLR treatment in THP1 cells. Further, they convincingly demonstrate that recombinant IL-1 cytokines are sufficient to reduce cell-to-cell fusion mediated by the S protein. Using chemical inhibitors and CRISPR knock-out of key IL-1 receptor signaling components in HEK293 cells, they demonstrate that components of the myddosome (MYD88, IRAK1/4, and TRAF6) are required for fusion inhibition, but that downstream canonical signaling (i.e., TAK1 and NFKB activation) is not required. Instead, they provide evidence that IL-1-dependent non-canonical activation of RhoA/Rock is important for this phenotype. Importantly, the authors demonstrate that expression of a constitutively active RhoA alone is sufficient to inhibit fusion and that chemical inhibition of Rock could reverse this inhibition. The authors followed up these in vitro experiments by examining the effects of IL-1 on SARS-COV-2 infection in vivo and they demonstrate that recombinant IL-1 can reduce viral burden and lung pathogenesis in a mouse model of infection. Use of a ROCK inhibitor in IL-1 treated mice restored the ability of SARS-CoV-2 to spread in the lung, suggesting that this inhibitory process functions in vivo.

Strengths:

(1) The bioluminescence cell-cell fusion assay provides a robust quantitative method to examine cytokine effects on viral glycoprotein-mediated fusion.

(2) The study identifies a new mechanism by which IL-1 cytokines can limit virus infection.

(3) The authors tested IL-1 mediated inhibition of fusion induced by many different coronavirus S proteins and several SARS-CoV-2 strains.

(4) The authors demonstrate that recombinant IL-1 mediated inhibition of SARS-CoV-2 infection in mice is dependent on the RhoA/Rock pathway.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

SARS-CoV-2 infection induces syncytia formation, which promotes viral transmission. In this paper, the authors aimed to understand how host-derived inflammatory cytokines IL-1α/β combat SARS-CoV-2 infection.

Strengths:

First, they used a cell-cell fusion assay developed previously to identify IL-1α/β as the cytokines that inhibit syncytia formation. They co-cultured cells expressing the spike protein and cells expressing ACE2 and found that IL-1β treatment decreased syncytia formation and S2' cleavage.

Second, they investigated the IL-1 signaling pathway in detail, using knockouts or pharmacological perturbation to understand the signaling proteins responsible for blocking cell fusion. They found that IL-1 prevents cell-cell fusion through MyD88/IRAK/TRAF6 but not TAK1/IKK/NF-κB, as only knocking out MyD88/IRAK/TRAF6 eliminates the inhibitory effect on cell-cell fusion in response to IL-1β. This revealed that the inhibition of cell fusion did not require a transcriptional response and was mediated by IL-1R proximal signaling effectors.

Third, the authors identified RhoA/ROCK activation by IL-1 as the basis for this inhibition of cell fusion. By visualizing a RhoA biosensor and actin, they found a redistribution of RhoA to the cell periphery and cell-cell junctions after IL-1 stimulation. This triggered the formation of actin bundles at cell-cell junctions, preventing fusion and syncytia formation. The authors confirmed this molecular mechanism by using constitutively active RhoA and an inhibitor of ROCK.

Diverse Cell types and in vivo models were used, and consistent results were shown across diverse models. These results were convincing and well-presented.

Weaknesses:

As the authors point out in the discussion, whether IL-1-mediated RhoA activation is specific to viral infection or regulates other RhoA-regulated processes is unclear. We would also require high-magnification images of the subcellular organization of the cytoskeleton to appreciate the effect of IL-1 stimulation.

Thanks for the suggestions. We tested the role of IL-1β in other RhoA-regulated processes, and found that IL-1β-mediated RhoA activation also reduced cell migration in a cell scratch assay (see Author response image 1). We also provided high-magnification images in the revised Figures 4 and 5, as well as their respective figure supplements.

Author response image 1.

(A) Cell scratch assay images of HEK293T cells treated with PBS or IL-1β. (B) Quantification of cell migration in (A).

Reviewer #2 (Public Review):

Summary:

In this study, Zheng et al investigated the role of inflammatory cytokines in protecting cells against SARS-CoV-2 infection. They demonstrate that soluble factors in the supernatants of TLR-stimulated THP1 cells reduce fusion events between HEK293 cells expressing SARS-CoV-2 S protein and the ACE2 receptor. Using qRT-PCR and ELISA, they demonstrate that IL-1 cytokines are (not surprisingly) upregulated by TLR treatment in THP1 cells. Further, they convincingly demonstrate that recombinant IL-1 cytokines are sufficient to reduce cell-to-cell fusion mediated by the S protein. Using chemical inhibitors and CRISPR knock-out of key IL-1 receptor signaling components in HEK293 cells, they demonstrate that components of the myddosome (MYD88, IRAK1/4, and TRAF6) are required for fusion inhibition, but that downstream canonical signaling (i.e., TAK1 and NFKB activation) is not required. Instead, they provide evidence that IL-1-dependent non-canonical activation of RhoA/Rock is important for this phenotype. Importantly, the authors demonstrate that expression of a constitutively active RhoA alone is sufficient to inhibit fusion and that chemical inhibition of Rock could reverse this inhibition. The authors followed up these in vitro experiments by examining the effects of IL-1 on SARS-COV-2 infection in vivo and they demonstrate that recombinant IL-1 can reduce viral burden and lung pathogenesis in a mouse model of infection. However, the contribution of the RhoA/Rock pathway and inhibition of fusion to IL-1-mediated control of SARS-CoV-2 infection in vivo remains unclear.

Strengths:

(1) The bioluminescence cell-cell fusion assay provides a robust quantitative method to examine cytokine effects on viral glycoprotein-mediated fusion.

(2) The study identifies a new mechanism by which IL-1 cytokines can limit virus infection.

(3) The authors tested IL-1 mediated inhibition of fusion induced by many different coronavirus S proteins and several SARS-CoV-2 strains.

Weaknesses:

(1) The qualitative assay demonstrating S2 cleavage and IL-1 mediated inhibition of this phenotype is extremely variable across the data figures. Sometimes it appears like S2 cleavage (S2') is reduced, while in other figures immunoblots show that total S2 protein is decreased. Based on the proposed model the expectation would be that S2 abundance would be rescued when cleavage is inhibited.

In our present manuscript, IL-1-mediated changes of the full-length spike showed some variation between authentic SARS-CoV-2 infection model and HEK293T-S + HEK293T-ACE2 coculture model, while IL-1 inhibited S2’ cleavage accompanied by a reduction of S2 subunit in both models.

In the authentic SARS-CoV-2 infection model, we observed that IL-1 inhibited S2' cleavage accompanied with a reduction in both S2 subunit and full-length spike protein. This is likely because the S2 subunit and full-length spike protein in this model are not only from infected cells, but also from intracellular viral particles. IL-1 inhibited SARS-CoV-2 induced cell-cell fusion and reduced the viral load in host cells, therefore the abundance of S2 subunit and full-length spike proteins were both reduced.

In the HEK293T-based co-culture model, IL-1 inhibited S2' cleavage accompanied with a reduction in S2 subunit, while the full-length spike protein was more or less rescued. Based on our previous study, R685A and ΔRRAR spike mutants cannot generate the S2 subunit, but still generated S2′ fragment to induce cell-cell fusion, and the S2' fragment produced from R685A and ΔRRAR spike mutants were only slightly reduced compared to wild-type spike protein, suggesting that the S2' fragment is mainly derived from the full-length spike directly, and to a minimal extent from the S2 subunit (Fig. 4B and 4G, PMID: 34930824). Thus, inhibition of S2’ cleavage by IL-1 mainly rescued the full-length spike protein.

(2) The text referencing Figure 1H suggests that TLR-stimulated THP-1 cell supernatants "significantly" reduce syncytia, but image quantification and statistics are not provided to support this statement.

Thanks for pointing out this issue. We have provided fluorescence image quantification and statistics in the revised version of our manuscript (Figure 1D, Figure 1-figure supplement 1A, Figure 1H-1I, Figure 2H-2I, Figure 1-figure supplement 1D-1E, Figure 1-figure supplement 1H-1I, Figure 2-figure supplement 1C-1D, Figure 2-figure supplement 2B-2E, Figure 2-figure supplement 2G-2H, Figure 2-figure supplement 6A-6B, Figure 2-figure supplement 7F-7G).

(3) The authors conclude that because IL-1 accumulates in TLR2-stimulated THP1 monocyte supernatants, this cytokine accounts for the ability of these supernatants to inhibit cell-cell fusion. However, they do not directly test whether IL-1 is required for the phenotype. Inhibition of the IL-1 receptor in supernatant-treated cells would help support their conclusion.

Thanks for the suggestion. Accordingly, we performed experiment and found that IL-1RA treatment reduced the inhibitory effect of PGN-stimulated THP-1 cell culture supernatant on cell-cell fusion, suggesting that IL-1 is required for the inhibition. This result has been added in our revised manuscript (Figure 2J and Figure2-figure supplement 4C).

(4) Immunoblot analysis of IL-1 treated HEK293 cells suggests that this cytokine does not reduce the abundance of ACE2 or total S protein in cells. However, it is possible that IL-1 signaling reduces the abundance of these proteins on the cell surface, which would result in a similar inhibition of cell-cell fusion. The authors should confirm that IL-1 treatment of their cells does not change Ace2 or S protein on the cell surface.

Thanks for the suggestion. Accordingly, we applied Wheat Germ Agglutinin (WGA) to stain cell surface in HKE293T cells and observed that IL-1β treatment did not change ACE2 or Spike protein on the cell surface. This result has been added in our revised manuscript (Figure 5-figure supplement 3A-D).

(5) In Figure 5A, expression of constitutively active RhoA appears to have profound effects on how ACE2 runs by SDS-PAGE, suggesting that RhoA may have additional effects on ACE2 biology that might account for the decreased cell-cell fusion. This phenotype should be addressed in the text and explored in more detail.

Thanks for pointing out this. We also noticed that the occurrence of cell-cell fusion reduced the amount of ACE2, whereas inhibition of cell-cell fusion restored the ACE2 abundance. Take the original Figure 5A (revised Figure 4-figure supplement 2B) as example, the increased ACE2 protein should be attributed to the decreased cell-cell fusion upon RhoA-CA transfection, as Spike binding with ACE2 leads to clathrin- and AP2-dependent endocytosis, resulting in ACE2 degradation in the lysosome (PMID: 36287912).

In addition, we have examined the potential effect of RhoA-CA on ACE2, and found that RhoA-CA did not affect ACE2 expression, nor Spike binding to ACE2 (revised Figure 5-figure supplement 2E); it did not affect ACE2 distribution on cell surface either (revised Figure 5-figure supplement 2F and G).

(6) The experiments linking IL-1 mediated restriction of SARS-COV-2 fusion to the control of virus infection in vivo are incomplete. The reported data demonstrate that recombinant IL-1 can restrict virus replication in vivo, but they fall short of confirming that the in vitro mechanism described (reduced fusion) contributes to the control of SARS-CoV2 replication in vivo. A critical piece of data that is missing is the demonstration that the ROCK inhibitor phenocopies IL-1RA treatment of SARS-COV-2 infected mice (viral infection and pathology).

Thanks for this suggestion. Accordingly, we applied the ROCK inhibitor in vivo to confirm its role in SARS-CoV-2-infected mice, and found similar phenotype as the IL-1RA treatment experiment. That is to say, Y-26732 treatment prevented the formation of IL-1β-induced actin bundles at cell-cell junctions, thus promoted syncytia formation and further viral transmission in vivo (revised Figure 7).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I suggest providing single-channel images in a supplementary figure for the live-cell images in Figures 4 and 5. Higher magnification images would also help distinguish the subcellular details of the cytoskeleton organization.

Thanks for the suggestion. We have provided the single channel images and higher magnification images in the revised Figures 4 and 5, as well as their respective figure supplements.

In Figure 4, the authors showed that IL-1 activates RhoA and induces the accumulation of activated RhoA at the cell-cell junctions. They also showed that IL-1 promotes the formation of actin bundles at cell-cell junctions. However, the authors have not shown any connection between RhoA and actin yet, but in lines 263-264, they claim that actin bundle formation is induced by RhoA. Evidence for this part was shown in later results, but at this moment, it is lacking. The same applies to lines 282-284; I think this conclusion that IL-1-induced actin bundle formation is through the RhoA-ROCK pathway should come after showing how RhoA affects actin bundle formation at cell-cell junctions. To this end, I suggest moving Supplementary Figures S12B and S12D to the main figure, as they provide strong evidence of the IL-1-RhoA-ROCK-actin pathway.

We appreciate these valuable comments. As suggested, we have moved the respective supplementary figures to the main figures to support our findings in the revised manuscript (Figure 4E and Figure 4-figure supplement 2B; Figure 5C and Figure 5-figure supplement 2A), the text has also been adjusted accordingly.

eLife Assessment

This fundamental study demonstrates the role of TRPV4 channels in mechano-transduction during carcinoma progression, showing how high cell confluence in DCIS cells activates pathways regulating calcium homeostasis, cell volume reduction, and invasiveness. The authors addressed all prior concerns, with the shRNA knockdown providing compelling evidence for TRPV4's role in metastasis.

Reviewer #1 (Public review):

Summary:

In this study, Bu et al examined the dynamics of TRPV4 channel in cell overcrowding in carcinoma conditions. They investigated how cell crowding (or high cell confluence) triggers a mechano-transduction pathway involving TRPV4 channels in high-grade ductal carcinoma in situ (DCIS) cells that leads to large cell volume reduction (or cell volume plasticity) and pro-invasive phenotype.

In vitro, this pathway is highly selective for highly malignant invasive cell lines derived from a normal breast epithelial cell line (MCF10CA) compared to the parent cell line, but not present in another triple-negative invasive breast epithelial cell line (MDA-MB-231). The authors convincingly showed that enhanced TRPV4 plasmamembrane localization correlates with high-grade DCIS cells in patient tissue samples. Specifically in invasive MCF10DCIS.com cells they showed that overcrowding or over-confluence leads to a decrease in cell volume and intracellular calcium levels. This condition also triggers the trafficking of TRPV4 channels from intracellular stores (nucleus and potentially endosomes), to the plasma membrane (PM). When these over-confluent cells are incubated with a TRPV4 activator, there is an acute and substantial influx of calcium, attesting the fact that there are high number of TRPV4 channels present on the PM. Long-term incubation of these over-confluent cells with the TRPV4 activator results in the internalization of the PM-localized TRPV4 channels.

In contrast, cells plated at lower confluence primarily have TRPV4 channels localized in the nucleus and cytosol. Long-term incubation of these cells at lower confluence with a TRPV4 inhibitor leads to the relocation of TRPV4 channels to the plasma membrane from intracellular stores and a subsequent reduction in cell volume. Similarly, incubation of these cells at low confluence with PEG 3000 (a hyperosmotic agent) promotes the trafficking of TRPV4 channels from intracellular stores to the plasma membrane.

Strengths:

The study is elegantly designed and the findings are novel. Their findings on this mechano-transduction pathway involving TRPV4 channels, calcium homeostasis, cell volume plasticity, motility and invasiveness will have a great impact in the cancer field and potentially applicable to other fields as well. Experiments are well-planned and executed, and the data is convincing. Authors investigated TRVP4 dynamics using multiple different strategies- overcrowding, hyperosmotic stress, pharmacological and genetic means, and showed a good correlation between different phenomena.

All of my previous concerns have been addressed. The quality of the manuscript has improved significantly.

Reviewer #2 (Public review):

Summary:

The metastasis poses a significant challenge in cancer treatment. During the transition from non-invasive cells to invasive metastasis cells, cancer cells usually experience mechanical stress due to a crowded cellular environment. The molecular mechanisms underlying mechanical signaling during this transition remain largely elusive. In this work, the authors utilize an in vitro cell culture system and advanced imaging techniques to investigate how non-invasive and invasive cells respond to cell crowding, respectively.

The results clearly show that pre-malignant cells exhibit a more pronounced reduction in cell volume and are more prone to spreading compared to non-invasive cells. Furthermore, the study identifies that TRPV4, a calcium channel, relocates to the plasma membrane both in vitro and in vivo (patient's samples). Activation and inhibition of TRPV4 channel can modulate the cell volume and cell mobility. These results unveil a novel mechanism of mechanical sensing in cancer cells, potentially offering new avenues for therapeutic intervention targeting cancer metastasis by modulating TRPV4 activity. This is a very comprehensive study, and the data presented in the paper are clear and convincing. The study represents a very important advance in our understanding of the mechanical biology of cancer.

Author response:

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

Reviewer #1 (Public review):

Summary:

In this study, Bu et al examined the dynamics of TRPV4 channel in cell overcrowding in carcinoma conditions. They investigated how cell crowding (or high cell confluence) triggers a mechano-transduction pathway involving TRPV4 channels in high-grade ductal carcinoma in situ (DCIS) cells that leads to large cell volume reduction (or cell volume plasticity) and proinvasive phenotype.

In vitro, this pathway is highly selective for highly malignant invasive cell lines derived from a normal breast epithelial cell line (MCF10CA) compared to the parent cell line, but not present in another triple-negative invasive breast epithelial cell line (MDA-MB-231). The authors convincingly showed that enhanced TRPV4 plasma membrane localization correlates with highgrade DCIS cells in patient tissue samples.

Specifically in invasive MCF10DCIS.com cells, they showed that overcrowding or overconfluence leads to a decrease in cell volume and intracellular calcium levels. This condition also triggers the trafficking of TRPV4 channels from intracellular stores (nucleus and potentially endosomes), to the plasma membrane (PM). When these over-confluent cells are incubated with a TRPV4 activator, there is an acute and substantial influx of calcium, attesting to the fact that there are a high number of TRPV4 channels present on the PM. Long-term incubation of these over-confluent cells with the TRPV4 activator results in the internalization of the PMlocalized TRPV4 channels.

In contrast, cells plated at lower confluence primarily have TRPV4 channels localized in the nucleus and cytosol. Long-term incubation of these cells at lower confluence with a TRPV4 inhibitor leads to the relocation of TRPV4 channels to the plasma membrane from intracellular stores and a subsequent reduction in cell volume. Similarly, incubation of these cells at low confluence with PEG 3000 (a hyperosmotic agent) promotes the trafficking of TRPV4 channels from intracellular stores to the plasma membrane.

Strengths:

The study is elegantly designed and the findings are novel. Their findings on this mechanotransduction pathway involving TRPV4 channels, calcium homeostasis, cell volume plasticity, motility, and invasiveness will have a great impact in the cancer field and are potentially applicable to other fields as well. Experiments are well-planned and executed, and the data is convincing. The authors investigated TRVP4 dynamics using multiple different strategies- overcrowding, hyperosmotic stress, and pharmacological means, and showed a good correlation between different phenomena.

Weaknesses:

A major emphasis in the study is on pharmacological means to relate TRPV4 channel function to the phenotype. I believe the use of genetic means would greatly enhance the impact and provide compelling proof for the involvement of TRPV4 channels in the associated phenotype.

In this regard, I wonder if siRNA-mediated knockdown of TRPV4 in over-confluent cells (or knockout) would lead to an increase in cell volume and normalize the intracellular calcium levels back to normal, thus ultimately leading to a decrease in cell invasiveness.

We greatly appreciate the positive feedback regarding the design of our study and the novelty of our findings. We also acknowledge the valuable suggestion to complement our pharmacological approaches with genetic manipulation of TRPV4.

In response to the comment regarding siRNA-mediated knockdown or knockout of TRPV4, we fully agree that this would further substantiate our findings. In the revised manuscript, we implemented shRNA targeting TRPV4 to investigate its functional effects on intracellular calcium level changes, cell volume plasticity, and invasiveness phenotypes, assessed through singlecell motility assays under cell crowding or hyperosmotic stress. These results have been incorporated into the revised manuscript, and detailed descriptions of these findings are included below.

Using the shRNA approach that resulted in ~50% reduction of TRPV4 expression

(Supplementary Figure 6A and 6B show TRPV4 expression levels via IF and immunoblots, respectively), we examined the effect of reduced TRPV4 on intracellular calcium levels in MCF10DCIS.com cells under normal density (ND) and stress conditions (confluent; Con and hyperosmotic; PEG) using Fluo-4 AM imaging (Fig. 4S-X). We found that shRNA TRPV4 slightly decreased calcium levels in ND cells, likely due to fewer active calcium channels at the plasma membrane resulting from lower TRPV4 expression (as shown in the summary plot in Fig. 4W). With fewer active calcium channels, cells treated with shRNA TRPV4 exhibited less reduction in intracellular calcium levels under cell crowding conditions compared to control cells. Additionally, hyperosmotic stress using PEG 300 induced smaller calcium spikes in shRNA cells compared to the significant spike observed in control cells. This reduced calcium response to Con and hyperosmotic stress in shRNA cells was reflected in the decreased cell volume reduction by PEG 300 shown in Fig. 4Y. Consequently, shRNA-mediated TRPV4 reduction impaired cell volume plasticity in MCF10DCIS.com cells and abolished the pro-invasive mechanotransduction capability involving cell volume reduction, as evidenced by no increase in cell motility (both cell diffusivity and directionality) under hyperosmotic conditions (Fig. 5H-J). These findings demonstrate the critical role of TRPV4 in conferring pro-invasive

mechanotransduction capability to MCF10DCIS.com cells through cell volume reduction.

Reviewer #2 (Public review):

Summary:

The metastasis poses a significant challenge in cancer treatment. During the transition from non-invasive cells to invasive metastasis cells, cancer cells usually experience mechanical stress due to a crowded cellular environment. The molecular mechanisms underlying mechanical signaling during this transition remain largely elusive. In this work, the authors utilize an in vitro cell culture system and advanced imaging techniques to investigate how non-invasive and invasive cells respond to cell crowding, respectively.

Strengths:

The results clearly show that pre-malignant cells exhibit a more pronounced reduction in cell volume and are more prone to spreading compared to non-invasive cells. Furthermore, the study identifies that TRPV4, a calcium channel, relocates to the plasma membrane both in vitro and in vivo (patient samples). Activation and inhibition of the TRPV4 channel can modulate the cell volume and cell mobility. These results unveil a novel mechanism of mechanical sensing in cancer cells, potentially offering new avenues for therapeutic intervention targeting cancer metastasis by modulating TRPV4 activity. This is a very comprehensive study, and the data presented in the paper are clear and convincing. The study represents a very important advance in our understanding of the mechanical biology of cancer.

Weaknesses:

However, I do think that there are several additional experiments that could strengthen the conclusions of this work. A critical limitation is the absence of genetic ablation of the TRPV4 gene to confirm its essential role in the response to cell crowding.

We are deeply grateful for the positive assessment of our study and its contribution to advancing our understanding of mechanical signaling in cancer progression. We also greatly appreciate the suggestion to incorporate genetic ablation experiments to further validate the role of TRPV4 in cell crowding responses.

As noted in our response to Reviewer #1, we employed an shRNA approach to investigate the functional effects of TRPV4 knockdown on intracellular calcium level changes, cell volume plasticity, and invasiveness phenotypes. We assessed these effects using Fluo-4 AM calcium assay, single-cell volume measurements, and single-cell motility assays under cell crowding or hyperosmotic stress. These results have been incorporated into the revised manuscript and are described in detail in our response to Reviewer #1's "weaknesses" comment.

Reducing TRPV4 expression levels by shRNA diminished mechanosensing intracellular calcium changes under cell crowding and hyperosmotic conditions using PEG 300 treatment. Furthermore, a significantly reduced cell volume plasticity was observed under hyperosmotic conditions in shRNA treated cells compared to control cells (Fig. 4S-X). This diminished mechanosensing capability abolished the pro-invasive mechanotransduction effect, as assessed by single cell motility under hyperosmotic conditions (Fig. 5H-J). These findings demonstrate the critical role of TRPV4 in conferring pro-invasive mechanotransduction capability to MCF10DCIS.com cells through cell volume reduction.

Reviewer #1 (Recommendations for the authors):

The way the results or discussion section is written. It was a little confusing for me to relate to some phenomena. For example, it is not clear how TRPV4 inhibition (due to overcrowding) leads to a decrease in intercellular calcium levels, especially when TRPV4 channels were intercellular (not on the PM) to begin with (in normal density (ND) conditions). Along the same lines, how GSK219 causes a dip in calcium levels in ND cells when TRPV4 channels are primarily intercellular (Figure 4E). If most of the TRPV4 channels that are translocated to the PM in response to cell crowding are in an inactive state, how do they confer enhanced cell volume plasticity relative to non-invasive cell lines?

Thank you very much for raising this important point. We fully agree with your concern and have significantly revised the manuscript to clarify this aspect. Specifically, we have emphasized that a modest level of TRPV4 channels are constitutively active at the plasma membrane in normal density (ND) cells. This is now discussed in detail in the context of Fig. 4:

Page 14: “Considering these factors, we hypothesized that cell crowding might inhibit calcium-permeant ion channels that are constitutively active at the plasma membrane, including TRPV4, which would then lower intracellular calcium levels and subsequently reduce cell volume via osmotic water movement.”

Page 16-17: “… However, the temporal profile of Fluo-4 intensity in Fig. 4E, which corresponds to the time points marked in Fig. 4D (t₁: baseline and t₂: dip), clearly shows the dip at t₂, indicated by ΔCa (the vertical dashed line between the dip and baseline). This modest Fluo-4 dip at t₂ represents the inhibition of activity by GSK219 on a small population of constitutively active TRPV4 channels at the plasma membrane under ND conditions.

In Con cells, 1 nM GSK219 caused a smaller dip in Fluo-4 intensity compared to the one observed in ND cells, with no subsequent changes. This is likely due to fewer constitutively active TRPV4 at the plasma membrane in Con cells than in ND cells. …These findings suggest that a substantial portion of TRPV4 channels relocated to the plasma membrane under cell crowding was inactive, and some constitutively active TRPV4 channels already present in the membrane became inactive as a result of cell crowding.”

'Internalization' might be a better word than 'uptake' in the following line in the results section

"...activating TRPV4 under cell crowding conditions triggered channel uptake, indicating that TRPV4 trafficking depended on the channel's activation status."

Thank you very much for this suggestion. As recommended, we replaced ‘uptake’ with internalization’ on page 18: 

“However, in Con cells, where a large number of inactive TRPV4 channels are likely located at the plasma membrane, GSK101 treatment notably reduced plasma membrane-associated TRPV4 in a dose-dependent manner through internalization (Fig. 4O, 4Q), consistent with previous findings65. These data suggest that plasma membrane TRPV4 levels were largely

regulated by the channel activity status. Specifically, channel activation led to the internalization of TRPV4, while channel inhibition promoted the relocation of TRPV4 to the plasma membrane.”

1. Out of curiosity:

2. Is there any information on what the intercellular TRPV4 channels are doing in the cytosol and in the nucleus? Is there any role of intercellular calcium stores in the proposed pathway?

We greatly appreciate this insightful question. Although we were unable to find studies specifically exploring the roles of cytosolic TRPV4, a recent study (Reference 74) identified a role for nuclear TRPV4 in regulating calcium within the nucleus. We speculate that when TRPV4 activity is severely impaired, such as with additional TRPV4 inhibition under cell crowding conditions, some TRPV4 channels may be redirected to the nucleus. This redistribution could help maintain nuclear calcium homeostasis.

This discussion is included on page 18 of the manuscript:

“These findings suggest that further TRPV4 inhibition under crowding conditions triggers a distinct trafficking alteration. Recent studies have implicated nuclear TRPV4 in regulating nuclear Ca2+ homeostasis and Ca2+-regulated transcription74. In light of this study and our findings, TRPV4 may relocate to the nucleus as a compensatory mechanism to maintain nuclear calcium regulation. This relocation could reflect an adaptive response to preserve calcium-dependent transcriptional programs or other nuclear processes essential for cell survival under mechanical stress.”

One recommendation is to add some explanation or some minor details for the convenience of the reader. For example:

At normal or lower confluence, cells show an acute large dip in intercellular calcium when an inhibitor is applied implying that there are a few TRPV4 channels on the PM and they are constitutively active.

Thank you very much for highlighting this important point and for the helpful suggestion to improve clarity. We have significantly revised the text associated with Fig. 4 to ensure this point is clear. Specifically, we have added the following explanation on page 16:

"This modest Fluo-4 dip at t2 represents the inhibition of activity by GSK219 on a small population of constitutively active TRPV4 channels at the plasma membrane under ND conditions."

Reviewer #2 (Recommendations for the authors):

(1) Figure 1. The authors frequently change the medium to prevent acidification in overconfluent cultures. A cell viability assay should be performed to ensure that the over-confluent cells remain healthy and viable during the experiments. There are commercial kits that can be easily used to quantify the number of viable cells and the extent of cell toxicity. The number of viable cells would provide a more reliable basis for comparison between normal density and overconfluent conditions.

Thank you very much for raising this important point. We have consistently observed that cell crowding does not induce significant cell death in MCF10DCIS.com cells. To address your recommendation, we performed a viability assay using propidium iodide (PI) to selectively stain dead cells and WGA-488 to stain all live cells. Cell death was quantified under normal density (ND) conditions and at 1, 3, 5, 7, and 10 days post-confluence.

Our results indicate that cells remain similarly viable post-confluence, with minimal cell death

(~1.5%) compared to ND cells (~0.75%). These findings are summarized in Supplementary Figure 2, demonstrating that over-confluent cultures remain healthy and viable during the experiments.

(2) Figure 2. To determine whether the reduction in cell volume is reversible, over-confluent cells can be further diluted back to normal density. Additionally, the reversibility of TRPV4 channel trafficking to the plasma membrane should be assessed under these conditions in IF experiments and cell surface biotinylation.

Thank you for this suggestion. We reseeded the previously overcrowded (OC) cells at normal density and observed that their TRPV4 distribution predominantly returned to being intracellular, with only modest plasma membrane localization, as shown by line analysis (Supplementary Figure 10A-C, page 13). Furthermore, their invasiveness decreased to levels comparable to the original normal density (ND) cells (Supplementary Figure 3C and 3E, page 6). These results demonstrate the reversibility of TRPV4 trafficking changes and the increase in invasiveness under mechanical stress.

Page 6. "The enhanced invasiveness of MCF10DCIS.com cells under cell crowding was largely reversible. When OC cells were reseeded at normal density for invasion assays, their invasive cell fraction decreased to approximately 15%, slightly lower (p = 0.012) than the initial value of around 24% (Suppl. Fig. 3C, 3E)."

Page 13. “We investigated whether TRPV4 relocation to the plasma membrane induced by cell crowding is reversible, as suggested by its impact on invasiveness (Suppl. Fig. 3E). To test this, previously OC MCF10DCIS.com cells were reseeded under ND conditions. We then assessed TRPV4 localization via immunofluorescence (IF) imaging to determine if most channels returned to the cytoplasm and could be relocated to the plasma membrane under mechanical stress, such as hyperosmotic conditions. Consistent with their initial ND state, reseeded ND MCF10DCIS.com cells displayed intracellular TRPV4 distribution (Suppl. Fig. 10A). Upon exposure to hyperosmotic stress (74.4 mOsm/Kg PEG300), TRPV4 was again relocated to the plasma membrane (Suppl. Fig. 10B). These findings, quantified through line analysis (Suppl. Fig. 10C), demonstrate that the mechanosensing response of MCF10DCIS.com cells is reversible.”

(3) Figure 3B. A control using intracellular proteins such as GAPDH or Tubulin is missing. Including this control would help exclude the possibility of cell rupture or compromised cell membranes in crowded environments, which is very common in a cell crowding environment.

Thank you very much for pointing this out. The control lanes (GAPDH) were already included in the full gel results shown in Supplementary Figure 5. For the immunoprecipitation and immunoblotting of surface-biotinylated cell lysates, we did not expect to detect GAPDH; however, some GAPDH signals were still observed. As shown for MCF10DCIS.com cells, less GAPDH was detected under OC conditions, but the immunoprecipitated samples displayed significantly higher levels of TRPV4 on the cell surface compared to ND cells (Supplementary Figure 5A). For the whole cell lysates, TRPV4 protein levels were comparable across different cell lines based on the immunoblot results, with consistent GAPDH signals serving as a loading control (Supplementary Figure 5B).

(4) Figure 4. To convincingly demonstrate TRPV4 relocation to the plasma membrane, IF should be performed under non-permeable conditions (i.e., without detergents like saponin). This approach ensures that only plasma membrane proteins are accessible to antibodies, reducing intracellular background. The same approach should be applied to Piezo1 and TfR.

Thank you for this suggestion. We observed that under non-permeable conditions, primary antibodies could still access intracellular proteins. To address this issue, we employed extracellular-binding TRPV4 antibodies to selectively detect TRPV4 relocation to the plasma membrane under hyperosmotic conditions (74.4 mOsm/kg PEG 300) in live MCF10DCIS.com cells, as shown in Supplementary Figure 9. These results clearly demonstrate the plasma membrane relocation of TRPV4 under hyperosmotic conditions, distinguishing it from control conditions. Unfortunately, we were unable to identify high-affinity extracellular-binding antibodies for Piezo1 and TfR. Nevertheless, our findings strongly support the mechanosensing plasma membrane relocation of TRPV4.

Essential Weakness:

Throughout the study, only TRPV4 inhibitors and activators were used to show that TRPV4 relocation is associated with intracellular calcium concentration and cell size changes. It is crucial to use TRPV4 KO or KD cells to confirm that the observed effects are specific to TRPV4 and not due to off-target effects on other proteins. Additionally, fusing a plasma membrane targeting sequence to TRPV4 to make a constitutive plasma membrane-localized construct could demonstrate the opposite effect.

Thank you very much for this important comment. As noted in our response to Reviewer #1, we employed an shRNA approach to investigate the functional effects of TRPV4 knockdown on intracellular calcium level changes, cell volume plasticity, and invasiveness phenotypes. We assessed these effects using Fluo-4 AM calcium assay, single-cell volume measurements, and single-cell motility assays under cell crowding or hyperosmotic stress. These results have been incorporated into the revised manuscript and are described in detail in our response to Reviewer #1's "weaknesses" comment.

Reducing TRPV4 expression levels by shRNA diminished mechanosensing intracellular calcium changes under cell crowding and hyperosmotic conditions using PEG 300 treatment. Furthermore, a significantly reduced cell volume plasticity was observed under hyperosmotic conditions in shRNA treated cells compared to control cells (Fig. 4S-X). This diminished mechanosensing capability abolished the pro-invasive mechanotransduction effect, as assessed by single cell motility under hyperosmotic conditions (Fig. 5H-J). These findings demonstrate the critical role of TRPV4 in conferring pro-invasive mechanotransduction capability to MCF10DCIS.com cells through cell volume reduction.

Minor Points:

The introduction section is poorly written; many results currently included in the introduction would be more appropriately placed in the discussion section. The long redundant introduction makes the article hard to read through.

Thank you very much for pointing this out. In the revised introduction, we have significantly reduced references to the results, streamlining the section to make it more concise and focused. This adjustment ensures the introduction is clearer and avoids redundancy, improving the readability of the manuscript.

eLife Assessment

In an important fMRI study with an elegant experimental design and rigorous cross-decoding analyses, this work shows a convincing dissociation between two parietal regions in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions, while SPL is sensitive to the patterns of body motion involved in those actions. The work will be of broad interest to cognitive neuroscientists, particularly vision and action researchers.

Reviewer #1 (Public review):

Summary:

The authors report a study aimed at understanding the brain's representations of viewed actions, with a particular aim to distinguish regions that encode observed body movements, from those that encode the effects of actions on objects. They adopt a cross-decoding multivariate fMRI approach, scanning adult observers who viewed full-cue actions, pantomimes of those actions, minimal skeletal depictions of those actions, and abstract animations that captured analogous effects to those actions. Decoding across different pairs of these action conditions allowed the authors to pull out the contributions of different action features in a given region's representation. The main hypothesis, which was largely confirmed, was that the superior parietal lobe (SPL) more strongly encodes movements of the body, whereas the anterior inferior parietal lobe (aIPL) codes for action effects of outcomes. Specifically, region of interest analyses showed dissociations in the successful cross-decoding of action category across full-cue and skeletal or abstract depictions. Their analyses also highlight the importance of the lateral occipito-temporal cortex (LOTC) in coding action effects. They also find some preliminary evidence about the organisation of action kinds in the regions examined, and take some steps to distinguishing the differences and similarities of action-evoked patterns in primary visual cortex and the other examined regions.

Strengths:

The paper is well-written, and it addresses a topic of emerging interest where social vision and intuitive physics intersect. The use of cross-decoding to examine actions and their effects across four different stimulus formats is a strength of the study. Likewise the a priori identification of regions of interest (supplemented by additional full-brain analyses) is a strength. Finally, the authors successfully deployed a representational-similarity approach that provides more detailed evidence about the different kinds of action features that seem to be captured in each of the regions that were examined.

Weaknesses:

Globally, the findings provide support for the predicted anatomical distinctions, and for the distinction between body-focused representations of actions and more abstract "action effect structures". Viewed more narrowly, the picture is rather complex, and the patterns of (dis)similarity in the activity evoked by different action kinds do not always divide neatly. Probably, examining many more kinds of actions with the multi-format decoding approach developed here will be needed to more effectively disentangle the various contributions of movement, posture, low-level visual properties, and action outcomes/effects.

Author response:

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

eLife Assessment

In an important fMRI study with an elegant experimental design and rigorous cross-decoding analyses, this work shows a solid dissociation between two parietal regions in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions, while SPL is sensitive to the patterns of body motion involved in those actions. Additional analysis and explanation would help to determine the strength of evidence and the mechanistic underpinnings would benefit from closer consideration. Nevertheless, the work will be of broad interest to cognitive neuroscientists, particularly vision and action researchers.

We thank the editor and the reviewers for their assessment and their excellent comments and suggestions. We really believe they helped us to provide a stronger and more nuanced paper. In our revision, we addressed all points raised by the reviewers. Most importantly, we added a new section on a series of analyses to characterize in more detail the representations isolated by the action-animation and action-PLD cross-decoding. Together, these analyses strengthen the conclusion that aIPL and LOTC represent action effect structures at a categorical rather than specific level, that is, the type of change (e.g., of location or configuration) rather than the specific effect type (e.g. division, compression). SPL is sensitive to body-specific representations, specifically manuality (unimanual vs. bimanual) and movement kinematics. We also added several other analyses and addressed each point of the reviewers. Please find our responses below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors report a study aimed at understanding the brain's representations of viewed actions, with a particular aim to distinguish regions that encode observed body movements, from those that encode the effects of actions on objects. They adopt a cross-decoding multivariate fMRI approach, scanning adult observers who viewed full-cue actions, pantomimes of those actions, minimal skeletal depictions of those actions, and abstract animations that captured analogous effects to those actions. Decoding across different pairs of these actions allowed the authors to pull out the contributions of different action features in a given region's representation. The main hypothesis, which was largely confirmed, was that the superior parietal lobe (SPL) more strongly encodes movements of the body, whereas the anterior inferior parietal lobe (aIPL) codes for action effects of outcomes. Specifically, region of interest analyses showed dissociations in the successful cross-decoding of action category across full-cue and skeletal or abstract depictions. Their analyses also highlight the importance of the lateral occipito-temporal cortex (LOTC) in coding action effects. They also find some preliminary evidence about the organisation of action kinds in the regions examined.

Strengths:

The paper is well-written, and it addresses a topic of emerging interest where social vision and intuitive physics intersect. The use of cross-decoding to examine actions and their effects across four different stimulus formats is a strength of the study. Likewise, the a priori identification of regions of interest (supplemented by additional full-brain analyses) is a strength.

Weaknesses:

I found that the main limitation of the article was in the underpinning theoretical reasoning. The authors appeal to the idea of "action effect structures (AES)", as an abstract representation of the consequences of an action that does not specify (as I understand it) the exact means by which that effect is caused, nor the specific objects involved. This concept has some face validity, but it is not developed very fully in the paper, rather simply asserted. The authors make the claim that "The identification of action effect structure representations in aIPL has implications for theories of action understanding" but it would have been nice to hear more about what those theoretical implications are. More generally, I was not very clear on the direction of the claim here. Is there independent evidence for AES (if so, what is it?) and this study tests the following prediction, that AES should be associated with a specific brain region that does not also code other action properties such as body movements? Or, is the idea that this finding -- that there is a brain region that is sensitive to outcomes more than movements -- is the key new evidence for AES?

Thank you for raising this important issue. We reasoned that AES should exist to support the recognition of perceptually variable actions, including those that we have never experienced before. To the best of our knowledge, there is only indirect evidence for the existence of AES, namely that humans effortlessly and automatically recognize actions (and underlying intentions and feelings) in movements of abstract shapes, as in the famous Heider and Simmel (1949) animations. As these animations do not contain any body posture or movement information at all, the only available cues are the spatiotemporal relations between entities and entity parts in the perceived scene. We think that the effortless and automatic attribution of actions to these stimuli points toward an evolutionary optimized mechanism to capture action effect structures from highly variable action instantiations (so general that it even works for abstract animations). Our study thus aimed to test for the existence of such a level of representation in the brain. We clarified this point in the introduction.

In our revised manuscript, we also revised our discussion of the implications of the finding of AES representations in the brain:

"The identification of action effect structure representations in aIPL and LOTC has implications for theories of action understanding: Current theories (see for review e.g. Zentgraf et al., 2011; Kemmerer, 2021; Lingnau and Downing, 2024) largely ignore the fact that the recognition of many goal-directed actions requires a physical analysis of the action-induced effect, that is, a state change of the action target. Moreover, premotor and inferior parietal cortex are usually associated with motor- or body-related processing during action observation. Our results, together with the finding that premotor and inferior parietal cortex are similarly sensitive to actions and inanimate object events (Karakose-Akbiyik et al., 2023), suggest that large parts of the 'action observation network' are less specific for body-related processing in action perception than usually thought. Rather, this network might provide a substrate for the physical analysis and predictive simulation of dynamic events in general (Schubotz, 2007; Fischer, 2024). In addition, our finding that the (body-independent) representation of action effects substantially draws on right LOTC contradicts strong formulations of a 'social perception' pathway in LOTC that is selectively tuned to the processing of moving faces and bodies (Pitcher and Ungerleider, 2021). The finding of action effect representation in right LOTC/pSTS might also offer a novel interpretation of a right pSTS subregion thought to specialized for social interaction recognition: Right pSTS shows increased activation for the observation of contingent action-reaction pairs (e.g. agent A points toward object; agent B picks up object) as compared to two independent actions (i.e., the action of agent A has no effect on the action of agent B) (Isik et al., 2017). Perhaps the activation reflects the representation of a social action effect - the change of an agent's state induced by someone else's action. Thus, the representation of action effects might not be limited to physical object changes but might also comprise social effects not induced by a physical interaction between entities. Finally, not all actions induce an observable change in the world. It remains to be tested whether the recognition of, e.g., communication (e.g. speaking, gesturing) and perception actions (e.g. observing, smelling) similarly relies on structural action representations in aIPL and LOTC"

On a more specific but still important point, I was not always clear that the significant, but numerically rather small, decoding effects are sufficient to support strong claims about what is encoded or represented in a region. This concern of course applies to many multivariate decoding neuroimaging studies. In this instance, I wondered specifically whether the decoding effects necessarily reflected fully five-way distinction amongst the action kinds, or instead (for example) a significantly different pattern evoked by one action compared to all of the other four (which in turn might be similar). This concern is partly increased by the confusion matrices that are presented in the supplementary materials, which don't necessarily convey a strong classification amongst action kinds. The cluster analyses are interesting and appear to be somewhat regular over the different regions, which helps. However: it is hard to assess these findings statistically, and it may be that similar clusters would be found in early visual areas too.

We agree that in our original manuscript, we did not statistically test what precisely drives the decoding, e.g., specific actions or rather broader categories. In our revised manuscript, we included a representational similarity analysis (RSA) that addressed this point. In short, we found that the action-animation decoding was driven by categorical distinctions between groups of actions (e.g. hit/place vs. the remaining actions) rather than a fully five-way distinction amongst all action kinds. The action-PLD decoding was mostly driven by , specifically manuality (unimanual vs. bimanual)) and movement kinematics; in left and right LOTC we found additional evidence for action-specific representations.

Please find below the new paragraph on the RSA:

"To explore in more detail what types of information were isolated by the action-animation and action-PLD cross-decoding, we performed a representational similarity analysis.

We first focus on the representations identified by the action-animation decoding. To inspect and compare the representational organization in the ROIs, we extracted the confusion matrices of the action-animation decoding from the ROIs (Fig. 5A) and compared them with different similarity models (Fig. 5B) using multiple regression. Specifically, we aimed at testing at which level of granularity action effect structures are represented in aIPL and LOTC: Do these regions encode the broad type of action effects (change of shape, change of location, ingestion) or do they encode specific action effects (compression, division, etc.)? In addition, we aimed at testing whether the effects observed in EVC can be explained by a motion energy model that captures the similarities between actions and animations that we observed in the stimulus-based action-animation decoding using motion energy features. We therefore included V1 in the ROI analysis. We found clear evidence that the representational content in right aIPL and bilateral LOTC can be explained by the effect type model but not by the action-specific model (all p < 0.005; two-sided paired t-tests between models; Fig. 5C). In left V1, we found that the motion energy model could indeed explain some representational variance; however, in both left and right V1 we also found effects for the effect type model. We assume that there were additional visual similarities between the broad types of actions and animations that were not captured by the motion energy model (or other visual models; see Supplementary Information). A searchlight RSA revealed converging results, and additionally found effects for the effect type model in the ventral part of left aIPL and for the action-specific model in the left anterior temporal lobe, left dorsal central gyrus, and right EVC (Fig. 5D). The latter findings were unexpected and should be interpreted with caution, as these regions (except right EVC) were not found in the action-animation cross-decoding and therefore should not be considered reliable (Ritchie et al., 2017). The motion energy model did not reveal effects that survived the correction for multiple comparison, but a more lenient uncorrected threshold of p = 0.005 revealed clusters in left EVC and bilateral posterior SPL.

To characterize the representations identified by the action-PLD cross-decoding, we used a manuality model that captures whether the actions were performed with both hands vs. one hand, an action-specific model as used in the action-animation RSA above, and a kinematics model that was based on the 3D kinematic marker positions of the PLDs (Fig. 6B). Since pSTS is a key region for biological motion perception, we included this region in the ROI analysis. The manuality model explained the representational variance in the parietal ROIs, pSTS, and LOTC, but not in V1 (all p < 0.002; two-sided paired t-tests between V1 and other ROIs; Fig. 6C). By contrast, the action-specific model revealed significant effects in V1 and LOTC, but not in pSTS and parietal ROIs (but note that effects in V1 and pSTS did not differ significantly from each other; all other two-sided paired t-tests between mentioned ROIs were significant at p < 0.0005). The kinematics model explained the representational variance in all ROIs. A searchlight RSA revealed converging results, and additionally found effects for the manuality model in bilateral dorsal/medial prefrontal cortex and in right ventral prefrontal cortex and insula (Fig. 6D).”

We also included an ROI covering early visual cortex (V1) in our analysis. While there was significant decoding for action-animation in V1, the representational organization did not substantially match the organization found in aIPL and LOTC: A cluster analysis revealed much higher similarity between LOTC and aIPL than between these regions and V1:

(please note that in this analysis we included the action-PLD RDMs as reference, and to test whether aIPL shows a similar representational organization in action-anim and action-PLD; see below)

Given these results, we think that V1 captured different aspects in the action-animation cross-decoding than aIPL and LOTC. We address this point in more detail in our response to the "Recommendations for The Authors".

Reviewer #2 (Public Review):

Summary:

This study uses an elegant design, using cross-decoding of multivariate fMRI patterns across different types of stimuli, to convincingly show a functional dissociation between two sub-regions of the parietal cortex, the anterior inferior parietal lobe (aIPL) and superior parietal lobe (SPL) in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions (e.g. whether an action causes an object to compress or to break into two parts), and SPL to the motion patterns of the body in executing those actions.

To show this, the authors assess how well linear classifiers trained to distinguish fMRI patterns of response to actions in one stimulus type can generalize to another stimulus type. They choose stimulus types that abstract away specific dimensions of interest. To reveal sensitivity to the causal effects of actions, regardless of low-level details or motion patterns, they use abstract animations that depict a particular kind of object manipulation: e.g. breaking, hitting, or squashing an object. To reveal sensitivity to motion patterns, independently of causal effects on objects, they use point-light displays (PLDs) of figures performing the same actions. Finally, full videos of actors performing actions are used as the stimuli providing the most complete, and naturalistic information. Pantomime videos, with actors mimicking the execution of an action without visible objects, are used as an intermediate condition providing more cues than PLDs but less than real action videos (e.g. the hands are visible, unlike in PLDs, but the object is absent and has to be inferred). By training classifiers on animations, and testing their generalization to full-action videos, the classifiers' sensitivity to the causal effect of actions, independently of visual appearance, can be assessed. By training them on PLDs and testing them on videos, their sensitivity to motion patterns, independent of the causal effect of actions, can be assessed, as PLDs contain no information about an action's effect on objects.

These analyses reveal that aIPL can generalize between animations and videos, indicating that it is sensitive to action effects. Conversely, SPL is found to generalize between PLDs and videos, showing that it is more sensitive to motion patterns. A searchlight analysis confirms this pattern of results, particularly showing that action-animation decoding is specific to right aIPL, and revealing an additional cluster in LOTC, which is included in subsequent analyses. Action-PLD decoding is more widespread across the whole action observation network.

This study provides a valuable contribution to the understanding of functional specialization in the action observation network. It uses an original and robust experimental design to provide convincing evidence that understanding the causal effects of actions is a meaningful component of visual action processing and that it is specifically localized in aIPL and LOTC.

Strengths:

The authors cleverly managed to isolate specific aspects of real-world actions (causal effects, motion patterns) in an elegant experimental design, and by testing generalization across different stimulus types rather than within-category decoding performance, they show results that are convincing and readily interpretable. Moreover, they clearly took great care to eliminate potential confounds in their experimental design (for example, by carefully ordering scanning sessions by increasing realism, such that the participants could not associate animation with the corresponding real-world action), and to increase stimulus diversity for different stimulus types. They also carefully examine their own analysis pipeline, and transparently expose it to the reader (for example, by showing asymmetries across decoding directions in Figure S3). Overall, this is an extremely careful and robust paper.

Weaknesses:

I list several ways in which the paper could be improved below. More than 'weaknesses', these are either ambiguities in the exact claims made, or points that could be strengthened by additional analyses. I don't believe any of the claims or analyses presented in the paper show any strong weaknesses, problematic confounds, or anything that requires revising the claims substantially.

(1) Functional specialization claims: throughout the paper, it is not clear what the exact claims of functional specialization are. While, as can be seen in Figure 3A, the difference between action-animation cross-decoding is significantly higher in aIPL, decoding performance is also above chance in right SPL, although this is not a strong effect. More importantly, action-PLD cross-decoding is robustly above chance in both right and left aIPL, implying that this region is sensitive to motion patterns as well as causal effects. I am not questioning that the difference between the two ROIs exists - that is very convincingly shown. But sentences such as "distinct neural systems for the processing of observed body movements in SPL and the effect they induce in aIPL" (lines 111-112, Introduction) and "aIPL encodes abstract representations of action effect structures independently of motion and object identity" (lines 127-128, Introduction) do not seem fully justified when action-PLD cross-decoding is overall stronger than action-animation cross-decoding in aIPL. Is the claim, then, that in addition to being sensitive to motion patterns, aIPL contains a neural code for abstracted causal effects, e.g. involving a separate neural subpopulation or a different coding scheme. Moreover, if sensitivity to motion patterns is not specific to SPL, but can be found in a broad network of areas (including aIPL itself), can it really be claimed that this area plays a specific role, similar to the specific role of aIPL in encoding causal effects? There is indeed, as can be seen in Figure 3A, a difference between action-PLD decoding in SPL and aIPL, but based on the searchlight map shown in Figure 3B I would guess that a similar difference would be found by comparing aIPL to several other regions. The authors should clarify these ambiguities.

We thank the reviewer for this careful assessment. The observation of action-PLD cross-decoding in aIPL is indeed not straightforward to interpret: It could mean that aIPL encodes both body movements and action effect structures by different neural subpopulations. Or it could mean that representations of action effect structures were also activated by the PLDs, which lead to successful decoding in the action-PLD cross-decoding. Our revision allows a more nuanced view on this issue:

First, we included the results of a behavioral test show that PLDs at least weakly allow for recognition of the specific actions (see our response to the second comment), which in turn might activate action effect structure representations. Second, the finding that also the cross-decoding between animations and PLDs revealed effects in left and right aIPL (as pointed out by the reviewer in the second comment) supports the interpretation that PLDs have activated, to some extent, action effect structure representations.

On the other hand, if aIPL encodes only action-effect-structures, that were also captured in the action-PLD cross-decoding, we would expect that the RDMs in aIPL are similar for the action-PLD and action-animation cross-decoding. However, the cluster analysis (see our response to Reviewer 1 above) does not show this; rather, all action-PLD RDMs are representationally more similar with each other than with action-animation RDMs, specifically with regard to aIPL. In addition, the RSA revealed sensitivity to manuality and kinematics also in aIPL. This suggests that the action-PLD decoding in aIPL was at least partially driven by representations related to body movements.

Taken together, these findings suggest that aIPL encodes also body movements. In fact, we didn't want to make the strong claim that aIPL is selectively representing action effect structures. Rather, we think that our results show that aIPL and SPL are disproportionally sensitive to action effects and body movements, respectively. We added this in our revised discussion:

"The action-PLD cross-decoding revealed widespread effects in LOTC and parietal cortex, including aIPL. What type of representation drove the decoding in aIPL? One possible interpretation is that aIPL encodes both body movements (isolated by the action-PLD cross-decoding) and action effect structures (isolated by the action-animation cross-decoding). Alternatively, aIPL selectively encodes action effect structures, which have been activated by the PLDs. A behavioral test showed that PLDs at least weakly allow for recognition of the specific actions (Tab. S2), which might have activated corresponding action effect structure representations. In addition, the finding that aIPL revealed effects for the cross-decoding between animations and PLDs further supports the interpretation that PLDs have activated, at least to some extent, action effect structure representations.  On the other hand, if aIPL encodes only action effect structures, we would expect that the representational similarity patterns in aIPL are similar for the action-PLD and action-animation cross-decoding. However, this was not the case; rather, the representational similarity pattern in aIPL was more similar to SPL for the action-PLD decoding, which argues against distinct representational content in aIPL vs. SPL isolated by the action-PLD decoding. In addition, the RSA revealed sensitivity to manuality and kinematics also in aIPL, which suggests that the action-PLD decoding in aIPL was at least partially driven by representations related to body movements. Taken together, these findings suggest that aIPL encodes not only action effect structures, but also representations related to body movements. Likewise, also SPL shows some sensitivity to action effect structures, as demonstrated by effects in SPL for the action-animation and pantomime-animation cross-decoding. Thus, our results suggest that aIPL and SPL are not selectively but disproportionally sensitive to action effects and body movements, respectively."

A clarification to the sentence "aIPL encodes abstract representations of action effect structures independently of motion and object identity": Here we are referring to the action-animation cross decoding only; specifically, the fact that because the animations did not show body motion and concrete objects, the representations isolated in the action-animation cross decoding must be independent of body motion and concrete objects. This does not rule out that the same region encodes other kinds of representations in addition.

And another side note to the RSA: It might be tempting to test the "effects" model (distinguishing change of shape, change of location and ingest) also in the action-PLD multiple regression RSA in order to test whether this model explains additional variance in aIPL, which would point towards action effect structure representations. However, the "effect type" model is relatively strongly correlated with the "manuality" model (VIF=4.2), indicating that multicollinearity might exist. We therefore decided to not include this model in the RSA. However, we nonetheless tested the inclusion of this model and did not find clear effects for the "effects" model in aIPL (but in LOTC). The other models revealed largely similar effects as the RSA without the "effects" model, but the effects appeared overall noisier. In general, we would like to emphasize that an RSA with just 5 actions is not ideal because of the small number of pairwise comparisons, which increases the chance for coincidental similarities between model and neural RDMs. We therefore marked this analysis as "exploratory" in the article.

(2) Causal effect information in PLDs: the reasoning behind the use of PLD stimuli is to have a condition that isolates motion patterns from the causal effects of actions. However, it is not clear whether PLDs really contain as little information about action effects as claimed. Cross-decoding between animations and PLDs is significant in both aIPL and LOTC, as shown in Figure 4. This indicates that PLDs do contain some information about action effects. This could also be tested behaviorally by asking participants to assign PLDs to the correct action category. In general, disentangling the roles of motion patterns and implied causal effects in driving action-PLD cross-decoding (which is the main dependent variable in the paper) would strengthen the paper's message. For example, it is possible that the strong action-PLD cross-decoding observed in aIPL relies on a substantially different encoding from, say, SPL, an encoding that perhaps reflects causal effects more than motion patterns. One way to exploratively assess this would be to integrate the clustering analysis shown in Figure S1 with a more complete picture, including animation-PLD and action-PLD decoding in aIPL.

With regard to the suggestion to behaviorally test how well participants can grasp the underlying action effect structures: We indeed did a behavioral experiment to assess the recognizability of actions in the PLD stick figures (as well as in the pantomimes). In short, this experiment revealed that participants could not well recognize the actions in the PLD stick figures and often confused them with kinematically similar but conceptually different actions (e.g. breaking --> shaking, hitting --> swiping, squashing --> knitting). However, the results also show that it was not possible to completely eliminate that PLDs contain some information about action effects.

Because we considered this behavioral experiment as a standard assessment of the quality of the stimuli, we did not report them in the original manuscript. We now added an additional section to the methods that describes the behavioral experiments in detail:

"To assess how much the animations, PLD stick figures, and pantomimes were associated with the specific action meanings of the naturalistic actions, we performed a behavioral experiment. 14 participants observed videos of the animations, PLDs (without stick figures), and pantomimes in three separate sessions (in that order) and were asked to describe what kind of actions the animations depict and give confidence ratings on a Likert scale from 1 (not confident at all) to 10 (very confident). Because the results for PLDs were unsatisfying (several participants did not recognize human motion in the PLDs), we added stick figures to the PLDs as described above and repeated the rating for PLD stick figures with 7 new participants, as reported below.

A general observation was that almost no participant used verb-noun phrases (e.g. "breaking a stick") in their descriptions for all stimulus types. For the animations, the participants used more abstract verbs or nouns to describe the actions (e.g. dividing, splitting, division; Tab. S1). These abstract descriptions matched the intended action structures quite well, and participants were relatively confident about their responses (mean confidences between 6 and 7.8). These results suggest that the animations were not substantially associated with specific action meanings (e.g. "breaking a stick") but captured the coarse action structures. For the PLD stick figures (Tab. S2), responses were more variable and actions were often confused with kinematically similar but conceptually different actions (e.g. breaking --> shaking, hitting --> turning page, squashing --> knitting). Confidence ratings were relatively low (mean confidences between 3 and 5.1). These results suggest that PLD stick figures, too, were not substantially associated with specific action meanings and additionally did not clearly reveal the underlying action effect structures. Finally, pantomimes were recognized much better, which was also reflected in high confidence ratings (mean confidences between 8 and 9.2; Tab. S3). This suggests that, unlike PLD stick figures, pantomimes allowed much better to access the underlying action effect structures."

We also agree with the second suggestion to investigate in more detail the representational profiles in aIPL and SPL. We think that the best way to do so is the RSA that we reported above. However, to provide a complete picture of the results, we also added the whole brain maps and RDMs for the animation-pantomime, animation-PLD, pantomime-PLD, and action-pantomime to the supplementary information.

(3) Nature of the motion representations: it is not clear what the nature of the putatively motion-driven representation driving action-PLD cross-decoding is. While, as you note in the Introduction, other regions such as the superior temporal sulcus have been extensively studied, with the understanding that they are part of a feedforward network of areas analyzing increasingly complex motion patterns (e.g. Riese & Poggio, Nature Reviews Neuroscience 2003), it doesn't seem like the way in which SPL represents these stimuli are similarly well-understood. While the action-PLD cross-decoding shown here is a convincing additional piece of evidence for a motion-based representation in SPL, an interesting additional analysis would be to compare, for example, RDMs of different actions in this region with explicit computational models. These could be, for example, classic motion energy models inspired by the response characteristics of regions such as V5/MT, which have been shown to predict cortical responses and psychophysical performance both for natural videos (e.g. Nishimoto et al., Current Biology 2011) and PLDs (Casile & Giese Journal of Vision 2005). A similar cross-decoding analysis between videos and PLDs as that conducted on the fMRI patterns could be done on these models' features, obtaining RDMs that could directly be compared with those from SPL. This would be a very informative analysis that could enrich our knowledge of a relatively unexplored region in action recognition. Please note, however, that action recognition is not my field of expertise, so it is possible that there are practical difficulties in conducting such an analysis that I am not aware of. In this case, I kindly ask the authors to explain what these difficulties could be.

Thank you for this very interesting suggestion. We conducted a cross-decoding analysis that was based on the features of motion energy models as described in Nishimoto et al. (2011). Control analyses within each stimulus type revealed high decoding accuracies (animations: 100%, PLDs: 100%, pantomimes: 65%, actions: 55%), which suggests that the motion energy data generally contains information that can be detected by a classifier. However, the cross-decoding between actions and PLDs was at chance (20%), and the classification matrix did not resemble the neural RDMs. We also tested optical flow vectors as input to the decoding, which revealed similarly high decoding for the within-stimulus-type decoding (animations: 75%, PLDs: 100%, pantomimes: 65%, actions: 40%), but again at-chance decoding for action-PLD (20%), notably with a very different classification pattern:

Author response image 1.

Given these mixed results, we decided not to use these models for a statistical comparison with the neural action-PLD RDMs.

It is notable that the cross-decoding worked generally less well for decoding schemes that involve PLDs, which is likely due to highly different feature complexity of actions and PLDs: Naturalistic actions have much richer visual details, texture, and more complex motion cues. Therefore, motion energy features extracted from these videos likely capture a mixture of both fine-grained and broad motion information across different spatial frequencies. By contrast, motion energy features of PLDs are sparse and might not match the features of naturalistic actions. In a way, this was intended, as we were interested in higher-level body kinematics rather than lower-level motion features. We therefore decided to use a different approach to investigate the representational structure found in the action-PLD cross-decoding: As the PLDs were based on kinematic recordings of actions that were carried out in exactly the same manner as the naturalistic actions, we computed the dissimilarity of the 5 actions based on the kinematic marker positions. Specifically, we averaged the kinematic data across the 2 exemplars per PLD, vectorized the 3D marker positions of all time points of the PLDs (3 dimensions x 13 markers x 200 time points), computed the pairwise correlations between the 5 vectors, and converted the correlations into dissimilarity values by subtracting 1 - r. This RDM was then compared with the neural RDMs extracted from the action-PLD cross-decoding. This was done using a multiple regression RSA (see also our response to Reviewer 1's public comment 2), which allowed us to statistically test the kinematic model against other dissimilarity models: a categorical model of manuality (uni- vs. bimanual) and an action-specific model that discriminates each specific action from each other with equal distance.

This analysis revealed interesting results: the kinematic model explained the representational variance in bilateral SPL and (particularly right) pSTS as well as in right fusiform cortex and early visual cortex. The action-specific model revealed effects restricted to bilateral LOTC. The manuality model revealed widespread effects throughout the action observation network but not in EVC.

(4) Clustering analysis: I found the clustering analysis shown in Figure S1 very clever and informative. However, there are two things that I think the authors should clarify. First, it's not clear whether the three categories of object change were inferred post-hoc from the data or determined beforehand. It is completely fine if these were just inferred post-hoc, I just believe this ambiguity should be clarified explicitly. Second, while action-anim decoding in aIPL and LOTC looks like it is consistently clustered, the clustering of action-PLD decoding in SPL and LOTC looks less reliable. The authors interpret this clustering as corresponding to the manual vs. bimanual distinction, but for example "drink" (a unimanual action) is grouped with "break" and "squash" (bimanual actions) in left SPL and grouped entirely separately from the unimanual and bimanual clusters in left LOTC. Statistically testing the robustness of these clusters would help clarify whether it is the case that action-PLD in SPL and LOTC has no semantically interpretable organizing principle, as might be the case for a representation based entirely on motion pattern, or rather that it is a different organizing principle from action-anim, such as the manual vs. bimanual distinction proposed by the authors. I don't have much experience with statistical testing of clustering analyses, but I think a permutation-based approach, wherein a measure of cluster robustness, such as the Silhouette score, is computed for the clusters found in the data and compared to a null distribution of such measures obtained by permuting the data labels, should be feasible. In a quick literature search, I have found several papers describing similar approaches: e.g. Hennig (2007), "Cluster-wise assessment of cluster stability"; Tibshirani et al. (2001) "Estimating the Number of Clusters in a Data Set Via the Gap Statistic". These are just pointers to potentially useful approaches, the authors are much better qualified to pick the most appropriate and convenient method. However, I do think such a statistical test would strengthen the clustering analysis shown here. With this statistical test, and the more exhaustive exposition of results I suggested in point 2 above (e.g. including animation-PLD and action-PLD decoding in aIPL), I believe the clustering analysis could even be moved to the main text and occupy a more prominent position in the paper.

With regard to the first point, we clarified in the methods that we inferred the 3 broad action effect categories after the stimulus selection: "This categorization was not planned before designing the study but resulted from the stimulus selection."

Thank you for your suggestion to test more specifically the representational organization in the action-PLD and action-animation RDMs. However, after a careful assessment, we decided to replace the cluster analysis with an RSA. We did this for two reasons:

First, we think that RSA is a better (and more conventional) approach to statistically investigate the representational structure in the ROIs (and in the whole brain). The RSA allowed us, for example, to specifically test the mentioned distinction between unimanual and bimanual actions, and to test it against other models, i.e., a kinematic model and an action-specific model. This indeed revealed interesting distinct representational profiles of SPL and LOTC.

Second, we learned that the small number of items (5) is generally not ideal for cluster analyses (absolute minimum for meaningful interpretability is 4, but to form at least 2-3 clusters a minimum of 10-15 items is usually recommended). A similar rule of thumb applies to methods to statistically assess the reliability of cluster solutions (e.g., Silhouette Scores, Cophenetic Correlation Coefficient, Jaccard Coefficient). Finally, the small number of items is not ideal to run a permutation test because the number of unique permutations (for shuffling the data labels: 5! = 30) is insufficient to generate a meaningful null distribution. We therefore think it is best to discard the cluster analysis altogether. We hope you agree with this decision.

(5) ROI selection: this is a minor point, related to the method used for assigning voxels to a specific ROI. In the description in the Methods (page 16, lines 514-24), the authors mention using the MNI coordinates of the center locations of Brodmann areas. Does this mean that then they extracted a sphere around this location, or did they use a mask based on the entire Brodmann area? The latter approach is what I'm most familiar with, so if the authors chose to use a sphere instead, could they clarify why? Or, if they did use the entire Brodmann area as a mask, and not just its center coordinates, this should be made clearer in the text.

We indeed used a sphere around the center coordinate of the Brodmann areas. This was done to keep the ROI sizes / number of voxels constant across ROIs. Since we aimed at comparing the decoding accuracies between aIPL and SPL, we thereby minimized the possibility that differences in decoding accuracy between ROIs are due to ROI size differences. The approach of using spherical ROIs is a quite well established practice that we are using in our lab by default (e.g. Wurm & Caramazza, NatComm, 2019; Wurm & Caramazza, NeuroImage, 2019; Karakose, Caramazza, & Wurm, NatComm, 2023). We clarified that we used spherical ROIs to keep the ROI sizes constant in the revised manuscript.

Reviewer #3 (Public Review):

This study tests for dissociable neural representations of an observed action's kinematics vs. its physical effect in the world. Overall, it is a thoughtfully conducted study that convincingly shows that representations of action effects are more prominent in the anterior inferior parietal lobe (aIPL) than the superior parietal lobe (SPL), and vice versa for the representation of the observed body movement itself. The findings make a fundamental contribution to our understanding of the neural mechanisms of goal-directed action recognition, but there are a couple of caveats to the interpretation of the results that are worth noting:

(1) Both a strength of this study and ultimately a challenge for its interpretation is the fact that the animations are so different in their visual content than the other three categories of stimuli. On one hand, as highlighted in the paper, it allows for a test of action effects that is independent of specific motion patterns and object identities. On the other hand, the consequence is also that Action-PLD cross-decoding is generally better than Action-Anim cross-decoding across the board (Figure 3A) - not surprising because the spatiotemporal structure is quite different between the actions and the animations. This pattern of results makes it difficult to interpret a direct comparison of the two conditions within a given ROI. For example, it would have strengthened the argument of the paper to show that Action-Anim decoding was better than Action-PLD decoding in aIPL; this result was not obtained, but that could simply be because the Action and PLD conditions are more visually similar to each other in a number of ways that influence decoding. Still, looking WITHIN each of the Action-Anim and Action-PLD conditions yields clear evidence for the main conclusion of the study.

The reviewer is absolutely right: Because the PLDs are more similar to the actions than the animations, a comparison of the effects of the two decoding schemes is not informative. As we also clarified in our response to Reviewer 2, we cannot rule out that the action-PLD decoding picked up information related to action effect structures. Thus, the only firm conclusion that we can draw from our study is that aIPL and SPL are disproportionally sensitive to action effects and body movements, respectively. We clarified this point in our revised discussion.

(2) The second set of analyses in the paper, shown in Figure 4, follows from the notion that inferring action effects from body movements alone (i.e., when the object is unseen) is easier via pantomimes than with PLD stick figures. That makes sense, but it doesn't necessarily imply that the richness of the inferred action effect is the only or main difference between these conditions. There is more visual information overall in the pantomime case. So, although it's likely true that observers can more vividly infer action effects from pantomimes vs stick figures, it's not a given that contrasting these two conditions is an effective way to isolate inferred action effects. The results in Figure 4 are therefore intriguing but do not unequivocally establish that aIPL is representing inferred rather than observed action effects.

We agree that higher decoding accuracies for Action-Pant vs. Action-PLD and Pant-PLD could also be due to visual details (in particular of hands and body) that are more similar in actions and pantomimes relative to PLDs. However, please note that for this reason we included also the comparison of Anim-Pant vs. Anim-PLD. For this comparison, visual details should not influence the decoding. We clarified this point in our revision.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

It struck me that there are structural distinctions amongst the 5 action kinds that were not highlighted and may have been unintentional. Specifically, three of the actions are "unary" in a sense: break(object), squash(object), hit(object). One is "binary": place(object, surface), and the fifth (drink) is perhaps ternary - transfer(liquid, cup, mouth)? Might these distinctions be important for the organization of action effects (or actions generally)?

This is an interesting aspect that we did not think of yet. We agree that for the organization of actions (and perhaps action effects) this distinction might be relevant. One issue we noticed, however, is that for the animations the suggested organization might be less clear, in particular for "drink" as ternary, and perhaps also for "place" as binary. Thus, in the action-animation cross-decoding, this distinction - if it exists in the brain - might be harder to capture. We nonetheless tested this distinction. Specifically, we constructed a dissimilarity model (using the proposed organization, valency model hereafter) and tested it in a multiple regression RSA against an effect type model and two other models for specific actions (discriminating each action from each other with the same distance) and motion energy (as a visual control model). This analysis revealed no effects for the "valency" model in the ROI-based RSA. Also a searchlight analysis revealed no effects for this model. Since we think that the valency model is not ideally suited to test representations of action effects (using data from the action-animation cross-decoding) and to make the description of the RSA not unnecessarily complicated, we decided to not include this model in the final RSA reported in the manuscript.

In general, I found it surprising that the authors treated their LOTC findings as surprising or unexpected. Given the long literature associating this region with several high-level visual functions related to body perception, action perception, and action execution, I thought there were plenty of a priori reasons to investigate the LOTC's behaviour in this study. Looking at the supplementary materials, indeed some of the strongest effects seem to be in that region.

(Likewise, classically, the posterior superior temporal sulcus is strongly associated with the perception of others' body movements; why not also examine this region of interest?)

One control analysis that would considerably add to the strength of the authors' conclusions would be to examine how actions could be cross-decoded (or not) in the early visual cortex. Especially in comparisons of, for example, pantomime to full-cue video, we might expect a high degree of decoding accuracy, which might influence the way we interpret similar decoding in other "higher level" regions.

We agree that it makes sense to also look into LOTC and pSTS, and also EVC. We therefore added ROIs for these regions: For EVC and LOTC we used the same approach based on Brodmann areas as for aIPL and SPL, i.e., we used BA 17 for V1 and BA 19 for LOTC. For pSTS, we defined the ROI based on a meta analysis contrast for human vs. non-human body movements (Grobras et al., HBM 2012). Indeed we find that the strongest effects (for both action effect structures and body movements) can be found in LOTC. We also found effects in EVC that, at least for the action-animation cross-decoding, are more difficult to interpret. To test for a coincidental visual confound between actions and animations, we included a control model for motion energy in the multiple regression RSA, which could indeed explain some of the representational content in V1. However, also the effect type model revealed effects in V1, suggesting that there were additional visual features that caused the action-animation cross-decoding in V1. Notably, as pointed out in our response to the Public comments, the representational organization in V1 was relatively distinct from the representational organization in aIPL and LOTC, which argues against the interpretation that effects in aIPL and LOTC were driven by the same (visual) features as in V1.

Regarding the analyses reported in Figure 4: wouldn't it be important to also report similar tests for SPL?

In the analysis of implied action effect structures, we focused on the brain regions that revealed robust effects for action-animation decoding in the ROI and the searchlight analysis, that is, aIPL and SPL. However, we performed a whole brain conjunction analysis to search for other brain regions that show a profile for implied action effect representation. This analysis (that we forgot to mention in our original manuscript; now corrected) did not find evidence for implied action effect representations in SPL.

However, for completeness, we also added a ROI analysis for SPL. This analysis revealed a surprisingly complex pattern of results: We observed stronger decoding for Anim-Pant vs. Anim-PLD, whereas there were no differences for the comparisons of Action-Pant with Action-PLD and Pant-PLD:

This pattern of results is not straightforward to explain: First, the equally strong decoding for Action-Pant, Action-PLD, and Pant-PLD suggests that SPL is not substantially sensitive to body part details. Rather, the decoding relied on the coarse body part movements, independently of the specific stimulus type (action, pantomime, PLD). However, the stronger difference between Anim-Pant and Anim-PLD suggests that SPL is also sensitive to implied AES. This appears unlikely, because no effects (in left aIPL) or only weak effects (in right SPL) were found for the more canonical Action-Anim cross-decoding. The Anim-Pant cross-decoding was even stronger than the Action-Anim cross-decoding, which is counterintuitive because naturalistic actions contain more information than pantomimes, specifically with regard to action effect structures. How can this pattern of results be interpreted? Perhaps, for pantomimes and animations, not only aIPL and LOTC but also SPL is involved in inferring (implied) action effect structures. However, for this conclusion, also differences for the comparison of Action-Pant with Action-PLD and for Action-Pant with Pant-PLD should be found. Another non-mutually exclusive interpretation is that both animations and pantomimes are more ambiguous in terms of the specific action, as opposed to naturalistic actions. For example, the squashing animation and pantomime are both ambiguous in terms of what is squashed/compressed, which might require additional load to infer both the action and the induced effect. The increased activation of action-related information might in turn increase the chance for a match between neural activation patterns of animations and pantomimes.

In any case, these additional results in SPL do not question the effects reported in the main text, that is, disproportionate sensitivity for action effect structures in right aIPL and LOTC and for body movements in SPL and other AON regions. The evidence for implied action effect structures representation in SPL is mixed and should be interpreted with caution.

We added this analysis and discussion as supplementary information.

Statistical arguments that rely on "but not" are not very strong, e.g. "We found higher cross-decoding for animation-pantomime vs. animation-PLD in right aIPL and bilateral LOTC (all t(23) > 3.09, all p < 0.0025; one-tailed), but not in left aIPL (t(23) = 0.73, p = 0.23, one-tailed)." Without a direct statistical test between regions, it's not really possible to support a claim that they have different response profiles.

Absolutely correct. Notably, we did not make claims about different profiles of the tested ROIs with regard to implied action effect representations. But of course it make sense to test for differential profiles of left vs. right aIPL, so we have added a repeated measures ANOVA to test for an interaction between TEST (animation-pantomime, animation-PLD) and ROI (left aIPL, right aIPL), which, however, was not significant (F(1,23)=3.66, p = 0.068). We included this analysis in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

(1) I haven't found any information about data and code availability in the paper: is the plan to release them upon publication? This should be made clear.

Stimuli, MRI data, and code are deposited at the Open Science Framework (https://osf.io/am346/). We included this information in the revised manuscript.

(2) Samples of videos of the stimuli (or even the full set) would be very informative for the reader to know exactly what participants were looking at.

We have uploaded the full set of stimuli on OSF (https://osf.io/am346/).

(3) Throughout the paper, decoding accuracies are averaged across decoding directions (A->B and B->A). To my knowledge, this approach was proposed in van den Hurk & Op de Beeck (2019), "Generalization asymmetry in multivariate cross-classification: When representation A generalizes better to representation B than B to A". I believe it would be fair to cite this paper.

Absolutely, thank you very much for the hint. We included this reference in our revised manuscript.

(4) Page 3, line 70: this is a very nitpicky point, but "This suggests that body movements and the effects they induce are at least partially processed independently from each other." is a bit of an inferential leap from "these are distinct aspects of real-world actions" to "then they should be processed independently in the brain". The fact that a distinction exists in the world is a prerequisite for this distinction existing in the brain in terms of functional specialization, but it's not in itself a reason to believe that functional specialization exists. It is a reason to hypothesize that the specialization might exist and to test that hypothesis. So I think this sentence should be rephrased as "This suggests that body movements and the effects they induce might be at least partially processed independently from each other.", or something to that effect.

Your reasoning is absolutely correct. We revised the sentence following your suggestion.

(5) Page 7, line 182: the text says "stronger decoding for action-animation vs. action-PLD" (main effect of TEST), which is the opposite of what can be seen in the figure. I assume this is a typo?

Thanks for spotting this, it was indeed a typo. We corrected it: “…stronger decoding for action-PLD vs. action-animation cross-decoding..”

(6) Page 7, Figure 3B: since the searchlight analysis is used to corroborate the distinction between aIPL and SPL, it would be useful to overlay the contours of these ROIs (and perhaps LOTC as well) on the brain maps.

We found that overlaying the contours of the ROIs onto the decoding searchlight maps would make the figure too busy, and the contours would partially hide effects. However, we added a brain map with all ROIs in the supplementary information.

(7) Page 9, Figure 4A: since the distinction between the significant difference between anim-pant and anim-PLD is quite relevant in the text, I believe highlighting the lack of difference between the two decoding schemes in left aIPL (for example, by writing "ns") in the figure would help guide the reader to see the relevant information. It is generally quite hard to notice the absence of something.

We added “n.s.” to the left aIPL in Fig. 4A.

(8) Page 11, line 300: "Left aIPL appears to be more sensitive to the type of interaction between entities, e.g. how a body part or an object exerts a force onto a target object" since the distinction between this and the effect induced by that interaction" is quite nuanced, I believe a concrete example would clarify this for the reader: e.g. I guess the former would involve a representation of the contact between hand and object when an object is pushed, while the latter would represent only the object's displacement following the push?

Thank you for the suggestion. We added a concrete example: “Left aIPL appears to be more sensitive to the type of interaction between entities, that is, how a body part or an object exerts a force onto a target object (e.g. how a hand makes contact with an object to push it), whereas right aIPL appears to be more sensitive to the effect induced by that interaction (the displacement of the object following the push).”

(9) Page 12, line 376: "Informed consent, and consent to publish, was obtained from the participant in Figure 2." What does this refer to? Was the person shown in the figure both a participant in the study and an actor in the stimulus videos? Since this is in the section about participants in the experiment, it sounds like all participants also appeared in the videos, which I guess is not the case. This ambiguity should be clarified.

Right, the statement sounds misleading in the “Participants” section. We rephrased it and moved it to the “Stimuli” section: “actions…were shown in 4 different formats: naturalistic actions, pantomimes, point light display (PLD) stick figures, and abstract animations (Fig. 2; informed consent, and consent to publish, was obtained from the actor shown in the figure).”

(10) Page 15, line 492: Here, "within-session analyses" are mentioned. However, these analyses are not mentioned in the text (only shown in Figure S2) and their purpose is not clarified. I imagine they were a sanity check to ensure that the stimuli within each stimulus type could be reliably distinguished. This should be explained somewhere.

We clarified the purpose of the within session decoding analyses in the methods section: "Within-session decoding analyses were performed as sanity checks to ensure that for all stimulus types, the 5 actions could be reliably decoded (Fig. S2)."

(11) Page 20, Figure S1: I recommend using the same color ranges for the two decoding schemes (action-anim and action-PLD) in A and C, to make them more directly comparable.

Ok, done.

Reviewer #3 (Recommendations For The Authors):

(1) When first looking at Figure 1B, I had a hard time discerning what action effect was being shown (I thought maybe it was "passing through") Figure 2 later clarified it for me, but it would be helpful to note in the caption that it depicts breaking.

Thank you for the suggestion. Done.

(2) It would be helpful to show an image of the aIPL and SPL ROIs on a brain to help orient readers - both to help them examine the whole brain cross-decoding accuracy and to aid in comparisons with other studies.

We added a brain map with all ROIs in the supplementary information.

(3) Line 181: I'm wondering if there's an error, or if I'm reading it incorrectly. The line states "Moreover, we found ANOVA main effects of TEST (F(1,24)=33.08, p=7.4E-06), indicating stronger decoding for action-animation vs. action-PLD cross-decoding..." But generally, in Figure 3A, it looks like accuracy is lower for Action-Anim than Action-PLD in both hemispheres.

You are absolutely right, thank you very much for spotting this error. We corrected the sentence: “…stronger decoding for action-PLD vs. action-animation cross-decoding..”

(4) It might be useful to devote some more space in the Introduction to clarifying the idea of action-effect structures. E.g., as I read the manuscript I found myself wondering whether there is a difference between action effect structures and physical outcomes in general... would the same result be obtained if the physical outcomes occurred without a human actor involved? This question is raised in the discussion, but it may be helpful to set the stage up front.

We clarified this point in the introduction:

In our study, we define action effects as induced by intentional agents. However, the notion of action effect structures might be generalizable to physical outcomes or object changes as such (e.g. an object's change of location or configuration, independently of whether the change is induced by an agent or not).

(5) Regarding my public comment #2, it would perhaps strengthen the argument to run the same analysis in the SPL ROIs. At least for the comparison of Anim-Pant with Anim-PLD, the prediction would be no difference, correct?

The prediction would indeed be that there is no difference for the comparison of Anim-Pant with Anim-PLD, but also for the comparison of Action-Pant with Action-PLD and for Action-Pant with Pant-PLD, there should be no difference. As explained in our response to the public comment #2, we ran a whole brain conjunction (Fig. 4B) to test for the combination of these effects and did not find SPL in this analysis. However, we did found differences for Anim-Pant vs. Anim-PLD, which is not straightforward to interpret (see our response to your public comment #2 for a discussion of this finding).

eLife Assessment

Wang et al. presented visual (dot) motion and/or the sound of a walking person and found solid evidence that EEG activity tracks the step rhythm, as well as the gait (2-step cycle) rhythm, with some demonstration that the gait rhythm is tracked superadditively (power for A+V condition is higher than the sum of the A-only and V-only condition). The valuable findings will be of wide interest to those examining biological motion perception and oscillatory processes more broadly. Some of the theoretical interpretations concerning entrainment must remain speculative when the authors cannot dissociate evoked responses from entrained oscillatory effects.

Reviewer #1 (Public review):

Summary:

Shen et al. conducted three experiments to study the cortical tracking of the natural rhythms involved in biological motion (BM), and whether these involve audiovisual integration (AVI). They presented participants with visual (dot) motion and/or the sound of a walking person. They found that EEG activity tracks the step rhythm, as well as the gait (2-step cycle) rhythm. The gait rhythm specifically is tracked superadditively (power for A+V condition is higher than the sum of the A-only and V-only condition, Experiments 1a/b), which is independent of the specific step frequency (Experiment 1b). Furthermore, audiovisual integration during tracking of gait was specific to BM, as it was absent (that is, the audiovisual congruency effect) when the walking dot motion was vertically inverted (Experiment 2). Finally, the study shows that an individual's autistic traits are negatively correlated with the BM-AVI congruency effect.

Strengths:

The three experiments are well designed and the various conditions are well controlled. The rationale of the study is clear, and the manuscript is pleasant to read. The analysis choices are easy to follow, and mostly appropriate.

Weaknesses:

On revision, the authors are careful not to overinterpret an analysis where the statistical test is not independent from the data (channel) selection criterion.

Reviewer #2 (Public review):

Summary:

The authors evaluate spectral changes in electroencephalography (EEG) data as a function of the congruency of audio and visual information associated with biological motion (BM) or non-biological motion. The results show supra-additive power gains in the neural response to gait dynamics, with trials in which audio and visual information was presented simultaneously producing higher average amplitude than the combined average power for auditory and visual conditions alone. Further analyses suggest that such supra-additivity is specific to BM and emerges from temporoparietal areas. The authors also find that the BM-specific supra-additivity is negatively correlated with autism traits.

Strengths:

The manuscript is well-written, with a concise and clear writing style. The visual presentation is largely clear. The study involves multiple experiments with different participant groups. Each experiment involves specific considered changes to the experimental paradigm that both replicate the previous experiment's finding yet extend it in a relevant manner.

In the first revisions of the paper, the manuscript better relays the results and anticipates analyses, and this version adequately resolves some concerns I had about analysis details. In a further revision, it is clarified better how the results relate to the various competing hypotheses on how biological motion is processed.

Weaknesses:

Still, it is my view that the findings of the study are basic neural correlate results that offer only minimal constraint towards the question of how the brain realizes the integration of multisensory information in the service of biological motion perception, and the data do not address the causal relevance of observed neural effects towards behavior and cognition. The presence of an inversion effect suggests that the supra-additivity is related to cognition, but that leaves open whether any detected neural pattern is actually consequential for multi-sensory integration (i.e., correlation is not causation). In other words, the fact that frequency-specific neural responses to the [audio & visual] condition are stronger than those to [audio] and [visual] combined does not mean this has implications for behavioral performance. While the correlation to autism traits could suggest some relation to behavior and is interesting in its own right, this correlation is a highly indirect way of assessing behavioral relevance. It would be helpful to test the relevance of supra-additive cortical tracking on a behavioral task directly related to the processing of biological motion to justify the claim that inputs are being integrated in the service of behavior. Under either framework, cortical tracking or entrainment, the causal relevance of neural findings toward cognition is lacking.

Overall, I believe this study finds neural correlates of biological motion that offer some constraint toward mechanism, and it is possible that the effects are behaviorally relevant, but based on the current task and associated analyses this has not been shown (or could not have been, given the paradigm).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Shen et al. conducted three experiments to study the cortical tracking of the natural rhythms involved in biological motion (BM), and whether these involve audiovisual integration (AVI). They presented participants with visual (dot) motion and/or the sound of a walking person. They found that EEG activity tracks the step rhythm, as well as the gait (2-step cycle) rhythm. The gait rhythm specifically is tracked superadditively (power for A+V condition is higher than the sum of the A-only and V-only condition, Experiments 1a/b), which is independent of the specific step frequency (Experiment 1b). Furthermore, audiovisual integration during tracking of gait was specific to BM, as it was absent (that is, the audiovisual congruency effect) when the walking dot motion was vertically inverted (Experiment 2). Finally, the study shows that an individual's autistic traits are negatively correlated with the BM-AVI congruency effect.

Strengths:

The three experiments are well designed and the various conditions are well controlled. The rationale of the study is clear, and the manuscript is pleasant to read. The analysis choices are easy to follow, and mostly appropriate.

Weaknesses:

There is a concern of double-dipping in one of the tests (Experiment 2, Figure 3: interaction of Upright/Inverted X Congruent/Incongruent). I raised this concern on the original submission, and it has not been resolved properly. The follow-up statistical test (after channel selection using the interaction contrast permutation test) still is geared towards that same contrast, even though the latter is now being tested differently. (Perhaps not explicitly testing the interaction, but in essence still testing the same.) A very simple solution would be to remove the post-hoc statistical tests and simply acknowledge that you're comparing simple means, while the statistical assessment was already taken care of using the permutation test. (In other words: the data appear compelling because of the cluster test, but NOT because of the subsequent t-tests.)

We are sorry that we did not explain this issue clearly before, which might have caused some misunderstanding. When performing the cluster-based permutation test, we only tested whether the audiovisual congruency effect (congruent vs. incongruent) between the upright and inverted conditions was significantly different [i.e., (UprCon – UprInc) vs. (InvCon – InvInc)], without conducting extra statistical analyses on whether the congruency effect was significant in each orientation condition. Such an analysis yielded a cluster with a significant interaction between audiovisual integration and BM orientation for the cortical tracking effect at 1Hz (but not at 2Hz). However, this does not provide valid information about whether the audiovisual congruency effect at this cluster is significant in each orientation condition, given that a significant interaction effect may result from various patterns of data across conditions: such as significant congruency effects in both orientation conditions (Author response image 1a), a significant congruency effect in the upright condition and a non-significant effect in the inverted condition (Author response image 1b), or even non-significant yet opposite effects in the two conditions (Author response image 1c). Here, our results conform to the second pattern, indicating that cortical tracking of the high-order gait cycles involves a domain-specific process exclusively engaged in the AVI of BM. In a similar vein, the non-significant interaction found at 2Hz does not necessarily indicate that the congruency effect is non-significant in each orientation condition (Author response image 1f&e). Indeed, the congruency effect was significant in both the upright and inverted conditions at 2Hz in our study despite the non-significant interaction, suggesting that neural tracking of the lower-order step cycles is associated with a domain-general AVI process mostly driven by temporal correspondence in physical stimuli.

Therefore, we need to perform subsequent t-tests to examine the significance of the simple effects in the two orientation conditions, which do not duplicate the clusterbased permutation test (for interaction only) and cause no double-dipping. Results from interaction and simple effects, put together, provide solid evidence that the cortical tracking of higher-order and lower-order rhythms involves BM-specific and domaingeneral audiovisual processing, respectively.

To avoid ambiguity, we have removed the sentence “We calculated the audiovisual congruency effect for the upright and the inverted conditions” (line 194, which referred to the calculation of the indices rather than any statistical tests) from the manuscript. We have also clarified the meanings of the findings based on the interaction and simple effects together at the two temporal scales, respectively (Lines 205-207; Lines 213-215).

Author response image 1.

Examples of different patterns of data yielding a significant or nonsignificant interaction effect.

Reviewer #2 (Public review):

Summary:

The authors evaluate spectral changes in electroencephalography (EEG) data as a function of the congruency of audio and visual information associated with biological motion (BM) or non-biological motion. The results show supra-additive power gains in the neural response to gait dynamics, with trials in which audio and visual information was presented simultaneously producing higher average amplitude than the combined average power for auditory and visual conditions alone. Further analyses suggest that such supra-additivity is specific to BM and emerges from temporoparietal areas. The authors also find that the BM-specific supra-additivity is negatively correlated with autism traits.

Strengths:

The manuscript is well-written, with a concise and clear writing style. The visual presentation is largely clear. The study involves multiple experiments with different participant groups. Each experiment involves specific considered changes to the experimental paradigm that both replicate the previous experiment's finding yet extend it in a relevant manner.

Weaknesses:

In the revised version of the paper, the manuscript better relays the results and anticipates analyses, and this version adequately resolves some concerns I had about analysis details. Still, it is my view that the findings of the study are basic neural correlate results that do not provide insights into neural mechanisms or the causal relevance of neural effects towards behavior and cognition. The presence of an inversion effect suggests that the supra-additivity is related to cognition, but that leaves open whether any detected neural pattern is actually consequential for multi-sensory integration (i.e., correlation is not causation). In other words, the fact that frequency-specific neural responses to the [audio & visual] condition are stronger than those to [audio] and [visual] combined does not mean this has implications for behavioral performance. While the correlation to autism traits could suggest some relation to behavior and is interesting in its own right, this correlation is a highly indirect way of assessing behavioral relevance. It would be helpful to test the relevance of supra-additive cortical tracking on a behavioral task directly related to the processing of biological motion to justify the claim that inputs are being integrated in the service of behavior. Under either framework, cortical tracking or entrainment, the causal relevance of neural findings toward cognition is lacking.

Overall, I believe this study finds neural correlates of biological motion, and it is possible that such neural correlates relate to behaviorally relevant neural mechanisms, but based on the current task and associated analyses this has not been shown.

Thank you for providing these thoughtful comments regarding the theoretical implications of our neural findings. Previous behavioral evidence highlights the specificity of the audiovisual integration (AVI) of biological motion (BM) and reveals the impairment of such ability in individuals with autism spectrum disorder. However, the neural implementation underlying the AVI of BM, its specificity, and its association with autistic traits remain largely unknown. The current study aimed to address these issues.

It is noteworthy that the operation of multisensory integration does not always depend on specific tasks, as our brains tend to integrate signals from different sensory modalities even when there is no explicit task. Hence, many studies have investigated multisensory integration at the neural level without examining its correlation with behavioral performance. For example, the widely known super-additivity mode for multisensory integration proposed by Perrault and colleagues was based on single-cell recording findings without behavioral tasks (Perrault et al., 2003, 2005). As we mentioned in the manuscript, the super-additive and sub-additive modes indicate non-linear interaction processing, either with potentiated neural activation to facilitate the perception or detection of near-threshold signals (super-additive) or a deactivation mechanism to minimize the processing of redundant information cross-modally (subadditive) (Laurienti et al., 2005; Metzger et al., 2020; Stanford et al., 2005; Wright et al., 2003). Meanwhile, the additive integration mode represents a linear combination between two modalities. Distinguishing among these integration modes helps elucidate the neural mechanism underlying AVI in specific contexts, even though sometimes, the neural-level AVI effects do not directly correspond to a significant behavioral-level AVI effect (Ahmed et al., 2023; Metzger et al., 2020). In the current study, we unveiled the dissociation of multisensory integration modes between neural responses at two temporal scales (Exps. 1a & 1b), which may involve the cooperation of a domain-specific and a domain-general AVI processes (Exp. 2). While these findings were not expected to be captured by a single behavioral index, they revealed the multifaceted mechanism whereby hierarchical cortical activity supports audiovisual BM integration. They also advance our understanding of the emerging view that multi-timescale neural dynamics coordinate multisensory integration (Senkowski & Engel, 2024), especially from the perspective of natural stimuli processing.

Meanwhile, our finding that the cortical tracking of higher-order rhythmic structure in audiovisual BM specifically correlated with individual autistic traits extends previous behavioral evidence that ASD children exhibited reduced orienting to audiovisual synchrony in BM (Falck-Ytter et al., 2018), offering new evidence that individual differences in audiovisual BM processing are present at the neural level and associated with autistic traits. This finding opens the possibility of utilizing the cortical tracking of BM as a potential neural maker to assist the diagnosis of autism spectrum disorder (see more details in our Discussion Lines 334-346).

However, despite the main objective of the current study focusing on the neural processing of BM, we agree with the reviewer that it would be helpful to test the relevance of supra-additive cortical tracking on a behavioral task directly related to BM perception, for further justifying that inputs are being integrated in the service of behavior. In the current study, we adopted a color-change detection task entirely unrelated to audiovisual correspondence but only for maintaining participants’ attention. The advantage of this design is that it allows us to investigate whether and how the human brain integrates audiovisual BM information under task-irrelevant settings, as people in daily life can integrate such information even without a relevant task. However, this advantage is accompanied by a limitation: the task does not facilitate the direct examination of the correlation between neural responses and behavioral performance, since the task performance was generally high (mean accuracy >98% in all experiments). Future research could investigate this issue by introducing behavioral tasks more relevant to BM perception (e.g., Shen et al., 2023). They could also apply advanced neuromodulation techniques to elucidate the causal relevance of the cortical tracking effect to behavior (e.g., Ko sem et al., 2018, 2020).

We have discussed the abovementioned points as a separate paragraph in the revised manuscript (Lines 322-333). In addition, since the scope of the current study does not involve a causal correlation with behavioral performance, we have removed or modified the descriptions related to "functional relevance" in the manuscript (Abstract; Introduction, lines 101-103; Results, lines 239; Discussion, line 336; Supplementary Information, line 794、803). Moreover, we have strengthened the descriptions of the theoretical implications of the current findings in the abstract.

We hope these changes adequately address your concern.

References

Ahmed, F., Nidiffer, A. R., O’Sullivan, A. E., Zuk, N. J., & Lalor, E. C. (2023). The integration of continuous audio and visual speech in a cocktail-party environment depends on attention. Neuroimage, 274, 120143. https://doi.org/10.1016/j.neuroimage.2023.120143

Falck-Ytter, T., Nystro m, P., Gredeba ck, G., Gliga, T., Bo lte, S., & the EASE team. (2018). Reduced orienting to audiovisual synchrony in infancy predicts autism diagnosis at 3 years of age. Journal of Child Psychology and Psychiatry, 59(8), 872–880. https://doi.org/10.1111/jcpp.12863

Ko sem, A., Bosker, H., Jensen, O., Hagoort, P., & Riecke, L. (2020). Biasing the Perception of Spoken Words with Transcranial Alternating Current Stimulation. Journal of Cognitive Neuroscience, 32, 1–10. https://doi.org/10.1162/jocn_a_01579

Ko sem, A., Bosker, H. R., Takashima, A., Meyer, A., Jensen, O., & Hagoort, P. (2018). Neural Entrainment Determines the Words We Hear. Current Biology, 28(18), 2867-2875.e3. https://doi.org/10.1016/j.cub.2018.07.023

Laurienti, P. J., Perrault, T. J., Stanford, T. R., Wallace, M. T., & Stein, B. E. (2005). On the use of superadditivity as a metric for characterizing multisensory integration in functional neuroimaging studies. Experimental Brain Research, 166(3), 289–297. https://doi.org/10.1007/s00221-005-2370-2

Metzger, B. A., Magnotti, J. F., Wang, Z., Nesbitt, E., Karas, P. J., Yoshor, D., & Beauchamp, M. S. (2020). Responses to Visual Speech in Human Posterior Superior Temporal Gyrus Examined with iEEG Deconvolution. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 40(36), 6938–6948. https://doi.org/10.1523/JNEUROSCI.0279-20.2020

Perrault, T. J., Vaughan, J. W., Stein, B. E., & Wallace, M. T. (2003). Neuron-Specific Response Characteristics Predict the Magnitude of Multisensory Integration. Journal of Neurophysiology, 90(6), 4022–4026. https://doi.org/10.1152/jn.00494.2003

Perrault, T. J., Vaughan, J. W., Stein, B. E., & Wallace, M. T. (2005). Superior Colliculus Neurons Use Distinct Operational Modes in the Integration of Multisensory Stimuli. Journal of Neurophysiology, 93(5), 2575–2586. https://doi.org/10.1152/jn.00926.2004

Senkowski, D., & Engel, A. K. (2024). Multi-timescale neural dynamics for multisensory integration. Nature Reviews Neuroscience, 25(9), 625–642. https://doi.org/10.1038/s41583-024-00845-7

Shen, L., Lu, X., Wang, Y., & Jiang, Y. (2023). Audiovisual correspondence facilitates the visual search for biological motion. Psychonomic Bulletin & Review, 30(6), 2272–2281. https://doi.org/10.3758/s13423-023-02308-z

Stanford, T. R., Quessy, S., & Stein, B. E. (2005). Evaluating the Operations Underlying Multisensory Integration in the Cat Superior Colliculus. Journal of Neuroscience, 25(28), 6499–6508. https://doi.org/10.1523/JNEUROSCI.5095-04.2005

Wright, T. M., Pelphrey, K. A., Allison, T., McKeown, M. J., & McCarthy, G. (2003). Polysensory Interactions along Lateral Temporal Regions Evoked by Audiovisual Speech. Cerebral Cortex, 13(10), 1034–1043. https://doi.org/10.1093/cercor/13.10.1034

eLife Assessment

This study empirically investigates the neural noise hypothesis of developmental dyslexia using electroencephalography (EEG) during a spoken language task and 7T magnetic resonance spectroscopy (MRS). The convincing findings indicate no evidence of an imbalance between excitatory and inhibitory (E/I) brain activity in adolescents and young adults with dyslexia compared to controls, thereby challenging the neural noise hypothesis. This research is valuable for advancing our understanding of the neural mechanisms underlying dyslexia and offers broader insights into the neural processes involved in reading development.

Reviewer #1 (Public review):

Summary:

"Neural noise", here operationalized as an imbalance between excitatory and inhibitory neural activity, has been posited as a core cause of developmental dyslexia, a prevalent learning disability that impacts reading accuracy and fluency. This is study is the first to systematically evaluate the neural noise hypothesis of dyslexia. Neural noise was measured using neurophysiological (electroencephalography [EEG]) and neurochemical (magnetic resonance spectroscopy [MRS]) in adolescents and young adults with and without dyslexia. The authors did not find evidence of elevated neural noise in the dyslexia group from EEG or MRS measures, and Bayes factors generally informed against including the grouping factor in the models. Although the comparisons between groups with and without dyslexia did not support the neural noise hypothesis, a mediation model that quantified phonological processing and reading abilities continuously revealed that EEG beta power in the left superior temporal sulcus was positively associated with reading ability via phonological awareness. This finding lends support for analysis of associations between neural excitatory/inhibitory factors and reading ability along a continuum, rather than as with a case/control approach, and indicates the relevance of phonological awareness as an intermediate trait that may provide a more proximal link between neurobiology and reading ability. Further research is needed across developmental stages and over a broader set of brain regions to more comprehensively assess the neural noise hypothesis of dyslexia, and alternative neurobiological mechanisms of this disorder should be explored.

Strengths:

The inclusion of multiple methods of assessing neural noise (neurophysiological and neurochemical) is a major advantage of this paper. MRS at 7T confers an advantage of more accurately distinguishing and quantifying glutamate, which is a primary target of this study. In addition, the subject-specific functional localization of the MRS acquisition is an innovative approach. MRS acquisition and processing details are noted in the supplementary materials using according to the experts' consensus recommended checklist (https://doi.org/10.1002/nbm.4484). Commenting on rigor the EEG methods is beyond my expertise as a reviewer.<br /> Participants recruited for this study included those with a clinical diagnosis of dyslexia, which strengthens confidence in the accuracy of the diagnosis. The assessment of reading and language abilities during the study further confirms the persistently poorer performance of the dyslexia group compared to the control group.<br /> The correlational analysis and mediation analysis provide complementary information to the main case-control analyses, and the examination of associations between EEG and MRS measures of neural noise is novel and interesting.<br /> The authors follow good practice for open science, including data and code sharing. They also apply statistical rigor, using Bayes Factors to support conclusions of null evidence rather than relying only on non-significant findings. In the discussion, they acknowledge the limitations and generalizability of the evidence and provide directions for future research on this topic.

Appraisal:

The authors present a thorough evaluation of the neural noise hypothesis of developmental dyslexia in a sample of adolescents and young adults using multiple methods of measuring excitatory/inhibitory imbalances as an indicator of neural noise. The authors concluded that there was not support for the neural noise hypothesis of dyslexia in their study based on null significance and Bayes factors. This conclusion is justified, and further research is called for to more broadly evaluate the neural noise hypothesis in developmental dyslexia.

Impact:

This study provides an exemplar foundation for the evaluation of the neural noise hypothesis of dyslexia. Other researcher may adopt the model applied in this paper to examine neural noise in various populations with/without dyslexia, or across a continuum of reading abilities, to more thoroughly examine evidence (or lack thereof) for this hypothesis. Notably, the lack of evidence here does not rule out the possibility for a role of neural noise in dyslexia, and the authors point out that presentation with co-occurring conditions, such as ADHD, may contribute to neural noise in dyslexia. Dyslexia remains a multi-faceted and heterogenous neurodevelopmental condition, and many genetic, neurobiological and environmental factors play a role. This study demonstrates one step toward evaluating neurobiological mechanisms that may contribute to reading difficulties.

Reviewer #2 (Public review):

Summary:

This study utilized two complimentary techniques (EEG and 7T MRI/MRS) to directly test a theory of dyslexia: the neural noise hypothesis. The authors report finding no evidence to support an excitatory/inhibitory balance, as quantified by beta in EEG and Glutamate/GABA ratio in MRS. This is important work and speaks to one potential mechanism by which increased neural noise may occur in dyslexia.

Strengths:

This is a well-conceived study with in depth analyses and publicly available data for independent review. The authors provide transparency with their statistics and display the raw data points along with the averages in figures for review and interpretation. The data suggest that an E/I balance issue may not underlie deficits in dyslexia and is a meaningful and needed test of a possible mechanism for increased neural noise.

Weaknesses:

The researchers did not include a visual print task in the EEG task, which limits analysis of reading specific regions such as the visual word form area, which is a commonly hypoactivated region in dyslexia. This region is a common one of interest in dyslexia, yet the researchers measured the I/E balance in only one region of interest, specific to the language network.

Reviewer #3 (Public review):

Summary:

This study by Glica and colleagues utilized EEG (i.e., Beta power, Gamma power, and aperiodic activity) and 7T MRS (i.e., MRS IE ratio, IE balance) to reevaluate the neural noise hypothesis in Dyslexia. Supported by Bayesian statistics, their results show convincing evidence of no differences in EI balance between groups, challenging the neural noise hypothesis.

Strengths:

Combining EEG and 7T MRS, this study utilized both the indirect (i.e., Beta power, Gamma power, and aperiodic activity) and direct (i.e., MRS IE ratio, IE balance) measures to reevaluate the neural noise hypothesis in Dyslexia.

Author response:

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

Reviewer #1 (Public review):

Summary:

"Neural noise", here operationalized as an imbalance between excitatory and inhibitory neural activity, has been posited as a core cause of developmental dyslexia, a prevalent learning disability that impacts reading accuracy and fluency. This is study is the first to systematically evaluate the neural noise hypothesis of dyslexia. Neural noise was measured using neurophysiological (electroencephalography [EEG]) and neurochemical (magnetic resonance spectroscopy [MRS]) in adolescents and young adults with and without dyslexia. The authors did not find evidence of elevated neural noise in the dyslexia group from EEG or MRS measures, and Bayes factors generally informed against including the grouping factor in the models. Although the comparisons between groups with and without dyslexia did not support the neural noise hypothesis, a mediation model that quantified phonological processing and reading abilities continuously revealed that EEG beta power in the left superior temporal sulcus was positively associated with reading ability via phonological awareness. This finding lends support for analysis of associations between neural excitatory/inhibitory factors and reading ability along a continuum, rather than as with a case/control approach, and indicates the relevance of phonological awareness as an intermediate trait that may provide a more proximal link between neurobiology and reading ability. Further research is needed across developmental stages and over a broader set of brain regions to more comprehensively assess the neural noise hypothesis of dyslexia, and alternative neurobiological mechanisms of this disorder should be explored.

Strengths:

The inclusion of multiple methods of assessing neural noise (neurophysiological and neurochemical) is a major advantage of this paper. MRS at 7T confers an advantage of more accurately distinguishing and quantifying glutamate, which is a primary target of this study. In addition, the subject-specific functional localization of the MRS acquisition is an innovative approach. MRS acquisition and processing details are noted in the supplementary materials using according to the experts' consensus recommended checklist (https://doi.org/10.1002/nbm.4484). Commenting on rigor the EEG methods is beyond my expertise as a reviewer.

Participants recruited for this study included those with a clinical diagnosis of dyslexia, which strengthens confidence in the accuracy of the diagnosis. The assessment of reading and language abilities during the study further confirms the persistently poorer performance of the dyslexia group compared to the control group.

The correlational analysis and mediation analysis provide complementary information to the main case-control analyses, and the examination of associations between EEG and MRS measures of neural noise is novel and interesting.

The authors follow good practice for open science, including data and code sharing. They also apply statistical rigor, using Bayes Factors to support conclusions of null evidence rather than relying only on non-significant findings. In the discussion, they acknowledge the limitations and generalizability of the evidence and provide directions for future research on this topic.

Weaknesses:

Though the methods employed in the paper are generally strong, the MRS acquisition was not optimized to quantify GABA, so the findings (or lack thereof) should be interpreted with caution. Specifically, while 7T MRS affords the benefit of quantifying metabolites, such as GABA, without spectral editing, this quantification is best achieved with echo times (TE) of 68 or 80 ms in order to minimize the spectral overlap between glutamate and GABA and reduce contamination from the macromolecular signal (Finkelman et al., 2022, https://doi.org/10.1016/j.neuroimage.2021.118810). The data in the present study were acquired at TE=28 ms, and are therefore likely affected by overlapping Glu and GABA peaks at 2.3 ppm that are much more difficult to resolve at this short TE, which could directly affect the measures that are meant to characterize the Glu/GABA+ ratio/imbalance. In future research, MRS acquisition schemes should be optimized for the acquisition of Glutamate, GABA, and their relative balance.

As the authors note in the discussion, additional factors such as MRS voxel location, participant age, and participant sex could influence associations between neural noise and reading abilities and should be considered in future studies.

We have modified Figure 2 and revised the paragraph discussing the MRS methodological limitations in accordance with Reviewer #1's recommendations. Additionally, we have included the CRLB and linewidth thresholds in the Results section. Furthermore, a new figure showing the correlations between EEG and MRS biomarkers has been added (Figure 3).

Appraisal:

The authors present a thorough evaluation of the neural noise hypothesis of developmental dyslexia in a sample of adolescents and young adults using multiple methods of measuring excitatory/inhibitory imbalances as an indicator of neural noise. The authors concluded that there was not support for the neural noise hypothesis of dyslexia in their study based on null significance and Bayes factors. This conclusion is justified, and further research is called for to more broadly evaluate the neural noise hypothesis in developmental dyslexia.

Impact:

This study provides an exemplar foundation for the evaluation of the neural noise hypothesis of dyslexia. Other researcher may adopt the model applied in this paper to examine neural noise in various populations with/without dyslexia, or across a continuum of reading abilities, to more thoroughly examine evidence (or lack thereof) for this hypothesis. Notably, the lack of evidence here does not rule out the possibility for a role of neural noise in dyslexia, and the authors point out that presentation with co-occurring conditions, such as ADHD, may contribute to neural noise in dyslexia. Dyslexia remains a multi-faceted and heterogenous neurodevelopmental condition, and many genetic, neurobiological and environmental factors play a role. This study demonstrates one step toward evaluating neurobiological mechanisms that may contribute to reading difficulties.

Reviewer #2 (Public review):

Summary:

This study utilized two complimentary techniques (EEG and 7T MRI/MRS) to directly test a theory of dyslexia: the neural noise hypothesis. The authors report finding no evidence to support an excitatory/inhibitory balance, as quantified by beta in EEG and Glutamate/GABA ratio in MRS. This is important work and speaks to one potential mechanism by which increased neural noise may occur in dyslexia.

Strengths:

This is a well conceived study with in depth analyses and publicly available data for independent review. The authors provide transparency with their statistics and display the raw data points along with the averages in figures for review and interpretation. The data suggest that an E/I balance issue may not underlie deficits in dyslexia and is a meaningful and needed test of a possible mechanism for increased neural noise.

Weaknesses:

The researchers did not include a visual print task in the EEG task, which limits analysis of reading specific regions such as the visual word form area, which is a commonly hypoactivated region in dyslexia. This region is a common one of interest in dyslexia, yet the researchers measured the I/E balance in only one region of interest, specific to the language network.

Reviewer #3 (Public review):

Summary:

This study by Glica and colleagues utilized EEG (i.e., Beta power, Gamma power, and aperiodic activity) and 7T MRS (i.e., MRS IE ratio, IE balance) to reevaluating the neural noise hypothesis in Dyslexia. Supported by Bayesian statistics, their results show convincing evidence of no differences in EI balance between groups, challenging the neural noise hypothesis.

Strengths:

Combining EEG and 7T MRS, this study utilized both the indirect (i.e., Beta power, Gamma power, and aperiodic activity) and direct (i.e., MRS IE ratio, IE balance) measures to reevaluating the neural noise hypothesis in Dyslexia.

eLife Assessment

In this valuable study, the authors studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila, which lacks homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain. They attempt to demonstrate deacetylase activity, however, whilst the revised manuscript has been improved, significant aspects of the data are still incomplete and require further refinement. The work will be of interest to microbiologists studying metabolism and post-translational modifications.

Reviewer #1 (Public review):

Summary:

This study by Wang et al. identifies a new type of deacetylase, CobQ, in Aeromonas hydrophila. Notably, the identification of this deacetylase reveals a lack of homology with eukaryotic counterparts, thus underscoring its unique evolutionary trajectory within the bacterial domain.

Strengths:

The manuscript convincingly illustrates CobQ's deacetylase activity through robust in vitro experiments, establishing its distinctiveness from known prokaryotic deacetylases. Additionally, the authors elucidate CobQ's potential cooperation with other deacetylases in vivo to regulate bacterial cellular processes. Furthermore, the study highlights CobQ's significance in the regulation of acetylation within prokaryotic cells.

Weaknesses:

The problem I raised has been well resolved. I have no further questions.

Reviewer #2 (Public review):

In recent years, lots of researchers tried to explore the existence of new acetyltransferase and deacetylase by using specific antibody enrichment technologies and high resolution mass spectrometry. Here is an example for this effort. Yuqian Wang et al. studied a novel Zn2+- and NAD+-independent KDAC protein, AhCobQ, in Aeromonas hydrophila. They studied the biological function of AhCobQ by using biochemistry method and MS identification technology to confirm it. These results extended our understanding of the regulatory mechanism of bacterial lysine acetylation modifications. However, I find this conclusion is a little speculative, and unfortunately it also doesn't totally support the conclusion as the authors provided.

Major concerns:

-It is a little arbitrary to come to the title "Aeromonas hydrophila CobQ is a new type of NAD+- and Zn2+-independent protein lysine deacetylase in prokaryotes." It should be modified to delete the "in the prokaryotes" except that the authors get new more evidence in the other prokaryotes for the existence of the AhCobQ.<br /> -I was confused about the arrangement of the supplementary results. Because there are no citations for Figures S9-S19.<br /> -Same to the above, there are no data about Tables S1-S6.<br /> -All the load control is not integrated. Please provide all of the load controls with whole PAGE gel or whole membrane western blot results. Without these whole results, it is not convincing to come the conclusion as the authors mentioned in the context.<br /> -Thoroughly review the materials & methods section. It is unclear to me what exactly the authors describe in the method. All the experimental designs and protocols should be described in detail, including growth conditions, assay conditions, and purification conditions, etc.<br /> -Include relevant information about the experiments performed in the figure legends, such as experimental conditions, replicates, etc. Often it is not clear what was done based on the figure legend description.

eLife Assessment

This work shows that newborn Thbs4-positive astrocytes generated in the adult subventricular zone (SVZ) respond to middle carotid artery occlusion (MCAO) by secreting hyaluronan at the lesion penumbra, and that hyaluronin is a chemoattractant to SVZ astrocytes. These findings are valuable, despite being mostly descriptive, as they point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. The methods, data and analyses are convincing and broadly support the claims made by the authors with only some weaknesses.

Reviewer #1 (Public review):

Summary:

The authiors show that SVZ derived astrocytes respond to a middle carotid artery occlusion (MCAO) hypoxia lesion by secreting and modulating hyaluronan at the edge of the lesion (penumbra) and that hyaluronin is a chemoattractant to SVZ astrocytes. They use lineage tracing of SVZ cells to determine their origin. They also find that SVZ derived astrocytes express Thbs-4 but astrocytes at the MCAO-induced scar do not. Also, they demonstrate that decreased HA in the SVZ is correlated with gliogenesis. While much of the paper is descriptive/correlative they do overexpress Hyaluronan synthase 2 via viral vectors and show this is sufficient to recruit astrocytes to the injury. Interestingly, astrocytes preferred to migrate to the MCAO than to the region of overexpressed HAS2.

Strengths:

The field has largely ignored the gliogenic response of the SVZ, especially with regards to astrocytic function. These cells and especially newborn cells may provide support for regeneration. Emigrated cells from the SVZ have been shown to be neuroprotective via creating pro-survival environments, but their expression and deposition of beneficial extracellular matrix molecules is poorly understood. Therefore, this study is timely and important. The paper is very well written and the flow of results logical.

Comments on revised version:

The authors have addressed my points and the paper is much improved. Here are the salient remaining issues that I suggest be addressed.

The authors have still not shown, using loss of function studies, that Hyaluronan is necessary for SVZ astrogenesis and or migration to MCAO lesions.

(1) The co-expression of EGFr with Thbs4 and the literature examination is useful.

(2) Too bad they cannot explain the lack of effect of the MCAO on type C cells. The comparison with kainate-induced epilepsy in the hippocampus may or may not be relevant.

(3) Thanks for including the orthogonal confocal views in Fig S6D.

(4) The statement that "BrdU+/Thbs4+ cells mostly in the dorsal area" and therefore they mostly focused on that region is strange. Figure 8 clearly shows Thbs4 staining all along the striatal SVZ. Do they mean the dorsal segment of the striatal SVZ or the subcallosal SVZ? Fig. 4b and Fig 4f clearly show the "subcallosal" area as the one analysed but other figures show the dorsal striatal region (Fig. 2a). This is important because of the well-known embryological and neurogenic differences between the regions.

(5) It is good to know that the harsh MCAO's had already been excluded.

(6) Sorry for the lack of clarity - in addition to Thbs4, I was referring to mouse versus rat Hyaluronan degradation genes (Hyal1, Hyal2 and Hyal3) and hyaluronan synthase genes (HAS1 and HAS2) in order to address the overall species differences in hyaluronan biology thus justifying the "shift" from mouse to rat. You examine these in the (weirdly positioned) Fig. 8h,i. Please add a few sentences on mouse vs rat Thbs4 and Hyaluronan relevant genes.

(7) Thank you for the better justification of using the naked mole rat HA synthase.

Reviewer #3 (Public review):

Summary:

The authors aimed to study the activation of gliogenesis and the role of newborn astrocytes in a post-ischemic scenario. Combining immunofluorescence, BrdU-tracing and genetic cellular labelling, they tracked the migration of newborn astrocytes (expressing Thbs4) and found that Thbs4-positive astrocytes modulate the extracellular matrix at the lesion border by synthesis but also degradation of hyaluronan. Their results point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. This work's major strength is the fact that it is tackling the function of SVZ newborn astrocytes, whose role is undisclosed so far.

Strengths:

The article is innovative, of good quality, and clearly written, with properly described Materials and Methods, data analysis and presentation. In general, the methods are designed properly to answer the main question of the authors, being a major strength. Interpretation of the data is also in general well done, with results supporting the main conclusions of this article.

In this revised version, the points raised/weaknesses were clarified and discussed in the article.

Author response:

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

Reviewer #1 (Public Review):

The authors show that SVZ-derived astrocytes respond to a middle carotid artery occlusion (MCAO) hypoxia lesion by secreting and modulating hyaluronan at the edge of the lesion (penumbra) and that hyaluronan is a chemoattractant to SVZ astrocytes. They use lineage tracing of SVZ cells to determine their origin. They also find that SVZ-derived astrocytes express Thbs-4 but astrocytes at the MCAO-induced scar do not. Also, they demonstrate that decreased HA in the SVZ is correlated with gliogenesis. While much of the paper is descriptive/correlative they do overexpress Hyaluronan synthase 2 via viral vectors and show this is sufficient to recruit astrocytes to the injury. Interestingly, astrocytes preferred to migrate to the MCAO than to the region of overexpressed HAS2.

Strengths:

The field has largely ignored the gliogenic response of the SVZ, especially with regard to astrocytic function. These cells and especially newborn cells may provide support for regeneration. Emigrated cells from the SVZ have been shown to be neuroprotective via creating pro-survival environments, but their expression and deposition of beneficial extracellular matrix molecules are poorly understood. Therefore, this study is timely and important. The paper is very well written and the flow of results is logical.

Weaknesses:

The main problem is that they do not show that Hyaluronan is necessary for SVZ astrogenesis and or migration to MCAO lesions. Such loss of function studies have been carried out by studies they cite (e.g. Girard et al., 2014 and Benner et al., 2013). Similar approaches seem to be necessary in this work. 

We appreciate the comments by the reviewer. The article is, indeed, largely descriptive since we attempt to describe in detail what happens to newborn astrocytes after MCAO. Still, we have not attempted any modification to the model, such as amelioration of ischemic damage. This is a limitation of the study that we do not hide. However, we use several experimental approaches, such as lineage tracing and hyaluronan modification, to strengthen our conclusions.

Regarding the weaknesses found by the reviewer, we do not claim that hyaluronan is necessary for SVZ astrogenesis. Indeed, we observe that when the MCAO stimulus (i.e. inflammation) is present, the HMW-HA (AAV-Has2) stimulus is less powerful (we discuss this in line 330-332). We do claim, and we believe we successfully demonstrate, the reverse situation: that SVZ astrocytes modulate hyaluronan, not at the SVZ but at the site of MCAO, i.e. the scar. However, regarding whether hyaluronan is necessary for SVZ astrogenesis, we only show a correlation between its degradation and the time-course of astrogenesis. We suggest this result as a starting point for a follow-up study. We have included a phrase in the discussion (line 310), stating that further experiments are needed to fully establish a link between hyaluronan and astrogenesis in the SVZ.

Major points:

(1) How good of a marker for newborn astrocytes is Thbs4? Did you co-label with B cell markers like EGFr? Is the Thbs4 gene expressed in B cells? Do scRNAseq papers show it is expressed in B cells? Are they B1 or B2 cells?

We chose Thbs4 as a marker of newborn astrocytes based on published research (Beckervordersanforth et al., 2010; Benner et al., 2013; Llorens-Bobadilla et al. 2015, Codega et al, 2014; Basak et al., 2018; Mizrak et al., 2019; Kjell et al., 2020; Cebrian-Silla et al., 2021). From those studies, at least 3 associate Thbs4 to B-type cells based on scRNAseq data (LlorensBobadilla et al. 2015; Cebrian-Silla et al., 2021; Basak et al., 2018). We have included a sentence about this and the associated references, in line 92. 

We co-label Thbs4 with EGFR, but in the context of MCAO. We observed an increase of EGFR expression with MCAO, similar to the increase in Thbs4 alongside ischemia (see author ). We did not include this figure in the manuscript since we did not have available tissue from all the time points we used (7d, 60d post-ischemia). 

Author response image 1.

Thbs4 cells, in basal and ischemic conditions, only represent a small amount of IdU-positive cells (Fig 3F), suggesting that they are mostly quiescent cells, i.e., B1 cells. However, the scRNAseq literature is not consistent about this.

(2) It is curious that there was no increase in Type C cells after MCAO - do the authors propose a direct NSC-astrocyte differentiation?

Type C cells are fast-proliferating cells, and our BrdU/IdU experiment (Fig. 3) suggests that Thbs4 cells are slow-proliferating cells. Some authors suggest (Encinas lab, Spain) that when the hippocampus is challenged by a harsh stimulus, such as kainate-induced epilepsy, the NSCs differentiate directly into reactive astrocytes and deplete the DG neurogenic niche (Encinas et al., 2011, Cell Stem Cell; Sierra et al., 2015, Cell Stem Cell). We believe this might be the case in our MCAO model and the SVZ niche, since we observe a decrease in DCX labeling in the olfactory bulb (Fig S5) and an increase in astrocytes in the SVZ, which migrate to the ischemic lesion. We did not want to overcomplicate an already complicated paper, dwelling with direct NSC-astrocyte differentiation or with the reactive status of these newborn astrocytes. 

(3) The paper would be strengthened with orthogonal views of z projections to show colocalization.

We thank the reviewer for this observation. We have now included orthogonal projections in the critical colocalization IF of CD44 and hyaluronan (hyaluronan internalization) in Fig S6D, and a zoomed-in inset. Hyaluronan membrane synthesis is already depicted with orthogonal projection in Fig 6F.

(4) It is not clear why the dorsal SVZ is analysed and focused on in Figure 4. This region emanates from the developmental pallium (cerebral cortex anlagen). It generates some excitatory neurons early postnatally and is thought to have differential signalling such as Wnt (Raineteau group).

We decided to analyze in depth the dorsal SVZ after the BrdU experiment (Fig S3), where we observed an increase in BrdU+/Thbs4+ cells mostly in the dorsal area. Hence, the electrodes for electroporation were oriented in such a way as to label the dorsal area. We appreciate the paper by Raineteau lab, but we assume that this region may potentially exploit other roles (apart from excitatory neurons generated early postnatally) depending on the developmental stage (our model is in adults) and/or pathological conditions (MCAO). 

(5) Several of the images show the lesion and penumbra as being quite close to the SVZ. Did any of the lesions contact the SVZ? If so, I would strongly recommend excluding them from the analysis as such contact is known to hyperactivate the SVZ.

We thank the referee for the suggestion to exclude the harsher MCAO-lesioned animals from the analysis. Indeed, the MCAO ischemia, methodologically, can generate different tissue damages that cannot be easily controlled. Thus, based on TTC staining, we had already excluded the more severe tissue damage that contacted the SVZ, based on TTC staining.

(6) The authors switch to a rat in vitro analysis towards the end of the study. This needs to be better justified. How similar are the molecules involved between mouse and rat?

We chose the rat culture since it is a culture that we have already established in our lab, and that in our own hands, is much more reproducible than the mouse brain cell culture that we occasionally use (for transgenic animals only). Benito-Muñoz et al., Glia. 2016; Cavaliere et al., Front Cell Neurosci. 2013. It is true that there could be differences between the rat and mouse Thbs4-cell physiology, despite a 96% identity between rat and mouse Thbs4 protein sequence (BLASTp). In vitro, we only confirm the capacity of astrocytes to internalize hyaluronan, which was a finding that we did not expect in our in vivo experiments. Indeed, these observations, notwithstanding the obvious differences between in vivo and in vitro scenarios, suggest that the HA internalization by astrocytes is a cross-species event, at least in rodents. Regarding HA, hyaluronan is similar in all species, since it’s a glycan (this is why there are no antibodies against HA, and ones has to rely on binding proteins such as HABP to label it).

(7) Similar comment for overexpression of naked mole rat HA.

We chose the naked mole rat Hyaluronan synthase (HAS), because it is a HAS that produces HA of very high molecular weight, similar to the one found accumulated in the glial scar, at the lesion border. The naked-mole rat HAS used in mice (Gorbunova Lab) is a known tool in the ECM field. (Zhang et al, 2023, Nature; Tian et al., 2013, Nature).

Reviewer 1 (Recommendation to authors):

(1) Line 22: most of the cells that migrate out of the SVZ are not stem cells but cells further along in the lineage - neuroblasts and glioblasts.

We thank the reviewer for this clarification. We have modified the abstract accordingly. 

(2) In Figure 3d the MCAO group staining with GFAP looks suspiciously like ependymal cells which have been shown to be dramatically activated by stroke models.

The picture does show ependymal cells, which are located next to the ventricle and are indeed very proliferative in stroke. However, these cells do not express Thbs4 (Shah et al., 2018, Cell). In the quantifications from the SVZ of BrdU and IdU injected animals (Fig 3e and f), we only take into account Thbs4+ GFAP+ cells, no GFAP+ only. 

(3) The TTC injury shown in Figure 5c is too low mag.

We apologize for the low mag. We have increased the magnification two-fold without compromising resolution. The problem might also have arisen from the compression of TIF into JPEG in the PDF export process. We will address this in the revised version by carefully selecting export settings. The images we used are all publication quality (300 ppi).

(4) How specific to HA is HABP?

Hyaluronic Acid Binding Protein is a canonical marker for hyaluronan that is used also in ELISA to quantify it specifically, since it does not bind other glycosaminoglycans. The label has been used for years in the field for immunochemistry, and some controls and validations have been published: Deepa et al., 2006, JBC performed appropriate controls of HABP-biotin labeling using hyaluronidase (destroys labeling) and chondroitinase (preserves labeling). Soria et al., 2020, Nat Commun checked that (i) streptavidin does not label unspecifically, and (ii) that HABP staining is reduced after hyaluronan depletion in vivo with HAS inhibitor 4MU.

(5) A number of images are out of focus and thus difficult to interpret (e.g. SFig. 4e).

This is true. We realized that the PDF conversion process for the preprint version has severely compressed the larger images, such as the one found in Fig. S4e. We have submitted a revised version in a better-quality PDF (the final paper will have the original TIFF files). We apologize for the technical problem.

(6) "restructuration" is not a word.

We apologize for the mistake and thank the reviewer for the correction. We corrected “restructuration” with “reorganization” in line 67.

(7) While much of the manuscript is well-written and logical it could use an in-depth edit to remove awkward words and phrasings.

A native English speaker has revised the manuscript to correct these awkward phrases. All changes are labeled in red in the revised version.

(8) Please describe why and how you used skeleton analysis for HABP in the methods, this will be unfamiliar to most readers. The one-sentence description in the methods is insufficient.

We have modified the text accordingly, explaining in depth the logic behind the skeleton analysis. (Line 204). We also added several lines of text describing in detail the image analysis (CD44/HABP spots, fractal dimension, masks for membranal HABP, among others, in lines 484494) 

Reviewer #2 (Public Review)

Summary:

In their manuscript, Ardaya et al have addressed the impact of ischemia-induced gliogenesis from the adult SVZ and their effect on the remodeling of the extracellular matrix (ECM) in the glial scar. They use Thbs4, a marker previously identified to be expressed in astrocytes of the SVZ, to understand its role in ischemia-induced gliogenesis. First, the authors show that Thbs4 is expressed in the SVZ and that its expression levels increase upon ischemia. Next, they claim that ischemia induces the generation of newborn astrocyte from SVZ neural stem cells (NSCs), which migrate toward the ischemic regions to accumulate at the glial scar. Thbs4-expressing astrocytes are recruited to the lesion by Hyaluronan where they modulate ECM homeostasis.

Strengths:

The findings of these studies are in principle interesting and the experiments are in principle good.

Weaknesses:

The manuscript suffers from an evident lack of clarity and precision in regard to their findings and their interpretation.

We thank the reviewer for the valuable feedback. We hope the changes proposed improve clarity and precision throughout the manuscript.

(1) The authors talk about Thbs4 expression in NSCs and astrocytes, but neither of both is shown in Figure 1, nor have they used cell type-specific markers.

As we reported also to Referee #1 (major point 1), Thbs4 is widely considered in literature as a valid marker for newly formed astrocytes (Beckervordersanforth et al., 2010; Benner et al., 2013; Llorens-Bobadilla et al. 2015, Codega et al, 2014; Basak et al., 2018; Mizrak et al., 2019; Kjell et al., 2020; Cebrian-Silla et al., 2021). Some of the studies mentioned here and discussed in the manuscript text, also associate Thbs4 to B-type cells based on scRNAseq data (LlorensBobadilla et al. 2015; Cebrian-Silla et al., 2021; Basak et al., 2018). Moreover, we also showed colocalization of Thbs4 with activated stem cells marker nestin (Fig.2), glial marker GFAP (Fig. 3) and with dorsal NSCs marker tdTOM (from electroporation, Fig. 4). 

(2) Very important for all following experiments is to show that Thbs4 is not expressed outside of the SVZ, specifically in the areas where the lesion will take place. If Thbs4 was expressed there, the conclusion that Thbs4+ cells come from the SVZ to migrate to the lesion would be entirely wrong.

In Figure 1a, we show that Thbs4 is expressed in the telencephalon, exclusively in the neurogenic regions like SVZ, RMS and OB, together with cerebellum and VTA, which are likely not directly topographically connected to the damaged area (cortex and striatum). Regarding the origin of Thbs4+ cells, we demonstrated their SVZ origin by lineage tracking experiments after in vivo cell labeling (Fig. 4).

(3) Next, the authors want to confirm the expression level of Thbs4 by electroporation of pThbs4-eGFP at P1 and write that this results in 20% of total cells expressing GFP, especially in the rostral SVZ. I do not understand the benefit of this sentence. This may be a confirmation of expression, but it also shows that the GFP+ cells derive from early postnatal NSCs.

Furthermore, these cells look all like astrocytes, so the authors could have made a point here that indeed early postnatal NSCs expressing Thbs4 generate astrocytes alongside development. Here, it would have been interesting to see how many of the GFP+ cells are still NSCs.

We thank the reviewer for this useful remark. We have rephrased this paragraph in the results section (Line 99).

(4) In the next chapter, the authors show that Thbs4 increases in expression after brain injury. I do not understand the meaning of the graphs showing expression levels of distinct cell types of the neuronal lineage. Please specify why this is interesting and what to conclude from that.

Also here, the expression of Thbs4 should be shown outside of the SVZ as well.

In Fig 2, we show the temporal expression of two markers (besides Thbs4) in the SVZ. Nestin and DCX are the gold standard markers for NSCs, with DCX present in neuroblasts. This is already explained in line 119. What we didn’t explain, and now we say in line 124, is that Nestin and DCX decrease immediately after ischemia (7d time-point). This probably means that the NSCs stop differentiating into neuroblast to favor glioblast formation. This is also supported by the experiments in the olfactory bulb depicted in Fig. S5C-H.

(5) Next, the origin of newborn astrocytes from the SVZ upon ischemia is revealed. The graphs indicate that the authors perfused at different time points after tMCAO. Did they also show the data of the early time points? If only of the 30dpi, they should remove the additional time points indicated in the graph. In line 127 they talk about the origin of newborn astrocytes. Until now they have not even mentioned that new astrocytes are generated. Furthermore, the following sentences are imprecise: first they write that the number of slow proliferation NSCs is increased, then they talk about astrocytes. How exactly did they identify astrocytes and separate them from NSCs? Morphologically? Because both cell types express GFAP and Thbs4.

The same problem also occurs throughout the next chapter.

We thank the reviewer for this interesting comment. The experiment in Fig 3 combines BrdU and IdU. This is a tricky experiment, since chronic BrdU is normally analyzed after 30d, since the experimenter must wait for the wash out of BrdU (it labels slow-proliferating cells). Since we also wanted to label fast proliferative cells with IdU, we used IP injections of this nucleotide at the different time points, and perfused the day after. It wouldn’t make sense to show BrdU at earlier time points. We do so in Fig 3e, just to colocalize with Thbs4 to read the tendency of the experiment. However, the quantification of BrdU (not of IdU) is done only at 30 DPI, which is explained in the methods (line 407).

“In line 127, they talk about the origin of newborn astrocytes…” 

Indeed, we wanted to introduce in the paragraph title that ischemia induced the generation of new astrocytes, which is more clearly described in the text. We changed the paragraph title with “Characterization of Ischemia-induced cell populations”

“How exactly did they identify astrocytes and separate them from NSC?” 

With this experiment and using two different protocols to label proliferating cells (BrdU vs IdU) we wanted to track the precursor cells that derivate to astrocytes and that already expressed the marker Thbs4. Indeed, the different increase and rate of proliferation is only related to the progenitor cells that lately will differentiate in astrocytes. In this experiment we only referred to the astrocytes in the last sentence “These results suggest that, after ischemia, Thbs4positive astrocytes derive from the slow proliferative type B cells”

(6) "These results suggest that ischemia-induced astrogliogenesis in the SVZ occurs in type B cells from the dorsal region, and that these newborn Thbs4-positive astrocytes migrate to the ischemic areas." This sentence is a bit dangerous and bares at least one conceptual difficulty: if NSCs generate astrocytes under normal conditions and along the cause of postnatal development (which they do), then local astrocytes  (expressing the tdTom because they stem from a postnatal NSC ), may also react to MCAO and proliferate locally. So the astrocytes along the scar do not necessarily come from adult NSCs upon injury but from local astrocytes.  If the authors state that NSCs generate astrocytes that migrate to the lesion, I would like to see that no astrocytes inside the striatum carry the tdTom reporter before MCAO is committed.

We understand the referee’s concern about the postnatal origin of astrocytes that can also be labeled with tdTom. Our hypothesis, tested at the beginning of the paper, is that SVZ-derived astrocytes derive from slow proliferative NSC. Thus, it is reasonable that Tom+ cells can reach the cortical region in such a short time frame. This is why we assumed that local astrocytes can’t be positive for tdTom. We characterized the expression of tfTom in sham animals and we observed few tdTom+ cells in the cortex and striatum (Author response image 2 and Figure S4). The expression of tdTom mainly remains in the SVZ and the corpus callosum under physiological conditions. However, proliferation of local astrocytes labeled with tdTom expression (early postnatally astrocytes) could explain the small percentage of tdTom+ cells in the ischemic regions that do not express Thbs4, even though this percentage could represent other cell types such as OPCs or oligodendrocytes. 

Author response image 2.

(7) If astrocytes outside the SVZ do not express Thbs4, I would like to see it.  Otherwise, the discrimination of SVZ-derive GFAP+/Thbs4+ astrocytes and local astrocytes expressing only GFAP is shaky.

Regarding Thbs4 outside the SVZ, we already answered this in point 2 (please refer to Fig 1A). We also quantified the expression of Thbs4+/GFAP+ astrocytes in the corpus callosum, cortex and striatum of sham and MCAO mice (Figure S5a-b) and we did not observe that local astrocytes express Thbs4 under physiological conditions.

(8) Please briefly explain what a Skeleton analysis and a Fractal dimension analysis is, and what it is good for.

We apologized for the brief information on Skeleton and Fractal dimension analysis. We included a detailed explanation of these analyses in methods (line 484-494).

(9) The chapter on HA is again a bit difficult to follow. Please rewrite to clarify who produces HA and who removes it by again showing all astrocyte subtypes (GFAP+/Thbs4+ and GFAP+/Thbs4-).

We apologize for the lack of clarity. We rewrote some passages of those chapters (changes in red), trying to convey the ideas more clearly. We also changed a panel in Figure S6b-c to clarify all astrocytes subtypes that internalize hyaluronan (Thbs4+/GFAP+ and Thbs4-/GFAP+). See Author response image 3.

Author response image 3.

(10) Why did the authors separate dorsal, medial, and ventral SVZ so carefully? Do they comment on it? As far as I remember, astrogenesis in physiological conditions has some local preferences (dorsal?)

We performed the electroporation protocol in the dorsal SVZ based on previous results (Figure 3 and Figure S3). NSC produce specific neurons in the olfactory bulb according to their location in the SVZ. However, postnatal production of astrocytes mainly occurs through local astrocytes proliferation and the SVZ contribution is very limited at this time point. 

Reviewer #3 (Public Review)

Summary:

The authors aimed to study the activation of gliogenesis and the role of newborn astrocytes in a post-ischemic scenario. Combining immunofluorescence, BrdU-tracing, and genetic cellular labelling, they tracked the migration of newborn astrocytes (expressing Thbs4) and found that Thbs4-positive astrocytes modulate the extracellular matrix at the lesion border by synthesis but also degradation of hyaluronan. Their results point to a relevant function of SVZ newborn astrocytes in the modulation of the glial scar after brain ischemia. This work's major strength is the fact that it is tackling the function of SVZ newborn astrocytes, whose role is undisclosed so far.

Strengths:

The article is innovative, of good quality, and clearly written, with properly described Materials and Methods, data analysis, and presentation. In general, the methods are designed properly to answer the main question of the authors, being a major strength. Interpretation of the data is also in general well done, with results supporting the main conclusions of this article.

Weaknesses:

However, there are some points of this article that still need clarification to further improve this work.

(1) As a first general comment, is it possible that the increase in Thbs4-positive astrocytes can also happen locally close to the glia scar, through the proliferation of local astrocytes or even from local astrocytes at the SVZ? As it was shown in published articles most of the newborn astrocytes in the adult brain actually derive from proliferating astrocytes, and a smaller percentage is derived from NSCs. How can the authors rule out a contribution of local astrocytes to the increase of Thbs4-positive astrocytes? The authors also observed that only about one-third of the astrocytes in the glial scar derived from the SVZ.

We thank the reviewer for the interesting comment. We have extended the discussion about this topic in the manuscript, (lines 333-342), including the statement about a third of glial scar astrocytes being from the SVZ and not downplaying the role of local astrocytes.  Whether the glial scar is populated by newborn astrocytes derived from SVZ or from local astrocytes is under debate, since there are groups that found astrocytes contribution from local astrocytes (Frisèn group, Magnusson et al., 2014) but there are others that observed the opposite (Li et al., 2010; Benner et al., 2013; Faiz et al., 2015; Laug et al., 2019 & Pous et al., 2020). 

In our study we observed that Thbs4 expression is almost absent in the cortex and striatum of sham mice. To demonstrate that new-born astrocytes are derived from SVZ we used two techniques: the chronic BrdU treatment and the cell tracing which mainly labels SVZ neural stem cells. Fast proliferating cells lose BrdU quickly so local astrocytes under ischemic conditions do not express BrdU. In addition, we injected IdU the day before perfusion in order to see if local astrocytes express Thbs4 when they respond to the brain ischemia. However, we did not observe proliferating local astrocytes expressing Thbs4 after MCAO (see Author response image 4)

Author response image 4.

As mentioned in the response for reviewer 2, the cell tracing technique could label early postnatal astrocytes. We characterized the technique and only a small percentage of tdTom expression was found in the cortex and striatum of sham animals.  This tdTom population could explain the percentage of tdTom+ cells in the ischemic regions that do not express Thbs4 even though this percentage could represent other cell types such as OPCs or oligodendrocytes. Taking all together, evidences suggest that Thbs4+ astrocyte population derived from the SVZ. 

We indeed observed a small contribution of Thbs4+ astrocytes to the glial scar. However, Thbs4+ astrocytes arrive at the lesion at a critical temporal window - when local hyper-reactive astrocytes die or lose their function. We hypothesized that Thbs4+ astrocytes could help local astrocytes or replace them in reorganizing the extracellular space and the glial scar, an instrumental process for the recovery of the ischemic area. 

(2) It is known that the local, GFAP-reactive astrocytes at the scar can form the required ECM. The authors propose a role of Thbs4-positive astrocytes in the modulation, and perhaps maintenance, of the ECM at the scar, thus participating in scar formation likewise. So, this means that the function of newborn astrocytes is only to help the local astrocytes in the scar formation and thus contribute to tissue regeneration. Why do we need specifically the Thbs4positive astrocytes migrating from the SVZ to help the local astrocytes? Can you discuss this further?

Unfortunately, we could not demonstrate which molecular machinery is involved in these mechanisms, and we can only speculate the functional meaning of a second wave of glial activation. We added a lengthy discussion in lines 333-342.

(3) The authors observed that the number of BrdU- and DCX-positive cells decreased 15 dpi in all OB layers (Fig. S5). They further suggest that ischemia-induced a change in the neuroblasts ectopic migratory pathway, depriving the OB layers of the SVZ newborn neurons. Are the authors suggesting that these BrdU/DCX-positive cells now migrate also to the ischemic scar, or do they die? In fact, they see an increase in caspase-3 positive cells in the SVZ after ischemia, but they do not analyse which type of cells are dying. Alternatively, is there a change in the fate of the cells, and astrogliogenesis is increased at the expense of neurogenesis?  The authors should understand which cells are Cleaved-caspase-3 positive at the SVZ and clarify if there is a change in cell fate. Also please clarify what happens to the BrdU/DCX-positive cells that are born at the SVZ but do not migrate properly to the OB layers.

Actually, we cannot demonstrate the fate of missing BrdU/DCX cells in the OB. We can reasonably speculate that following the ischemic insult, the neurogenic machinery steers toward investing more energy in generating glial cells to support the lesion. We didn’t analyze the fate of the DCX that originally should migrate and differentiate to the OB, whether they die or if there is a shift in the differentiation program in the SVZ, since we consider that question is out of the study’s scope.   

(4) The authors showed decreased Nestin protein levels at 15 dpi by western blot and immunostaining shows a decrease already at 7div (Figure 2). These results mean that there is at least a transient depletion of NSCs due to the promotion of astrogliogenesis. However, the authors show that at 30dpi there is an increase of slow proliferating NSCs (Figure 3). Does this mean, that there is a reestablishment of the SVZ cytogenic process?  How does it happen, more specifically, how NSCs number is promoted at 30dpi?  Please explain how are the NSCs modulated throughout time after ischemia induction and its impact on the cytogenic process.

Based on the chronic BrdU treatment, results suggested a restoration of SVZ cytogenic process (also observed in the nestin and DCX proteins expression at 30dpi). However, we did not analyze how it happens (from asymmetric or symmetric divisions). As suggested by Encinas group, we hypothesized that the brain ischemia induces the exhaustion of the neurogenic niche of the SVZ by symmetric divisions of NSC into reactive astrocytes.

(5) The authors performed a classification of Thbs4-positive cells in the SVZ according to their morphology. This should be confirmed with markers expressed by each of the cell subtypes.

We thank the referee for the comment. Classifying NSC based on different markers could also be tricky because different NSC cell types share markers. This classification was made considering the specific morphology of each NSC cell type. In addition, Thbs4 expression in Btype cells is also observed in other studies (Llorens-Bobadilla et al. 2015; Cebrian-Silla et al.,

2021; Basak et al., 2018).

(6) In Figure S6, the authors quantified HABP spots inside Thbs4-positive astrocytes. Please show a higher magnification picture to show how this quantification was done.

We quantified HABP area and HABP spots inside Thbs4+ astrocytes with a custom FIJI script.

Thbs4 cell mask was done via automatic thresholding within the GFAP cell mask. Threshold for HABP marker was performed and binary image was processed with 1 pixel median filter (to eliminate 1 px noise-related spots). “Analyze particles” tool was used to sort HABP spots in the cell ROI. HABP spot number per compartment and population was exported to excel and data was normalized dividing HABP spots per ROI by total HABP spots. See Author response image 5.

Author response image 5.

eLife Assessment

This valuable study decoded target-associated information in prefrontal and sensory cortex during the preparatory period of a visual search task, suggesting a memory-driven attentional template. The evidence supporting this claim is convincing, based on multivariate pattern analyses of fMRI data. The results will be of interest to psychologists and cognitive neuroscientists.

Reviewer #1 (Public review):

When you search for something, you need to maintain some representation (a "template") of that target in your mind/brain. Otherwise, how would you know what you were looking for? If your phone is in a shocking pink case, you can guide your attention to pink things based on a target template that includes the attribute 'pink'. That guidance should get you to the phone pretty effectively if it is in view. Most real-world searches are more complicated. If you are looking for the toaster, you will make use of your knowledge of where toasters can be. Thus, if you are asked to find a toaster, you might first activate a template of a kitchen or a kitchen counter. You might worry about pulling up the toaster template only after you are reasonably sure you have restricted your attention to a sensible part of the scene.

Zhou and Geng are looking for evidence of this early stage of guidance by information about the surrounding scene in a search task. They train Os to associate four faces with four places. Then, with Os in the scanner, they show one face - the target for a subsequent search. After an 8 sec delay, they show a search display where the face is placed on the associated scene 75% of the time. Thus, attending to the associated scene is a good idea. The questions of interest are "When can the experimenters decode which face Os saw from fMRI recording?" "When can the experimenters decode the associated scene?" and "Where in the brain can the experimenters see evidence of this decoding? The answer is that the face but not the scene can be read out during the face's initial presentation. The key finding is that the scene can be read out (imperfectly but above chance) during the subsequent delay when Os are looking at just a fixation point. Apparently, seeing the face conjures up the scene in the mind's eye.

This is a solid and believable result. The only issue, for me, is whether it is telling us anything specifically about search. Suppose you trained Os on the face-scene pairing but never did anything connected to the search. If you presented the face, would you not see evidence of recall of the associated scene? Maybe you would see the activation of the scene in different areas and you could identify some areas as search specific. I don't think anything like that was discussed here.

You might also expect this result to be asymmetric. The idea is that the big scene gives the search information about the little face. The face should activate the larger useful scene more than the scene should activate the more incidental face, if the task was reversed. That might be true if the finding is related to a search where the scene context is presumed to be the useful attention guiding stimulus. You might not expect an asymmetry if Os were just learning an association.

It is clear in this study that the face and the scene have been associated and that this can be seen in the fMRI data. It is also clear that a valid scene background speeds the behavioral response in the search task. The linkage between these two results is not entirely clear but perhaps future research will shed more light.

It is also possible that I missed the clear evidence of the search-specific nature of the activation by the scene during the delay period. If so, I apologize and suggest that the point be underlined for readers like me.

Reviewer #2 (Public review):

Summary:

This work is one of the best instances of a well-controlled experiment and theoretically impactful findings within the literature on templates guiding attentional selection. I am a fan of the work that comes out of this lab and this particular manuscript is an excellent example as to why that is the case. Here, the authors use fMRI (employing MVPA) to test whether during the preparatory search period, a search template is invoked within the corresponding sensory regions, in the absence of physical stimulation. By associating faces with scenes, a strong association was created between two types of stimuli that recruit very specific neural processing regions - FFA for faces and PPA for scenes. The critical results showed that scene information that was associated with a particular cue could be decoded from PPA during the delay period. This result strongly supports the invoking of a very specific attentional template.

Strengths:

There is so much to be impressed with in this report. The writing of the manuscript is incredibly clear. The experimental design is clever and innovative. The analysis is sophisticated and also innovative. The results are solid and convincing.

Weaknesses:

I only have a few weaknesses to point out.<br /> This point is not so much of a weakness, but a further test of the hypothesis put forward by the authors. The delay period was long - 8 seconds. It would be interesting to split the delay period into the first 4seconds and the last 4seconds and run the same decoding analyses. The hypothesis here is that semantic associations take time to evolve, and it would be great to show that decoding gets stronger in the second delay period as opposed to the period right after the cue. I don't think this is necessary for publication, but I think it would be a stronger test of the template hypothesis.<br /> Type in the abstract "curing" vs "during."<br /> It is hard to know what to do with significant results in ROIs that are not motivated by specific hypotheses. However, for Figure 3, what are the explanations for ROIs that show significant differences above and beyond the direct hypotheses set out by the authors?

Reviewer #3 (Public review):

The manuscript contains a carefully designed fMRI study, using MVPA pattern analysis to investigate which high-level associate cortices contain target-related information to guide visual search. A special focus is hereby on so-called 'target-associated' information, that has previously been shown to help in guiding attention during visual search. For this purpose the author trained their participants and made them learn specific target-associations, in order to then test which brain regions may contain neural representations of those learnt associations. They found that at least some of the associations tested were encoded in prefrontal cortex during the cue and delay period.

The manuscript is very carefully prepared. As far as I can see, the statistical analyses are all sound and the results integrate well with previous findings.

I have no strong objections against the presented results and their interpretation.

Author response:

Public Reviews:

Reviewer #1 (Public review):

When you search for something, you need to maintain some representation (a "template") of that target in your mind/brain. Otherwise, how would you know what you were looking for? If your phone is in a shocking pink case, you can guide your attention to pink things based on a target template that includes the attribute 'pink'. That guidance should get you to the phone pretty effectively if it is in view. Most real-world searches are more complicated. If you are looking for the toaster, you will make use of your knowledge of where toasters can be. Thus, if you are asked to find a toaster, you might first activate a template of a kitchen or a kitchen counter. You might worry about pulling up the toaster template only after you are reasonably sure you have restricted your attention to a sensible part of the scene.

Zhou and Geng are looking for evidence of this early stage of guidance by information about the surrounding scene in a search task. They train Os to associate four faces with four places. Then, with Os in the scanner, they show one face - the target for a subsequent search. After an 8 sec delay, they show a search display where the face is placed on the associated scene 75% of the time. Thus, attending to the associated scene is a good idea. The questions of interest are "When can the experimenters decode which face Os saw from fMRI recording?" "When can the experimenters decode the associated scene?" and "Where in the brain can the experimenters see evidence of this decoding? The answer is that the face but not the scene can be read out during the face's initial presentation. The key finding is that the scene can be read out (imperfectly but above chance) during the subsequent delay when Os are looking at just a fixation point. Apparently, seeing the face conjures up the scene in the mind's eye.

This is a solid and believable result. The only issue, for me, is whether it is telling us anything specifically about search. Suppose you trained Os on the face-scene pairing but never did anything connected to the search. If you presented the face, would you not see evidence of recall of the associated scene? Maybe you would see the activation of the scene in different areas and you could identify some areas as search specific. I don't think anything like that was discussed here.

You might also expect this result to be asymmetric. The idea is that the big scene gives the search information about the little face. The face should activate the larger useful scene more than the scene should activate the more incidental face, if the task was reversed. That might be true if the finding is related to a search where the scene context is presumed to be the useful attention guiding stimulus. You might not expect an asymmetry if Os were just learning an association.

It is clear in this study that the face and the scene have been associated and that this can be seen in the fMRI data. It is also clear that a valid scene background speeds the behavioral response in the search task. The linkage between these two results is not entirely clear but perhaps future research will shed more light.

It is also possible that I missed the clear evidence of the search-specific nature of the activation by the scene during the delay period. If so, I apologize and suggest that the point be underlined for readers like me.

We will respond to this question by acknowledging that the reviewer is right in that the delay period activation of the scene is not necessarily search-specific. We will then discuss how this possibility affects the interpretation of our results and what kind of studies would need to be conducted in order to fully establish a causal link between delay period activity and visual search performance. We will also discuss the literature on cued attention and situate our work within the context of these other studies that have used similar task paradigms to infer attentional processes. Finally, we will discuss the interpretation of delay period activity in PPA and IFJ.

Reviewer #2 (Public review):

Summary:

This work is one of the best instances of a well-controlled experiment and theoretically impactful findings within the literature on templates guiding attentional selection. I am a fan of the work that comes out of this lab and this particular manuscript is an excellent example as to why that is the case. Here, the authors use fMRI (employing MVPA) to test whether during the preparatory search period, a search template is invoked within the corresponding sensory regions, in the absence of physical stimulation. By associating faces with scenes, a strong association was created between two types of stimuli that recruit very specific neural processing regions - FFA for faces and PPA for scenes. The critical results showed that scene information that was associated with a particular cue could be decoded from PPA during the delay period. This result strongly supports the invoking of a very specific attentional template.

Strengths:

There is so much to be impressed with in this report. The writing of the manuscript is incredibly clear. The experimental design is clever and innovative. The analysis is sophisticated and also innovative. The results are solid and convincing.

Weaknesses:

I only have a few weaknesses to point out.

This point is not so much of a weakness, but a further test of the hypothesis put forward by the authors. The delay period was long - 8 seconds. It would be interesting to split the delay period into the first 4seconds and the last 4seconds and run the same decoding analyses. The hypothesis here is that semantic associations take time to evolve, and it would be great to show that decoding gets stronger in the second delay period as opposed to the period right after the cue. I don't think this is necessary for publication, but I think it would be a stronger test of the template hypothesis.

We will conduct the suggested analysis. Depending on the outcome, we will include it in supplemental materials or the main text.

Type in the abstract "curing" vs "during."

We will fix this.

It is hard to know what to do with significant results in ROIs that are not motivated by specific hypotheses. However, for Figure 3, what are the explanations for ROIs that show significant differences above and beyond the direct hypotheses set out by the authors?

We will address how each of the ROIs wdas selected based on the use of a priori networks as masks with ROIs as sub-parcels. We will explain why specific ROIs were associated with the strongest hypotheses but how the entire networks are relevant and related to existing literatures on attentional control and working memory. This content will be included in the introduction and discussion sections.

Reviewer #3 (Public review):

The manuscript contains a carefully designed fMRI study, using MVPA pattern analysis to investigate which high-level associate cortices contain target-related information to guide visual search. A special focus is hereby on so-called 'target-associated' information, that has previously been shown to help in guiding attention during visual search. For this purpose the author trained their participants and made them learn specific target-associations, in order to then test which brain regions may contain neural representations of those learnt associations. They found that at least some of the associations tested were encoded in prefrontal cortex during the cue and delay period.

The manuscript is very carefully prepared. As far as I can see, the statistical analyses are all sound and the results integrate well with previous findings.

I have no strong objections against the presented results and their interpretation.

Thank you.

eLife Assessment

MGPfactXMBD is a novel computational method for investigating cell evolutionary trajectory for scRNA-seq samples. It is important, with several potential future applications. The authors benchmarked this method using synthetic and real-world samples and showed superior performance for some of the tasks in cell trajectory analysis compared to other methods with compelling evidence.

Reviewer #1 (Public review):

Summary:

Ren et al developed a novel computational method to investigate cell evolutionary trajectory for scRNA-seq samples. This method, MGPfact, estimates pseudotime and potential branches in the evolutionary path through explicitly modeling the bifurcations in a Gaussian process. They benchmarked this method using synthetic as well as real world samples and showed superior performance for some of the tasks in cell trajectory analysis. They further demonstrated the utilities of MGPfact using single cell RNA-seq samples derived from microglia or T cells and showed that it can accurately identify the differentiation timepoint and uncover biologically relevant gene signatures.

Strengths:

Overall I think this is a useful new tool that could deliver novel insights for the large body of scRNA-seq data generated in the public domain. The manuscript is written is a logical way and most parts of the method are well described.

Comments on revisions:

In this revision, the authors have sufficiently addressed all of my concerns. I don't have any follow-up comments.

Reviewer #2 (Public review):

Summary of the manuscript:

Authors present MGPfactXMBD, a novel model-based manifold-learning framework designed to address the challenges of interpreting complex cellular state spaces from single-cell RNA sequences. To overcome current limitations, MGPfactXMBD factorizes complex development trajectories into independent bifurcation processes of gene sets, enabling trajectory inference based on relevant features. As a result, it is expected that the method provides a deeper understanding of the biological processes underlying cellular trajectories and their potential determinants.

MGPfactXMBD was tested across 239 datasets, and the method demonstrated similar to slightly superior performance in key quality-control metrics to state-of-the-art methods. When applied to case studies, MGPfactXMBD successfully identified critical pathways and cell types in microglia development, validating experimentally identified regulons and markers. Additionally, it uncovered evolutionary trajectories of tumor-associated CD8+ T cells, revealing new subtypes with gene expression signatures that predict responses to immune checkpoint inhibitors in independent cohorts.

Overall, MGPfactXMBD represents a relevant tool in manifold-learning for scRNA-seq data, enabling feature selection for specific biological processes and enhancing our understanding of the biological determinants of cell fate.

Summary of the outcome:

The novel method addresses core state-of-the-art questions in biology related to trajectory identification. The design and the case studies are of relevance.

Comments on revisions:

The authors have addressed all my previous comments to satisfaction.

Author response:

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

Reviewer #1:

Comment#1: Ren et al developed a novel computational method to investigate cell evolutionary trajectory for scRNA-seq samples. This method, MGPfact, estimates pseudotime and potential branches in the evolutionary path by explicitly modeling the bifurcations in a Gaussian process. They benchmarked this method using synthetic as well as real-world samples and showed superior performance for some of the tasks in cell trajectory analysis. They further demonstrated the utilities of MGPfact using single-cell RNA-seq samples derived from microglia or T cells and showed that it can accurately identify the differentiation timepoint and uncover biologically relevant gene signatures. Overall I think this is a useful new tool that could deliver novel insights for the large body of scRNA-seq data generated in the public domain. The manuscript is written in a logical way and most parts of the method are well described.

Thank you for reviewing our manuscript and for your positive feedback on MGPfact. We are pleased that you find it useful for identifying differentiation timepoints and uncovering gene signatures. We will continue to refine MGPfact and explore its applications across diverse datasets. Your insights are invaluable, and we appreciate your support.

Comment#2: Some parts of the methods are not clear. It should be outlined in detail how pseudo time T is updated in Methods. It is currently unclear either in the description or Algorithm 1.

Thanks to the reviewers' comments. We've added a description of how pseudotime T is obtained between lines 138 and 147 in the article. In brief, the pseudotime of MGPfact is inferred through Gaussian process regression on the downsampled single-cell transcriptomic data. Specifically, T is treated as a continuous variable representing the progression of cells through the differentiation process. We describe the relationship between pseudotime and expression data using the formula:

Where f(T) is a Gaussian Process (GP) with covariance matrix S, and Ɛ represents the error term. The Gaussian process is defined as:

Where is the variance set to 1e-6.

During inference, we update the pseudotime by maximizing the posterior likelihood. Specifically, the posterior distribution of pseudotime T can be represented as:

Where is the likelihood function of the observed data Y*, and is the prior distribution of the Gaussian process. This posterior distribution integrates the observed data with model priors, enabling inference of pseudotime and trajectory simultaneously. Due to the high autocorrelation of  in the posterior distribution, we use Adaptive Metropolis within Gibbs (AMWG) sampling (Roberts and Rosenthal, 2009; Tierney, 1994). Other parameters are estimated using the more efficient SLICE sampling technique (Neal, 2003).

Comment#3: There should be a brief description in the main text of how synthetic data were generated, under what hypothesis, and specifically how bifurcation is embedded in the simulation.

Thank you for the reviewers' comments. We have added descriptions regarding the synthetic dataset in the methods section. The revised content is from line 487 to 493:

“The synthetic datasets were generated using four simulators: dyngen (Saelens et al., 2019), dyntoy (Saelens et al., 2019), PROSSTT (Papadopoulos et al., 2019), and Splatter (Zappia et al., 2017), each modeling different trajectory topologies such as linear, branching, and cyclic. Splatter simulates branching events by setting expression states and transition probabilities, dyntoy generates random expression gradients to reflect dynamic changes, and dyngen focuses on complex branching structures within gene regulatory networks.”

Comment#4: Please explain what the abbreviations mean at their first occurrence.

We appreciate the reviewers' feedback. We have thoroughly reviewed the entire manuscript and made sure that all abbreviations have had their full forms provided upon their first occurrence.

Comment#5: In the benchmark analysis (Figures 2/3), it would be helpful to include a few trajectory plots of the real-world data to visualize the results and to evaluate the accuracy.

We appreciate the reviewer's feedback. To more clearly demonstrate the performance of MGPfact, we selected three representative cases from the dataset for visual comparison. These cases represent different types of trajectory structures: linear, bifurcation, and multifurcation. The revised content is between line 220 and 226.

As shown in Supplementary Fig. 5, it is evident that MGPfact excels in capturing main developmental paths and identifying key bifurcation points. In the linear trajectory structure, MGPfact accurately predicted the linear structure without bifurcation events, showing high consistency with the ground truth (overall\=0.871). In the bifurcation trajectory structure, MGPfact accurately captured the main bifurcation event (overall\=0.636). In the multifurcation trajectory structure, although MGPfact predicted only one bifurcation point, its overall structure remains close to the ground truth, as evidenced by its high overall score (overall\=0.566). Overall, MGPfact demonstrates adaptability and accuracy in reconstructing various types of trajectory structures.

Comment#6: It is not clear how this method selects important genes/features at bifurcation. This should be elaborated on in the main text.

Thanks to the reviewers' comments. To enhance understanding, we've added detailed descriptions of gene selection in the main text and appendix, specifically from lines 150 to 161. In brief, MGPfact employs a Gaussian process mixture model to infer cell fate trajectories and identify independent branching events. We calculate load matrices using formulas 1 and 14 to assess each gene's contribution to the trajectories. Genes with an absolute weight greater than 0.05 are considered predominant in specific branching processes. Subsequently, SCENIC (Aibar et al., 2017; Bravo González-Blas et al., 2023) analysis was conducted to further infer the underlying regulons and annotate the biological processes of these genes.

Comment#7: It is not clear how survival analysis was performed in Figure 5. Specifically, were critical confounders, such as age, clinical stage, and tumor purity controlled?

To evaluate the predictive and prognostic impacts of the selected genes, we utilized the Cox multivariate regression model, where the effects of relevant covariates, including age, clinical stage, and tumor purity, were adjusted. We then conducted the Kaplan-Meier survival analysis again to ensure the reliability of the results. The revisions mainly include the following sections:

(1) We modified the description of adjusting for confounding factors in the survival analysis, from line 637 to 640:

“To adjust for possible confounding effects, the relevant clinical features including age, sex and tumor stage were used as covariates. The Cox regression model was implemented using R-4.2 package “survival”. And we generated Kaplan-Meier survival curves based on different classifiers to illustrate differences in survival time and report the statistical significance based on Log-rank test.”

(2) We updated the images in the main text regarding the survival analysis, including Fig. 5a-b, Fig. 6c, and Supplementary Fig. 8e.

Comment#8: I recommend that the authors perform some sort of 'robustness' analysis for the consensus tree built from the bifurcation Gaussian process. For example, subsample 80% of the cells to see if the bifurcations are similar between each bootstrap.

We appreciate the reviewers' feedback. We performed a robustness analysis of the consensus tree using 100 training datasets. This involved sampling the original data at different proportions, and then calculating the topological similarity between the consensus trajectory predictions of MGPfact and those without sampling, using the Hamming-Ipsen-Mikhailov (HIM ) metric. A higher score indicates greater robustness. The relevant figure is in Supplementary Fig. 4, and the description is in the main text from line 177 to 182.

The results indicate that the consensus trajectory predictions based on various sampling proportions of the original data maintain a high topological similarity with the unsampled results (HIM_mean=0.686). This demonstrates MGPfact’s robustness and generalizability under different data conditions, hence the capability of capturing bifurcative processes in the cells’ trajectory.

Reviewer #2:

Comment#1: The authors present MGPfact^XMBD, a novel model-based manifold-learning framework designed to address the challenges of interpreting complex cellular state spaces from single-cell RNA sequences. To overcome current limitations, MGPfact^XMBD factorizes complex development trajectories into independent bifurcation processes of gene sets, enabling trajectory inference based on relevant features. As a result, it is expected that the method provides a deeper understanding of the biological processes underlying cellular trajectories and their potential determinants. MGPfact^XMBD was tested across 239 datasets, and the method demonstrated similar to slightly superior performance in key quality-control metrics to state-of-the-art methods. When applied to case studies, MGPfact^XMBD successfully identified critical pathways and cell types in microglia development, validating experimentally identified regulons and markers. Additionally, it uncovered evolutionary trajectories of tumor-associated CD8+ T cells, revealing new subtypes with gene expression signatures that predict responses to immune checkpoint inhibitors in independent cohorts. Overall, MGPfact^XMBD represents a relevant tool in manifold learning for scRNA-seq data, enabling feature selection for specific biological processes and enhancing our understanding of the biological determinants of cell fate.

Thank you for your thoughtful review of our manuscript. We are thrilled to hear that you find MGPfact^XMBD beneficial for exploring cellular evolutionary paths in scRNA-seq data. Your insights are invaluable, and we look forward to incorporating them to further enrich our study. Thank you once again for your support and constructive feedback.

Comment#2: How the methods compare with existing Deep Learning based approaches such as TIGON is a question mark. If a comparison would be possible, it should be conducted; if not, it should be clarified why.

We appreciate the reviewer's comments. We have added a comparison with the sctour (Li, 2023) and TIGON methods (Sha, 2024).

It is important to note that the encapsulation and comparison of MGPfact are based on traditional differentiation trajectory construction. Saelens et al. established a systematic evaluation framework that categorizes differentiation trajectory structures into topological subtypes such as linear, bifurcation, multifurcation, graph, and tree, focusing on identifying branching structures in the cell differentiation process (Saelens et al., 2019). The sctour and TIGON methods mentioned by the reviewer are primarily used for estimating RNA velocity, focusing on continuous temporal evolution rather than explicit branching structures, and do not explicitly model branches. Therefore, we considered the predictions of these two methods as linear trajectories and compared them with MGPfact. While scTour explicitly estimates pseudotime, TIGON uses the concept of "growth," which is analogous to pseudotime, so we made the necessary adaptations.

Author response image 1 show that within this framework, compared to scTour (overall_mean=0.448) and TIGON (overall_mean=0.263), MGPfact still maintains a relatively high standard (overall_mean=0.534). This indicates that MGPfact has a significant advantage in accurately capturing branching structures in cell differentiation, especially in applications where explicit modeling of branches is required.

Author response image 1.

Comparison of MGPfact with scTour and TIGON in trajectory inference performance across 239 test datasets. a. Overall scores; b.F1_branches; c.HIM; d. cor_dist; e. wcor_features. All results are color-coded based on the trajectory types, with the black line representing the mean value. The “Overall” assessment is calculated as the geometric mean of all four metrics.

Comment#3: Missing Methods:

- The paper lacks a discussion of Deep Learning approaches for bifurcation analysis. e.g. scTour, Tigon.

- I am missing comments on methods such CellRank, and alternative approaches to delineate a trajectory.

We thank the reviewer for these comments.

(1) As mentioned in response to Comments#2, the scTour and TIGON methods are primarily used for estimating RNA velocity, focusing on continuous temporal evolution rather than explicit branching structures, and they do not explicitly model branches. We consider the predictions of these two methods as linear trajectories and compare them with MGPfact. The relevant description and discussion have been addressed in the response.

(2) We have added a description of RNA velocity estimation methods (scTour, TIGON, CellRank) in the introduction section. The revised content is from line 66 to 71:

“Moreover, recent studies based on RNA velocity has provided insights into cell state transitions. These methods measure RNA synthesis and degradation rates based on the abundance of spliced and unspliced mRNA, such as CellRank (Lange et al., 2022). Nevertheless, current RNA velocity analyses are still unable to resolve cell-fates with complex branching trajectory. Deep learning methods such as scTour (Li, 2023) and TIGON (Sha, 2024) circumvent some of these limitations, offering continuous state assumptions or requiring prior cell sampling information.”

Comment#4: Impact of MURP:

The rationale for using MURP is well-founded, especially for trajectory definition. However, its impact on the final results needs evaluation.

How does the algorithm compare with a random subselection of cells or the entire cell set?

Thank you for the comments. We fully agree that MURP is crucial in trajectory prediction. As a downsampling method, MURP is specifically designed to address noise issues in single-cell data by dividing the data into several subsets, thereby maximizing noise reduction while preserving the main structure of biological variation (Ren et al., 2022). In MGPfact, MURP typically reduces the data to fewer than 100 downsampled points, preserving the core biological structure while lowering computational complexity. To assess MURP's impact, we conducted experiments by randomly selecting 20, 40, 60, 80, and 100 cells for trajectory inference. These results were mapped back to the original data using the KNN graph structure for final predictions, which were then compared with the MURP downsampling results. Supplementary results can be found in Supplementary Fig. 3, with additional descriptions in the main text from line 170 to 176.

The results indicate that trajectory inference using randomly sampled cells has significantly lower prediction accuracy compared to that using MURP. This is particularly evident in branch assignment (F1_branches) and correlation cor_dist, where the average levels decrease by 20.5%-64.9%. In contrast, trajectory predictions using MURP for downsampling show an overall score improvement of 5.31%-185%, further highlighting MURP's role in enhancing trajectory inference within MGPfact.

Comment#5: What is the impact of the number of components selected?

Thank you for the comments. In essence, MGPfact consists of two main steps: 1) trajectory inference; 2) calculation of factorized scores and identification of high-weight genes. After step 1, MGPfact estimates parameters such as pseudotime T and bifurcation points B.  In step 2, we introduce a rotation matrix to obtain factor scores W_l  for each trajectory l by rotating Y*.

For all trajectories,

where e_l  is the error term for the -th trajectory. The number of features in Y* must match the dimensions of the rotation matrix R to ensure the factorized score matrix W contains factor scores for  trajectories, achieving effective feature representation and interpretation in the model.

Additionally, to further illustrate the impact of the number of principal components (PCs) on model performance in step 1, we conducted additional experiments. We used 3 PCs as the default and adjusted the number to evaluate changes from this baseline. As shown in Author response image 2, setting the number of PCs to 1 significantly decreases the overall performance score (overall_mean=0.363), as well as the wcor_features and wcor_dist metrics.  In contrast, increasing the number of PCs does not significantly affect the metrics. It ought to be mentioned that number of components used should be determined by the intrinsic biological characteristics of the cell fate-determination. Our experiment based on a limited number of datasets may not represent more complex scenarios in other cell types.

Author response image 2.

Robustness testing of the number of MURP PCA components on 100 training datasets. With the number of principal components (PCs) set to 3 by default; we tested the impact of different number of components (1-10) on the prediction results. In all box plots, the asterisk represents the mean value, while the whiskers extend to the farthest data points within 1.5 times the interquartile range. Significance is denoted as follows: not annotated indicates non-significant; * P < 0.05; ** P < 0.01; *** P < 0.001; two-sided paired Student’s T-tests.

Comment#6: Please comment on the selection of the kernel functions (rbf and polynomial) and explain why other options were discarded.

Thank you for the comments. We have added a description regarding the selection of radial basis functions and polynomial kernels in lines 126-130. As the reviewers mentioned, the choice of kernel functions is crucial in the MGPfact analysis pipeline for constructing the covariance matrix of the Gaussian process. We selected the radial basis function (RBF) kernel and the polynomial kernel to balance capturing data complexity and computational efficiency. The RBF kernel is chosen for its ability to effectively model smooth functions and capture local variations in the data, making it well-suited to the continuous and smooth characteristics of biological processes; its hyperparameters offer modeling flexibility. The polynomial kernel is used to capture more complex nonlinear relationships between input features, with its hyperparameters also allowing further customization of the model. In contrast, other complex kernels, such as Matérn or spectral kernels, were omitted due to their interpretability challenges and the risk of overfitting with limited data. However, as suggested by the reviewers, we will consider and test the impact of other kernel functions on the covariance matrix of the Gaussian process and their role in trajectory inference in our subsequent phases of algorithm design.

Comment#7: What is the impact of the Pseudotime method used initially? This section should be expanded with clear details on the techniques and parameters used in each analysis.

We are sorry for the confusion. We've added a description of how pseudotime T is obtained between line 138 and 147 in the main text. And the specific hyperparameters involved in the model and their prior settings are detailed in the supplementary information.

In brief, the pseudotime and related topological parameters of the bifurcative trajectories in MGPfact are inferred by Gaussian process regression from downsampled single-cell transcriptomic data (MURP). Specifically, T is treated as a continuous variable representing the progression of cells through the differentiation process. We describe the relationship between pseudotime and expression data as:

where f(T) is a Gaussian Process (GP) with covariance matrix S, and ε represents the error term. The Gaussian process is defined as:

where  is the variance set to 1e-6. During inference, we update the pseudotime by maximizing the posterior liklihood. Specifically, the posterior distribution of pseudotime is obtained by combining the observed data Y* with the prior distribution of the Gaussian process model.

We use the Markov Chain Monte Carlo method for parameter estimation, particularly employing the adaptive Metropolis-within-Gibbs (AMWG) sampling to handle the high autocorrelation of pseudotime.

Comment#8: Enhancing Readability: For clarity, provide intuitive descriptions of each evaluation function used in simulated and real data. The novel methodology performs well for some metrics but less so for others. A clear understanding of these measurements is essential.

To address the concern of readability, we have added descriptions of 5 evaluation metrics in the methodology section (Benchmarking MGPfact to state-of-the-art methods) in line 494 to 515. Additionally, we have included a summary and discussion of these metrics in the conclusion section in line 214-240 to help the readers better understand the significance and impact of these measurements.

(1) In brief, the Hamming-Ipsen-Mikhailov (HIM) distance measures the similarity between topological structures, combining the normalized Hamming distance and the Ipsen-Mikhailov distance, which focus on edge length differences and degree distribution similarity, respectively. The F1_branches is used to assess the accuracy of a model's branch assignment via Jaccard similarity between branch pairs. In trajectory inference, cor_dist quantifies the similarity of inter-cell distances between predicted and true trajectories, evaluating the accuracy of cell ordering. The wcor_features assesses the similarity of key features through weighted Pearson correlation, capturing biological variation. The Overall score is calculated as the geometric mean of these metrics, providing an assessment of overall performance.

(2) For MGPfact and the other seven methods included in the comparison, each has its own focus. MGPfact specializes in factorizing complex cell trajectories using Gaussian process mixture models, making it particularly capable of identifying bifurcation events. Therefore, it excels in the accuracy of branch partitioning and similarity of trajectory topology. Among other methods, scShaper (Smolander et al., 2022) and TSCAN(Ji and Ji, 2016) are more suited for generating linear trajectories and excel in linear datasets, accurately predicting pseudotime. The Monocle series, as typical representatives of tree methods, effectively capture complex topologies and are suitable for analyzing cell data with diversified differentiation paths.

Comment#9: Microglia Analysis:In Figures 3A-C, the genes mentioned in the text for each bifurcation do not always match those shown in the panels. Please confirm this.

Thank you for pointing this out. We have carefully reviewed the article and corrected the error where the genes shown in the figures did not correspond to the descriptions in the article. The specific corrections have been made between line 257 and 264:

“The first bifurcation determines the differentiated cell fates of PAM and HM, which involves a set of notable marker genes of both cell types, such as Apoe, Selplg (HM), and Gpnmb (PAM). The second bifurcation determines the proliferative status, which is crucial for the development and function of PAM and HM (Guzmán, n.d.; Li et al., 2019). The genes affected by the second bifurcation are associated with cell cycle and proliferation, such as Mki67, Tubb5, Top2a. The third bifurcation influences the development and maturity of microglia, of which the highly weighted genes, such as Tmem119, P2ry12, and Sepp1 are all previously annotated markers for establishment of the fates of microglia (Anderson et al., 2022; Li et al., 2019) (Supplementary Table 4).”

Comment#10: Regulons:

- The conclusions rely heavily on regulons. The Methods section describes using SCENIC, GENIE3, RCisTarget, and AUCell, but their relation to bifurcation analysis is unclear.

- Do you perform trajectory analysis on all MURP-derived cells or within each identified trajectory based on bifurcation? This point needs clarification to make the outcomes comprehensible. The legend of Figure 4 provides some ideas, but further clarity is required.

Thank you for the comments.

(1) To clarify, we used the tools like SCENIC to annotate the highly weighted genes (HWG) resulted from the bifurcation analysis for transcription factor regulation activity and possible impacts on biological processes. We have added descriptions to the analysis of our microglial data. The revised content is between line 265 and 266:

“Moreover, we retrieved highly active regulons from the HWG by MGPfact, of which the significance is quantified by the overall weights of the member genes.”

(2) We apologize for any confusion caused by our description. It is important to clarify that we performed an overall trajectory analysis on all MURP results, rather than analyzing within each identified trajectory. Specifically, we first used MURP to downsample all preprocessed cells, where each MURP subset represents a group of cells. We then conducted trajectory inference on all MURP subsets and identified bifurcation points. This process generated multiple independent differentiation trajectories, encompassing all MURP subsets. To clearly convey this point, we have added descriptions in the legend of Figure 4. The revised content is between line 276 and 283:

“Fig. 4. MGPfact reconstructed the developmental trajectory of microglia, recovering known determinants of microglia fate. a-c. The inferred independent bifurcation processes with respect to the unique cell types (color-coded) of microglia development, where phase 0 corresponds to the state before bifurcation; and phases 1 and 2 correspond to the states post-bifurcation. Each colored dot represents a metacell of unique cell type defined by MURP. The most highly weighted regulons in each trajectory were labeled by the corresponding transcription factors (left panels). The HWG of each bifurcation process include a set of highly weighted genes (HWG), of which the expression levels differ significantly among phases 1, 2, and 3 (right panels).”

Comment#11: CD8+ T Cells: The comparison is made against Monocle2, the method used in the publication, but it would be beneficial to compare it with more recent methods. Otherwise, the added value of MGPfact is unclear.

Per your request, we have expanded our comparative analysis to include not only Monocle2 but also more recent methods such as Monocle3 (Cao et al., 2019) and scFates Tree (Faure et al., 2023). We used adjusted R-squared values to evaluate each method's ability to explain trajectory variation. The results have been added to Table 2 and Supplementary Table 6. The revised content is between line 318 and 326:

We assessed the goodness-of-fit (adjusted R-square) of the consensus trajectory derived by MGPfact and three methods (Monocle 2, Monocle 3 and scFates Tree) for the CD8+ T cell subtypes described in the original studies (Guo et al., 2018; Zhang et al., 2018). The data showed that MGPfact significantly improved the explanatory power for most CD8+ T cell subtypes over Monocle 2, which was used in the original studies (P < 0.05, see Table 2 and Supplementary Table 6), except for the CD8-GZMK cells in the CRC dataset. Additionally, MGPfact demonstrated better explanatory power in specific cell types when compared to Monocle 3 and scFates Tree. For instance, in the NSCLC dataset, MGPfact exhibited higher explanatory power for CD8-LEF1 cells (Table 2, R-squared = 0.935), while Monocle 3 and scFates Tree perform better in other cell types.

Comment#12: Consensus Trajectory: A panel explaining how the consensus trajectory is generated would be helpful. Include both visual and textual explanations tailored to the journal's audience.

Thank you for the comments. Regarding how the consensus trajectory is constructed, we have illustrated and described this in Figure 1 and the supplementary methods. Taking the reviewers' suggestions into account, we have added more details about the generation process of the consensus trajectory in the methods section to enhance the completeness of the manuscript. The revised content is from line 599 to 606:

“Following MGPfact decomposition, we obtained multiple independent bifurcative trajectories, each corresponds to a binary tree within the temporal domain. These trajectories were then merged to construct a coherent diffusion tree, representing the consensus trajectory of cells’ fate. The merging process involves initially sorting all trajectories by their bifurcation time. The first (earliest) bifurcative trajectory is chosen as the initial framework, and subsequent trajectories are integrated to the initial framework iteratively by adding the corresponding branches at the bifurcation timepoints. As a result, the trajectories are ultimately merged into a comprehensive binary tree, serving as the consensus trajectory.”

Comment#13: Discussion:

- Check for typos, e.g., line 382 "pseudtime.".

- Avoid considering HVG as the entire feature space.

- The first three paragraphs are too similar to the Introduction. Consider shortening them to succinctly state the scenario and the implications of your contribution.

Thank you for pointing out the typos.

(1) We conducted a comprehensive review of the document to ensure there are no typographical errors.

(2) We restructured the first three paragraphs of the discussion section to clarify the limitations in the use of current manifold-learning methods and removed any absolute language regarding treating HVGs as the entire feature space. The revised content is from line 419 to 430:

“Single-cell RNA sequencing (scRNA-seq) provides a direct, quantitative snapshot of a population of cells in certain biological conditions, thereby revealing the actual cell states and functions. Although existing clustering and embedding algorithms can effectively reveal discrete biological states of cells, these methods become less efficient when depicting continuous evolving of cells over the temporal domain. The introduction of manifold learning offers a new dimension for discovery of relevant biological knowledge in cell fate determination, allowing for a better representation of continuous changes in cells, especially in time-dependent processes such as development, differentiation, and clonal evolution. However, current manifold learning methods face major limitations, such as the need for prior information on pseudotime and cell clustering, and lack of explainability, which restricts their applicability. Additionally, many existing trajectory inference methods do not support gene selection, making it difficult to annotate the results to known biological entities, thereby hindering the interpretation of results and subsequent functional studies.”

Comment#14: Minor Comments:

(1) Review the paragraph regarding the "current manifold-learning methods are faced with two major challenges." The message needs clarification.

(2) Increase the quality of the figures.

(3) Update the numbering of equations from #(.x) to (x).

We thank the reviewer for these detailed suggestions.

(1) We have thoroughly revised the discussion section, addressing overly absolute statements. The revised content is from line 426 to 428:

“However, current manifold learning methods face major limitations, such as the need for prior information on pseudotime and cell clustering, and lack of explainability, which restricts their applicability.”

(2) We conducted a comprehensive review of the figures in the article to more clearly present our results.

(3) We have meticulously reviewed the equations in the article to ensure there are no display issues with the indices.

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eLife Assessment

This study uses all-optical electrophysiology methods to provide a valuable insight into the organization of cortical networks and their ability to balance the activity of groups of neurons with similar functional tuning. The all-optical approach used in this study is impressive, but the claim that the effects of optical stimulation correspond to a specific homeostatic mechanism are incompletely supported by the statistical analysis of the results. The work will be of interest to neurobiologists and to developers of optical approaches for interrogating brain function.

Reviewer #1 (Public review):

Summary:

Kang et al. provide the first experimental insights from holographic stimulation of auditory cortex. Using stimulation of functionally-defined ensembles, they test whether overactivation of a specific subpopulation biases simultaneous and subsequent sensory-evoked network activations.

Strengths:

The investigators use a novel technique to investigate the sensory response properties in functionally defined cell assemblies in auditory cortex. These data provide the first evidence of how acutely perturbing specific frequency-tuned neurons impacts the tuning across a broader population.

Weaknesses:

I have several main concerns about the interpretation of these data:<br /> (1) The premise of the paper suggests that sensory responses are noisy at the level of neurons, but that population activity is reliable and that different neurons may participate in sensory coding on different trials. However, no analysis related to single trial variance or overall stability of population coding is provided. Specifically, showing that population activity is stable across trials in terms of total activity level or in some latent low dimensional representation would be required to support the concept of "homeostatic balancing".<br /> (2) Rebalancing would predict either that the responses of stimulated neurons would remain A) elevated after stimulation due to a hebbian mechanism or B) suppressed due to high activity levels on previous trials, a homeostatic mechanism. The authors report suppression in targeted neurons after stimulation blocks, but this appears similar to all other non-stimulated neurons. How do the authors interpret the post-stimulation effect in stimulated neurons?<br /> (3) The authors suggest that ACtx is different from visual cortex in that neurons with different tuning properties are intermingled. While that is true at the level of individual neurons, there is global order, as demonstrated by the authors own widefield imaging data and others at the single cell level (e.g. Tischbirek et al. 2019). Generally, distance is dismissed as a variable in the paper, but this is not convincing. Work across multiple sensory systems, including the authors own work, has demonstrated that cortical neuron connectivity is not random but varies as a function of distance (e.g. Watkins et al. 2014). Better justification is needed for the spatial pattern of neurons that were chosen for stimulation. Further, analyses that account for center of mass of stimulation, rather than just the distance from any stimulated neuron would be important to any negative result related to distance.<br /> (4) Data curation and presentation: Broadly, the way the data were curated and plotted makes it difficult to determine how well-supported the authors claims are. In terms of curation, the removal of outliers 3 standard deviations above the mean in the analysis of stimulation effects is questionable. Given the single-cell stimulation data presented in Figure 1, the reader is led to believe that holographic stimulation is quite specific. However, the justification for removing these outliers is that there may be direct stimulation 20-30 um from the target. Without plotting and considering the outliers as well, it is difficult to understand if these outsized responses are due to strong synaptic connections with neighboring neurons or rather just direct off-target stimulation. Relatedly, data presentation is limited to the mean + SEM for almost all main effects and pre-post stimulation effects are only compared indirectly. Whether stimulation effects are driven by just a few neurons that are particularly suppressed or distinct populations which are suppressed or enhanced remains unclear.

Reviewer #2 (Public review):

The goal of HiJee Kang et al. in this study is to explore the interaction between assemblies of neurons with similar pure-tone selectivity in mouse auditory cortex. Using holographic optogenetic stimulation in a small subset of target cells selective for a given pure tone (PTsel), while optically monitoring calcium activity in surrounding non-target cells, they discovered a subtle rebalancing process: co-tuned neurons that are not optogenetically stimulated tend to reduce their activity. The cortical network reacts as if an increased response to PTsel in some tuned assemblies is immediately offset by a reduction in activity in the rest of the PTsel-tuned assemblies, leaving the overall response to PTsel unchanged. The authors show that this rebalancing process affects only the responses of neurons to PTsel, not to other pure tones. They also show that assemblies of neurons that are not selective for PTsel don't participate in the rebalancing process. They conclude that assemblies of neurons with similar pure-tone selectivity must interact in some way to organize this rebalancing process, and they suggest that mechanisms based on homeostatic signaling may play a role.

The conclusions of this paper are very interesting but some aspects of the study including methods for optogenetic stimulation, statistical analysis of the results and interpretation of the underlying mechanisms need to be clarified and extended.

(1) This study uses an all-optical approach to excite a restricted group of neurons chosen for their functional characteristics (their frequency tuning), and simultaneously record from the entire network observable in the FOV. As stated by the authors, this approach is applied for the first time to the auditory cortex, which is a tour de force. However, such an approach is complex and requires precise controls to be convincing. In the manuscript, several methodological aspects are not sufficiently described to allow a proper understanding.<br /> (i) The use of CRmine together with GCaMP8s has been reported as problematic as the 2Ph excitation of GCaMP8s also excites the opsin. Here, the authors use a red-shifted version of CRmine to prevent such cross excitation by the imaging laser. To be convincing, they should explain how they controlled for the absence of rsCRmine activation by the 940nm light. Showing the fluorescence traces immediately after the onset of the imaging session would ensure that neurons are not excited as they are imaged.<br /> (ii) Holographic patterns used to excite 5 cells simultaneously may be associated with out-of-focus laser hot spots. Cells located outside of the FOV could be activated, therefore engaging other cells than the targeted ones in the stimulation. This would be problematic in this study as their tuning may be unrelated to the tuning of the targeted cells. To control for such an effect, one could in principle decouple the imaging and the excitation planes, and check for the absence of out-of-focus unwanted excitation.<br /> (iii) The control shown in Figure 1B is intended to demonstrate the precision of the optogenetic stimulation: when the stimulation spiral is played at a distance larger or equal to 20 µm from a cell, it does not activate it. However, in the rest of the study, the stimulation is applied with a holographic approach, targeting 5 cells simultaneously instead of just one. As the holographic pattern of light could produce out-of-focus hot spots (absent in the single cell control), we don't know what is the extent of the contamination from non-targeted cells in this case. This is important because it would determine an objective criterion to exclude non-targeted but excited cells (last paragraph of the Result section: "For the stimulation condition, we excluded non-target cells that were within 15 µm distance of the target cells...")

(2) A strength of this study comes from the design of the experimental protocol used to compare the activity in non-target co-tuned cells when the optogenetic stimulation is paired with their preferred tone versus a non-preferred pure tone. The difficulty lies in the co-occurrence of the rebalancing process and the adaptation to repeated auditory stimuli, especially when these auditory stimuli correspond to a cell's preferred pure tones. To distinguish between the two effects, the authors use a comparison with a control condition similar to the optogenetic stimulation conditions, except that the laser power is kept at 0 mW. The observed effect is shown as an extra reduction of activity in the condition with the optogenetic paired with the preferred tone, compared to the control condition. The specificity of this extra reduction when stimulation is synchronized with the preferred tone, but not with a non-preferred tone, is a potentially powerful result, as it points to an underlying mechanism that links the assemblies of cells that share the same preferred pure tones.<br /> The evidence for this specificity is shown in Figure 3A and 3D. However, the universality of this specificity is challenged by the fact that it is observed for 16kHz preferring cells, but not so clearly for 54kHz preferring cells: these 54kHz preferring cells also significantly (p = 0.044) reduce their response to 54kHz in the optogenetic stimulation condition applied to 16kHz preferring target cells compared to the control condition. The proposed explanation for this is the presence of many cells with a broad frequency tuning, meaning that these cells could have been categorized as 54kHz preferring cells, while they also responded significantly to a 16kHz pure tone. To account for this, the authors divide each category of pure tone cells into three subgroups with low, medium and high frequency preferences. Following the previous reasoning, one would expect at least the "high" subgroups to show a strong and significant specificity for an additional reduction only if the optogenetic stimulation is targeted to a group of cells with the same preferred frequency. Figure 3D fails to show this. The extra reduction for the "high" subgroups is significant only when the condition of opto-stimulation synchronized with the preferred frequency is compared to the control condition, but not when it is compared to the condition of opto-stimulation synchronized with the non-preferred frequency.<br /> Therefore, the claim that "these results indicate that the effect of holographic optogenetic stimulation depends not on the specific tuning of cells, but on the co-tuning between stimulated and non-stimulated neurons" (end of paragraph "Optogenetic holographic stimulation decreases activity in non-target co-tuned ensembles") seems somewhat exaggerated. Perhaps increasing the number of sessions in the 54kHz target cell optogenetic stimulation condition (12 FOV) to the number of sessions in the 16kHz target cell optogenetic stimulation condition (18 FOV) could help to reach significance levels consistent with this claim.

(3) To interpret the results of this study, the authors suggest that mechanisms based on homeostatic signaling could be important to allow the rebalancing of the activity of assemblies of co-tuned neurons. In particular, the authors try to rule out the possibility that inhibition plays a central role. Both mechanisms could produce effects on short timescales, making them potential candidates. The authors quantify the spatial distribution of the balanced non-targeted cells and show that they are not localized in the vicinity of the targeted cells. They conclude that local inhibition is unlikely to be responsible for the observed effect. This argument raises some questions. The method used to quantify spatial distribution calculates the minimum distance of a non-target cell to any target cell. If local inhibition is activated by the closest target cell, one would expect the decrease in activity to be stronger for non-target cells with a small minimum distance and to fade away for larger minimum distances. This is not what the authors observe (Figure 4B), so they reject inhibition as a plausible explanation. However, their quantification doesn't exclude the possibility that non-target cells in the minimum distance range could also be close and connected to the other 4 target cells, thus masking any inhibitory effect mediated by the closest target cell. In addition, the authors should provide a quantitative estimate of the range of local inhibition in layers 2/3 of the mouse auditory cortex to compare with the range of distances examined in this study (< 300 µm). Finally, the possibility that some target cells could be inhibitory cells themselves is considered unlikely by the authors, given the proportions of excitatory and inhibitory neurons in the upper cortical layers. On the other hand, it should be acknowledged that inhibitory cells are more electrically compact, making them easier to be activated optogenetically with low laser power.

Reviewer #3 (Public review):

Summary:

The authors optogenetically stimulate 5 neurons all preferring the same pure tone frequency (16 or 54 kHz) in the mouse auditory cortex using a holography-based single cell resolution optogenetics during sound presentation. They demonstrate that the response boosting of target neurons leads to a broad suppression of surrounding neurons, which is significantly more pronounced in neurons that have the same pure tone tuning as the target neurons. This effect is immediate and spans several hundred micrometers. This suggests that the auditory cortical network balances its activity in response to excess spikes, a phenomenon already seen in visual cortex.

Strengths:

The study is based on a technologically very solid approach based on single-cell resolution two-photon optogenetics. The authors demonstrate the potency and resolution of this approach. The inhibitory effects observed upon targeted stimulation are clear and the relative specificity to co-tuned neurons is statistically clear although the effect size is moderate.

Weaknesses:

The evaluation of the results is brief and some aspects of the observed homeostatic are not quantified. For example, it is unclear whether stimulation produces a net increase or decrease of population activity, or if the homeostatic phenomenon fully balances activity. A comparison of population activity for all imaged neurons with and without stimulation would be instructive. The selectivity for co-tuned neurons is significant but weak. Although it is difficult to evaluate this issue, this result may be trivial, as co-tuned neurons fire more strongly. Therefore, the net activity decrease is expected to be larger, in particular, for the number of non-co-tuned neurons which actually do not fire to the target sound. The net effect for the latter neurons will be zero just because they do not respond. The authors do not make a very strong case for a specific inhibition model in comparison to a broad and non-specific inhibitory effect. Complementary modeling work would be needed to fully establish this point.

Author response:

We would like to thank the editors and the reviewers for constructive feedback on our first version of the manuscript. Before submitting a fully revised version with detailed response to each point, we would like to provide a brief clarification on some of the key issues.

Reviewer 2 raised a concern about the precision and specificity of holographic stimulation, regarding its potential effect on out-of-focus stimulation points and planes. We further verified whether the laser power at the targeted z-plane influences cells’ activity at nearby z-planes. As the Reviewer pointed out, the previous x- and y-axis shifts were tested by single-cell stimulation. This time, we stimulated five cells simultaneously, to match the actual experiment setup and assess potential artifacts in other planes. We observed no stimulation-driven activity increase in cells at a z-planed shifted by 20 µm (Author response image 1). This confirms the holographic stimulation accurately manipulates the pre-selected target cells and the effects we observe is not likely due to out-of-focus stimulation artifacts. It is true that not all of pre-selected cells showing significant response changes prior to the main experiment are effectively activated t every trial during the experiments. While further analyses will be included in the revised manuscript, we varied the target cell distances across FOVs, from nearby cells to those farther apart within the FOV. We have not observed a significant relationship between the target cell distances and stimulation effect. Lastly, cells within < 15 µm of the target were excluded to prevent potential excitation due to the holographic stimulation power. Given the spontaneous movements of the FOV during imaging sessions due to animal’s movement, despite our efforts to minimize them, we believe that any excitation from these neighboring neurons would be directly from the stimulation rather than the light pattern artifact itself.

Author response image 1.

Stimulation effect on five pre-selected cells at the target z-plane (left) and 20 µm off-target z-plane (right). No stimulation-driven effect was observed on the off-target cells.

Reviewers also raised concerns regarding the interpretation of homeostatic balance. While we are working on further analyses to strengthen our findings based on the reviewers’ suggestions, the observed response changes in co-tuned neuronal ensembles, specifically during the processing of their preferred frequency information, highlights an interaction between sensory processing and network dynamics. We believe this specificity indicates a functional mechanism beyond broad suppression or simple inhibitory effects, possibly aligning with homeostatic principles in cortical circuits. Regarding the post-stimulation effect, it is true neither the stimulation nor the control condition showed further response changes during the post-stimulation session. For the control condition, this is likely due to the repetitive tone presentation that could already triggered neural adaptation to a plateau by first two imaging sessions (baseline and stimulation sessions), preventing further changes in the last session. However, as the stimulation condition induced a greater amplitude decrease during the stimulation session compared to the control condition, if this extra suppression had not persisted during the post-stimulation session, we would have expected response amplitudes to rebound, increasing between the stimulation and post-stimulation sessions, which was not the case. Therefore, we propose that the persistence of this rebalanced network state is more indicative of a potential homeostatic mechanism in response to the activity manipulation within the network.

eLife Assessment

This important study enhances our understanding of Pseudomonas aeruginosa's transcriptional regulatory network by revealing its hierarchical structure through analysis of transcription factor binding patterns. The conclusions are supported by compelling evidence and will appeal to researchers investigating P. aeruginosa and the regulatory mechanisms underlying its pathogenicity. The paper would be strengthened by clarifying implications of binding and regulatory networks with virulence, and transcription factor divergence across species.

Reviewer #1 (Public review):

Summary:<br /> This work done by Huang et.al. revealed the complex regulatory functions and transcription network of 172 unknown transcription factors of Pseudomonas aeruginosa PAO1. The authors utilized ChIP-seq to profile TFs binding site information across the genome, demonstrating diverse regulatory relationships among them via hierarchical networks with three levels. They further constructed thirteen ternary regulatory motifs in small subs and co-association atlas with 7 core associated clusters. The study also uncovered 24 virulence-related master regulators. The pan-genome analysis uncovered both the conservation and evolution of TFs with P. aeruginosa complex and related species. Furthermore, they established a web-based database combining both existing and novel data from HT-SELEX and ChIP-seq to provide TF binding site information. This study offered valuable insights into studying transcription regulatory networks in P. aeruginosa and other microbes.

Strengths:<br /> The results are presented with clarity, supported by well-organized figures and tables that not only illustrate the study's findings but also enhance the understanding of complex data patterns.

Weaknesses:<br /> The results of this manuscript are mainly presented in systematic figures and tables. Some of the results need to be discussed as an illustration how readers can utilize these datasets.

Reviewer #2 (Public review):

In this work, the authors comprehensively describe the transcriptional regulatory network of Pseudomonas aeruginosa through the analysis of transcription factor binding characteristics. They reveal the hierarchical structure of the network through ChIP-seq, categorizing transcription factors into top-, middle-, and bottom-level, and reveal a diverse set of relationships among the transcription factors. Additionally, the authors conduct a pangenome analysis across the Pseudomonas aeruginosa species complex as well as other species to study the evolution of transcription factors. Moreover, the authors present a database with new and existing data to enable the storage and search of transcription factor binding sites. The findings of this study broaden our knowledge on the transcriptome of P. aeruginosa.

This study sheds light on the complex interconnections between various cellular functions that contribute to the pathogenicity of P. aeruginosa, along with the associated regulatory mechanisms. Certain findings, such as the regulatory tendencies of DNA-binding domain-types, provides valuable insights on the possible functions of uncharacterized transcription factors and new functions of those that have already been characterized. The techniques used hold great potential for discovery of transcription factor functions in understudied organisms as well.

The study would benefit from a more clear discussion on the implications of various findings, such as binding preferences, regulatory preferences, and the link between regulatory crosstalk and virulence. Additionally, the pangenome analysis would be furthered through a discussion of the divergence of the transcription factors of P. aeruginosa PAO1across species in relation to the findings on the hierarchical structure of the transcriptional regulatory network.

eLife Assessment

This important study uses machine learning-based network analysis on transcriptomic data from different tissue cell types to identify a small set of conserved (pan-tissue) genes associated with changes in cell mechanics. The new method, which provides a new type of approach for mechanobiology, is accessible, compelling, and well-validated using in silico and experimental approaches. The study provides motivation for researchers to test hypotheses concerning the identified five-gene network, and the method will be strengthened over time with expanded sets of validations, such as testing genes with hitherto unknown roles and different perturbation techniques.

Reviewer #1 (Public review):

In this work, Urbanska and colleagues use a machine-learning based crossing of mechanical characterisations of various cells in different states and their transcriptional profiles. Using this approach, they identify a core set of five genes that systematically vary together with the mechanical state of the cells, although not always in the same direction depending on the conditions. They show that the combined transcriptional changes in this gene set is strongly predictive of a change in the cell mechanical properties, in systems that were not used to identify the genes (a validation set). Finally, they experimentally after the expression level of one of these genes, CAV1, that codes for the caveolin 1 protein, and show that, in a variety of cellular systems and contexts, perturbations in the expression level of CAV1 also induce changes in cell mechanics, cells with lower CAV1 expression being generally softer.

Overall the approach seems accessible, sound and is well described. My personal expertize is not suited to judge its validity, novelty or relevance, so I do not make comments on that. The results it provides seem to have been thoroughly tested by the authors (using different types of mechanical characterisations of the cells) and to be robust in their predictive value. The authors also show convincingly that one of the genes they identified, CAV1, is not only correlated with the mechanical properties of cells, but also that changing its expression level affects cell mechanics. At this stage, the study appears mostly focused on the description and validation of the methodological approach, and it is hard to really understand what the results obtain really mean, the importance of the biological finding - what is this set of 5 genes doing in the context of cell mechanics? Is it really central, or is it just one of the set of knobs on which the cell plays - and it is identified by this method because it is systematically modulated but maybe, for any given context, it is not the dominant player - all these fundamental questions remain unanswered at this stage. On one hand, it means that the study might have identified an important novel module of genes in cell mechanics, but on the other hand, it also reveals that it is not yet easy to interpret the results provided by this type of novel approach.

Comments on revisions:

In their point-by-point answer, the authors did a great effort to provide pedagogical answers that clarified most of the points I had raised. They also did more analysis, some of which are included as supplementary data, and added a few sentences to the main text and discussion. As far as I am concerned, I see no particular issue with the revised article. I think it will be interesting both as a new type of approach in mechanobiology, and also as a motivation for more experimentally oriented labs to test the hypothesis proposed in the article and the 'module' they found.

Author response:

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

In summary, the changes made in the revision process include:

An addition of a paragraph in the result section that discusses the absolute values of measured Young’s moduli in the light of probing frequencies, accompanied by a new supplementary figure and a supplementary table that support that discussion

- Fig. S10. Absolute Young’s modulus values across the frequencies characteristic for the three measurement methods.

- Table S9. Operation parameters of the three methods used for characterizing the mechanical properties of cells.

Three new supplementary figures that display the expression matrices for the genes from the identified modules in carcinoma datasets used for validation:

- Fig. S4. Expression of identified target genes in the CCLE microarray dataset used for validation.

- Fig. S5. Expression of identified target genes in the CCLE RNA-Seq dataset used for validation.

- Fig. S6. Expression of identified target genes in the Genentech dataset used for validation.

An addition of a paragraph in the discussion section that discusses the intracellular origins of resistance to deformation and the dominance of actin cortex at low deformations.

- Refinement of the manuscript text and figures based on the specific feedback from the Reviewers.

Please see below for detailed responses to the Reviewers’ comments.

Reviewer #1 (Public Review)

In this work, Urbanska and colleagues use a machine-learning based crossing of mechanical characterisations of various cells in different states and their transcriptional profiles. Using this approach, they identify a core set of five genes that systematically vary together with the mechanical state of the cells, although not always in the same direction depending on the conditions. They show that the combined transcriptional changes in this gene set is strongly predictive of a change in the cell mechanical properties, in systems that were not used to identify the genes (a validation set). Finally, they experimentally after the expression level of one of these genes, CAV1, that codes for the caveolin 1 protein, and show that, in a variety of cellular systems and contexts, perturbations in the expression level of CAV1 also induce changes in cell mechanics, cells with lower CAV1 expression being generally softer. 

Overall the approach seems accessible, sound and is well described. My personal expertise is not suited to judge its validity, novelty or relevance, so I do not make comments on that. The results it provides seem to have been thoroughly tested by the authors (using different types of mechanical characterisations of the cells) and to be robust in their predictive value. The authors also show convincingly that one of the genes they identified, CAV1, is not only correlated with the mechanical properties of cells, but also that changing its expression level affects cell mechanics. At this stage, the study appears mostly focused on the description and validation of the methodological approach, and it is hard to really understand what the results obtain really mean, the importance of the biological finding - what is this set of 5 genes doing in the context of cell mechanics? Is it really central, or is it just one of the set of knobs on which the cell plays - and it is identified by this method because it is systematically modulated but maybe, for any given context, it is not the dominant player - all these fundamental questions remain unanswered at this stage. On one hand, it means that the study might have identified an important novel module of genes in cell mechanics, but on the other hand, it also reveals that it is not yet easy to interpret the results provided by this type of novel approach. 

We thank the Reviewer #1 for the thoughtful evaluation of our manuscript. The primary goal of the manuscript was to present a demonstration of an unbiased approach for the identification of genes involved in the regulations of cell mechanics. The manuscript further provides a comprehensive computational validation of all genes from the identified network, and experimental validation of a selected gene, CAV1. 

We agree that at the current stage, far-reaching conclusions about the biological meaning of the identified network cannot be made. We are, however, convinced that the identification of an apparently central player such as CAV1 across various cellular systems is per se meaningful, in particular since CAV1 modulation shows clear effects on the cell mechanical state in several cell types. 

We anticipate that our findings will encourage more mechanistic studies in the future, investigating how these identified genes regulate mechanical properties and interact with each other. Notwithstanding, the identified genes (after testing in specific system of interest) can be readily used as genetic targets for modulating mechanical properties of cells. Access to such modifications is of huge relevance not only for performing further research on the functional consequence of cell mechanics changes (in particular in in-vivo systems where using chemical perturbations is not always possible), but also for the potential future implementation in modulating mechanical properties of the cells to prevent disease (for example to inhibit cancer metastasis or increase efficacy of cancer cell killing by cytotoxic T cells).

We have now added a following sentence in the first paragraph of discussion to acknowledge the open ends of our study:

“(...). Here we leveraged this opportunity by performing discriminative network analysis on transcriptomes associated with mechanical phenotype changes to elucidate a conserved module of five genes potentially involved in cell mechanical phenotype regulation. We provided evidence that the inferred conserved functional network module contains an ensemble of five genes that, in particular when combined in a unique combinatorial marker, are universal, specific and trustworthy markers of mechanical phenotype across the studied mouse and human systems. We further demonstrated on the example of a selected marker gene, CAV1, that its experimental up- and downregulation impacts the stiffness of the measured cells. This demonstrates that the level of CAV1 not only correlates with, but also is causative of mechanical phenotype change. The mechanistic insights into how precisely the identified genes are involved in regulating mechanical properties, how they interact with each other, and whether they are universal and dominant in various contexts all remain to be established in

future studies.”

Reviewer #2 (Public Review)

A key strength is the quantitative approaches all add rigor to what is being attempted. The approach with very different cell culture lines will in principle help identify constitutive genes that vary in a particular and predictable way. To my knowledge, one other study that should be cited posed a similar pan-tissue question using mass spectrometry proteomics instead of gene expression, and also identified a caveolae component (cavin-1, PTRF) that exhibited a trend with stiffness across all sampled tissues. The study focused instead on a nuclear lamina protein that was also perturbed in vitro and shown to follow the expected mechanical trend (Swift et al 2013). 

We thank the Reviewer #2 for the positive evaluation of the breadth of the results and for pointing us to the relevant reference for the proteomic analysis related to tissue stiffness (Swift et al., 2013). This study, which focused primarily on the tissue-level mechanical properties, identifying PTRF, a caveolar component, which links to our observation of another caveolar component, CAV1, at the single-cell level. 

We have now included the citation in the following paragraph of the discussion:

“To our knowledge, there are no prior studies that aim at identifying gene signatures associated with single-cell mechanical phenotype changes, in particular across different cell types. There are, however, several studies that investigated changes in expression upon exposure of specific cell types to mechanical stimuli such as compression (87, 88) or mechanical stretch (22, 80, 89), and one study that investigated difference in expression profiles between stiffer and softer cells sorted from the same population (90). Even though the studies concerned with response to mechanical stimuli answer a fundamentally different question (how gene expression changes upon exposure to external forces vs which genes are expressed in cells of different mechanical phenotype), we did observe some similarities in the identified genes. For example, in the differentially expressed genes identified in the lung epithelia exposed to compression (87), three genes from our module overlapped with the immediate response (CAV1, FHL2, TGLN) and four with the long-term one (CAV1, FHL2, TGLN, THBS1). We speculate that this substantial overlap is caused by the cells undergoing change in their stiffness during the response to compression (and concomitant unjamming transition). Another previous study explored the association between the stiffness of various tissues and their proteomes. Despite the focus on the tissue-scale rather than single-cell elasticity, the authors identified polymerase I and transcript release factor (PTRF, also known as cavin 1 and encoding for a structural component of the caveolae) as one of the proteins that scaled with tissue stiffness across samples (91).”

Reviewer #3 (Public Review)

In this work, Urbanska et al. link the mechanical phenotypes of human glioblastoma cell lines and murine iPSCs to their transcriptome, and using machine learning-based network analysis identify genes with putative roles in cell mechanics regulation. The authors identify 5 target genes whose transcription creates a combinatorial marker which can predict cell stiffness in human carcinoma and breast epithelium cell lines as well as in developing mouse neurons. For one of the target genes, caveolin1 (CAV1), the authors perform knockout, knockdown, overexpression and rescue experiments in human carcinoma and breast epithelium cell lines. They determine the cell stiffness via RT-DC, AFM indentation and AFM rheology and confirm that high CAV1 expression levels correlate with increased stiffness in those model systems. This work brings forward an interesting approach to identify novel genes in an unbiased manner, but surprisingly the authors validate caveolin 1, a target gene with known roles in cell mechanics regulation. 

I have two main concerns with the current version of this work: 

(1) The authors identify a network of 5 genes that can predict mechanics. What is the relationship between the 5 genes? If the authors aim to highlight the power of their approach by knockdown, knockout or over-expression of a single gene why choose CAV1 (which has an individual p-value of 0.16 in Fig S4)? To justify their choice, the authors claim that there is limited data supporting the direct impact of CAV1 on mechanical properties of cells but several studies have previously shown its role in for example zebrafish heart stiffness, where a knockout leads to higher stiffness (Grivas et al., Scientific Reports 2020), in cancer cells, where a knockdown leads to cell softening (Lin et al., Oncotarget 2015), or in endothelial cell, where a knockout leads to cell softening (Le Master et al., Scientific Reports 2022). 

We thank the reviewer for their comments. First, we do acknowledge that studying the relationship between the five identified genes is an intriguing question and would be a natural extension of the currently presented work. It is, however, beyond the scope of presented manuscript, in which our primarily goal was to introduce a general pipeline for de novo identification of genes related to cell mechanics. We did add a following statement in the discussion (yellow highlight) to acknowledge the open ends of our study:

“The mechanical phenotype of cells is recognized as a hallmark of many physiological and pathological processes. Understanding how to control it is a necessary next step that will facilitate exploring the impact of cell mechanics perturbations on cell and tissue function (76).

The increasing availability of transcriptional profiles accompanying cell state changes has recently been complemented by the ease of screening for mechanical phenotypes of cells thanks to the advent of high-throughput microfluidic methods (77). This provides an opportunity for data-driven identification of genes associated with the mechanical cell phenotype change in a hypothesis-free manner. Here we leveraged this opportunity by performing discriminative network analysis on transcriptomes associated with mechanical phenotype changes to elucidate a conserved module of five genes potentially involved in cell mechanical phenotype regulation. We provided evidence that the inferred conserved functional network module contains an ensemble of five genes that, in particular when combined in a unique combinatorial marker, are universal, specific and trustworthy markers of mechanical phenotype across the studied mouse and human systems. We further demonstrated on the example of a selected marker gene, CAV1, that its experimental up- and downregulation impacts the stiffness of the measured cells. This demonstrates that the level of CAV1 not only correlates with, but also is causative of mechanical phenotype change. The mechanistic insights into how precisely the identified genes are involved in regulating mechanical properties, how they interact with each other, and whether they are universal and dominant in various contexts all remain to be established in future studies.”

Regarding the selection of CAV1 as the gene that we used for validation experiment; as mentioned in the introductory paragraph of the result section “Perturbing expression levels of CAV1 changes cells stiffness” (copied below), we were encouraged by the previous data already linking CAV1 with cell mechanics when selecting it as our first target. The relationship between CAV1 and cell mechanics regulation, however, is not very well established (of note, two of the latest manuscripts came out after the initial findings of our study). 

Regarding the citations suggested by the reviewer: two are already included in the original manuscript (Lin et al., Oncotarget 2015 – Ref (63), Le Master –2022 Ref (67)), along with an additional one (Hsu et al 2018 (66)), and the third one (Grivas et al, 2020 (68)) is now also added to the manuscript. Though, we would like to highlight that even though Grivas et al state that the CAV1 KO cells are stiffer, the AFM indentation measurements were performed on the cardiac tissue, with a spherical tip of 30 μm radius and likely reflect primarily supracelluar, tissue-scale properties, as opposed to cell-scale measurements performed in our study (we used cultured cells which mostly lack the extracellular tissue structures, deformability cytometry was performed on dissociated cells and picks up on cell properties exclusively, and in case of AFM measurements a spherical tip with 5 μm radius was used).

“We decided to focus our attention on CAV1 as a potential target for modulating mechanical properties of cells, as it has previously been linked to processes intertwined with cell mechanics. In the context of mechanosensing, CAV1 is known to facilitate buffering of the membrane tension (45), play a role in β1-inegrin-dependent mechanotransduction (58) and modulate the mechanotransduction in response to substrate stiffness (59). CAV1 is also intimately linked with actin cytoskeleton — it was shown to be involved in cross-talk with Rho-signaling and actin cytoskeleton regulation (46, 60–62), filamin A-mediated interactions with actin filaments (63), and co-localization with peripheral actin (64). The evidence directly relating CAV1 levels with the mechanical properties of cells (47, 62, 65, 66) and tissues (66, 67) , is only beginning to emerge.”

Regarding the cited p-value of 0.16, we would like to clarify that it is the p-value associated with the coefficient of the crude linear regression model fitted to the data for illustrative purposes in Fig S4. This value only says that from the linear fit we cannot conclude much about the correlation of the level of Cav1 with the Young’s modulus change. Much more relevant parameters to look at are the AUC-ROC values and associated p-values reported in the Table 4 in the main text (see below), which show good performance of CAV1 in separating soft and stiff cell states. 

The positive hypothesis I assumes that markers are discriminative of samples with stiff/soft mechanical phenotype regardless of the studied biological system, and CAV1 has a clear trend with the minimum AUC-ROC on 3 datasets of 0.78, even though the p-value is below the significance level. The positive hypothesis II assumes that markers are discriminative of samples with stiff/soft mechanical phenotype in carcinoma regardless of data source, and CAV1 has a clear significance because the minimum AUC-ROC on 3 datasets is 0.89 and the p-value is 0.02.

(2) The authors do not show how much does PC-Corr outperforms classical co-expression network analysis or an alternative gold standard. It is worth noting that PC-Corr was previously published by the same authors to infer phenotype-associated functional network modules from omics datasets (Ciucci et al., Scientific Reports 2017). 

As pointed out by the Reviewer, PC-corr has been introduced and characterized in detail in a previous publication (Ciucci et al, 2017, Sci. Rep.), where it was compared against standard co-expression analysis (below reported as: p-value network) on molecules selected using univariate statistical analysis. 

See the following fragment of Discussion in Ciucci et al, 2017:

“The PC-corr networks were always compared to P-value networks. The first strategical difference lies in the way features are selected: while the PC-corr adopts a multivariate approach, i.e. it uses a combination of features that are responsible for the sample discrimination, in the P-value network the discriminating features are singly selected (one by one) with each Mann-Whitney test (followed by Benjamini-Hochberg procedure). The second strategical difference lies in the generation of the correlation weights in the network. PC-corr combines in parallel and at the same time in a unique formula the discrimination power of the PC-loadings and the association power of the Pearson correlation, directly providing in output discriminative omic associations. These are generated using a robust (because we use as merging factor the minimum operator, which is a very penalizing operator) mathematical trade-off between two important factors: multivariate discriminative significance and correlation association. In addition, as mentioned above, the minimum operator works as an AND logical gate in a digital circuit, therefore in order to have a high link weight in the PCcorr network, both the discrimination (the PC-loadings) and the association (the Pearson correlations) of the nodes adjacent to the link should be simultaneously high. Instead, the Pvalue procedure begins with the pre-selection of the significant omic features and, only in a second separated step, computes the associations between these features. Therefore, in P-value networks, the interaction weights are the result neither of multivariate discriminative significance, nor of a discrimination/association interplay.”

Here we implement PC-corr for a particular application and do not see it as central to the message of the present manuscript to compare it with other available methods. We considered it much more relevant to focus on an in-silico validation on dataset not used during the PCcorr analysis (see Table 3 and 4 for details).

Altogether, the authors provide an interesting approach to identify novel genes associated with cell mechanics changes, but the current version does not fulfill such potential by focusing on a single gene with known roles in cell mechanics. 

Our manuscript presents a demonstration of an overall approach for the identification of genes involved in the regulation of cell mechanics, and the perturbations performed on CAV1 have a demonstrative role (please also refer to the explanations of why we decided to perform the verification focused on CAV1 above). The fact that we identify CAV1, which has been implicated in regulating cell mechanics in a handful of studies, de novo and in an unbiased way speaks to the power of our approach. We do agree that investigation into the effect of manipulating the expression of the remaining genes from the identified network module, as well as into the mutual relationships between those genes and their covariance in perturbation experiments, constitutes a desirable follow-up on the presented results. It is, however, beyond the scope of the current manuscript. Regardless, the other genes identified can be readily tested in systems of interest and used as potential knobs for tuning mechanical properties on demand.

Reviewer #1 (Recommendations For Authors)

I am not a specialist of the bio-informatics methods used in this study, so I will not make any specific technical comments on them. 

In terms of mechanical characterisation of cells, the authors use well established methods and the fact that they systematically validate their findings with at least two independent methods (RT-DC and AFM for example) makes them very robust. So I have no concerns with this part.  The experiments of perturbations of CAV 1 are also performed to the best standards and the results are clear, no concern on that. 

My main concerns are rather questions I was asking myself and could not answer when reading the article. Maybe the authors could find ways to clarify them - the discussion of their article is already very long and maybe it should not be lengthened to much. In my opinion, some of the points discussed are not really essential and rather redundant with other parts of the paper. This could be improved to give some space to clarify some of the points below:  

We thank the Reviewer #1 for an overall positive evaluation of the manuscript as well as the points of criticism which we addressed in a point-by-point manner below.

(1) This might be a misunderstanding of the method on my side, but I was wondering whether it is possible to proceed through the same steps but choose other pairs of training datasets amongst the 5 systems available (there are 10 such pairs if I am not mistaken) and ask whether they always give the same set of 5 genes. And if not, are the other sets also then predictive, robust, etc. Or is it that there are 'better' pairs than others in this respect. Or the set of 5 genes is the only one that could be found amongst these 5 datasets - and then could it imply that it is the only group 'universal' group of predictive genes for cell mechanics (when applied to any other dataset comprising similar mechanical measures and expression profiles, for other cells, other conditions)? 

I apologize in case this question is just the result of a basic misunderstanding of the method on my side. But I could not answer the question myself based on what is in the article and it seems to be important to understand the significance of the finding and the robustness of the method. 

We thank the Reviewer for this question. To clarify: while in general it is possible to proceed through the same analysis steps choosing a different pair of datasets (see below for examples), we have purposefully chosen those two and not any other datasets because they encompassed the highest number of samples per condition in the RNAseq data (see Fig 4 and Table R1 below), originated from two different species and concerned least related tissues (the other option for mouse would be neural progenitors which in combination with the glioblastoma would likely result in focusing on genes expressed in neural tissues). This is briefly explained in the following fragment of the manuscript on Page 10:

“For the network construction, we chose two datasets that originate from different species, concern unrelated biological processes, and have a high number of samples included in the transcriptional analysis: human glioblastoma and murine iPSCs (Table 1).”

To further address the comment of the reviewer: there is indeed a total of 10 possible two-set combinations of datasets, 6 of those pairs are human-mouse combinations (highlighted in orange in Author response Table 1), 3 are human-human combinations (highlighted in blue), and 1 is mousemouse (marked in green).

Author response table 1.

Possible two-set combinations of datasets. For each combination, the number of common genes is indicated. The number on the diagonal represents total number of transcripts in the individual datasets, n corresponds to the number of samples in the respective datasets.  * include non-coding genes.

To reiterate, we have chosen the combination of set A (glioblastoma) and set D (iPSCs) to choose datasets from different species and with highest sample number. 

As for the other combinations of human-mouse datasets:

• set A & E lead to derivation of a conserved module, however as expected this module includes genes specific for neuronal tissues (such as brain & testis specific immunoglobulin IGSF11, or genes involved in neuronal development such as RFX4, SOX8)

Author response image 1.

• the remaining combinations (set B&D, B&E, C&D and C&E) do not lead to a derivation of a highly interconnected module

Author response image 2.

Author response image 3.

Author response image 4.

Author response image 5.

Finally, it would have also been possible to perform the combined PC-corr procedure on all 5 datasets. However, this would prevent us from doing validation using unknown datasets.

Hence, we decided to proceed with the 2 discovery and 4 validation datasets.

For the sake of completeness, we present below some of the networks obtained from the analysis performed on all 5 datasets (which intersect at 8059 genes).

Author response image 6.

The above network was created by calculating mean/minimum PC-corr among all five datasets and applying the threshold. The thresholding can be additionally restricted in that we:

a. constrain the directionality of the correlation between the genes (𝑠𝑔𝑛(𝑐) ) to be the same among all or at least n datasets

b. constrain the directionality of the correlation between the cell stiffness and gene expression level (𝑠𝑔𝑛(𝑉)) for individual genes.

Some of the resulting networks for such restrictions are presented below.

Author response image 7.

Author response image 8.

Of note, some of the nodes from the original network presented in the paper (CAV1, FHL2, and IGFBP7) are preserved in the 5-set network (and highlighted with blue rims),

(2) The authors already use several types of mechanical characterisation of the cells, but there are even more of them, in particular, some that might not directly correspond to global cell stiffness but to other aspects, like traction forces, or cell cortex rheology, or cell volume or passage time trough constrictions (active or passive) - they might all be in a way or another related, but they are a priori independent measures. Would the authors anticipate finding very different 'universal modules' for these other mechanical properties, or again the same one? Is there a way to get at least a hint based on some published characterisations for the cells used in the study? Basically, the question is whether the gene set identified is specific for a precise type of mechanical property of the cell, or is more generally related to cell mechanics modulation - maybe, as suggested by the authors because it is a set of molecular knobs acting upstream of general mechanics effectors like YAP/TAZ or acto-myosin? 

We thank the Reviewer for this comment. We would like to first note that in our study, we focused on single-cell mechanical phenotype understood as a response of the cells to deformation at a global (RT-DC) or semi-local (AFM indentation with 5-μm bead) level and comparatively low deformations (1-3 μm, see Table S9). There is of course a variety of other methods for measuring cell mechanics and mechanics-related features, such as traction force microscopy mentioned by the reviewer. Though, traction force microscopy probes how the cells apply forces and interact with their environment rather than the inherent mechanical properties of the cells themselves which were the main interest of our study. 

Nevertheless, as mentioned in the discussion, we found some overlap with the genes identified in other mechanical contexts, for example in the context of mechanical stretching of cells:

“Furthermore, CAV1 is known to modulate the activation of transcriptional cofactor yesassociated protein, YAP, in response to changes in stiffness of cell substrate (60) and in the mechanical stretch-induced mesothelial to mesenchymal transition (74).”

Which suggests that the genes identified here may be more broadly related to mechanical aspects of cells. 

Of note, we do have some insights connected to the changes of cell volume — one of the biophysical properties mentioned by the reviewer — from our experiments.  For all measurements performed with RT-DC, we can also calculate cell volumes from 2D cell contours (see Author response images 9, 10, and 11). For most of the cases (all apart from MEF CAV1KO), the stiffer phenotype of the cells, associated with higher levels of CAV1, shows a higher volume.

Author response image 9.

Cell volumes for the divergent cell states in the five characterized biological systems. (A) Glioblastoma. (B) Carcinoma, (C) MCF10A, (D) iPSCs, (E) Developing neurons. Data corresponds to Figure 2. Cell volumes were estimated using Shape-Out 1.0.10 by rotation of the cell contours.

Author response image 10.

Cell volumes for CAV1 perturbation experiments. (A) CAV1 knock down performed in TGBC cells. (B) CAV1 overexpression in ECC4 and TGBC cells. Data corresponds to Figure 5. Cell volumes were estimated using Shape-Out 1.0.10 by rotation of the cell contours.  

Author response image 11.

Cell volumes for WT and CAV1KO MEFs. Data corresponds to Figure S9. Cell volumes were estimated using Shape-Out 1.0.10 by rotation of the cell contours.  

(3) The authors have already tested a large number of conditions in which perturbations of the level of expression of CAV1 correlates with changes in cell mechanics, but I was wondering whether it also has some direct explanatory value for the initial datasets used - for example for the glioblastoma cells from Figure 2, in the different media, would a knock-down of CAV1 prevent the increase in stiffness observed upon addition of serum, or for the carcinoma cells from different tissues treated with different compounds - if I understand well, the authors have tested a subset of these (ECC4 versus TGBC in figure 5) - how did they choose these and how general is it that the mechanical phenotype changes reported in Figure 2 are all mostly dependant on CAV1 expression level? I must say that the way the text is written and the results shown, it is hard to tell whether CAV1 is really having a dominant effect on cell mechanics in most of these contexts or only a partial effect. I hope I am being clear in my question - I am not questioning the conclusions of Figures 5 and 6, but asking whether the level of expression of CAV1, in the datasets reported in Figure 2, is the dominant explanatory feature for the differences in cell mechanics. 

We thank reviewer for this comment and appreciate the value of the question about the generality and dominance of CAV1 in influencing cell mechanics.

On the computational side, we have addressed these issues by looking at the performance of CAV1 (among other identified genes) in classifying soft and stiff phenotypes across biological systems (positive hypothesis I), as well as across data of different type (sequencing vs microarray data) and origin (different research institutions) (positive hypothesis II). CAV1 showed strong classification performance (Table 4), suggesting it is a general marker of stiffness changes.  

On the experimental side, we conducted the perturbation experiments in two systems of choice: two intestinal carcinoma cell lines (ECC4 and TGBC) and the MCF10A breast epithelial cell line. These choices were driven by ease of handling, accessibility, as well as (for MCF10A) connection with a former study (Taveres et al, 2017). While we observed correlations between CAV1 expression and cell mechanics in wide range of datasets, the precise role of CAV1 in each system may vary, and further perturbation experiments in specific systems could be performed to solidify the direct/dominant role of CAV1 in cell mechanics. We hypothesize that the suggested knockdown of CAV1 upon serum addition in glioblastoma cells could reduce or prevent the increase in stiffness observed, though this experiment has not been performed. 

In conclusion, while the computational analysis gives us confidence that CAV1 is a good indicator of cell stiffness, we predict that it acts in concert with other genes and in specific context could be replaced by other changes. We suggest that the suitability of CAV1 for manipulation of the mechanical properties should be tested in each system of interested before use. 

To highlight the fact that the relevance of CAV1 for modulating cell mechanics in specific systems of interest should be tested and the mechanistic insights into how CAV1 regulates cell mechanics are still missing, we have added the following sentence in the discussion:

“The mechanical phenotype of cells is recognized as a hallmark of many physiological and pathological processes. Understanding how to control it is a necessary next step that will facilitate exploring the impact of cell mechanics perturbations on cell and tissue function (76). The increasing availability of transcriptional profiles accompanying cell state changes has recently been complemented by the ease of screening for mechanical phenotypes of cells thanks to the advent of high-throughput microfluidic methods (77). This provides an opportunity for data-driven identification of genes associated with the mechanical cell phenotype change in a hypothesis-free manner. Here we leveraged this opportunity by performing discriminative network analysis on transcriptomes associated with mechanical phenotype changes to elucidate a conserved module of five genes potentially involved in cell mechanical phenotype regulation. We provided evidence that the inferred conserved functional network module contains an ensemble of five genes that, in particular when combined in a unique combinatorial marker, are universal, specific and trustworthy markers of mechanical phenotype across the studied mouse and human systems. We further demonstrated on the example of a selected marker gene, CAV1, that its experimental up- and downregulation impacts the stiffness of the measured cells. This demonstrates that the level of CAV1 not only correlates with, but also is causative of mechanical phenotype change. The mechanistic insights into how precisely the identified genes are involved in regulating mechanical properties, how they interact with each other, and whether they are universal and dominant in various contexts all remain to be established in future studies.”

(4) It would be nice that the authors try to more directly address, in their discussion, what is the biological meaning of the set of 5 genes that they found - is it really mostly a product of the methodology used, useful but with little specific relevance to any biology, or does it have a deeper meaning? Either at a system level, or at an evolutionary level. 

We would like to highlight that our manuscript is focused on the method that we introduce to identify sets of genes involved in the regulation of cell mechanics. The first implementation included here is only the beginning of this line of work which, in the future, will include looking in detail at the biological meaning and the interconnectivity of the genes identified. Most likely, there is a deeper meaning of the identified module which could be revealed with a lot of dedicated future work. As it is a mere speculation at this point, we would like to refrain from going into more detail about it in the current manuscript. We provide below a few words of extended explanation and additional analysis that can shed light on the current limited knowledge of the connections between the genes and evolutionary preservation of the genes. 

While it is difficult to prove at present, we do believe that the identified node of genes may have an actual biological meaning and is not a mere product of the used methodology. The PC-corr score used for applying the threshold and obtaining the gene network is high only if the Pearson’s correlation between the two genes is high, meaning that the high connected module of genes identified show corelated expression and is likely co-regulated. Additionally, we performed the GO Term analysis using DAVID to assess the connections between the genes (Figure S3). We have now performed an additional analysis using two orthogonal tools the functional protein association tool STRING and KEGG Mapper. 

With STRING, we found a moderate connectivity using the five network nodes identified in our study, and many of the obtained connections were based on text mining and co-expression, rather than direct experimental evidence (Author response image 12A). A more connected network can be obtained by allowing STRING to introduce further nodes (Author response image 12B). Interestingly, some of the nodes included by STRING in the extended network are nodes identified with milder PCcorr thresholds in our study (such as CNN2 or IGFBP3, see Table S3). 

With KEGG Mapper, we did not find an obvious pathway-based clustering of the genes from the module either. A maximum of two genes were assigned to one pathway and those included: 

• focal adhesions (pathway hsa04510): CAV1 and THBS1

• cytoskeleton in muscle cells (pathway hsa04820): FHL2 and THBS1

• proteoglycans in cancer (pathway hsa05205): CAV1 and THBS1.

As for the BRITE hierarchy, following classification was found:

• membrane trafficking(hsa04131): CAV1, IGFBP7, TAGLN, THBS, with following subcategories:

- endocytosis / lipid raft mediated endocytosis/caveolin-mediated endocytosis:

CAV1

- endocytosis / phagocytosis / opsonins: THBS1

- endocytosis / others/ insulin-like growth factor-binding proteins: IGFBP7 o others / actin-binding proteins/others: TAGLN.

Taken together, all that analyses (DAVID, STRING, KEGG) show that at present no direct relationship/single pathway can be found that integrates all the genes from the identified modules. Future experiments, including investigations of how other module nodes are affected when one of the genes is manipulated, will help to establish actual physical or regulatory interactions between the genes from our module. 

To touch upon the evolutionary perspective, we provide an overview of occurrence of the genes from the identified module across the evolutionary tree. This overview shows that the five identified genes are preserved in phylum Chordata with quite high sequence similarity, and even more so within mammals (Author response image 13).

Author response image 12.

Visualisation of interactions between the nodes in the identified module using functional protein association networks tool STRING. (A) Connections obtained using multiple proteins search and entering the five network nodes. (B) Extended network that includes further genes to increase indirect connectivity. The genes are added automatically by STRING. Online version of STRING v12.0 was used with Homo sapiens as species of interest.   

Author response image 13.

Co-occurrence of genes from the network module across the evolutionary tree. Mammals are indicated with the green frame, glires (include mouse), as well as primates (include human) are indicated with yellow frames. The view was generated using online version of STRING 12.0.

Reviewer #2 (Recommendations For Authors) 

(1) The authors need to discuss the level of sensitivity of their mechanical measurements with RT-DC for changes to the membrane compared to changes in microtubules, nucleus, etc. The limited AFM measurements also seem membrane/cortex focused. For these and further reasons below, "universal" doesn't seem appropriate in the title or abstract, and should be deleted. 

We thank the reviewer for this comment. Indeed, RT-DC is a technique that deforms the entire cell to a relatively low degree (inducing ca 17% mean strain, i.e. a deformation of approximately 2.5 µm on a cell with a 15 µm diameter, see Table S9 and Urbanska et al., Nat Methods 2020). Similarly, the AFM indentation experiments performed in this study (using a 5-µm diameter colloidal probe and 1 µm indentation) induce low strains, at which, according to current knowledge, the actin cortex dominates the measured deformations. However, other cellular components, including the membrane, microtubules, intermediate filaments, nucleus, other organelles, and cytoplasmic packing, can also contribute. We have reviewed these contributions in detail in a recent publication (Urbanska and Guck, 2024, Ann Rev Biophys., PMID 38382116). For a particular system, it is hard to speculate without further investigation which parts of the cell have a dominant effect on the measured deformability. We have added now a following paragraph in the discussion to include this information:

“The mechanical phenotype of single cells is a global readout of cell’s resistance to deformation that integrates contributions from all cellular components. The two techniques implemented for measuring cell mechanical in this study — RT-DC and AFM indentation using a spherical indenter with 5 µm radius — exert comparatively low strain on cells (< 3 µm, see Table S9), at which the actin cortex is believed to dominate the measured response. However, other cellular components, including the membrane, microtubules, intermediate filaments, nucleus, other organelles, and cytoplasmic packing, also contribute to the measured deformations (reviewed in detail in (79)) and, for a particular system, it is hard to speculate without further investigation which parts of the cell have a dominant effect on the measured deformability.”

The key strength of measuring the global mechanics is that such measurements are agnostic of the specific origin of the resistance to shape change. As such, the term “universal” could be seen as rather appropriate, as we are not testing specific contributions to cell mechanics, and we see the two methods used (RT-DC and AFM indentation) as representative when it comes to measuring global cell mechanics. And we highlighted many times throughout the text that we are measuring global single-cell mechanical phenotype. 

Most importantly, however, we have used the term “universal” to capture that the genes are preserved across different systems and species, not in relation to the type of mechanical measurements performed and as such we would like to retain the term in the title.

(2) Fig.2 cartoons of tissues is a good idea to quickly illustrate the range of cell culture lines studied. However, it obligates the authors to examine the relevant primary cell types in singlecell RNAseq of human and/or mouse tissues (e.g. Tabula Muris). They need to show CAV1 is expressed in glioblastoma, iPSCs, etc and not a cell culture artifact. CAV1 and the other genes also need to be plotted with literature values of tissue stiffness.  

We thank the reviewer for this the comment; however, we do believe that the cartoons in Figure 2 should assist the reader to readily understand whether cultured cells derived from the respective tissues were used (see cartoons representing dishes), or the cells directly isolated from the tissue were measured (this is the case for the developing neurons dataset). 

We did, however, follow the suggestion of the reviewer to use available resources and checked the expression of genes from the identified network module across various tissues in mouse and human. We first used the Mouse Genome Informatics (MGI; https://www.informatics.jax.org/) to visualize the expression of the genes across organs and organ systems (Author response image 14) as well as across more specific tissue structures (Author response image 15). These two figures show that the five identified genes are expressed quite broadly in mouse. We next looked at the expression of the five genes in the scRNASeq dataset from Tabula Muris (Author response image 16). Here, the expression of respective genes seemed more restricted to specific cell clusters. Finally, we also collected the cross-tissue expression of the genes from our module in human tissues from Human Protein Atlas v23 at both mRNA (Author response image 17) and protein (Author response image 18) levels. CAV1, IGFBP7, and THBS1 showed low tissue specificity at mRNA level, FHL2 was enriched in heart muscle and ovary (the heart enrichment is also visible in Author response image 15 for mouse) and TAGLN in endometrium and intestine. Interestingly, the expression at the protein level (Author response image 18) did not seem to follow faithfully the mRNA levels (Author response image 17). Overall, we conclude that the identified genes are expressed quite broadly across mouse and human tissues. 

Author response image 14.

Expression of genes from the identified module across various organ and organ systems in mouse. The expression matrices for organs (A) and organ systems (B) were generated using Tissue x Gene Matrix tool of Gene eXpression Database (https://www.informatics.jax.org/gxd/, accessed on 22nd September 2024). No pre-selection of stage (age) and assay type (includes RNA and protein-based assays) was applied. The colors in the grid (blues for expression detected and reds for expression not detected) get progressively darker when there are more supporting annotations. The darker colors do not denote higher or lower levels of expression, just more evidence.

Author response image 15.

Expression of genes from the identified module across various mouse tissue structures. The expression matrices for age-selected mouse marked as adult (A) or young individuals (collected ages labelled P42-84 / P w6-w12 / P m1.5-3.0) (B) are presented and were generated using RNASeq Heatmap tool of Gene eXpression Database (https://www.informatics.jax.org/gxd/, accessed on 2nd October 2024).

Author response image 16.

Expression of genes from the identified module across various cell types and organs in t-SNE embedding of Tabula Muris dataset. (A) t-SNE clustering color-coded by organ. (B-F) t-SNE clustering colorcoded for expression of CAV1 (B), IGFBP7 (C), FHL2 (D), TAGLN (E), and THBS1 (F). The plots were generated using FACS-collected cells data through the visualisation tool available at https://tabulamuris.sf.czbiohub.org/ (accessed on 22nd September 2024).

Author response image 17.

Expression of genes from the identified module at the mRNA level across various human tissues. (A-E) Expression levels of CAV1 (A), IGFBP7 (B), FHL2 (C), TAGLN (D), and THBS1 (E). The plots were generated using consensus dataset from Human Protein Atlas v23 https://www.proteinatlas.org/ (accessed on 22nd September 2024).

Author response image 18.

Protein levels of genes from the identified module across various human tissues. (A-E) Protein levels of CAV1 (A), IGFBP7 (B), FHL2 (C), TAGLN (D), and THBS1 (E). The plots were generated using Human Protein Atlas v23 https://www.proteinatlas.org/ (accessed on 22nd September 2024).

Regarding literature values and tissue stiffness, we would like to argue that cell stiffness is not equivalent to tissue stiffness, and we are interested in the former. Tissue stiffness is governed by a combination of cell mechanical properties, cell adhesions, packing and the extracellular matrix. There can be, in fact, mechanically distinct cell types (for example characterized by different metabolic state, malignancy level etc) within one tissue of given stiffness. Hence, we consider that testing for the correlation between tissue stiffness and expression of identified genes is not immediately relevant.

(3) Fig.5D,H show important time-dependent mechanics that need to be used to provide explanations of the differences in RT-DC (5B,F) and in standard AFM indentation expts (5C,G). In particular, it looks to me that RT-DC is a high-f/short-time measurement compared to the AFM indentation, and an additional Main or Supp Fig needs to somehow combine all of this data to clarify this issue. 

We thank the reviewer for this comment. It is indeed the case, that cells typically display higher stiffness when probed at higher rates. We have now expanded on this aspect of the results and added a supplementary figure (Fig. S10) that illustrates the frequencies used in different methods and summarizes the apparent Young’s moduli values into one plot in a frequencyordered manner. Of note, we typically acquire RT-DC measurements at up to three flowrates, and the increase in measurement flow rates accompanying increase in flow rate also results in higher extracted apparent Young’s moduli (see Fig. S10 B,D). We have further added Table S9 that summarizes operating parameters of all three methods used for probing cell mechanics in this manuscript:

“The three techniques for characterizing mechanical properties of cells — RT-DC, AFM indentation and AFM microrheology — differ in several aspects (summarized in Table S9), most notably in the frequency at which the force is applied to cells during the measurements, with RT-DC operating at the highest frequency (~600 Hz), AFM microrheology at a range of frequencies in-between (3–200 Hz), and AFM indentation operating at lowest frequency (5 Hz) (see Table S9 and Figure S10A). Even though the apparent Young’s moduli obtained for TGBCS cells were consistently higher than those for ECC4 cells across all three methods, the absolute values measured for a given cell line varied depending on the methods: RT-DC measurements yielded higher apparent Young’s moduli compared to AFM indentation, while the apparent Young’s moduli derived from AFM microrheology measurements were frequency-dependent and fell between the other two methods (Fig. 5B–D, Fig. S10B). The observed increase in apparent Young’s modulus with probing frequency aligns with previous findings on cell stiffening with increased probing rates observed for both AFM indentation (68, 69) and microrheology assays (70–72).”

(4) The plots in Fig.S4 are important as main Figs, particularly given the cartoons of different tissues in Fig.1,2. However, positive correlations for a few genes (CAV1, IGFBP7, TAGLN) are most clear for the multiple lineages that are the same (stomach) or similar (gli, neural & pluri). The authors need to add green lines and pink lines in all plots to indicate the 'lineagespecific' correlations, and provide measures where possible. Some genes clearly don't show the same trends and should be discussed. 

We thank reviewer for this comment. It is indeed an interesting observation (and worth highlighting by adding the fits to lineage-restricted data) that the relationship between relative change in Young’s modulus and the selected gene expression becomes steeper for samples from similar tissue contexts. 

For the sake of keeping the main manuscript compact, we decided to keep Fig. S7 (formerly Fig. S4) in the supplement, however, we did add the linear fit to the glioblastoma dataset (pink line) and a fit to the related neural/embryonic datasets (gli, neural & pluri – purple line) as advised — see below.

We did not pool the stomach data since it is represented by a single point in the figure, aligning with how the data is presented in the main text—stomach adenocarcinoma cell lines (MKN1 and MKN45) are pooled in Fig. 1B (see below).

We have also amended the respective results section to emphasize that, in certain instances, the correlation between changes in mechanical phenotype and alterations in the expression of analysed genes may be less pronounced:

“The relation between normalized apparent Young’s modulus change and fold-change in the expression of the target genes is presented in Fig. S7. The direction of changes in the expression levels between the soft and stiff cell states in the validation datasets was not always following the same direction (Fig. 4, C to F, Fig. S7). This suggests that the genes associated with cell mechanics may not have a monotonic relationship with cell stiffness, but rather are characterized by different expression regimes in which the expression change in opposite directions can have the same effect on cell stiffness. Additionally, in specific cases a relatively high change in Young’s modulus did not correspond to marked expression changes of a given gene — see for example low CAV1 changes observed in MCF10A PIK3CA mutant (Fig. S7A), or low IGFBP7 changes in intestine and lung carcinoma samples (Fig. S7C). This indicates that the importance of specific targets for the mechanical phenotype change may vary depending on the origin of the sample.”

(5) Table-1 neuro: Perhaps I missed the use of the AFM measurements, but these need to be included more clearly in the Results somewhere. 

To clarify: there were no AFM measurements performed for the developing neurons (neuro) dataset, and it is not marked as such in Table 1. There are previously published AFM measurements for the iPSCs dataset (maybe that caused the confusion?), and we referred to them as such in the table by citing the source (Urbanska et al (30)) as opposed to the statement “this paper” (see the last column of Table 1). We did not consider it necessary to include these previously published data. We have added additional horizontal lines to the table that will hopefully help in the table readability.

Reviewer #3 (For Authors) 

Major 

-  I strongly encourage the authors to validate their approach with a gene for which mechanical data does not exist yet, or explore how the combination of the 5 identified genes is the novel regulator of cell mechanics. 

We appreciate the reviewer’s insightful comment and agree that it would be highly interesting to validate further targets and perform combinatorial perturbations. However, it is not feasible at this point to expand the experimental data beyond the one already provided. We hope that in the future, the collective effort of the cell mechanics community will establish more genes that can be used for tuning of mechanical properties of cells.

- If this paper aims at highlighting the power of PC-Corr as a novel inference approach, the authors should compare its predictive power to that of classical co-expression network analysis or an alternative gold standard. 

We thank the reviewer for the suggestion to compare the predictive power of PC-Corr with classical co-expression network analysis or an alternative gold standard. PC-corr has been introduced and characterized in detail in a previous publication (Ciucci et al, 2017, Sci. Rep.), where it was compared against standard co-expression analysis methods. Here we implement PC-corr for a particular application. Thus, we do not see it as central to the message of the present manuscript to compare it with other available methods again.

- The authors call their 5 identified genes "universal, trustworthy and specific". While they provide a great amount of data all is derived from human and mouse cell lines. I suggest toning this down. 

We thank the reviewers for this comment. To clarify, the terms universal, trustworthy and specific are based on the specific hypotheses tested in the validation part of the manuscript, but we understand that it may cause confusion. We have now toned that the statement by adding “universal, trustworthy and specific across the studied mouse and human systems” in the following text fragments:

(1) Abstract

“(…) We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. (...)”

(2) Last paragraph of the introduction

“(…) We then test the ability of each gene to classify cell states according to cell stiffness in silico on six further transcriptomic datasets and show that the individual genes, as well as their compression into a combinatorial marker, are universally, specifically and trustworthily associated with the mechanical phenotype across the studied mouse and human systems. (…)”

(3) First paragraph of the discussion

“We provided strong evidence that the inferred conserved functional network module contains an ensemble of five genes that, in particular when combined in a unique combinatorial marker, are universal, specific and trustworthy markers of mechanical phenotype across the studied mouse and human systems.”

Minor suggestions 

-  The authors point out how genes that regulate mechanics often display non-monotonic relations with their mechanical outcome. Indeed, in Fig.4 developing neurons have lower CAV1 in the stiff group. Perturbing CAV1 expression in that model could show the nonmonotonic relation and strengthen their claim. 

We thank reviewer for highlighting this important point. It would indeed be interesting to explore the changes in cell stiffness upon perturbation of CAV1 in a system that has a potential to show an opposing behavior. Unfortunately, we are unable to expand the experimental part of the manuscript at this time. We do hope that this point can be addressed in future research, either by our team or other researchers in the field. 

-  In their gene ontology enrichment assay, the authors claim that their results point towards reduced transcriptional activity and reduced growth/proliferation in stiff compared to soft cells. Proving this with a simple proliferation assay would be a nice addition to the paper. 

This is a valuable suggestion that should be followed up on in detail in the future. To give a preliminary insight into this line of investigation, we have had a look at the cell count data for the CAV1 knock down experiments in TGBC cells. Since CAV1 is associated with the GO Term “negative regulation of proliferation/transcription” (high CAV1 – low proliferation), we would expect that lowering the levels of CAV1 results in increased proliferation and higher cell counts at the end of experiment (3 days post transfection). As illustrated in Author response image 19 below, the cell counts were higher for the samples treated with CAV1 siRNAs, though, not in a statistically significant way. Interestingly, the magnitude of the effect partially mirrored the trends observed for the cell stiffness (Figure 5F).

Author response image 19.

The impact of CAV1 knock down on cell counts in TGBC cells. (A) Absolute cell counts per condition in a 6-well format. Cell counts were performed when harvesting for RT-DC measurements using an automated cell counter (Countess II, Thermo Fisher Scientific). (B) The event rates observed during the RT-DC measurements. The harvested cells are resuspended in a specific volume of measuring buffer standardized per experiment (50-100 μl); thus, the event rates reflect the absolute cell numbers in the respective samples. Horizontal lines delineate medians with mean absolute deviation (MAD) as error, datapoints represent individual measurement replicates, with symbols corresponding to matching measurement days. Statistical analysis was performed using two sample two-sided Wilcoxon rank sum test.

Methods

- The AFM indentation experiments are performed with a very soft cantilever at very high speeds. Why? Also, please mention whether the complete AFM curve was fitted with the Hertz/Sneddon model or only a certain area around the contact point. 

We thank the reviewer for this comment. However, we believe that the spring constants and indentation speeds used in our study are typical for measurements of cells and not a cause of concern. 

For the indentation experiments, we used Arrow-TL1 cantilevers (nominal spring constant k = 0.035-0.045 N m⁻¹, Nanoworld, Switzerland) which are used routinely for cell indentation (with over 200 search results on Google Scholar using the term: "Arrow-TL1"+"cell", and several former publications from our group, including Munder et al 2016, Tavares et al 2017, Urbanska et al 2017, Taubenberger et al 2019, Abuhattum et al 2022, among others). Additionally, cantilevers with the spring constants as low as 0.01 N m−1 can be used for cell measurements (Radmacher 2002, Thomas et al, 2013). 

The indentation speed of 5 µm s⁻¹ is not unusually high and does not result in significant hydrodynamic drag. 

For the microrheology experiments, we used slightly stiffer and shorter (100/200 µm compared to 500 µm for Arrow-TL1) cantilevers: PNP-TR-TL (nominal spring constant k = 0.08 N m⁻¹, Nanoworld, Switzerland). The measurement frequencies of 3-200 Hz correspond to movements slightly faster than 5 µm s⁻¹, but cells were indented only to 100 nm, and the data were corrected for the hydrodynamic drag (see equation (8) in Methods section).

Author response image 20.

Exemplary indentation curve obtained using arrow-TL1 decorated with a 5-µm sphere on a ECC4 cell. The shown plot is exported directly from JPK Data Processing software. The area shaded in grey is the area used for fitting the Sneddon model.  

In the indentation experiments, the curves were fitted to a maximal indentation of 1.5 μm (rarely exceeded, see Author response image 20). We have now added this information to the methods section:

- Could the authors include the dataset wt #1 in Fig 4D? Does it display the same trend? 

We thank the reviewer for this comment. To clarify: in the MCF10A dataset (GEO: GSE69822) there are exactly three replicates of each wt (wild type) and ki (knock-in, referring to the H1047R mutation in the PIK3CA) samples. The numbering wt#2, wt#3, wt#4 originated from the short names that were used in the working files containing non-averaged RPKM (possibly to three different measurement replicates that may have not been exactly paired with the ki samples). We have now renamed the samples as wt#1, wt#2 and wt#3 to avoid the confusion. This naming also reflects better the sample description as deposited in the GSE69822 dataset (see Author response table 2).

Author response table 2.

- Reference (3) is an opinion article with the last author as the sole author. It is used twice as a self-standing reference, which is confusing, as it suggests there is previous experimental evidence. 

We thank the reviewer for pointing this out and agree that it may not be appropriate to cite the article (Guck 2019 Biophysical Reviews, formerly Reference (3), currently Reference (76)) in all instances. The references to this opinion article have now been removed from the introduction:

“The extent to which cells can be deformed by external loads is determined by their mechanical properties, such as cell stiffness. Since the mechanical phenotype of cells has been shown to reflect functional cell changes, it is now well established as a sensitive label-free biophysical marker of cell state in health and disease (1-2).”

“Alternatively, the problem can be reverse-engineered, in that omics datasets for systems with known mechanical phenotype changes are used for prediction of genes involved in the regulation of mechanical phenotype in a mechanomics approach.”

But has been kept in the discussion:

“The mechanical phenotype of cells is recognized as a hallmark of many physiological and pathological processes. Understanding how to control it is a necessary next step that will facilitate exploring the impact of cell mechanics perturbations on cell and tissue function

(76).”.

This reference seems appropriate to us as it expands on the point that our ability to control cell mechanics will enable the exploration of its impact on cell and tissue function, which is central to the discussion of the current manuscript. 

-The authors should mention what PC-corr means. Principle component correlation? Pearson's coefficient correlation? 

PC-corr is a combination of loadings from the principal component (PC) analysis and Pearson’s correlation for each gene pair. We have aimed at conveying this in the “Discriminative network analysis on prediction datasets” result section. We have now added and extra sentence at the first appearance of PC-corr to clarify that for the readers from the start:

“After characterizing the mechanical phenotype of the cell states, we set out to use the accompanying transcriptomic data to elucidate genes associated with the mechanical phenotype changes across the different model systems. To this end, we utilized a method for inferring phenotype-associated functional network modules from omics datasets termed PCCorr (28), that relies on combining loadings obtained from the principal component (PC) analysis and Pearson’s correlation (Corr) for every pair of genes. PC-Corr was performed individually on two prediction datasets, and the obtained results were overlayed to derive a conserved network module. Owing to the combination of the Pearson’s correlation coefficient and the discriminative information included in the PC loadings, the PC-corr analysis does not only consider gene co-expression — as is the case for classical co-expression network analysis — but also incorporates the relative relevance of each feature for discriminating between two or more conditions; in our case, the conditions representing soft and stiff phenotypes. The overlaying of the results from two different datasets allows for a multi-view analysis (utilizing multiple sets of features) and effectively merges the information from two different biological systems.”

- The formatting of Table 1 is confusing. Horizontal lines should be added to make it clear to the reader which datasets are human and which mouse as well as which accession numbers belong to the carcinomas. 

Horizontal lines have now been added to improve the readability of Table 1. We hope that makes the table easier to follow and satisfies the request. We assume that further modifications to the table appearance may occur during publishing process in accordance with the publisher’s guidelines. 

- In many figures, data points are shown in different shapes without an explanation of what the shapes represent. 

We thank the reviewer for this comment and apologize for not adding this information earlier. We have added explanations of the symbols to captions of Figures 2, 3, 5, and 6 in the main text:

“Fig. 2. Mechanical properties of divergent cell states in five biological systems. Schematic overviews of the systems used in our study, alongside with the cell stiffness of individual cell states parametrized by Young’s moduli E. (…) Statistical analysis was performed using generalized linear mixed effects model. The symbol shapes represent measurements of cell lines derived from three different patients (A), matched experimental replicates (C), two different reprogramming series (D), and four different cell isolations (E). Data presented in (A) and (D) were previously published in ref (29) and (30), respectively.”

“Fig. 3. Identification of putative targets involved in cell mechanics regulation. (A) Glioblastoma and iPSC transcriptomes used for the target prediction intersect at 9,452 genes. (B, C) PCA separation along two first principal components of the mechanically distinct cell states in the glioblastoma (B) and iPSC (C) datasets. The analysis was performed using the gene expression data from the intersection presented in (A). The symbol shapes in (B) represent cell lines derived from three different patients. (…)”

“Fig. 5. Perturbing levels of CAV1 affects the mechanical phenotype of intestine carcinoma cells. (…) In (E), (F), (I), and (J), the symbol shapes represent experiment replicates.”

“Fig. 6. Perturbations of CAV1 levels in MCF10A-ER-Src cells result in cell stiffness changes. (…)  Statistical analysis was performed using a two-sided Wilcoxon rank sum test. In (B), (D), and (E), the symbol shapes represent experiment replicates.”

As well as to Figures S2, S9, and S11 in the supplementary material (in Figure S2, the symbol explanation was added to the legends in the figure panels as well): 

“Fig. S2. Plots of area vs deformation for different cell states in the characterized systems. Panels correspond to the following systems: (A) glioblastoma, (B) carcinoma, (C) non-tumorigenic breast epithelia MCF10A, (D) induced pluripotent stem cells (iPSCs), and (E) developing neurons. 95%- and 50% density contours of data pooled from all measurements of given cell state are indicated by shaded areas and continuous lines, respectively. Datapoints indicate medians of individual measurements. The symbol shapes represent cell lines derived from three different patients (A), two different reprogramming series (D), and four different cell isolations (E), as indicated in the respective panels. (…).”

“Fig. S9. CAV1 knock-out mouse embryonic fibroblasts (CAV1KO) have lower stiffness compared to the wild type cells (WT). (…) (C) Apparent Young’s modulus values estimated for WT and CAV1KO cells using areadeformation data in (B). The symbol shapes represent experimental replicates. (…)”

“Fig. S11. Plots of area vs deformation from RT-DC measurements of cells with perturbed CAV1 levels. Panels correspond to the following experiments: (A and B) CAV1 knock-down in TGBC cells using esiRNA (A) and ONTarget siRNA (B), (C and D) transient CAV1 overexpression in ECC4 cells (C) and TGBC cells (D). Datapoints indicate medians of individual measurement replicates. The isoelasticity lines in the background (gray) indicate regions of of same apparent Young’s moduli. The symbol shapes represent experimental replicates.”

- In Figure 2, the difference in stiffness appears bigger than it actually is because the y-axes are not starting at 0. 

While we acknowledge that starting the y-axes at a value other than 0 is generally not ideal, we chose this approach to better display data variability and minimize empty space in the plots.

A similar effect can be achieved with logarithmic scaling, which is a common practice (see  Author response image 21 for visualization). We believe our choice of axes cut-off enhances the interpretability of the data without misleading the viewer.

Author response image 21.

Visualization of different axis scaling strategies applied to the five datasets presented in Figure 2 of the manuscript. 

Of note, apparent Young’s moduli obtained from RT-DC measurements typically span 0.5-3.0 kPa (see Figure 2.3 from Urbanska et al 2021, PhD thesis). Differences between treatments rarely exceed a few hundred pascals. For example, in an siRNA screen of mitotic cell mechanics regulators in Drosophila cells (Kc167), the strongest hits (e.g., Rho1, Rok, dia) showed changes in stiffness of 100-150 Pa (see Supplementary Figure 11 from Rosendahl, Plak et al 2018, Nature Methods 15(5): 355-358).

- In Figure 3, I don't personally see the benefit of showing different cut-offs for PC-corr. In the end, the paper focuses on the 5 genes in the pentagram. I think only showing one of the cutoffs and better explaining why those target genes were picked would be sufficient and make it clearer for the reader. 

We believe it is beneficial to show the extended networks for a few reasons. First, it demonstrates how the selected targets connect to the broader panel of the genes, and that the selected module is indeed much more interconnected than other nodes. Secondly, the chosen PC-corr cut-off is somewhat arbitrary and it may be interesting to look through the genes from the extended network as well, as they are likely also important for regulating cell mechanics. This broader view may help readers identify familiar genes and recognizing the connections to relevant signaling networks and processes of interest.

- In Figure 4C, I suggest explaining why the FANTOM5 and not another dataset was used for the visualization here and mentioning whether the other datasets were similar. 

In Figure 4C, we have chosen to present data corresponding to FANTOM5, because that was the only carcinoma dataset in which all the cell lines tested mechanically are presented. We have now added this information to the caption of Figure 4. Additionally, the clustergrams corresponding to the remaining carcinoma datasets (CCLE RNASeq, Genetech ) are presented in supplementary figures S4-S6. 

“The target genes show clear differences in expression levels between the soft and stiff cell states and provide for clustering of the samples corresponding to different cell stiffnesses in both prediction and validation datasets (Fig. 4, Figs. S4-S6).”

Typos 

We would like to thank the Reviewer#3 for their detailed comments on the typos and details listed below. This is much appreciated as it improved the quality of our manuscript.

-  In the first paragraph of the results section the 'and' should be removed from this sentence: Each dataset encompasses two or more cell states characterized by a distinct mechanical phenotype, and for which transcriptomic data is available. 

The sentence has been corrected and now reads:

“Each dataset encompasses two or more cell states characterized by a distinct mechanical phenotype, and for which transcriptomic data is available.”

-  In the methods in the MCF10A PIK3CA cell lines part, it says cell liens instead of cell lines. 

The sentence has been corrected and now reads:

“The wt cells were additionally supplemented with 10 ng ml⁻¹ EGF (E9644, Sigma-Aldrich), while mutant cell lienes were maintained without EGF.”

-  In the legend of Figure 6 "accession number: GSE17941, data previously published in ())" the reference is missing. 

The reference has been added.

-  In the legend of Figure 5 "(E) Verification of CAV1 knock-down in TGBC cells using two knock-down system" 'a' between using and two is missing. 

The legend has been corrected (no ‘a’ is missing, but it should say systems (plural)):

-  In Figure 5B one horizontal line is missing. 

The Figure 5B has been corrected accordingly. 

-  Terms such as de novo or in silico should be written in cursive. 

We thank the Reviewer for this comment; however, we believe that in the style used by eLife, common Latin expressions such as de novo or in vitro are used in regular font.

-  In the heading of Table 4 "The results presented in this table can be reproducible using the code and data available under the GitHub link reported in the methods section." It should say reproduced instead of reproducible. 

Yes, indeed. It has been corrected.

-  The citation of reference 20 contains several author names multiple times. 

Indeed, it has been fixed now:

-  In Figure S2 there is a vertical line in the zeros of the y axis labels. 

I am not sure if there was some rendering issue, but we did not see a vertical line in the zeros of the y axis label in Figure S2.

- The Text in Figure S4 is too small.                   

We thank the reviewer for pointing this out. We have now revised Figure S7 (formerly Figure S4) to increase the text size, ensuring better readability. (It has also been updated to include additional fits as requested by Reviewer #2).

- In Table 3 "positive hypothesis II markers are discriminative of samples with stiff/soft independent of data source" the words 'mechanical phenotype' are missing. 

The column headings in Table 3 have now been updated accordingly.

- In Table S3 explain in the table headline what vi1, vi2 and v are. I assume the loading for PC1, the loading for PC2 and the average of the previous two values. But it should be mentioned somewhere.

The caption of table S3 has been updated to explain the meaning of vi1, vi2 and v.

eLife Assessment

This study addresses a novel and interesting question about how the rise of the Qinghai-Tibet Plateau influenced patterns of bird migration, employing a multi-faceted approach that combines species distribution data with environmental modeling. The findings are valuable for understanding avian migration within a subfield, but the strength of evidence is incomplete due to critical methodological assumptions about historical species-environment correlations, limited tracking data, and insufficient clarity in species selection criteria. Addressing these weaknesses would significantly enhance the reliability and interpretability of the results.

Reviewer #1 (Public review):

Strengths:

This is an interesting topic and a novel theme. The visualisations and presentation are to a very high standard. The Introduction is very well-written and introduces the main concepts well, with a clear logical structure and good use of the literature. The methods are detailed and well described and written in such a fashion that they are transparent and repeatable.

Weaknesses:

I only have one major issue, which is possibly a product of the structure requirements of the paper/journal. This relates to the Results and Discussion, line 91 onwards. I understand the structure of the paper necessitates delving immediately into the results, but it is quite hard to follow due to a lack of background information. In comparison to the Methods, which are incredibly detailed, the Results in the main section reads as quite superficial. They provide broad overviews of broad findings but I found it very hard to actually get a picture of the main results in its current form. For example, how the different species factor in, etc.

Reviewer #2 (Public review):

Summary:

The study tries to assess how the rise of the Qinghai-Tibet Plateau affected patterns of bird migration between their breeding and wintering sites. They do so by correlating the present distribution of the species with a set of environmental variables. The data on species distributions come from eBird. The main issue lies in the problematic assumption that species correlations between their current distribution and environment were about the same before the rise of the Plateau. There is no ground truthing and the study relies on Movebank data of only 7 species which are not even listed in the study. Similarly, the study does not outline the boundaries of breeding sites NE of the Plateau. Thus it is absolutely unclear potentially which breeding populations it covers.

Strengths:

I like the approach for how you combined various environmental datasets for the modelling part.

Weaknesses:

The major weakness of the study lies in the assumption that species correlations between their current distribution and environments found today are back-projected to the far past before the rise of the Q-T Plateau. This would mean that species responses to the environmental cues do not evolve which is clearly not true. Thus, your study is a very nice intellectual exercise of too many ifs.

The second major drawback lies in the way you estimate the migratory routes of particular birds. No matter how good the data eBird provides is, you do not know population-specific connections between wintering and breeding sites. Some might overwinter in India, some populations in Africa and you will never know the teleconnections between breeding and wintering sites of particular species. The few available tracking studies (seven!) are too coarse and with limited aspects of migratory connectivity to give answer on the target questions of your study.

Your set of species is unclear, selection criteria for the 50 species are unknown and variability in their migratory strategies is likely to affect the direction of the effects. In addition, the position of the breeding sites relative to the Q-T plate will affect the azimuths and resulting migratory flyways. So in fact, we have no idea what your estimates mean in Figure 2.

There is no way one can assess the performance of your statistical exercises, e.g. performances of the models.

Author response:

eLife Assessment

This study addresses a novel and interesting question about how the rise of the Qinghai-Tibet Plateau influenced patterns of bird migration, employing a multi-faceted approach that combines species distribution data with environmental modeling. The findings are valuable for understanding avian migration within a subfield, but the strength of evidence is incomplete due to critical methodological assumptions about historical species-environment correlations, limited tracking data, and insufficient clarity in species selection criteria. Addressing these weaknesses would significantly enhance the reliability and interpretability of the results.

We would like to thank you and two anonymous reviewers for your careful, thoughtful, and constructive feedback on our manuscript. These reviews made us revisit a lot of our assumptions and we believe the paper will be much improved as a result. In addition to minor points, we will make three main changes to our manuscript in response to the reviews. First, we will address the concerns on the assumptions of historical species-environment correlations from perspectives of both theoretical and empirical evidence. Second, we will discuss the benefits and limitations of using tracking data in our study and demonstrate how the findings of our study are consolidated with results of previous studies. Third, we will clarify our criteria for selecting species in terms of both eBird and tracking data.

Below, we respond to each comment in turn. Once again, we thank you all for your feedback.

Reviewer #1 (Public review):

Strengths:

This is an interesting topic and a novel theme. The visualisations and presentation are to a very high standard. The Introduction is very well-written and introduces the main concepts well, with a clear logical structure and good use of the literature. The methods are detailed and well described and written in such a fashion that they are transparent and repeatable.

We appreciate the reviewer’s careful reading of our manuscript, encouraging comments and constructive suggestions.

Weaknesses:

I only have one major issue, which is possibly a product of the structure requirements of the paper/journal. This relates to the Results and Discussion, line 91 onwards. I understand the structure of the paper necessitates delving immediately into the results, but it is quite hard to follow due to a lack of background information. In comparison to the Methods, which are incredibly detailed, the Results in the main section reads as quite superficial. They provide broad overviews of broad findings but I found it very hard to actually get a picture of the main results in its current form. For example, how the different species factor in, etc.

Yes, it is the journal request to format in this way (Methods follows the Results and Discussion) for the article type of short reports. As suggested, in the revision we will elaborate on details of our findings, especially the species-specific responses, in terms of (i) shifts of distribution of avian breeding and wintering areas under the influence of the uplift of the Qinghai-Tibetan Plateau, and (ii) major factors that shape current migration patterns of birds in the Plateau. We will also better reference the approaches we used in the study.

Reviewer #2 (Public review):

Summary:

The study tries to assess how the rise of the Qinghai-Tibet Plateau affected patterns of bird migration between their breeding and wintering sites. They do so by correlating the present distribution of the species with a set of environmental variables. The data on species distributions come from eBird. The main issue lies in the problematic assumption that species correlations between their current distribution and environment were about the same before the rise of the Plateau. There is no ground truthing and the study relies on Movebank data of only 7 species which are not even listed in the study. Similarly, the study does not outline the boundaries of breeding sites NE of the Plateau. Thus it is absolutely unclear potentially which breeding populations it covers.

We are very grateful for the careful review and helpful suggestions. We will revise the manuscript carefully in response to the reviewer’s comments and believe that it will be much improved as a result. Below are our point-by-point replies to the comments.

Strengths:

I like the approach for how you combined various environmental datasets for the modelling part.

We appreciate the reviewer’s encouragement.

Weaknesses:

The major weakness of the study lies in the assumption that species correlations between their current distribution and environments found today are back-projected to the far past before the rise of the Q-T Plateau. This would mean that species responses to the environmental cues do not evolve which is clearly not true. Thus, your study is a very nice intellectual exercise of too many ifs.

This is a valid concern. We will address this from both the perspectives of the theoretical design of our study and empirical evidence.

First, we agree with the reviewer that species responses to environmental cues might vary over time. Nonetheless, the simulated environments before the uplift of the plateau serve as a counterfactual state in our study. Counterfactual is an important concept to support causation claims by comparing what happened to what would have happened in a hypothetical situation: “If event X had not occurred, event Y would not have occurred” (Lewis 1973). Recent years have seen an increasing application of the counterfactual approach to detect biodiversity change, i.e., comparing diversity between the counterfactual state and real estimates to attribute the factors causing such changes (e.g., Gonzalez et al. 2023). Whilst we do not aim to provide causal inferences for avian distributional change, using the counterfactual approach, we are able to estimate the influence of the plateau uplift by detecting the changes of avian distributions, i.e., by comparing where the birds would have distributed without the plateau to where they currently distributed. We regard the counterfactual environments as a powerful tool for eliminating, to the extent possible, vagueness, as opposed to simply description of current distributions of birds. Therefore, we assume species’ responses to environments are conservative and their evolution should not discount our findings. We will clarify this in both the Introduction and Methods.

Second, we used species distribution modelling to contrast the distributions of birds before and after the uplift of the plateau under the assumption that species tend to keep their ancestral ecological traits over time (i.e., niche conservatism). This indicates a high probability for species to distribute in similar environments wherever suitable. Particularly, considering birds are more likely to be influenced by food resources (Martins et al. 2024), and the distribution of available food before the uplift (Jia et al. 2020), we believe the findings can provide valuable insights into the influence of the plateau on avian migratory patterns. Having said that, we acknowledge other factors, e.g., carbon dioxide concentrations (Zhang et al. 2022), can influence the simulations of environments and our prediction of avian distribution. We will clarify the assumptions and evidence we have for the modelling in Methods. We will further point out the direction for future studies in the Discussion.

The second major drawback lies in the way you estimate the migratory routes of particular birds. No matter how good the data eBird provides is, you do not know population-specific connections between wintering and breeding sites. Some might overwinter in India, some populations in Africa and you will never know the teleconnections between breeding and wintering sites of particular species. The few available tracking studies (seven!) are too coarse and with limited aspects of migratory connectivity to give answer on the target questions of your study.

We agree with the reviewer that establishing interconnections for birds is important for estimating the migration patterns of birds. We employed a dynamic model to assess their weekly distributions. Thus, we can track the movement of species every week, and capture the breeding and wintering areas for specific populations. That being said, we acknowledge that our approach can be subjected to the patchy sampling of eBird data. We will better demonstrate this in the main text.  

Tracking data can provide valuable insights into the movement patterns of species but are limited to small numbers of species due to the considerable costs and time needed. We aimed to adopt the tracking data to examine the influence of focal factors on avian migration patterns, but only seven species, to the best of our ability, were acquired. Moreover, similar results were found in studies that used tracking data to estimate the distribution of breeding and wintering areas of birds in the plateau (e.g., Prosser et al. 2011, Zhang et al. 2011, Zhang et al. 2014, Liu et al. 2018, Kumar et al. 2020, Wang et al. 2020, Pu and Guo 2023, Yu et al. 2024, Zhao et al. 2024). We believe the conclusions based on seven species are rigour, but their implications could be restricted by the number of tracking species we obtained. We will demonstrate how our findings on breeding and wintering areas of birds are reinforced by other studies reporting the locations of those areas. We will also add a separate caveat section to discuss the limitations stated above.

Your set of species is unclear, selection criteria for the 50 species are unknown and variability in their migratory strategies is likely to affect the direction of the effects.

We will clarify the selection criteria for the 50 species). We first obtained a full list of birds in the plateau from Prins and Namgail (2017). We then extracted species identified as full migrants in Birdlife International (https://datazone.birdlife.org/species/spcdistPOS) from the full list.

In addition, the position of the breeding sites relative to the Q-T plate will affect the azimuths and resulting migratory flyways. So in fact, we have no idea what your estimates mean in Figure 2.

We calculated the azimuths not only by the angles between breeding sites and wintering sites but also based on the angles between the stopovers of birds. Therefore, the azimuths are influenced by the relative positions of breeding, wintering and stopover sites. We will better explain this both in the Methods and legend of Figure 2.

There is no way one can assess the performance of your statistical exercises, e.g. performances of the models.

As suggested, we will add the AUC values to assess the performances of the models.

References

Gonzalez, A., J. M. Chase, and M. I. O'Connor. 2023. A framework for the detection and attribution of biodiversity change. Philosophical Transactions of the Royal Society B: Biological Sciences 378: 20220182.

Jia, Y., H. Wu, S. Zhu, Q. Li, C. Zhang, Y. Yu, and A. Sun. 2020. Cenozoic aridification in Northwest China evidenced by paleovegetation evolution. Palaeogeography, Palaeoclimatology, Palaeoecology 557:109907.

Kumar, N., U. Gupta, Y. V. Jhala, Q. Qureshi, A. G. Gosler, and F. Sergio. 2020. GPS-telemetry unveils the regular high-elevation crossing of the Himalayas by a migratory raptor: implications for definition of a “Central Asian Flyway”. Scientific Reports 10:15988.

Lewis, D. 1973. Counterfactuals. Oxford: Blackwell.

Liu, D., G. Zhang, H. Jiang, and J. Lu. 2018. Detours in long-distance migration across the Qinghai-Tibetan Plateau: individual consistency and habitat associations. PeerJ 6:e4304.

Martins, L. P., D. B. Stouffer, P. G. Blendinger, K. Böhning-Gaese, J. M. Costa, D. M. Dehling, C. I. Donatti, C. Emer, M. Galetti, R. Heleno, Í. Menezes, J. C. Morante-Filho, M. C. Muñoz, E. L. Neuschulz, M. A. Pizo, M. Quitián, R. A. Ruggera, F. Saavedra, V. Santillán, M. Schleuning, L. P. da Silva, F. Ribeiro da Silva, J. A. Tobias, A. Traveset, M. G. R. Vollstädt, and J. M. Tylianakis. 2024. Birds optimize fruit size consumed near their geographic range limits. Science 385:331-336.

Prins, H. H. T., and T. Namgail. 2017. Bird migration across the Himalayas : wetland functioning amidst mountains and glaciers. Cambridge University Press, Cambridge.

Prosser, D. J., P. Cui, J. Y. Takekawa, M. Tang, Y. Hou, B. M. Collins, B. Yan, N. J. Hill, T. Li, Y. Li, F. Lei, S. Guo, Z. Xing, Y. He, Y. Zhou, D. C. Douglas, W. M. Perry, and S. H. Newman. 2011. Wild bird migration across the Qinghai-Tibetan Plateau: a transmission route for highly pathogenic H5N1. PloS One 6:e17622.

Pu, Z., and Y. Guo. 2023. Autumn migration of black-necked crane (Grus nigricollis) on the Qinghai-Tibetan and Yunnan-Guizhou plateaus. Ecology and Evolution 13:e10492.

Wang, Y., C. Mi, and Y. Guo. 2020. Satellite tracking reveals a new migration route of black-necked cranes (Grus nigricollis) in Qinghai-Tibet Plateau. PeerJ 8:e9715.

Yu, X., G. Song, H. Wang, Q. Wei, C. Jia, and F. Lei. 2024. Migratory flyways and connectivity of brown headed gulls (Chroicocephalus brunnicephalus) revealed by GPS tracking. Global Ecology and Conservation 56:e03340.

Zhang, G.G., D.P. Liu, Y.Q. Hou, H.X. Jiang, M. Dai, F.W. Qian, J. Lu, T. Ma, L.X. Chen, and Z. Xing. 2014. Migration routes and stopover sites of Pallas’s gulls Larus ichthyaetus breeding at Qinghai Lake, China, determined by satellite tracking. Forktail 30:104-108.

Zhang, G.G., D.P. Liu, Y.Q. Hou, H.X. Jiang, M. Dai, F.W. Qian, J. Lu, Z. Xing, and F.S. Li. 2011. Migration routes and stop-over sites determined with satellite tracking of bar-headed geese (Anser indicus) breeding at Qinghai Lake, China. Waterbirds 34:112-116, 115.

Zhang, R., D. Jiang, C. Zhang, and Z. Zhang. 2022. Distinct effects of Tibetan Plateau growth and global cooling on the eastern and central Asian climates during the Cenozoic. Global and Planetary Change 218:103969.

Zhao, T., W. Heim, R. Nussbaumer, M. van Toor, G. Zhang, A. Andersson, J. Bäckman, Z. Liu, G. Song, M. Hellström, J. Roved, Y. Liu, S. Bensch, B. Wertheim, F. Lei, and B. Helm. 2024. Seasonal migration patterns of Siberian Rubythroat (Calliope calliope) facing the Qinghai–Tibet Plateau. Movement Ecology 12:54.

eLife Assessment

This study presents important findings demonstrating that FZD5 and FZD8, two of the ten Frizzled proteins, undergo Wnt-mediated endocytosis. The E3 ubiquitin ligases RNF43/ZNRF3 regulate their degradation in a Wnt-dependent manner, a mechanism that was not previously recognized. The evidence supporting the claims of the authors is solid. This research will be of interest to biologists specializing in Wnt signaling, cancer, and regenerative medicine.

Reviewer #1 (Public review):

Summary:

The mechanism by which WNT signals are received and transduced into the cell has been the topic of extensive research. Cell surface levels of the WNT receptors of the FZD family are subject to tight control and it's well established that the transmembrane ubiquitin ligases ZNRF3 and RNF43 target FZDs for degradation and that proteins of the R-spondin family block this effect. This manuscript explores the role that WNT proteins play in receptor internalization, recycling and degradation, and the authors provide evidence that WNTs promote interactions of FZD with the ubiquitin ligases. Using cells mutant in all 3 DVL genes, the authors demonstrate that this effect of WNT on FZD is DVL-independent.

Strengths:

Overall, the data are of good quality and support the authors' hypothesis. Strengths of this study are the use of CRISPR-mutated cell lines to establish genetic requirements for the various components. The finding that FZD internalization and degradation is WNT dependent and does not involve DVL is novel.

Weaknesses:

Weaknesses of the work include a heavy reliance on overexpression and monitoring the effects in a single cell line, HEK293. In addition, the claim of specificity - only FZD5 and FZD8 participate in this process - is not strongly supported.