Author response:

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

We thank you for sending our manuscript for the second round of review.  We are encouraged by the comments from reviewer #2 that our supplementary work on naïve T cells and antibody blockade work satisfied their previous concerns and is important for our work.

The Editors raised concerns that we have shared preliminary data on Nrn1 and AMPAR double knockout mice.  We apologize for our enthusiasm for these studies.  Because of the publication model by eLife, we shared that data not because we needed to persuade the reviewer for publication purposes but rather to agree with the reviewer that the molecular target of Nrn1 is important, and we are progressing in understanding this subject.

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

To Reviewer #1:

Thank you for your thorough review and comments on our work, which you described as “the role of neuritin in T cell biology studied here is new and interesting.”.  We have summarized your comments into two categories: biology and investigation approach, experimental rigor, and data presentation.

Biology and Investigation approach comments:

(1) Questions regarding the T cell anergy model:

Major point “(4) Figure 1E-H. The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this. It would be useful to show that T cells are indeed anergic in this model, especially those that are OVA-specific. The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVA-specific cells, rather than by an anergic status.”

T cell anergy is a well-established concept first described by Schwartz’s group. It refers to the hyporesponsive T cell functional state in antigen-experienced CD4 T cells (Chappert and Schwartz, 2010; Fathman and Lineberry, 2007; Jenkins and Schwartz, 1987; Quill and Schwartz, 1987).  Anergic T cells are characterized by their inability to expand and to produce IL2 upon subsequent antigen re-challenge. In this paper, we have borrowed the existing in vivo T cell anergy induction model used by Mueller’s group for T cell anergy induction (Vanasek et al., 2006).  Specifically, Thy1.1+ Ctrl or Nrn1-/- TCR transgenic OTII cells were co-transferred with the congenically marked Thy1.2+ WT polyclonal Treg cells into TCR-/- mice.  After anergy induction, the congenically marked TCR transgenic T cells were recovered by sorting based on Thy1.1+ congenic marker, and subsequently re-stimulation ex vivo with OVA323-339 peptide. We evaluated the T cell anergic state based on OTII cell expansion in vivo and IL2 production upon OVA323-339 restimulation ex vivo.  

“The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this.”

Because the anergy model by Mueller's group is well established (Vanasek et al., 2006), we did not feel that additional effort was required to validate this model as the reviewer suggested. Moreover, the limited IL2 production among the control cells upon restimulation confirms the validity of this model.

“The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVAspecific cells, rather than by an anergic status”.

Cells from Ctrl and Nrn1-/- mice on a homogeneous TCR transgenic (OTII) background were used in these experiments. The possibility that substantial variability of TCR expression or different expression levels of the transgenic TCR could have impacted IL2 production rather than anergy induction is unlikely.

Overall, we used this in vivo anergy model to evaluate the Nrn1-/- T cell functional state in comparison to Ctrl cells under the anergy induction condition following the evaluation of Nrn1 expression, particularly in anergic T cells.  Through studies using this anergy model, we observed a significant change in Treg induction among OTII cells. We decided to pursue the role of Nrn1 in Treg cell development and function rather than the biology of T cell anergy as evidenced by subsequent experiments.

Minor points “(6) On which markers are anergic cells sorted for RNAseq analysis?”

Cells were sorted out based on their congenic marker marking Ctrl or Nrn1-/- OTII cells transferred into the host mice.  We did not specifically isolate anergic cells for sequencing.

(2) Question regarding the validity of iTreg differentiation model.

Major point: “(5) Figure 2A-C and Figure 3. The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance. In any case, they are different from pTreg cells generated in vivo. Working with pTreg may be challenging, that is why I would suggest generating data with purified nTreg. Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript. Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”.

We thank Reviewer #1 for their feedback. While it is true that iTregs made in vitro and in vivo generated pTregs display several distinctions (e. g., differences in Foxp3 expression stability, for example), we strongly disagree with this statement by Revieweer#1 “The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance.”  The induced Treg cell (iTreg) model was established over 20 years ago (Chen et al., 2003; Zheng et al., 2002), and the model is widely adopted with over 2000 citations. Further, it has been instrumental in understanding different aspects of regulatory T cell biology (Hurrell et al., 2022; John et al., 2022; Schmitt and Williams, 2013; Sugiura et al., 2022).   

Because we have observed reduced pTreg generation in vivo, we choose to use the in vitro iTreg model system to understand the mechanistic changes involved in Treg cell differentiation and function, specifically, neuritin’s role in this process. We have made no claim that iTreg cell biology is identical to pTreg generated in vivo or nTreg cells. However, the iTreg culture system has proved to be a good in vitro system for deciphering molecular events involved in complex processes. As such, it remains a commonly used approach by many research groups in the Treg cell field (Hurrell et al., 2022; John et al., 2022; Sugiura et al., 2022). Moreover, applying the iTreg in vitro culture system has been instrumental in helping us identify the cell electrical state change in Nrn1-/- CD4 cells and revealed the biological link between Nrn1 and the ionotropic AMPA receptor (AMPAR), which we will discuss in the subsequent discussion. It is technically challenging to use nTreg cells for T cell electrical state studies due to their heterogeneous nature from development in an in vivo environment and the effect of manipulation during the nTreg cell isolation process, which can both affect the T cell electrical state.   

“Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript.” 

We have also carried out nTreg studies in vitro in addition to iTreg cells. Similar to Gonzalez-Figueroa et al.'s findings, we did not observe differences in suppression function between Nrn1-/- and WT nTreg using the in vitro suppression assay. However, Nrn1-/- nTreg cells revealed reduced suppression function in vivo (Fig. 2D-L). In fact, Gonzalez-Figueroa et al. observed reduced plasma cell formation after OVA immunization in Treg-specific Nrn1-/- mice, implicating reduced suppression from Nrn1-/- follicular regulatory T (Tfr) cells. Thus, our observation of the reduced suppression function of Nrn1-/- nTreg toward effector T cell expansion, as presented in Fig. 2D-L, does not contradict the results from Gonzalez-Figueroa et al. Rather, the conclusions of these two studies agree that Nrn1 can play important roles in immune suppression observable in vivo that are not captured readily by the in vitro suppression assay.

“Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”

We have stated in the manuscript on page 7 line 208 that “Similar proportions of Foxp3+ cells were observed in Nrn1-/- and Ctrl cells under the iTreg culture condition, suggesting that Nrn1 deficiency does not significantly impact Foxp3+ cell differentiation”. In the revised manuscript, we will include the data on the proportion of Foxp3+ cells before iTreg restimulation.

(3) Confirmation of transcriptomic data regarding amino acids or electrolytes transport change

Minor point“(3) Would not it be possible to perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane? This would be a more interesting demonstration than transcriptomic data.”

We appreciate Review# 1’s suggestion regarding “perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane”.  We have indeed already performed such experiments corroborating the transcriptomics data on differential amino acid and nutrient transporter expression. Specifically, we loaded either iTreg or Th0 cells with membrane potential (MP) dye and measured MP level change after adding the complete set of amino acids (complete AA).  Upon entry, the charge carried by AAs may transiently affect cell membrane potential. Different AA transporter expression patterns may show different MP change patterns upon AA entry, as we showed in Author response image 1. We observed reduced MP change in Nrn1-/- iTreg compared to the Ctrl, whereas in the context of Th0 cells, Nrn1-/- showed enhanced MP change than the Ctrl. We can certainly include these data in the revised manuscript.

Author response image 1.

Membrane potential change induced by amino acids entry. a. Nrn1-/- or WT iTreg cells loaded with MP dye and MP change was measured upon the addition of a complete set of AAs. b. Nrn1-/- or WT Th0 cells loaded with MP dye and MP change was measured upon the addition of a complete set of AAs.

(4) EAE experiment data assessment

Minor point ”(5) Figure 5F. How are cells re-stimulated? If polyclonal stimulation is used, the experiment is not interesting because the analysis is done with lymph node cells. This analysis should either be performed with cells from the CNS or with MOG restimulation with lymph node cells.”

In the EAE study, the Nrn1-/- mice exhibit similar disease onset but a protracted non-resolving disease phenotype compared to the WT control mice.  Several reasons may contribute to this phenotype: 1. Enhanced T effector cell infiltration/persistence in the central nervous system (CNS); 2. Reduced Treg cell-mediated suppression to the T effector cells in the CNS; 3. Protracted non-resolving inflammation at the immunization site has the potential to continue sending T effector cells into CNS, contributing to persistent inflammation. Based on this reasoning, we examined the infiltrating T effector cell number and Treg cell proportion in the CNS.  We also restimulated cells from draining lymph nodes close to the inflammation site, looking for evidence of persistent inflammation.  When mice were harvested around day 16 after immunization, the inflammation at the local draining lymph node should be at the contraction stage.  We stimulated cells with PMA and ionomycin intended to observe all potential T effector cells involved in the draining lymph node rather than only MOG antigen-specific cells.  We disagree with Reviewer #1’s assumption that “This analysis should either be performed with cells from the CNS or with MOG restimulation with lymph node cells.”. We think the experimental approach we have taken has been appropriately tailored to the biological questions we intended to answer.

Experimental rigor and data presentation.

(1) data labeling and additional supporting data

Major points

(2) The authors use Nrn1+/+ and Nrn1+/- cells indiscriminately as control cells on the basis of similar biology between Nrn1+/+ and Nrn1+/- cells at homeostasis. However, it is quite possible that the Nrn1+/- cells have a phenotype in situations of in vitro activation or in vivo inflammation (cancer, EAE). It would be important to discriminate Nrn1+/- and Nrn1+/+ cells in the data or to show that both cell types have the same phenotype in these conditions too.

(3) Figure 1A-D. Since the authors are using the Nrp1 KO mice, it would be important to confirm the specificity of the anti-Nrn1 mAb by FACS. Once verified, it would be important to add FACS results with this mAb in Figures 1A-C to have single-cell and quantitative data as well.

Minor points  

(1) Line 119, 120 of the text. It is said that one of the most up-regulated genes in anergic cells is Nrn1 but the data is not shown.

(2) For all figures showing %, the titles of the Y axes are written in an odd way. For example, it is written "Foxp3% CD4". It would be more conventional and clearer to write "% Foxp3+ / CD4+" or "% Foxp3+ among CD4+".

(4) For certain staining (Figure 3E, H) it would be important to show the raw data, in addition to MFI or % values.

We can adapt the labeling and provide additional data, including Nrn1 staining on Treg cells and flow graphs for pmTOR and pS6 staining (Fig. 3H), as requested by Reviewer #1.

(2) Experimental rigor:

General comments:

“However, it is disappointing that reading this manuscript leaves an impression of incomplete work done too quickly.”

We were discouraged to receive the comment, “this manuscript leaves an impression of incomplete work done too quickly.” Our study of this novel molecule began without any existing biological tools such as antibodies, knockout mice, etc.  Over the past several years, we have established our own antibodies for Nrn1 detection, obtained and characterized Nrn1 knockout mice, and utilized multiple approaches to identify the molecular mechanism of Nrn1 function. Through the use of the in vitro iTreg system described in this manuscript, we identified the association of Nrn1 deficiency with cell electrical state change, potentially connected to AMPAR function. We have further corroborated our findings by generating Nrn1 and AMPAR T cell specific double knockout mice and confirmed that T cell specific AMPAR deletion could abrogate the phenotype caused by the Nrn1 deficiency (see Support Figure 2).  We did not include the double knockout data in the current manuscript because AMPAR function has not yet been studied thoroughly in T cell biology, and we feel this topic warrants examination in its own right.  However, the unpublished data support the finding that Nrn1 modulates the T cell electrical state and, consequently, metabolism, ultimately influencing tolerance and immunity.  In its current form, the manuscript represents the first characterization of the novel molecule Nrn1 in anergic cells, Tregs, and effector T cells. While this work has led to several exciting additional questions, we disagree that the novel characterization we have presented Is incomplete. We feel that our present data set, which squarely highlights Nrn1’s role as an important immune regulator while shedding unprecedented light on the molecular events involved, will be of considerable interest to a broad field of researchers.

“Multiple models have been used, but none has been studied thoroughly enough to provide really conclusive and unambiguous data. For example, 5 different models were used to study T cells in vivo. It would have been preferable to use fewer, but to go further in the study of mechanisms.”

We have indeed used multiple in vivo models to reveal Nrn1's function in Treg differentiation, Treg suppression function, T effector cell differentiation and function, and the overall impact on autoimmune disease. Because the impact of ion channel function is often context-dependent, we examined the biological outcome of Nrn1 deficiency in several in vivo contexts.  We would appreciate it if Reviewer#1 would provide a specific example, given the Nrn1 phenotype, of how to proceed deeper to investigate the electrical change in the in vivo models.

“Major points

(1) A real weakness of this work is the fact that in most of the results shown, there are few biological replicates with differences that are often small between Ctrl and Nrn1 -/-. The systematic use of student's t-test may lead to thinking that the differences are significant, which is often misleading given the small number of samples, which makes it impossible to know whether the distributions are Gaussian and whether a parametric test can be used. RNAseq bulk data are based on biological duplicates, which is open to criticism.”

We respectfully disagree with Reviewer #1 on the question of statistical power and significance to our work. We have used 5-8 mice/group for each in vivo model and 3-4 technical replicates for the in vitro studies, with a minimum of 2-3 replicate experiments. These group sizes and replication numbers are in line with those seen in high-impact publications. While some differences between Ctrl and Nrn1-/- appear small, they have significant biological consequences, as evidenced by the various Nrn1-/- in vivo phenotypes. Furthermore, we believe we have subjected our data to the appropriate statistical tests to ensure rigorous analysis and representation of our findings.

To Reviewer #2.

We thank Reviewer #2 for the careful review of the manuscript. We especially appreciate the comments that “The characterizations of T cell Nrn1 expression both in vitro and in vivo are comprehensive and convincing. The in vivo functional studies of anergy development, Treg suppression, and EAE development are also well done to strengthen the notion that Nrn1 is an important regulator of CD4 responsiveness.”

“The major weakness of this study stems from a lack of a clear molecular mechanism involving Nrn1. “  

We fully understand this comment from Reviewer #2. The main mechanism we identified contributing to the functional defect of Nrn1-/- T cells involves novel effects on the electric and metabolic state of the cells. Although we referenced neuronal studies that indicate Nrn1 is the auxiliary protein for the ionotropic AMPA-type glutamate receptor (AMPAR) and may affect AMPAR function, we did not provide any evidence in this manuscript as the topic requires further in-depth study.   

For the benefit of this discussion, we include our preliminary Nrn1 and AMPAR double knockout data (Author response image 2), which indicates that abrogating AMPAR expression can compensate for the defect caused by Nrn1 deficiency in vitro and in vivo. This preliminary data supports the notion that Nrn1 modulates AMPAR function, which causes changes in T cell electric and metabolic state, influencing T cell differentiation and function.  

Author response image 2.

Deletion of AMPAR expression in T cells compensates for the defect caused by Nrn1 deficiency. Nrn1-/- mice were crossed with T cell-specific AMPAR knockout mice (AMPARfl/flCD4Cre+) mice. The following mice were generated and used in the experiment: T cell specific AMPAR-knockout and Nrn1 knockout mice (AKONKO), Nrn1 knockout mice (AWTNKO), Ctrl mice (AWTNWT). a. Deletion of AMPAR compensates for the iTreg cell defect observed in Nrn1-/- CD4 cells. iTreg live cell proportion, cell number, and Ki67 expression among Foxp3+ cells 3 days after aCD3 restimulation. b. Deletion of AMPAR in T cells abrogates the enhanced autoimmune response in Nrn1-/- Mouse in the EAE disease model. Mouse relative weight change and disease score progression after EAE disease induction.  

Ion channels can influence cell metabolism through multiple means (Vaeth and Feske, 2018; Wang et al., 2020). First, ion channels are involved in maintaining cell resting membrane potential. This electrical potential difference across the cell membrane is essential for various cellular processes, including metabolism (Abdul Kadir et al., 2018; Blackiston et al., 2009; Nagy et al., 2018; Yu et al., 2022). Second, ion channels facilitate the movement of ions across cell membranes. These ions are essential for various metabolic processes. For example, ions like calcium (Ca2+), potassium (K+), and sodium (Na+) play crucial roles in signaling pathways that regulate metabolism (Kahlfuss et al., 2020). Third, ion channel activity can influence cellular energy balance due to ATP consumption associated with ion transport to maintain ion balances (Erecińska and Dagani, 1990; Gerkau et al., 2019). This, in turn, can impact processes like ATP production, which is central to cellular metabolism. Thus, ion channel expression and function determine the cell’s bioelectric state and contribute to cell metabolism (Levin, 2021).

Because the AMPAR function has not been thoroughly studied using a genetic approach in T cells, we do not intend to include the double knockout data in this manuscript before fully characterizing the T cell-specific AMPAR knockout mice.  

“Although the biochemical and informatics studies are well-performed, it is my opinion that these results are inconclusive in part due to the absence of key "naive" control groups. This limits my ability to understand the significance of these data.

Specifically, studies of the electrical and metabolic state of Nrn1-/- inducible Treg cells (iTregs) would benefit from similar data collected from wild-type and Nrn1-/- naive CD4 T cells.”

We appreciate the reviewer’s comments. This comment reflects two concerns in data interpretation:

(1) Are Nrn1-/- naïve T cells fundamentally different from WT cells? Does this fundamental difference contribute to the observed electrical and metabolic phenotype in iTreg or Th0 cells? This is a very good question we will perform the experiments as the reviewer suggested. While Nrn1 is expressed at a basal (low) level in naïve T cells, deletion of Nrn1 may cause changes in naïve T cell phenotype.   

(2) Is the Nrn1-/- phenotype caused by Nrn1 functional deficiency or due to the secondary effect of Nrn1 deletion, such as non-physiological cell membrane structure changes?

We have done the following experiment to address this concern.  We have cultured WT T cells in the presence of Nrn1 antibody and compared the outcome with Nrn1-/- iTreg cells (Figure 3-figure supplement 2D,E,F). WT iTreg cells under antibody blockade exhibited similar changes as Nrn1-/- iTreg cells, confirming the physiological relevance of the Nrn1-/- phenotype.

Manuscript Revision based on the Reviewer’s suggestions:

Reviewer #1:

Major points (3) Figure 1A-D. Since the authors are using the Nrp1 KO mice, it would be important to confirm the specificity of the anti-Nrn1 mAb by FACS. 

Following the suggestion by Reviewer#1, We have included the Nrn1 Ab staining on activated Nrn1-/- CD4 cells in Figure 1D. We have also added the staining of cell surface Nrn1 on Treg cells in Figure 1-figure supplement 1D.

Major point: (5) “Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”

In the revised manuscript, we have included the proportion of Foxp3+ cells among Nrn1-/- and ctrl iTreg cells developed under the iTreg culture condition in Figure 2A.

Minor points  

(2) For all figures showing %, the titles of the Y axes are written in an odd way. For example, it is written "Foxp3% CD4". It would be more conventional and clearer to write "% Foxp3+ / CD4+" or "% Foxp3+ among CD4+".

Following reviewer#1’s suggestion, we have changed the Y-axis label in all the relevant figures.

(3) Would not it be possible to perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane? This would be a more interesting demonstration than transcriptomic data.”

We appreciate Review# 1’s suggestion regarding “perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane”.  We have used AAinduced cellular MP changes to confirm differential AA transporter expression patterns and their impact on cellular MP levels.  The data are included in the revised manuscript in Figure 3H and Figure 4K.

(4) For certain staining (Figure 3E, H) it would be important to show the raw data, in addition to MFI or % values.

We appreciated Reviewer #1’s suggestion and have included the histogram staining data for Figure 3E. We have moved the original Figure 3H to the supplemental figure and included the histogram staining data in Figure 3-figure supplement 1C.  Similarly, we have included the histogram staining data in Figure 4-figure supplement 1C.

Reviewer#2:

“Although the biochemical and informatics studies are well-performed, it is my opinion that these results are inconclusive in part due to the absence of key "naive" control groups. This limits my ability to understand the significance of these data.

Specifically, studies of the electrical and metabolic state of Nrn1-/- inducible Treg cells (iTregs) would benefit from similar data collected from wild-type and Nrn1-/- naive CD4 T cells.”

We greatly appreciate Reviewer#2’s suggestion and have carried out experiments on naïve CD4 cells derived from Nrn1-/- and WT mice. We have compared membrane potential, AA-induced MP change between Nrn1-/- and WT naïve T cells, and the metabolic state of Nrn1-/- and WT naïve T cells by carrying out glucose stress tests and mitochondria stress tests using a seahorse assay.  Moreover, to investigate whether the phenotype revealed in Nrn1-/- CD4 cells was caused by a secondary effect of cell membrane structure change due to Nrn1 deletion, we carried out Nrn1 antibody blockade in WT CD4 cells and investigated the phenotypic change. These new results are included in Figure 3-figure supplement 2.

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Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study addresses how faces and bodies are integrated in two STS face areas revealed by fMRI in the primate brain. It builds upon recordings and analysis of the responses of large populations of neurons to three sets of images, that vary face and body positions. These sets allowed the authors to thoroughly investigate invariance to position on the screen (MC HC), to pose (P1 P2), to rotation (0 45 90 135 180 225 270 315), to inversion, to possible and impossible postures (all vs straight), to the presentation of head and body together or in isolation. By analyzing neuronal responses, they found that different neurons showed preferences for body orientation, head orientation, or the interaction between the two. By using a linear support vector machine classifier, they show that the neuronal population can decode head-body angle presented across orientations, in the anterior aSTS patch (but not middle mSTS patch), except for mirror orientation.

Strengths:

These results extend prior work on the role of Anterior STS fundus face area in face-body integration and its invariance to mirror symmetry, with a rigorous set of stimuli revealing the workings of these neuronal populations in processing individuals as a whole, in an important series of carefully designed conditions.

Minor issues and questions that could be addressed by the authors:

(1) Methods. While monkeys certainly infer/recognize that individual pictures refer to the same pose with varying orientations based on prior studies (Wang et al.), I am wondering whether in this study monkeys saw a full rotation of each of the monkey poses as a video before seeing the individual pictures of the different orientations, during recordings.

The monkeys had not been exposed to videos of a rotating monkey pose before the recordings. However, they were reared and housed with other monkeys, providing them with ample experience of monkey poses from different viewpoints.

(2) Experiment 1. The authors mention that neurons are preselected as face-selective, body-selective, or both-selective. Do the Monkey Sum Index and ANOVA main effects change per Neuron type?

We have performed a new analysis to assess whether the Monkey Sum Index is related to the response strength for the face versus the body as measured in the Selectivity Test of Experiment 1. To do this we selected face- and body-category selective neurons, as well as neurons responding selectively to both faces and bodies. First, we selected those neurons that responded significantly to either faces, bodies, or the two control object categories, using a split-plot ANOVA for these 40 stimuli. From those neurons, we selected face-selective ones having at least a twofold larger mean net response to faces compared to bodies (faces > 2 * bodies) and the control objects for faces (faces  > 2* objects). Similarly, a body-selective neuron was defined by a twofold larger mean net response to bodies compared to faces and the control objects for bodies. A body-and-face selective neuron was defined as having a twofold larger net response to the faces compared to their control objects, and to bodies compared to their control objects, with the ratio between mean response to bodies and faces being less than twofold. Then, we compared the distribution of the Monkey Sum Index (MSI) for each region (aSTS; mSTS), pose (P1, P2), and centering (head- (HC) or monkey-centered (MC)) condition. Too few body-and-face selective neurons were present in each combination of region, pose, and centering (a maximum of 7) to allow a comparison of their MSI distribution with the other neuron types. The Figure below shows the distribution of the MSI for the different orientation-neuron combinations for the body- and face-selective neurons (same format as in Figure 3a, main text). The number of body-selective neurons, according to the employed criteria, varied from 21 to 29, whereas the number of face-selective neurons ranged from 14 to 24 (pooled across monkeys). The data of the two subjects are shown in a different color and the number of cases for each subject is indicated (n1: number of cases for M1; n2: number of cases for M2). The arrows indicate the medians for the data pooled across the monkey subjects. For the MC condition, the MSI tended to be more negative (i.e. relatively less response to the monkey compared to the sum of the body and face responses) for the face compared to the body cells, but this was significant only for mSTS and P1 (p = 0.043; Wilcoxon rank sum test; tested after averaging the indices per neuron to avoid dependence of indices within a neuron). No consistent, nor significant tendencies were observed for the HC stimuli. This absence of a consistent relationship between MSI and face- versus body-selectivity is in line with the absence of a correlation between the MSI and face- versus body-selectivity using natural images of monkeys in a previous study (Zafirova Y, Bognár A, Vogels R. Configuration-sensitive face-body interactions in primate visual cortex. Prog Neurobiol. 2024 Jan;232:102545).

We did not perform a similar analysis for the main effects of the two-way ANOVA because the very large majority of neurons showed a significant effect of body orientation and thus no meaningful difference between the two neuron types can be expected.

Author response image 1.

(3) I might have missed this information, but the correlation between P1 and P2 seems to not be tested although they carry similar behavioral relevance in terms of where attention is allocated and where the body is facing for each given head-body orientation.

Indeed, we did not compute this correlation between the responses to the sitting (P1) and standing (P2) pose avatar images. However, as pointed out by the reviewer, one might expect such correlations because of the same head orientations and body-facing directions. Thus, we computed the correlation between the 64 head-body orientation conditions of P1 and P2 for those neurons that were tested with both poses and showed a response for both poses (Split-plot ANOVA). This was performed for the Head-Centered and Monkey-Centered tests of Experiment 1 for each monkey and region. Note that not all neurons were tested with both poses (because of failure to maintain isolation of the single unit in both tests or the monkey stopped working) and not all neurons that were recorded in both tests showed a significant response for both poses, which is not unexpected since these neurons can be pose selective. The distribution of the Pearson correlation coefficients of the neurons with a significant response in both tests is shown in Figure S1. The median correlation coefficient was significantly larger than zero for each region, monkey, and centering condition (outcome of Wilcoxon tests, testing whether the median was different from zero (p1 = p-value for M1; p2: p-value for M2) in Figure), indicating that the effect of head and/or body orientation generalizes across pose. We have noted this now in the Results (page 12) and added the Figure (New Figure S1) in the Suppl. Material.

(4) Is the invariance for position HC-MC larger in aSTS neurons compared to mSTS neurons, as could be expected from their larger receptive fields?

Yes, the position tolerance of the interaction of body and head orientation was significantly larger for aSTS compared to mSTS neurons, as we described on pages 11 and 12 of the Results. This is in line with larger receptive fields in aSTS than in mSTS. However, we did not plot receptive fields in the present study.

(5) L492 "The body-inversion effect likely results from greater exposure to upright than inverted bodies during development". Monkeys display more hanging upside-down behavior than humans, however, does the head appear more tilted in these natural configurations?

Indeed, infant monkeys do spend some time hanging upside down from their mother's belly. While we lack quantitative data on this behavior, casual observations suggest that even young monkeys spend more time upright. The tilt of the head while hanging upside down can vary, just as it does in standing or sitting monkeys (as when they search for food or orient to other individuals). To our knowledge, no quantitative data exist on the frequency of head tilts in upright versus upside-down monkeys. Therefore, we refrain from further speculation on this interesting point, which warrants more attention.

(6) Methods in Experiment 1. SVM. How many neurons are sufficient to decode the orientation?

The number of neurons that are needed to decode the head-body orientation angle depends on which neurons are included, as we show in a novel analysis of the data of Experiment 1. We employed a neuron-dropping analysis, similar to Chiang et al. (Chiang FK, Wallis JD, Rich EL. Cognitive strategies shift information from single neurons to populations in prefrontal cortex. Neuron. 2022 Feb 16;110(4):709-721) to assess the positive (or negative) contribution of each neuron to the decoding performance. We performed cross-validated linear SVM decoding N times, each time leaving out a different neuron (using N-1 neurons; 2000 resamplings of pseudo-population vectors). We then ranked decoding accuracies from highest to lowest, identifying the ‘worst’ (rank 1) to ‘best’ (rank N) neurons. Next, we conducted N decodings, incrementally increasing the number of included neurons from 1 to N, starting with the worst-ranked neuron (rank 1) and sequentially adding the next (rank 2, rank 3, etc.). This analysis focused on zero versus straight angle decoding in the aSTS, as it yielded the highest accuracy. We applied it when training on MC and testing on HC for each pose. Plotting accuracy as a function of the number of included neurons suggested that less than half contributed positively to decoding. We show also the ten “best” neurons for each centering condition and pose. These have a variety of tuning patterns for head and body orientation suggesting that the decoding of head-body orientation angle depends on a population code. Notably, the best-ranked (rank N) neuron alone achieved above-chance accuracy. We have added this interesting and novel result to the Results (page 16) and Suppl. Material (new Figure S3).

(7) Figure 3D 3E. Could the authors please indicate for each of these neurons whether they show a main effect of face, body, or interaction, as well as their median corrected correlation to get a flavor of these numbers for these examples?

We have indicated these now in Figure 3.

(8) Methods and Figure 1A. It could be informative to precise whether the recordings are carried in the lateral part of the STS or in the fundus of the STS both for aSTS and mSTS for comparison to other studies that are using these distinctions (AF, AL, MF, ML).

In experiment 1, the recording locations were not as medial as the fundus. For experiments 2 and 3, the ventral part of the fundus was included, as described in the Methods. We have added this to the Methods now (page 31).

Wang, G., Obama, S., Yamashita, W. et al. Prior experience of rotation is not required for recognizing objects seen from different angles. Nat Neurosci 8, 1768-1775 (2005). https://doi-org.insb.bib.cnrs.fr/10.1038/nn1600

Reviewer #2 (Public review):

Summary:

This paper investigates the neuronal encoding of the relationship between head and body orientations in the brain. Specifically, the authors focus on the angular relationship between the head and body by employing virtual avatars. Neuronal responses were recorded electrophysiologically from two fMRI-defined areas in the superior temporal sulcus and analyzed using decoding methods. They found that: (1) anterior STS neurons encode head-body angle configurations; (2) these neurons distinguish aligned and opposite head-body configurations effectively, whereas mirror-symmetric configurations are more difficult to differentiate; and (3) an upside-down inversion diminishes the encoding of head-body angles. These findings advance our understanding of how visual perception of individuals is mediated, providing a fundamental clue as to how the primate brain processes the relationship between head and body - a process that is crucial for social communication.

Strengths:

The paper is clearly written, and the experimental design is thoughtfully constructed and detailed. The use of electrophysiological recordings from fMRI-defined areas elucidated the mechanism of head-body angle encoding at the level of local neuronal populations. Multiple experiments, control conditions, and detailed analyses thoroughly examined various factors that could affect the decoding results. The decoding methods effectively and consistently revealed the encoding of head-body angles in the anterior STS neurons. Consequently, this study offers valuable insights into the neuronal mechanisms underlying our capacity to integrate head and body cues for social cognition-a topic that is likely to captivate readers in this field.

Weaknesses:

I did not identify any major weaknesses in this paper; I only have a few minor comments and suggestions to enhance clarity and further strengthen the manuscript, as detailed in the Private Recommendations section.

Reviewer #3 (Public review):

Summary:

Zafirova et al. investigated the interaction of head and body orientation in the macaque superior temporal sulcus (STS). Combining fMRI and electrophysiology, they recorded responses of visual neurons to a monkey avatar with varying head and body orientations. They found that STS neurons integrate head and body information in a nonlinear way, showing selectivity for specific combinations of head-body orientations. Head-body configuration angles can be reliably decoded, particularly for neurons in the anterior STS. Furthermore, body inversion resulted in reduced decoding of head-body configuration angles. Compared to previous work that examined face or body alone, this study demonstrates how head and body information are integrated to compute a socially meaningful signal.

Strengths:

This work presents an elegant design of visual stimuli, with a monkey avatar of varying head and body orientations, making the analysis and interpretation straightforward. Together with several control experiments, the authors systematically investigated different aspects of head-body integration in the macaque STS. The results and analyses of the paper are mostly convincing.

Weaknesses:

(1) Using ANOVA, the authors demonstrate the existence of nonlinear interactions between head and body orientations. While this is a conventional way of identifying nonlinear interactions, it does not specify the exact type of the interaction. Although the computation of the head-body configuration angle requires some nonlinearity, it's unclear whether these interactions actually contribute. Figure 3 shows some example neurons, but a more detailed analysis is needed to reveal the diversity of the interactions. One suggestion would be to examine the relationship between the presence of an interaction and the neural encoding of the configuration angle.

This is an excellent suggestion. To do this, one needs to identify the neurons that contribute to the decoding of head-body orientation angles. For that, we employed a neuron-dropping analysis, similar to Chiang et al. (Chiang FK, Wallis JD, Rich EL. Cognitive strategies shift information from single neurons to populations in prefrontal cortex. Neuron. 2022 Feb 16;110(4):709-721.) to assess the positive (or negative) contribution of each neuron to the decoding performance. We performed cross-validated linear SVM decoding N times, each time leaving out a different neuron (using N-1 neurons; 2000 resamplings of pseudo-population vectors). We then ranked decoding accuracies from highest to lowest, identifying the ‘worst’ (rank 1) to ‘best’ (rank N) neurons. Next, we conducted N decodings, incrementally increasing the number of included neurons from 1 to N, starting with the worst-ranked neuron (rank 1) and sequentially adding the next (rank 2, rank 3, etc.). This analysis focused on zero versus straight angle decoding in the aSTS, as it yielded the highest accuracy. We applied it when training on MC and testing on HC for each pose. Plotting accuracy as a function of the number of included neurons suggested that less than half contributed positively to decoding (see Figure S3). We examined the tuning for head and body orientation of the 10 “best” neurons (Figure S3). For half or more of those the two-way ANOVA showed a significant interaction. These are indicated by the red color in the Figure. They showed a variety of tuning patterns for head and body orientation, suggesting that the decoding of the head-body orientation angle results from a combination of neurons with different tuning profiles. Based on a suggestion from reviewer 2, we performed for each neuron of experiment 1 a one-way ANOVA with as factor head-body orientation angle. To do that, we combined all 64 trials that had the same head-body orientation angle. The percentage of neurons (required to be responsive in the tested condition) for which this one-way ANOVA was significant was low but larger than the expected 5% (Type 1 error), with a median of 16.5% (range: 3 to 23%) in aSTS and 8% for mSTS (range: 0-19%). However, a higher percentage of the 10 best neurons for each pose (indicated by the star) showed a significant one-way ANOVA for angle (for P1, MC: 50% (95% confidence interval (CI): 19% – 81%); P1, HC: 70% (CI: 35% - 93%); P2, MC: 70% (CI: 35% – 93%); P2: HC: 50% (CI: 19%-81%)). These percentages were significantly higher than expected for a random sample from the population of neurons for each pose-centering combination (expected percentages listed in the same order as above: 16%, 13%, 16%, and 10%; all outside CI). Thus, for at least half of the “best” neurons, the response differed significantly among the head-orientation angles at the single neuron level. Nonetheless, the tuning profiles were diverse, suggesting a populationl code for head-body orientation angle. We have added this interesting and novel result to the Results (page 16) and Suppl. Material (Figure S3).

(2) Figure 4 of the paper shows a better decoding of the configuration angle in the anterior STS than in the middle STS. This is an interesting result, suggesting a transformation in the neural representation between these two areas. However, some control analyses are needed to further elucidate the nature of this transformation. For example, what about the decoding of head and body orientations - dose absolute orientation information decrease along the hierarchy, accompanying the increase in configuration information?

We have performed now two additional analyses, one in which we decoded the orientation of the head and another one in which we decoded the orientation of the body. We employed the responses to the avatar of experiment 1, using the same sample of neurons of which we decoded the head-body orientation angle. To decode the head orientation, the trials with identical head orientation, irrespective of their body orientation, were given the same label. For this, we employed only responses in the head-centered condition. To decode the body orientation, the trials with identical body orientation, irrespective of their head orientation, had the same label, and we employed only responses in the body-centered condition. The decoding was performed separately for each pose (P1 and P2) and region. We decoded either the responses of 20 neurons (10 randomly sampled from each monkey for each of the 1000 resamplings), 40 neurons (20 randomly sampled per monkey), or 60 neurons (30 neurons per monkey) since the sample of 60 neurons yielded close to ceiling performance for the body orientation decoding. For each pose, the body orientation decoding was worse for aSTS than for mSTS, although this difference reached significance only for P1 and for the 40 neurons sample of P2 (p < 0.025; two-tailed test; same procedure as employed for testing the significance of the decoding of whole-body orientation for upright versus inverted avatars (Experiment 3))). Face orientation decoding was significantly worse for aSTS compared to mSTS. These results are in line with the previously reported decreased decoding of face orientation in the anterior compared to mid-STS face patches (Meyers EM, Borzello M, Freiwald WA, Tsao D. Intelligent information loss: the coding of facial identity, head pose, and non-face information in the macaque face patch system. J Neurosci. 2015 May 6;35(18):7069-81), and decreased decoding of body orientation in anterior compared to mid-STS body patches (Kumar S, Popivanov ID, Vogels R. Transformation of Visual Representations Across Ventral Stream Body-selective Patches. Cereb Cortex. 2019 Jan 1;29(1):215-229). As mentioned by the reviewer, this contrasts with the decoding of the head-body orientation angle, which increases when moving more anteriorly. We mention this finding now in the Discussion (page 27) and present the new Figure S10 in the Suppl. Material.    

(3) While this work has characterized the neural integration of head and body information in detail, it's unclear how the neural representation relates to the animal's perception. Behavioural experiments using the same set of stimuli could help address this question, but I agree that these additional experiments may be beyond the scope of the current paper. I think the authors should at least discuss the potential outcomes of such experiments, which can be tested in future studies.

Unfortunately, we do not have behavioral data. One prediction would be that the discrimination of head-body orientation angle, irrespective of the viewpoint of the avatar, would be more accurate for zero versus straight angles compared to the right versus left angles. We have added this to the Discussion (page 28).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) P22 L373. It should read Figure S5C instead of S4C.

Thanks; corrected.

(2) Figure 7B. All inverted decoding accuracies, although significantly lower than upright decoding accuracies, appear significantly above baseline. Should the title be amended accordingly?

Thanks for pointing this out. To avoid future misunderstanding we have changed the title to:

“Integration of head and body orientations in the macaque superior temporal sulcus is stronger for upright bodies”

(3) Discussion L432-33. "with some neurons being tuned to a particular orientation of both the head and the body". Wouldn't that be visible as a diagonal profile on the normalized net responses in Fig 3D? Or can the Anova evidence such a tuning?

We meant to say that some neurons were tuned to a particular combination of head and body orientation, like the third aSTS example neuron shown in Figure 3D. We have corrected the sentence.

Reviewer #2 (Recommendations for the authors):

Major comment:

This paper effectively demonstrates that the angular relationship between the head and body can be decoded from population responses in the anterior STS. In other words, these neurons encode information about the head-body angle. However, how exactly do these neurons encode this information? Given that the study employed electrophysiological recordings from a local population of neurons, it might be possible to provide additional data on the response patterns of individual neurons to shed light on the underlying encoding mechanisms.

Although the paper already presents example response patterns (Figures 3D, E) and shows that STS neurons encode interactions between head and body orientations (Figure 3B), it remains unclear whether the angle difference between the head and body has a systematic effect on neuronal responses. For instance, a description of whether some neurons preferentially encode specific head-body angle differences (e.g., a "45-degree angle neuron"), or additional population analyses such as a one-way ANOVA with angle difference as the main effect (or two-way ANOVA with angle difference as one of the main effect), would be very informative. Such data could offer valuable insights into how individual neurons contribute to the encoding of head-body angle differences-a detail that may also be reflected in the decoding results. Alternatively, it is possible that the encoding of head-body angle is inherently complex and only discernible via decoding methods applied to population activity. Either scenario would provide interesting and useful information to the field.

We have performed two additional analyses which are relevant to this comment. First, we attempted to relate the tuning for body and head orientation with the decoding of the head-body orientation angle. To do this, one needs to identify the neurons that contribute to the decoding of head-body orientation angles. For that, we employed a neuron-dropping analysis, similar to Chiang et al. (Chiang FK, Wallis JD, Rich EL. Cognitive strategies shift information from single neurons to populations in prefrontal cortex. Neuron. 2022 Feb 16;110(4):709-721.) to assess the positive (or negative) contribution of each neuron to the decoding performance. We performed cross-validated linear SVM decoding N times, each time leaving out a different neuron (using N-1 neurons; 2000 resamplings of pseudo-population vectors). We then ranked decoding accuracies from highest to lowest, identifying the ‘worst’ (rank 1) to ‘best’ (rank N) neurons. Next, we conducted N decodings, incrementally increasing the number of included neurons from 1 to N, starting with the worst-ranked neuron (rank 1) and sequentially adding the next (rank 2, rank 3, etc.). This analysis focused on zero versus straight angle decoding in the aSTS, as it yielded the highest accuracy. We applied it when training on MC and testing on HC for each pose. Plotting accuracy as a function of the number of included neurons suggested that less than half contributed positively to decoding (see Figure S3). We examined the tuning for head and body orientation of the 10 “best” neurons (Figure S3). For half or more of those the two-way ANOVA showed a significant interaction. These are indicated by the red color in the Figure. They showed a variety of tuning patterns for head and body orientation, suggesting that the decoding of the head-body orientation angle results from a combination of neurons with different tuning profiles.

Second, we have followed the suggestion of the reviewer to perform for each neuron of experiment 1 a one-way ANOVA with as factor head-body orientation angle. To do that, we combined all 64 trials that had the same head-body orientation angle. The percentage of neurons (required to be responsive in the tested condition) for which this one-way ANOVA was significant is shown in the Tables below for each region, separately for each pose (P1, P2), centering condition (MC = monkey-centered; HC = head-centered) and monkey subject (M1, M2). The percentages were low but larger than the expected 5% (Type 1 error), with a median of 16.5% (range: 3 to 23%) in aSTS and 8% for mSTS (range: 0-19%).

Author response table 1.

Interestingly, a higher percentage of the 10 best neurons for each pose (indicated by the star in the Figure above) showed a significant one-way ANOVA for angle (for P1, MC: 50% (95% confidence interval (CI): 19% – 81%); P1, HC: 70% (CI: 35% - 93%); P2, MC: 70% (CI: 35% – 93%); P2: HC: 50% (CI: 19%-81%)). These percentages were significantly higher than expected for a random sample from the population of neurons for each pose-centering combination (expected percentages listed in the same order as above: 16%, 13%, 16%, and 10%; all outside CI). Thus, for at least half of the “best” neurons, the response differed significantly among the head-orientation angles at the single neuron level. Nonetheless, the tuning profiles were quite diverse, suggesting population coding of head-body orientation angle. We have added this interesting and novel result to the Results (page 16) and Suppl. Material (Figure S3).    

Minor comments:

(1) Figure 4A, Fourth Row Example (Zero Angle vs. Straight Angle, Bottom of the P2 Examples): The order of the example stimuli might be incorrect- the 0{degree sign} head with 180{degree sign} body stimulus (leftmost) might be swapped with the 180{degree sign} head with 0{degree sign} body stimulus (5th from the left). While this ordering may be acceptable, please double-check whether it reflects the authors' intended arrangement.

We have changed the order of the two stimuli in Figure 4A, following the suggestion of the reviewer.

(2) Page 12, Lines 192-194: The text states, "Interestingly, some neurons (e.g. Figure 3D) were tuned to a particular combination of a head and body irrespective of centering." However, Figure 3D displays data for a total of 10 neurons. Could you please specify which of these neurons are being referred to in this context?

The wording was not optimal. We meant to say that some neurons were tuned to a particular combination of head and body orientation, like the third aSTS example neuron of Figure 3D. We have rephrased the sentence and clarified which example neuron we referred to.

(3) Page 28, Lines 470-471: The text states, "We observed no difference in response strength between anatomically possible and impossible configurations." Please clarify which data were compared for response strength, as I could not locate the corresponding analyses.

The anatomically possible and impossible configurations differ in the head-body orientation angle. However, as we reported before in the Results, there was no effect of head-body orientation angle on mean response strength across poses (Friedman ANOVA; all p-values for both poses and centerings > 0.1). We have clarified this now in the Discussion (page 28).

(4) Pages 40-43, Decoding Analyses: In experiments 2 and 3, were the decoding analyses performed on simultaneously recorded neurons? If so, such analyses might leverage trial-by-trial correlations and thus avoid confounds from trial-to-trial variability. In contrast, experiment 1, which used single-shank electrodes, would lack this temporal information. Please clarify how trial numbers were assigned to neurons in each experiment and how this assignment may have influenced the decoding performance.

For the decoding analyses of experiments 2 and 3, we combined data from different daily penetrations, with only units from the same penetration being recorded simultaneously. In the decoding analyses of each experiment, the trials were assigned randomly to the pseudo-population vectors, shuffling on each resampling the trial order per neuron. This shuffling abolishes noise correlations in the analysis of each experiment.

(5) Page 41, Lines 792-802: The authors state that "To assess the significance of the differences in classification scores between pairs of angles ... we computed the difference in classification score between the two pairs for each resampling and the percentile of 0 difference corresponded to the p-value." In a two-sided test under the null hypothesis of no difference between the distributions, the conventional approach would be to compute the p-value as the proportion of resampled differences that are as extreme or more extreme than the observed difference. Since a zero difference might be relatively rare, relying solely on its percentile could potentially misrepresent the tail probabilities relevant to a two-sided test. Could you clarify how their method addresses this issue?

This test is based on the computation of the distribution of the difference between classification accuracies across resamplings. This is similar to the computation of the confidence interval of a  difference. Thus, we assess whether the theoretical zero value (= no difference; = null hypothesis) is outside the 2.5 and 97.5 percentile interval of the computed distribution of the empirically observed differences. We clarified now in the Methods (page 41) that for a two-tailed test the computed p-value (the percentile of the zero value) should be smaller than 0.025.

(6) Page 43, Lines 829-834: The manuscript explains: "The mean of 10 classification accuracies (i.e., of 10 resamplings) was employed to obtain a distribution (n=100) of the differences in classification accuracy ... The reported standard deviations of the classification accuracies are computed using also the means of 10 resamplings." I am unfamiliar with this type of analysis and am unclear about the rationale for calculating distributions and standard deviations based on the means of 10 resamplings rather than using the original distribution of classification accuracies. This resampling procedure appears to yield a narrower distribution and smaller standard deviations than the original data. Could you please justify this approach?

The logic of the analysis is to reduce the noise in the data, by averaging across 10 randomly selected resamplings, but still keeping a sufficient number of data (100 values) for a test.

Reviewer #3 (Recommendations for the authors):

(1) Some sentences are too long and difficult to parse. For example, in line 177: "the correlations between the responses to the 64 head-body orientation conditions of the two centerings for the neuron and pose combinations showing significant head-body interactions for the two centerings were similar to those observed for the whole population."

We have modified this sentence: For neuron and pose combinations with significant head-body interactions in both centerings, the correlations between responses to the 64 head-body orientation conditions were similar to those observed in the whole population.

(2) The authors argue in line 485: "in our study, a search bias cannot explain the body-inversion effect since we selected responsive units using both upright and inverted images." However, the body-selective patches were localized using upright images, correct?

The monkey-selective patches were localized using upright images indeed. However, we recorded in experiment 3 (and 2) also outside the localized patches (as we noted before in the Methods:  “In experiments 2 and 3 we recorded from a wider region, which overlapped with the two monkey patches and the recording locations of experiment 1”). Furthermore, the preference for upright monkey images is not an all-or-nothing phenomenon: most units still responded to inverted monkeys. Also, we believe it is likely that the mean responses to the inverted bodies in the monkey patches, defined by upright bodies versus objects, would be larger than those to objects and we would be surprised to learn that there is a patch selective for inverted bodies that we would have missed with our localizer.

(3) Typo: line 447, "this independent"->"is independent"?

Corrected.

Author Response

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

Reviewer #1

Public Review

Summary:

(1) This work describes a simple mechanical model of worm locomotion, using a series of rigid segments connected by damped torsional springs and immersed in a viscous fluid.

(2) It uses this model to simulate forward crawling movement, as well as omega turns.

Strengths:

(3) The primary strength is in applying a biomechanical model to omega-turn behaviors.

(4) The biomechanics of nematode turning behaviors are relatively less well described and understood than forward crawling.

(5) The model itself may be a useful implementation to other researchers, particularly owing to its simplicity.

Weaknesses:

(6) The strength of the model presented in this work relative to prior approaches is not well supported, and in general, the paper would be improved with a better description of the broader context of existing modeling literature related to undulatory locomotion.

(7) This paper claims to improve on previous approaches to taking body shapes as inputs.

(8) However, the sole nematode model cited aims to do something different, and arguably more significant, which is to use experimentally derived parameters to model both the neural circuits that induce locomotion as well as the biomechanics and to subsequently compare the model to experimental data.

(9) Other modeling approaches do take experimental body kinematics as inputs and use them to produce force fields, however, they are not cited or discussed.

(10) Finally, the overall novelty of the approach is questionable.

(11) A functionally similar approach was developed in 2012 to describe worm locomotion in lattices (Majmudar, 2012, Roy. Soc. Int.), which is not discussed and would provide an interesting comparison and needed context.

9-11: The paper you recommended and our manuscript have some similarities and differences.

Similarities

Firstly, the components constituting the worm are similar in both models. ElegansBot models the worm as a chain of n rods, while the study by Majmudar et al. (2012) models it as a chain of n beads. Each bead in the Majmudar et al. model has a directional vector, making it very similar to ElegansBot's rod. However, there's a notable difference: in the Majmudar et al. model, each bead has an area for detecting contact between the obstacle and the bead, while in ElegansBot, the rod does not feature such an area.

Secondly, the types of forces and torques acting on the components constituting the worm are similar. Each rod in ElegansBot receives frictional force, muscle force, and joint force. Each bead in the Majmudar et al. model receives a constraint force, viscous force, and a repulsive force from obstacles. Each rod in ElegansBot receives frictional torque, muscle torque, and joint torque. Each bead in the Majmudar et al. model receives elastic torque, constraint torque, drive torque, and viscous torque. The Majmudar et al. model's constraint force and torque are similar to ElegansBot's joint force and torque in that they prevent two connected components of the worm from separating. The Majmudar et al. model's viscous force and torque are similar to ElegansBot's frictional force and torque in that they are forces exchanged between the worm and its surrounding environment (ground surface). The Majmudar et al. model's drive torque is similar to ElegansBot's muscle force and muscle torque as a cause of the worm's motion. However, unlike ElegansBot, the Majmudar et al. model did not consider the force generating the drive torque, and there are differences in how each force and torque is calculated. This will be discussed in more detail below.

Differences

Firstly, the medium in which the worm locomotes is different. ElegansBot is a model describing motion in a homogeneous medium like agar or water without obstacles, while the Majmudar et al. model describes motion in water with circular obstacles fixed at each lattice point. This is because the purposes of the models are different. ElegansBot analyzes locomotion patterns based on the friction coefficient, while the Majmudar et al. model analyzes locomotion patterns based on the characteristics of the obstacle lattice, such as the distance between obstacles. Also, for this reason, the Majmudar et al. model's bead, unlike ElegansBot's rod, receives a repulsive force from obstacles.

Secondly, the specific methods of calculating similar types of forces differ. ElegansBot calculates joint forces by substituting frictional forces, muscle forces, frictional torques, and muscle torques into an equation derived from differentiating a boundary condition equation twice over time, where two neighboring rods always meet at one point. This involves determining the process through which various forces and torques are transmitted across the worm. Specifically, it entails calculating how the frictional forces and torques, as well as the muscle forces and torques acting on each rod, are distributed throughout the entire length of the worm. In contrast, The Majmudar et al. model uses Lagrange multipliers method based on a boundary condition that the curve length determined by each bead's tangential angle does not change, to calculate the constraint force and torque before calculating the drive torque and viscous force. This implies that the Majmudar et al. model did not consider the mechanism by which the drive torque and viscous force received by one bead are distributed throughout the worm. ElegansBot's rod receives an anisotropic Stokes frictional force from the ground surface, while the Majmudar et al. model considered the frictional force according to the Navier-Stokes equation for incompressible fluid, assuming the fluid velocity at the bead's location as the bead's velocity.

Thirdly, unlike the Majmudar et al. model, ElegansBot considers the inertia of the worm components. Therefore, ElegansBot can simulate regardless of how low or high the ground surface's friction coefficient is. the Majmudar et al. model is not like this.

(12) The idea of applying biomechanical models to describe omega turns in C. elegans is a good one, however, the kinematic basis of the model as used in this paper (the authors do note that the control angle could be connected to a neural model, but don't do so in this work) limits the generation of neuromechanical control hypotheses.

8, 12: We do not agree with the claim that ElegansBot could limit other researchers in generating neuromechanical control hypotheses. The term θ_("ctrl" ,i)^((t) ) used in our model is designed to be replaceable with neuromechanical control in the future.

(13) The model may provide insights into the biomechanics of such behaviors, however, the results described are very minimal and are purely qualitative.

(14-1) Overall, direct comparisons to the experiments are lacking or unclear.

14-1: If you look at the text explaining Fig. 2 and 5 (Fig. 2 and 4 in old version), it directly compares the velocity, wave-number, and period as numerical indicators representing the behavior of the worm, between the experiment and ElegansBot.

(14-2) Furthermore, the paper claims the value of the model is to produce the force fields from a given body shape, but the force fields from omega turns are only pictured qualitatively.

13, 14-2: We gratefully accept the point that our analysis of the omega-turn is qualitative. Therefore, we have conducted additional quantitative analysis on the omega-turn and inserted the results into the new Fig. 4. We have considered the term 'Force field' as referring to the force vector received by each rod. We have created numerical indicators representing various behaviors of the worm and included them in the revised manuscript.

(15) No comparison is made to other behaviors (the force experienced during crawling relative to turning for example might be interesting to consider) and the dependence of the behavior on the model parameters is not explored (for example, how does the omega turn change as the drag coefficients are changed).

Thank you for the great idea. To compare behaviors, first, a clear criterion for distinguishing behaviors is needed. Therefore, we have created a new mathematical definition for behavior classification in the revised manuscript (“Defining Behavioral Categories” in Method). After that, we compared the force and power (energy consuming rate) between each forward locomotion, backward locomotion, and omega-turn (Fig. 4). And in the revised manuscript, we newly analyzed how the turning behavior changes with variations in the friction coefficients in Figs. S4-S7.

(16) If the purpose of this paper is to recapitulate the swim-to-crawl transition with a simple model, and then apply the model to new behaviors, a more detailed analysis of the behavior of the model variables and their dependence on the variables would make for a stronger result.

In our revised manuscript, we have quantitatively analyzed the changes occurring in turning behavior from water to agar, and the results are presented in Figs. S9 and S10.

(17) In some sense, because the model takes kinematics as an input and uses previously established techniques to model mechanics, it is unsurprising that it can reproduce experimentally observed kinematics, however, the forces calculated and the variation of parameters could be of interest.

(18) Relatedly, a justification of why the drag coefficients had to be changed by a factor of 100 should be explored.

(19) Plate conditions are difficult to replicate and the rheology of plates likely depends on a number of factors, but is for example, changes in hydration level likely to produce a 100-fold change in drag? or something more interesting/subtle within the model producing the discrepancy?

18, 19: As mentioned in the paper, we do not know if the friction coefficients in the study of Boyle et al. (2012) and the friction coefficients in the experiment of Stephens et al. (2016) are the same. In our revised manuscript, we have explored more in detail the effects of the friction coefficient's scale factor, and explained why we chose a scale factor of 1/100 (“Proper Selection of Friction Coefficients” in Supplementary Information). In summary, we analyzed the changes in trajectory due to scaling of the friction coefficient, and chose the scale factor 1/100 as it allowed ElegansBot to accurately reproduce the worm's trajectory while also being close to the friction coefficients in the Boyle et al. paper.

(20) Finally, the language used to distinguish different modeling approaches was often unclear.

(21) For example, it was unclear in what sense the model presented in Boyle, 2012 was a "kinetic model" and in many situations, it appeared that the term kinematic might have been more appropriate. Thank you for the feedback. As you pointed it out, we have corrected that part to 'kinematic' in the revised manuscript.

(22) Other phrases like "frictional forces caused by the tension of its muscles" were unclear at first glance, and might benefit from revision and more canonical usage of terms.

We agree that the expression may not be immediately clear. This is due to the word limit for the abstract (the abstract of eLife VOR should be under 200 words, and our paper's abstract is 198 words), which forced us to convey the causality in a limited number of words. Therefore, although we will not change the abstract, the expression in question means that the muscle tension, which is the cause of the worm's locomotion, ultimately generates the frictional force between the worm and the ground surface.

Recommendations For The Authors

(23) As I stated in my public review, I think the paper could be made much stronger if a more detailed exploration of turning mechanics was presented.

(24) Relatedly, rather than restricting the analysis to individual videos of turning behaviors, I wonder if a parameterized model of the turning kinematics would be fruitful to study, to try to understand how different turning gaits might be more or less energetically favorable.

We thank the reviewer once again for their suggestion. Thanks to their proposal, we were able to conduct additional quantitative analysis on turning behavior.

Reviewer #2

Public Review

Summary:

(1) Developing a mechanical model of C. elegans is difficult to do from basic principles because it moves at a low (but not very small) Reynolds number, is itself visco-elastic, and often is measured moving at a solid/liquid interface.

(2) The ElegansBot is a good first step at a kinetic model that reproduces a wide range of C. elegans motiliy behavior.

Strengths: (3) The model is general due to its simplicity and likely useful for various undulatory movements.

(4) The model reproduces experimental movement data using realistic physical parameters (e.g. drags, forces, etc).

(5) The model is predictive (semi?) as shown in the liquid-to-solid gait transition.

(6) The model is straightforward in implementation and so likely is adaptable to modification and addition of control circuits.

Weaknesses:

(7) Since the inputs to the model are the actual shape changes in time, parameterized as angles (or curvature), the ability of the model to reproduce a realistic facsimile of C. elegans motion is not really a huge surprise. (8) The authors do not include some important physical parameters in the model and should explain in the text these assumptions.

(9. 1) The cuticle stiffness is significant and has been measured [1].

(10. 2) The body of C. elegans is under high hydrostatic pressure which adds an additional stiffness [2].

(11. 3) The visco-elasticity of C. elegans body has been measured. [3]

Thank you for asking. The stiffness of C. elegans is an important consideration. We took this into account when creating ElegansBot, but did not explain it in the paper. The detailed explanation is as follows. C. elegans indeed has stiffness due to its cuticle and internal pressure. This stiffness is treated as a passive elastic force (elastic force term of lateral passive body force) in the paper of Boyle et al. (2012). However, the maximum spring constant of the passive elastic force is 1/20 of the maximum spring constant of the active elastic force. If we consider this fact in our model, the elastic term of the muscle torque is as follows: ( is the active torque elasticity coefficient, is the passive torque elasticity coefficient)

where

Therefore, there is no need to describe the active and passive terms separately in

Furthermore, since , assuming , then and .

(12) There is only a very brief mention of proprioception.

(13) The lack of inclusion of proprioception in the model should be mentioned and referenced in more detail in my opinion.

As you emphasized, proprioception is an important aspect in the study of C. elegans' locomotion. In our paper, its importance is briefly introduced with a sentence each in the introduction and discussion. However, our research is a model about the process of the creation of body motion originated from muscle forces, and it does not model the sensory system that senses body posture. Therefore, there is no mention of using proprioception in our paper's results section. What is mentioned in the discussion is that ElegansBot can be applied as the kinetic body model part in a combination model of a kinetic body model and a neuronal circuit model that receives proprioception as a sensory signal.

(14) These are just suggested references.

(15) There may be more relevant ones available.

The papers you provided contain specific information about the Young's modulus of the C. elegans body. The first paper (Rahimi et al., 2022) measured the Young's modulus of the cuticle after chemically isolating it from C. elegans, while the second paper (Park et al., 2007) and third paper (Backholm et al., 2013) measured the elasticity and Young's modulus of C. elegans without separating the cuticle. Based on the Young's modulus provided in each paper (although the second and third papers did not measure stiffness in the longitudinal direction), we derived the elastic coefficient (assuming a worm radius of 25 μm, cuticle thickness of 0.5 μm, and 1/25 of longitudinal length of the cuticle of 40 μm). The range was quite broad, from 9.82ⅹ1011 μg/sec2 (from the first paper) to 2.16 ⅹ 108 μg / sec2 (from the third paper). Although the elastic coefficient value in our paper falls within this range, since the range of the elastic coefficient is wide, we think we can modify the elastic coefficient in our paper and will be able to reapply our model if more accurate values become known in the future.

Reviewer #3

Public Review

Summary:

(1) A mechanical model is used with input force patterns to generate output curvature patterns, corresponding to a number of different locomotion behaviors in C. elegans

Strengths:

(2) The use of a mechanical model to study a variety of locomotor sequences and the grounding in empirical data are strengths.

(3) The matching of speeds (though qualitative and shown only on agar) is a strength.

Weaknesses:

(4) What is the relation between input and output data?

ElegansBot takes the worm's body control angle as the input, and produces trajectory and force of each segment of the worm as the output.

(5) How does the input-output relation depend on the parameters of the model?

If 'parameter' is understood as vertical and horizontal friction coefficients, then the explanation for this can be found in Fig. 5 (Fig. 4 in the old version).

(6) What biological questions are addressed and can significant model predictions be made?

Equation of motion deciphering locomotion of C. elegans including turning behaviors which were relatively less well understood.

Recommendations For The Authors

(7) The novelty and significance of the paper should be clarified.

We have added quantitative analyses of turning behavior in the revised manuscript, and we hope this will be helpful to you.

(8) Previously much more detailed models have been published, as compared to this one.

We hope the reviewer can point out any previous model that we may have missed.

(9) The mechanics here are simplified (e.g. no information about dorsal/ventral innervation but only a bending angle) setting limitations on the capacity for model predictiveness.

(10) Such limitations should be discussed.

We view the difference between dorsal/ventral innervation and bending angle not as a matter of simplification, but rather as a reflection of the hierarchy that our model implements. Our model does not consider dorsal/ventral innervation, but it uses the bending angle to reproduce behavior in various input and frictional environments, which signifies the strong predictiveness of ElegansBot (Figure 2, 3, 5 (2, 3, 4 in the old version)). Moreover, if the midline of C. elegans is incompressible, then modeling by dividing into dorsal/ventral, as opposed to modeling solely with the bending angle, does not increase the degree of freedom of the worm model, and therefore does not increase its predictiveness.

(11) The aims of the paper and results need to be supported quantitatively and analyzed through parameter sweeps and intervention.

We have conducted additional quantitative analyses on turning behavior as suggested by Reviewer #1 (Fig. 4, S4-S7, S9, and S10).

(12) The methods are given only in broad brushstrokes, and need to be much more clear (and ideally sharing all code).

We have thoroughly detailed every aspect of this research, from deriving the physical constants of C. elegans, agar, and water to developing the formulas and proofs necessary for operating ElegansBot and its applications. This comprehensive information is all presented in the Results, Methods, and Supplementary Information sections, as well as in the source code. Moreover, we have already ensured that our research can be easily reproduced by providing detailed explanations and by making ElegansBot accessible through public software databases (PyPI, GitHub). To further aid in its application and understanding, especially for those less familiar with the subject, we have also included minimal code as examples in the database. This code is designed to simplify the process of reproducing the results of the paper, thereby making our research more accessible and understandable. Therefore, we believe that readers will easily gain significant assistance from the extensive information we have provided. Should readers require further help, they can always contact us, and we will be readily available to offer support.

(13) The supporting figures and movies need to include a detailed analysis to evidence the claims.

We have conducted and provided additional quantitative analyses on turning behavior as suggested by Reviewer #1 (Fig. 4, S4-S7, S9, and S10).

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Chen et al. used cryo-ET and in vitro reconstituted system to demonstrate that the autoinhibited form of LRRK2 can also assemble into filaments that wrap around the microtubule, although the filaments are typically shorter and less regular compared to the previously reported active-LRRK2 filaments. The structure revealed a new interface involving the N-terminal repeats that were disordered in the previous active-LRRK2 filament structure. The autoinhibited-LRRK2 filament also has different helical parameters compared to the active form.

Strengths:

The structure obtained in this study is the highest resolution of LRRK2 filaments done by subtomogram averaging, representing a major technical advance compared to the previous Cell paper from the same group. Overall, I think the data are well presented with beautiful graphic rendering, and valuable insights can be gained from this structural study.

Weaknesses:

(1) There are only three main figures, together with 9 supplemental figures. The authors may consider breaking the currently overwhelming Figures 1 and 3 into smaller figures and moving some of the supplemental figures to the main figure, e.g., Figure S7.

(2) The key analysis of this manuscript is to compare the current structure with the previous active-LRRK2 filament structure. Currently, such a comparison is buried in Figure 3H. It should be part of Figure 1.

We thank the reviewer for this suggestion. As suggested, we have rearranged the figures, split Figure 1 and 3 into smaller Figures, and moved the comparison analysis in Figure 3H to the new Figure 1. Specifically, the old Figure 1 is separated into two figures, introducing the model-building process and describing the two symmetric axes. The old Figure 3 is also separated into two small figures, describing the geometric analysis and model comparison, respectively.

Reviewer #2 (Public review):

The authors of this paper have done much pioneering work to decipher and understand LRRK2 structure and function, to uncover the mechanism by which LRRK2 binds to microtubules, and to study the roles that this may play in biology. Their previous data demonstrated that LRRK2 in the active conformation (pathogenic mutation or Type I inhibitor complex) bound to microtubule filaments in an ordered helical arrangement. This they showed induced a "roadblock" in the microtubule impacting vesicular trafficking. The authors have postulated that this is a potentially serious flaw with Type 1 inhibitors and that companies should consider generating Type 2 inhibitors in which the LRRK2 is trapped in the inactive conformation. Indeed the authors have published much data that LRRK2 complexed to Type 2 inhibitors does not seem to associate with microtubules and cause roadblocks in parallel experiments to those undertaken with type 1 inhibitors published above.

In the current study, the authors have undertaken an in vitro reconstitution of microtubule-bound filaments of LRRK2 in the inactive conformation, which surprisingly revealed that inactive LRRK2 can also interact with microtubules in its auto-inhibited state. The authors' data shows that while the same interphases are seen with both the active LRRK2 and inactive microtubule bound forms of LRRK2, they identified a new interphase that involves the WD40-ARM-ANK- domains that reportedly contributes to the ability of the inactive form of LRRK2 to bind to microtubule filaments. The structures of the inactive LRRK2 complexed to microtubules are of medium resolution and do not allow visualisation of side chains.

This study is extremely well-written and the figures are incredibly clear and well-presented. The finding that LRRK2 in the inactive autoinhibited form can be associated with microtubules is an important observation that merits further investigation. This new observation makes an important contribution to the literature and builds upon the pioneering research that this team of researchers has contributed to the LRRK2 fields. However, in my opinion, there is still significant work that could be considered to further investigate this question and understand the physiological significance of this observation.

We thank the reviewer for the positive comments and we agree that more work can be done next to understand the physiological significance of the autoinhibited LRRK2 in cellular environments. We are actively working on understanding how the stability of autoinhibited full-length LRRK2 is regulated, especially how the transfer between autoinhibited and active forms of LRRK2 can happen. Our in situ data (Watabane et al. 2020) indicates that overexpressed hyperactive PD-mutant LRRK2 mainly adopts its active-like conformation in cells. Thus, learning how the state transfer occurs will allow us to target autoinhibited LRRK2 specifically and efficiently in cells and study its structure and function in physiological conditions.

Reviewer #3 (Public review):

Summary:

The manuscript by Chen et al examines the structure of the inactive LRRK2 bound to microtubules using cryo-EM tomography. Mutations in this protein have been shown to be linked to Parkinson's Disease. It is already shown that the active-like conformation of LRRK2 binds to the MT lattice, but this investigation shows that full-length LRRk2 can oligomerize on MTs in its autoinhibited state with different helical parameters than were observed with the active-like state. The structural studies suggest that the autoinhibited state is less stable on MTs.

Strengths:

The protein of interest is very important biomedically and a novel conformational binding to microtubules in the proposed.

Weaknesses:

(1) The structures are all low resolution.

We thank the reviewer for the comments on both the strengths and weaknesses of the manuscript. We agree with the reviewer that higher resolution would provide more information about how LRRK2 interacts with microtubules and oligomerizes in its autoinhibited form. However, with the current resolution, our model-building benefited significantly from the published high-resolution models and the alpha-fold predictions. We used cryo-ET and subtomogram analysis to solve the structure because this filament is less regular than the right-handed active LRRK2 filament, preventing us from using conventional single-particle analysis. As highlighted by reviewer 1, being able to push the resolution to sub-nanometer is an important advance reflecting state-of-the-art subtomogram analysis, especially for a heterogeneous sample.  Notably, the microtubule reconstruction reached higher resolution, comparable to our previous single-particle studies on LRRK2-RCKW (Snead and Matyszewski et al.), confirming the data quality.

(2) There are no measurements of the affinity of the various LRRK2 molecules (with and without inhibitors) to microtubules. This should be addressed through biochemical sedimentation assay.

We thank the reviewer for the suggestion and we agree that learning the binding affinity between LRRK2 and microtubules would be informative. We attempted to purify the LRRK2 with mutants on the WD40:ARM/ANK interface we identified in the manuscript.. Unfortunately, either LRRK2 or LRRK2^I2020T with N-terminal mutants (R521A/F573A/E854K), the yield and purity of the final samples are significantly worse than our routine LRRK2 prep. Our chromatography and gel electrophoresis results indicate that proteins are degrading during purification.

Author response image 1.

While we have attached the results here, and it would be interesting to investigate why N-terminal mutations destabilize LRRK2, we anticipate that significant efforts would be required for further experiments, which we respectfully consider outside of the scope of this manuscript. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In Figure S9, the graphic definition of "chain length" in panel A is misleading. The authors can simply note in the figure legend that "chain length is the number of asymmetric units in a continuous chain".

We thank the reviewer for the suggestion. The updated figure and legend have incorporated the changes.

(2) In Figure S7B, the conformation changes of the 'G-loop' and the 'DYG' motifs are not so convincing at the current resolution.

We thank the reviewer for pointing it out. We agree that our model resolution is not high enough to support the unbiased observation of the conformation changes of the key kinase motifs. In the revised manuscript, we avoided emphasizing the comparison between the two models. Instead, we state that for both the MLi-2 bound map and the GZD-824 bound map, the corresponding published high-resolution models fit into each kinase map, but the MLi-2 bound model doesn’t fit as well in the GZD-824 bound map, with a correlation value dropped from 0.44 to 0.4, supporting our statement that “full-length LRRK2 bound to microtubules is in its autoinhibited state in our reconstituted system”.

Reviewer #2 (Recommendations for the authors):

(1) Are there any cellular experiments that could be done to demonstrate that inactive LRRK2 associates with microtubules in cells?

We thank the reviewer for pointing out this direction for future studies. We are studying the physiological significance of the autoinhibited LRRK2 in cells, but haven’t yet been successful at demonstrating physiological binding to microtubules. Further, as noted in our response to reviewer #3, we are also actively working on understanding how the stability of autoinhibited full-length LRRK2 is regulated, especially how the transfer between autoinhibited and active forms of LRRK2 can happen. Our in situ data (Watabane et al. 2020) indicates that hyperactive PD-mutant overexpressed LRRK2 mainly adopts its active-like conformation in cells. Thus, learning how the state transfer occurs will allow us to target autoinhibited LRRK2 specifically and efficiently in cells and study its structure and function in physiological conditions.

(2) Previous work that the authors and others have undertaken has suggested that only LRRK2 in its active conformation can associate with microtubule filaments and the authors have shown that this leads to a roadblock in vesicular transport only when LRRK2 is complexed with Type 1 but not Type 2 inhibitors. There seems to be some discrepancy here that is not addressed in the paper as based on the current results one would also expect LRRK2 bound to Type 2 inhibitors to induce roadblocks in microtubule filaments. How can this be explained?

We thank the reviewer for raising this important question. Taking all of our published data together, we believe that LRRK2 can introduce roadblocks with Type 1 inhibitor bound in the active-like conformation, where N-terminus LRRK2 domains are flexible and don’t block the kinase active site. In other words, full-length LRRK2 can form roadblocks when it behaves more like the truncated LRRK2^RCKW variant. The autoinhibited LRRK2 forms shorter and less stable oligomers on microtubules, making it harder to block transport. Consistent with this, our in situ LRRK2-microtubule structure was observed in cells where LRRK2 is in an active-like conformation, and the LRRK2 N-terminus appeared to be flexible and away from the microtubule when forming right-handed filaments.

(3) Does the finding that inactive LRRK2 only binds to microtubules as a short filament, explain the differences between the inactive and active forms of LRRK2 binding to microtubules and causing roadblocks?

We thank the reviewer for discussing this point with us and asking the question. As we replied in the previous comment, the reviewer’s conclusion explains how the roadblock phenomenon occurs only under certain circumstances. We expanded our discussion to add the following and address the question:

“Notably, we previously demonstrated that active‐like LRRK2, when bound to a Type I inhibitor, can form roadblocks that impair vesicular transport. Since autoinhibited LRRK2 assembles into shorter, less stable oligomers on microtubules, we anticipate it will exert reduced road‐blocking effects in cells, regardless of the inhibitor bound.”

(4) Could the authors undertake further characterization of the new WD40-ARM-ANK interphase that they have identified? Is this important for the binding of the autoinhibited mutant? Could mutants be made in this interphase to see if this prevents the autoinhibited but not the active conformation of LRRK2 binding to microtubules?

We thank the reviewer for the comment. As mentioned in our response to Reviewer #2, public comment #2, we attempted to purify the LRRK2 with mutants on the WD40:ARM/ANK interface we identified in the manuscript multiple times. Unfortunately, either LRRK2 or LRRK2^I2020T with N-terminal mutants (R521A/F573A/E854K), the yield and purity of the final samples are significantly worse than our routine LRRK2 prep. Our chromatography and gel electrophoresis results indicate that proteins are degrading during purification.

(5) The authors identify several disease-relevant missense mutations that appear to lie within the novel interphase that the authors have characterised in this study. Although this is discussed in the Discussion, some experimental data demonstrating how these missense mutations impact the ability of inactive LRRK2 to bind to microtubule filaments in the presence or absence of Type 1 and Type 2 compounds could provide further experimental data that emphasises the physiological importance of the results presented in this study.

We thank the reviewer for discussing this interesting direction. The disease-relevant missense mutations can have a direct or indirect impact on the binding of autoinhibited LRRK2 to microtubules, and we agree that it would be interesting to test it out in the future. However, we anticipate that significant effort would be required for further experiments. Alas, our funding for this project ended suddenly and we want to report our results to the community.

(6) For the data that is shown in Figure 1, could the authors explain how this differs from results in previous papers of the authors showing that the active form of LRRK2 binds microtubules? How does the binding observed here differ from that observed in the previous studies? To a non-specialist reader, the data looks fairly like what has previously been reported.

We thank the reviewer for asking the question. As mentioned in the response to the public review, the detailed comparison between the data and the previous papers is described in Figure 3, and we agree that it is helpful to incorporate this information in Figure 1. In the revised manuscript, we have incorporated the comparison panel in Figure 1.

(7) The finding that the autoinhibited LRRK2 forms short and sparse oligomers on microtubules raises the question of how physiological this observation is. Having some data that suggests that this is physiologically relevant would boost the impact of this study.

We agree with the reviewer on this comment. As discussed in the response to the first comment from the reviewer, we have not been able to assess the physiological relevance of LRRK2 binding to microtubules in either active or inactive state, but continue to pursue this line of research. We are aware and regret that this lessens the impact of this work.

(8) For the more general reader the authors could potentially better highlight why the key finding in this paper is important.

We thank the reviewer for the suggestion. To further address the significance of the key findings, especially how it can open up more possibilities for inhibitor-based drug development, we expand our discussion section to include the following:

“Understanding how Type I and Type II inhibitors’ binding to LRRK2 affects its mechanism is vital to the design of inhibitor-based PD drug development strategies. Our findings revealed that different LRRK2 kinase inhibitors bind to autoinhibited LRRK2 similarly either in solution or on microtubules. Furthermore, the observation of autoinhibited LRRK2 forming short, less stable oligomers on microtubules opens new possibilities to inhibit LRRK2 activity in PD patients. A Type I inhibitor specifically targeting autoinhibited LRRK2 may alleviate the effect of LRRK2 roadblocks on microtubules. Alternatively, a promising strategy of LRRK2 inhibitor design can focus on the stabilization of allosteric N-terminus blocking on the kinase domain, which favors the formation of autoinhibited LRRK2 oligomers on microtubules and causes fewer side effects.”

Reviewer #3 (Recommendations for the authors):

In the third paragraph of the introduction, expand on whether type-1 inhibitors which "capture kinases in a closed, "active-like" conformation still inhibit the kinase activity.

We thank the reviewer for the request to expand this paragraph. We added the following explanation for better understanding in the third paragraph:

“Type-I inhibitors bind to the ATP binding site and target the kinase in its ‘active-like' conformation, inhibiting its kinase activity.”

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The aim of this paper is to describe a novel method for genetic labelling of animals or cell populations, using a system of DNA/RNA barcodes.

Strengths:

• The author's attempt at providing a straightforward method for multiplexing Drosophila samples prior to scRNA-seq is commendable. The perspective of being able to load multiple samples on a 10X Chromium without antibody labelling is appealing.

• The authors are generally honest about potential issues in their method, and areas that would benefit from future improvement.

• The article reads well. Graphs and figures are clear and easy to understand.

We thank the reviewer for these positive comments.

Weaknesses:

• The usefulness of TaG-EM for phototaxis, egg laying or fecundity experiments is questionable. The behaviours presented here are all easily quantifiable, either manually or using automated image-based quantification, even when they include a relatively large number of groups and replicates. Despite their claims (e.g., L311-313), the authors do not present any real evidence about the cost- or time-effectiveness of their method in comparison to existing quantification methods.

While the behaviors that were quantified in the original manuscript were indeed relatively easy to quantify through other methods, they nonetheless demonstrated that sequencing-based TaG-EM measurements faithfully recapitulated manual behavioral measurements. In response to the reviewer’s comment, we have added additional experiments that demonstrate the utility of TaG-EM-based behavioral quantification in the context of a more labor-intensive phenotypic assay (measuring gut motility via food transit times in Drosophila larvae, Figure 4, Supplemental Figure 7). We found that food transit times in the presence and absence of caffeine are subtly different and that, as with larger effect size behaviors, TaG-EM data recapitulates the results of the manual assay. This experiment demonstrates both that TaG-EM can be used to streamline labor-intensive behavioral assays (we have included an estimate of the savings in hands-on labor for this assay by using a multiplexed sequencing approach, Supplemental Figure 8) and that TaG-EM can quantify small differences between experimental groups. We also note in the discussion that an additional benefit of TaGEM-based behavioral assays is that the observed is blinded as to the experimental conditions as they are intermingled in a single multiplexed assay. We have added the following text to the paper describing these experiments.

Results:

“Quantifying food transit time in the larval gut using TaG-EM

Gut motility defects underlie a number of functional gastrointestinal disorders in humans (Keller et al., 2018). To study gut motility in Drosophila, we have developed an assay based on the time it takes a food bolus to transit the larval gut (Figure 4A), similar to approaches that have been employed for studying the role of the microbiome in human gut motility (Asnicar et al., 2021). Third instar larvae were starved for 90 minutes and then fed food containing a blue dye. After 60 minutes, larvae in which a blue bolus of food was visible were transferred to plates containing non-dyed food, and food transit (indicated by loss of the blue food bolus) was scored every 30 minutes for five hours (Supplemental Figure 7). 

Because this assay is highly labor-intensive and requires hands-on effort for the entire five-hour observation period, there is a limit on how many conditions or replicates can be scored in one session (~8 plates maximum). Thus, we decided to test whether food transit could be quantified in a more streamlined and scalable fashion by using TaG-EM (Figure 4B). Using the manual assay, we observed that while caffeinecontaining food is aversive to larvae, the presence of caffeine reduces transit time through the gut (Figure 4C, Supplemental Figure 7). This is consistent with previous observations in adult flies that bitter compounds (including caffeine) activate enteric neurons via serotonin-mediated signaling and promote gut motility (Yao and Scott, 2022). We tested whether TaG-EM could be used to measure the effect of caffeine on food transit time in larvae. As with prior behavioral tests, the TaG-EM data recapitulated the results seen in the manual assay (Figure 4D). Conducting the transit assay via TaGEM enables several labor-saving steps. First, rather than counting the number of larvae with and without a food bolus at each time point, one simply needs to transfer nonbolus-containing larvae to a collection tube. Second, because the TaG-EM lines are genetically barcoded, all the conditions can be tested at once on a single plate, removing the need to separately count each replicate of each experimental condition. This reduces the hands-on time for the assay to just a few minutes per hour.  A summary of the anticipated cost and labor savings for the TaG-EM-based food transit assay is shown in Supplemental Figure 8.”

Discussion:

“While the utility of TaG-EM barcode-based quantification will vary based on the number of conditions being analyzed and the ease of quantifying the behavior or phenotype by other means, we demonstrate that TaG-EM can be employed to cost-effectively streamline labor-intensive assays and to quantify phenotypes with small effect sizes (Figure 4, Supplemental Figure 8). An additional benefit of multiplexed TaG-EM behavioral measurements is that the experimental conditions are effectively blinded as the multiplexed conditions are intermingled in a single assay.”

Methods:

“Larval gut motility experiments

Preparing Yeast Food Plates

Yeast agar plates were prepared by making a solution containing 20% Red Star Active Dry Yeast 32oz (Red Star Yeast) and 2.4% Agar Powder/Flakes (Fisher) and a separate solution containing 20% Glucose (Sigma-Aldrich). Both mixtures were autoclaved with a 45-minute liquid cycle and then transferred to a water bath at 55ºC. After cooling to 55ºC, the solutions were combined and mixed, and approximately 5 mL of the combined solution was transferred into 100 x 15 mm petri dishes (VWR) in a PCR hood or contamination-free area. For blue-dyed yeast food plates, 0.4% Blue Food Color (McCormick) was added to the yeast solution. For the caffeine assays, 300 µL of a solution of 100 mM 99% pure caffeine (Sigma-Aldrich) was pipetted onto the blue-dyed yeast plate and allowed to absorb into the food during the 90-minute starvation period.

Manual Gut Motility Assay

Third instar Drosophila larvae were transferred to empty conical tubes that had been misted with water to prevent the larvae from drying out. After a 90-minute starvation period the larvae were moved from the conical to a blue-dyed yeast plate with or without caffeine and allowed to feed for 60 minutes. Following the feeding period, the larvae were transferred to an undyed yeast plate. Larvae were scored for the presence or absence of a food bolus every 30 minutes over a 5-hour period. Up to 8 experimental replicates/conditions were scored simultaneously. 

TaG-EM Gut Motility Assay

Third instar larvae were starved and fed blue dye-containing food with or without caffeine as described above. An equal number of larvae from each experimental condition/replicate were transferred to an undyed yeast plate. During the 5-hour observation period, larvae were examined every 30 minutes and larvae lacking a food bolus were transferred to a microcentrifuge tube labeled for the timepoint. Any larvae that died during the experiment were placed in a separate microcentrifuge tube and any larvae that failed to pass the food bolus were transferred to a microcentrifuge tube at the end of the experiment. DNA was extracted from the larvae in each tube and TaG-EM barcode libraries were prepared and sequenced as described above.”

• Behavioural assays presented in this article have clear outcomes, with large effect sizes, and therefore do not really challenge the efficiency of TaG-EM. By showing a Tmaze in Fig 1B, the authors suggest that their method could be used to quantify more complex behaviours. Not exploring this possibility in this manuscript seems like a missed opportunity.

See the response to the previous point.

• Experiments in Figs S3 and S6 suggest that some tags have a detrimental effect on certain behaviours or on GFP expression. Whereas the authors rightly acknowledge these issues, they do not investigate their causes. Unfortunately, this question the overall suitability of TaG-EM, as other barcodes may also affect certain aspects of the animal's physiology or behaviour. Revising barcode design will be crucial to make sure that sequences with potential regulatory function are excluded.

We have determined that the barcode (BC#8) that had no detectable Gal4induced gene expression in Figure S6 (now Supplemental Figure 9) has a deletion in the GFP coding region that ablates GFP function. Interestingly, the expressed TaG-EM barcode transcript is still detectable in single cell sequencing experiments, but obviously this line cannot be used for cell enrichment (at least based solely on GFP expression from the TaG-EM construct). While it is unclear how this line came to have a lesion in the GFP gene, we have subsequently generated >150 additional TaG-EM stocks and we have tested the GFP expression of these newly established stocks by crossing them to Mhc-Gal4. All of the additional stocks had GFP expression in the expected pattern, indicating that the BC#8 construct is an outlier with respect to inducibility of GFP. We have added the following text to the results section to address this point:

“No GFP expression was visible for TaG-EM barcode number 8, which upon molecular characterization had an 853 bp deletion within the GFP coding region (data not shown). We generated and tested GFP expression of an additional 156 TaG-EM barcode lines (Alegria et al., 2024), by crossing them to Mhc-Gal4 and observing expression in the adult thorax. All 156 additional TaG-EM lines had robust GFP expression (data not shown).”

It is certainly the case that future improvements to the construct design may be necessary or desirable and that back-crossing could likely be used to alleviate line-toline differences for specific phenotypes, we also address this point in the discussion with the following text:

“We excluded this poor performing barcode line from the fecundity tests, however, backcrossing is often used to bring reagents into a consistent genetic background for behavioral experiments and could also potentially be used to address behavior-specific issues with specific TaG-EM lines. In addition, other strategies such as averaging across multiple barcode lines or permutation of barcode assignment across replicates could also mitigate such deficiencies.”

• For their single-cell experiments, the authors have used the 10X Genomics method, which relies on sequencing just a short segment of each transcript (usually 50-250bp - unknown for this study as read length information was not provided) to enable its identification, with the matching paired-end read providing cell barcode and UMI information (Macosko et al., 2015). With average fragment length after tagmentation usually ranging from 300-700bp, a large number of GFP reads will likely not include the 14bp TaG-EM barcode. 

The 10x Genomics 3’ workflows that were used for sequencing TaG-EM samples reads the cell barcode and UMI in read one and the expressed RNA sequence in read two. We sequenced the samples shown in Figure 5 in the initial manuscript using a run configuration that generated 150 bp for read two. The TaG-EM barcodes are located just upstream of the poly-adenylation sites (based on the sequencing data, we observe two different poly-A sites and the TaG-EM barcode is located 35 and 60 bp upstream of these sites). Based on the location of the TaG-EM barcodes,150 bp reads is sufficient to see the barcode in any GFP-associated read (when using the 3’ gene expression workflow). In addition to detecting the expression of the TaG-EM barcodes in the 10x Genomics gene expression library, it is possible to make a separate library that enriches the barcode sequence (similar to hashtag or CITE-Seq feature barcode libraries). We have added experimental data where we successfully performed an enrichment of the TaG-EM barcodes and sequenced this as a separate hashtag library (Supplemental Figure 18). We have added text to the results describing this work and also included a detailed information in the methods for performing TaG-EM barcode enrichment during 10x library prep. 

Results:

“In antibody-conjugated oligo cell hashing approaches, sparsity of barcode representation is overcome by spiking in an additional primer at the cDNA amplification step and amplifying the hashtag oligo by PCR. We employed a similar approach to attempt to enrich for TaG-EM barcodes in an additional library sequenced separately from the 10x Genomics gene expression library. Our initial attempts at barcode enrichment using spike-in and enrichment primers corresponding to the TaG-EM PCR handle were unsuccessful (Supplemental Figure 18). However, we subsequently optimized the TaG-EM barcode enrichment by 1) using a longer spike-in primer that more closely matches the annealing temperature used during the 10x Genomics cDNA creation step, and 2) using a nested PCR approach to amplify the cell-barcode and unique molecular identifier (UMI)-labeled TaG-EM barcodes (Supplemental Figure 18). Using the enriched library, TaG-EM barcodes were detected in nearly 100% of the cells at high sequencing depths (Supplemental Figure 19). However, although we used a polymerase that has been engineered to have high processivity and that has been shown to reduce the formation of chimeric reads in other contexts (Gohl et al., 2016), it is possible that PCR chimeras could lead to unreliable detection events for some cells. Indeed, many cells had a mixture of barcodes detected with low counts and single or low numbers of associated UMIS. To assess the reliability of detection, we analyzed the correlation between barcodes detected in the gene expression library and the enriched TaG-EM barcode library as a function of the purity of TaG-EM barcode detection for each cell (the percentage of the most abundant detected TaG-EM barcode, Supplemental Figure 19). For TaG-EM barcode detections where the most abundance barcode was a high percentage of the total barcode reads detected (~75%-99.99%), there was a high correlation between the barcode detected in the gene expression library and the enriched TaG-EM barcode library. Below this threshold, the correlation was substantially reduced. 

In the enriched library, we identified 26.8% of cells with a TaG-EM barcode reliably detected, a very modest improvement over the gene expression library alone (23.96%), indicating that at least for this experiment, the main constraint is sufficient expression of the TaG-EM barcode and not detection. To identify TaG-EM barcodes in the combined data set, we counted a positive detection as any barcode either identified in the gene expression library or any barcode identified in the enriched library with a purity of >75%. In the case of conflicting barcode calls, we assigned the barcode that was detected directly in the gene expression library. This increased the total fraction of cells where a barcode was identified to approximately 37% (Figure 6B).”

Methods:

“The resulting pool was prepared for sequencing following the 10x Genomics Single Cell 3’ protocol (version CG000315 Rev C), At step 2.2 of the protocol, cDNA amplification, 1 µl of TaG-EM spike-in primer (10 µM) was added to the reaction to amplify cDNA with the TaG-EM barcode. Gene expression cDNA and TaG-EM cDNA were separated using a double-sided SPRIselect (Beckman Coulter) bead clean up following 10x Genomics Single Cell 3’ Feature Barcode protocol, step 2.3 (version CG000317 Rev E). The gene expression cDNA was created into a library following the CG000315 Rev C protocol starting at section 3. Custom nested primers were used for enrichment of TaG-EM barcodes after cDNA creation using PCR.  The following primers were tested (see Supplemental Figure 18):

UMGC_IL_TaGEM_SpikeIn_v1:

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTTCCAACAACCGGAAGT*G*A UMGC_IL_TaGEM_SpikeIn_v2:

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCAGCTTATAACTTCCAACAACCGGAAGT*G*A

UMGC_IL_TaGEM_SpikeIn_v3:

TGTGCTCTTCCGATCTGCAGCTTATAACTTCCAACAACCGGAAGT*G*A D701_TaGEM:

CAAGCAGAAGACGGCATACGAGATCGAGTAATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCAGC*T*T

SI PCR Primer:

AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGC*T*C

UMGC_IL_DoubleNest:

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCAGCTTATAACTTCCAACAACCGG*A*A

P5: AATGATACGGCGACCACCGA

D701:

GATCGGAAGAGCACACGTCTGAACTCCAGTCACATTACTCGATCTCGTATGCCGTCTTCTGCTTG

D702:

GATCGGAAGAGCACACGTCTGAACTCCAGTCACTCCGGAGAATCTCGTATGCCGTCTTCTGCTTG

After multiple optimization trials, the following steps yielded ~96% on-target reads for the TaG-EM library (Supplemental Figure 18, note that for the enriched barcode data shown in Figure 6 and Supplemental Figure 19, a similar amplification protocol was used TaG-EM barcodes were amplified from the gene expression library cDNA and not the SPRI-selected barcode pool). TaG-EM cDNA was amplified with the following PCR reaction: 5 µl purified TaG-EM cDNA, 50 µl 2x KAPA HiFi ReadyMix (Roche), 2.5 µl UMGC_IL_DoubleNest primer (10 µM), 2.5 µl SI_PCR primer (10 µM), and 40 µl nuclease-free water. The reaction was amplified using the following cycling conditions: 98ºC for 2 minutes, followed by 15 cycles of 98ºC for 20 seconds, 63ºC for 30 seconds, 72ºC for 20 seconds, followed by 72ºC for 5 minutes. After the first PCR, the amplified cDNA was purified with a 1.2x SPRIselect (Beckman Coulter) bead cleanup with 80% ethanol washes and eluted into 40 µL of nuclease-water. A second round of PCR was run with following reaction: 5 µl purified TaG-EM cDNA, 50 µl 2x KAPA HiFi ReadyMix (Roche), 2.5 µl D702 primer (10 µM), 2.5 µl p5 Primer (10 µM), and 40 µl nuclease-free water. The reaction was amplified using the following cycling conditions: 98ºC for 2 minutes, followed by 10 cycles of 98ºC for 20 seconds, 63ºC for 30 seconds, 72ºC for 20 seconds, followed by 72ºC for 5 minutes. After the second PCR, the amplified cDNA was purified with a 1.2x SPRIselect (Beckman Coulter) bead cleanup with 80% ethanol washes and eluted into 40uL of nuclease-water. The resulting 3’ gene expression library and TaG-EM enrichment library were sequenced together following Scenario 1 of the BioLegend “Total-Seq-A Antibodies and Cell Hashing with 10x Single Cell 3’ Reagents Kit v3 or v3.1” protocol. Additional sequencing of the enriched TaG-EM library also done following Scenario 2 from the same protocol.” 

When a given cell barcode is not associated with any TaG-EM barcode, then demultiplexing is impossible. This is a major problem, which is particularly visible in Figs 5 and S13. In 5F, BC4 is only detected in a couple of dozen cells, even though the Jon99Ciii marker of enterocytes is present in a much larger population (Fig 5C). Therefore, in this particular case, TaG-EM fails to detect most of the GFP-expressing cells. 

Figure 5 in the original manuscript represented data from an experiment in which there were eight different TaG-EM barcoded samples present, including four replicates of the pan-midgut driver (each of which included enterocyte populations). One would not expect the BC4 enterocyte driver expression to be observed in all of the Jon99Ciii cells, since the majority of the GFP+ cells shown in the UMAP plot were likely derived from and are labeled by the pan-midgut driver-associated barcodes. Thus, the design and presentation of this particular experiment (in particular, the presence of eight distinct samples in the data set) is making the detection of the TaG-EM barcodes look sparser than it actually is. We have added a panel in both Figure 6B and Supplemental Figure 17B that shows the overall detection of barcodes in the enriched barcode library and gene expression library or the gene expression library only, respectively, for this experiment.

However, the reviewer’s overall point regarding barcode detection is still valid in that if we consider all eight barcodes, we only see TaG-EM barcode labeling associated with about a quarter of all the cells in this gene expression library, or about 37% of cells when we include the enriched TaG-EM barcode library. While improving barcode detection will improve the yield and is necessary for some applications (such as robust detection of multiplets), we would argue that even at the current level of success this approach has significant utility. First, if one’s goal is to unambiguously label a cell cluster and trace it to a defined cell population in vivo, sparse labeling may be sufficient. Second, demultiplexing is still possible (as we demonstrate) but involves a trade off in yield (not every cell is recovered and there is some extra sequencing cost as some sequenced cells cannot be assigned to a barcode). 

Similarly, in S13, most cells should express one of the four barcodes, however many of them (maybe up to half - this should be quantified) do not. Therefore, the claim (L277278) that "the pan-midgut driver were broadly distributed across the cell clusters" is misleading. Moreover, the hypothesis that "low expressing driver lines may result in particularly sparse labelling" (L331-333) is at least partially wrong, as Fig S13 shows that the same Gal4 driver can lead to very different levels of barcode coverage.

As described above, since this experiment included eight different TaG-EM barcodes expressed by five different drivers, the expectation is that only about half of the cells in Figure S13 (now Figure S20) should express a TaG-EM barcode. It is not clear why BC2 is underrepresented in terms of the number of cells labeled and BC7 is overrepresented. We agree with the reviewer that this should be described more accurately in the paper and that it does impact our interpretation related to driver strength and barcode detection. We have revised this sentence in the discussion and also added additional text in the results describing the within driver variability seen in this experiment.

Results text:

“As expected, the barcodes expressed by the pan-midgut driver were broadly distributed across the cell clusters (Supplemental Figure 20). However, the number of cells recovered varied significantly among the four pan-midgut driver associated barcodes.”

Discussion text:

“It is likely that the strength of the Gal4 driver contributes to the labeling density. However, we also observed variable recovery of TaG-EM barcodes that were all driven by the same pan-midgut Gal4 driver (Supplemental Figure 20).”

• Comparisons between TaG-EM and other, simpler methods for labelling individual cell populations are missing. For example, how would TaG-EM compare with expression of different fluorescent reporters, or a strategy based on the brainbow/flybow principle?

The advantage of TaG-EM is that an arbitrarily large number of DNA barcodes can be used (contingent upon the availability of transgenic lines – we described 20 barcoded lines in our initial manuscript and we have now extended this collection to over 170 lines), while the number of distinguishable FPs is much lower. Brainbow/Flybow uses combinatorial expression of different FPs, but because this combinatorial expression is stochastic, tracing a single cell transcriptome to a defined cell population in vivo based on the FP signature of a Brainbow animal would likely not be possible (and would almost certainly be impossible at scale).

• FACS data is missing throughout the paper. The authors should include data from their comparative flow cytometry experiment of TaG-EM cells with or without additional hexameric GFP, as well as FSC/SSC and fluorescence scatter plots for the FACS steps that they performed prior to scRNA-seq, at least in supplementary figures.

We have added Supplemental Figures with the FACS data for all of the single cell sequencing data presented in the manuscript (Supplemental Figures 12 and 14).

• The authors should show the whole data described in L229, including the cluster that they chose to delete. At least, they should provide more information about how many cells were removed. In any case, the fact that their data still contains a large number of debris and dead cells despite sorting out PI negative cells with FACS and filtering low abundance barcodes with Cellranger is concerning.

This description was referring to the unprocessed Cellranger output (not filtered for low abundance barcodes). Prior to filtering for cell barcodes with high mitochondria or rRNA (or other processing in Seurat/Scanpy), we saw two clusters, one with low UMI counts and enrichment of mitochondrial genes (see Cellranger report below). 

Author response image 1.

These cell barcodes were removed by downstream quality filtering and the remaining cells showed expression of expected intestinal stem cell and enteroblast marker genes.

Overall, although a method for genetic tagging cell populations prior to multiplexing in single-cell experiments would be extremely useful, the method presented here is inadequate. However, despite all the weaknesses listed above, the idea of barcodes expressed specifically in cells of interest deserves more consideration. If the authors manage to improve their design to resolve the major issues and demonstrate the benefits of their method more clearly, then TaG-EM could become an interesting option for certain applications.

We thank the reviewer for this comment and hope that the above responses and additional experiments and data that we have added have helped to alleviate the noted weaknesses.

Reviewer #2 (Public Review):

In this manuscript, Mendana et al developed a multiplexing method - Targeted Genetically-Encoded Multiplexing or TaG-EM - by inserting a DNA barcode upstream of the polyadenylation site in a Gal4-inducible UAS-GFP construct. This Multiplexing method can be used for population-scale behavioral measurements or can potentially be used in single-cell sequencing experiments to pool flies from different populations. The authors created 20 distinctly barcoded fly lines. First, TaG-EM was used to measure phototaxis and oviposition behaviors. Then, TaG-EM was applied to the fly gut cell types to demonstrate its applications in single-cell RNA-seq for cell type annotation and cell origin retrieving.

This TaG-EM system can be useful for multiplexed behavioral studies from nextgeneration sequencing (NGS) of pooled samples and for Transcriptomic Studies. I don't have major concerns for the first application, but I think the scRNA-seq part has several major issues and needs to be further optimized.

Major concerns:

(1) It seems the barcode detection rate is low according to Fig S9 and Fig 5F, J and N. Could the authors evaluate the detection rate? If the detection rate is too low, it can cause problems when it is used to decode cell types.

See responses to Reviewer #1 on this topic above.  

(2) Unsuccessful amplification of TaG-EM barcodes: The authors attempted to amplify the TaG-EM barcodes in parallel to the gene expression library preparation but encountered difficulties, as the resulting sequencing reads were predominantly offtarget. This unsuccessful amplification raises concerns about the reliability and feasibility of this amplification approach, which could affect the detection and analysis of the TaG-EM barcodes in future experiments.

As noted above, we have now established a successful amplification protocol for the TaG-EM barcodes. This data is shown in Figure 6, and Supplemental Figures 18-19 and we have included a detailed information in the methods for performing TaG-EM barcode enrichment during 10x library prep. We have also included code in the paper’s Github repository for assigning TaG-EM barcodes from the enriched library to the associated 10x Genomics cell barcodes.

(3) For Fig 5, the singe-cell clusters are not annotated. It is not clear what cell types are corresponding to which clusters. So, it is difficult to evaluate the accuracy of the assignment of barcodes.

We have added annotation information for the cell clusters based on expression of cell-type-specific marker genes (Figure 6A, Supplemental Figures 16-17).

(4) The scRNA-seq UMAP in Fig 5 is a bit strange to me. The fly gut epithelium contains only a few major cell types, including ISC, EB, EC, and EE. However, the authors showed 38 clusters in fig 5B. It is true that some cell types, like EE (Guo et al., 2019, Cell Reports), have sub-populations, but I don't expect they will form these many subtypes. There are many peripheral small clusters that are not shown in other gut scRNAseq studies (Hung et al., 2020; Li et al., 2022 Fly Cell Atlas; Lu et al., 2023 Aging Fly Cell Atlas). I suggest the authors try different data-processing methods to validate their clustering result.

For all of the single cell experiments, after doublet and ambient RNA removal (as suggested below), we have reclustered the datasets and evaluated different resolutions using Clustree. As the Reviewer points out, there are different EE subtypes, as well as regionalized expression differences in EC and other cell populations, so more than four clusters are expected (an analysis of the adult midgut identified 22 distinct cell types). With this revised analysis our results more closely match the cell populations observed in other studies (though it should be noted that the referenced studies largely focus on the adult and not the larval stage).  

(5) Different gut drivers, PMC-, PC-, EB-, EC-, and EE-GAL4, were used. The authors should carefully characterize these GAL4 expression in larval guts and validate sequencing data. For example, does the ratio of each cell type in Fig 5B reflect the in vivo cell type ratio? The authors used cell-type markers mostly based on the knowledge from adult guts, but there are significant morphological and cell ratio differences between larval and adult guts (e.g., Mathur...Ohlstein, 2010 Science).

We have characterized the PC driver which is highlighted in Supplemental Figure 13, and the EC and EE drivers which are highlighted in Figure 6G-N in detail in larval guts and have added this data to the paper (Supplemental Figure 21). The EB driver was not characterized histologically as EB-specific antibodies are not currently available. The PMG-Gal4 line exhibits strong expression throughout the larval gut (Figure 5B and barcodes are recovered from essentially all of the larval gut cell clusters using this driver (Supplemental Figure 20). We don’t necessarily expect the ratios of cells observed in the scRNA-Seq data to reflect the ratios typically observed in the gut as we performed pooled flow sorting on a multiplexed set of eight genotypes and driver expression levels, flow sorting, and possibly other processing steps could all influence the relative abundance of different cell types. However, detailed characterization of these driver lines did reveal spatial expression patterns that help explain aspects of the scRNA-Seq data. We have also added the following text to the paper to further describe the characterization of the drivers:

Results:

“Detailed characterization of the EC-Gal4 line indicated that although this line labeled a high percentage of enterocytes, expression was restricted to an area at the anterior and middle of the midgut, with gaps between these regions and at the posterior (Supplemental Figure 21). This could explain the absence of subsets of enterocytes, such as those labeled by betaTry, which exhibits regional expression in R2 of the adult midgut (Buchon et al., 2013).”

“Detailed characterization of the EE-Gal4 driver line indicated that ~80-85% of Prospero-positive enteroendocrine cells are labeled in the anterior and middle of the larval midgut, with a lower percentage (~65%) of Prospero-positive cells labeled in the posterior midgut (Supplemental Figure 21). As with the enterocyte labeling, and consistent with the Gal4 driver expression pattern, the EE-Gal4 expressed TaG-EM barcode 9 did not label all classes of enteroendocrine cells and other clusters of presumptive enteroendocrine cells expressing other neuropeptides such as Orcokinin, AstA, and AstC, or neuropeptide receptors such as CCHa2 (not shown) were also observed.”

Methods:

“Dissection and immunostaining

Midguts from third instar larvae of driver lines crossed to UAS-GFP.nls or UAS-mCherry were dissected in 1xPBS and fixed with 4% paraformaldehyde (PFA) overnight at 4ºC. Fixed samples were washed with 0.1% PBTx (1xPBS + 0.1% Triton X-100) three times for 10 minutes each and blocked in PBTxGS (0.1% PBTx + 3% Normal Goat Serum) for 2–4 hours at RT. After blocking, midguts were incubated in primary antibody solution overnight at 4ºC. The next day samples were washed with 0.1% PBTx three times for 20 minutes each and were incubated in secondary antibody solution for 2–3 hours at RT (protected from light) followed by three washes with 0.1% PBTx for 20 minutes each. One µg/ml DAPI solution prepared in 0.1% PBTx was added to the sample and incubated for 10 minutes followed by washing with 0.1% PBTx three times for 10 minutes each. Finally, samples were mounted on a slide glass with 70% glycerol and imaged using a Nikon AX R confocal microscope. Confocal images were processed using Fiji software. 

The primary antibodies used were rabbit anti-GFP (A6455,1:1000 Invitrogen), mouse anti-mCherry (3A11, 1:20 DSHB), mouse anti-Prospero (MR1A, 1:50 DSHB) and mouse anti-Pdm1 (Nub 2D4, 1:30 DSHB). The secondary antibodies used were goat antimouse and goat anti-rabbit IgG conjugated to Alexa 647 and Alexa 488 (1:200) (Invitrogen), respectively. Five larval gut specimens per Gal4 line were dissected and examined.”

(6) Doublets are removed based on the co-expression of two barcodes in Fig 5A. However, there are also other possible doublets, for example, from the same barcode cells or when one cell doesn't have detectable barcode. Did the authors try other computational approaches to remove doublets, like DoubleFinder (McGinnis et al., 2019) and Scrublet (Wolock et al., 2019)?

We have included DoubleFinder-based doublet removal in our data analysis pipeline. This is now described in the methods (see below).

(7) Did the authors remove ambient RNA which is a common issue for scRNA-seq experiments?

We have also used DecontX to remove ambient RNA. This is now described in the methods:

“Datasets were first mapped and analyzed using the Cell Ranger analysis pipeline (10x Genomics). A custom Drosophila genome reference was made by combining the BDGP.28 reference genome assembly and Ensembl gene annotations. Custom gene definitions for each of the TaG-EM barcodes were added to the fasta genome file and .gtf gene annotation file. A Cell Ranger reference package was generated with the Cell Ranger mkref command. Subsequent single-cell data analysis was performed using the R package Seurat (Satija et al., 2015). Cells expressing less than 200 genes and genes expressed in fewer than three cells were filtered from the expression matrix. Next, percent mitochondrial reads, percent ribosomal reads cells counts, and cell features were graphed to determine optimal filtering parameters. DecontX (Yang et al., 2020) was used to identify empty droplets, to evaluate ambient RNA contamination, and to remove empty cells and cells with high ambient RNA expression. DoubletFinder (McGinnis et al., 2019) to identify droplet multiplets and remove cells classified as multiplets. Clustree (Zappia and Oshlack, 2018) was used to visualize different clustering resolutions and to determine the optimal clustering resolution for downstream analysis. Finally, SingleR (Aran et al., 2019) was used for automated cell annotation with a gut single-cell reference from the Fly Cell Atlas (Li et al., 2022). The dataset was manually annotated using the expression patterns of marker genes known to be associated with cell types of interest. To correlate TaG-EM barcodes with cell IDs in the enriched TaG-EM barcode library, a custom Python script was used (TaGEM_barcode_Cell_barcode_correlation.py), which is available via Github: https://github.com/darylgohl/TaG-EM.”

(8) Why does TaG-EM barcode #4, driven by EC-GAL4, not label other classes of enterocyte cells such as betaTry+ positive ECs (Figures 5D-E)? similarly, why does TaG-EM barcode #9, driven by EE-GAL4, not label all EEs? Again, it is difficult to evaluate this part without proper data processing and accurate cell type annotation.

As noted in the response to a comment by Reviewer #1 above, part of this apparent sparsity of labeling is due to the way that this experiment was designed and visualized. We have added a new Figure panel in both Figure 6B and Supplemental Figure 17B that shows the overall detection of barcodes in the enriched barcode library and gene expression library or the gene expression library only, respectively, to better illustrate the efficacy of barcode detection. See also the response to point 5 above. Both the lack of labelling of betaTry+ ECs and subsets of EEs is consistent with the expression patterns of the EC-Gal4 and EE-Gal4 drivers.

(9) For Figure 2, when the authors tested different combinations of groups with various numbers of barcodes. They found remarkable consistency for the even groups. Once the numbers start to increase to 64, barcode abundance becomes highly variable (range of 12-18% for both male and female). I think this would be problematic because the differences seen in two groups for example may be due to the barcode selection rather than an actual biologically meaningful difference.

While there is some barcode-to-barcode variability for different amplification conditions, the magnitude of this variation is relatively consistent across the conditions tested. We looked at the coefficient of variation for the evenly pooled barcodes or for the staggered barcodes pooled at different relative levels. While the absolute magnitude of the variation is higher for the highly abundant barcodes in the staggered conditions, the CVs for these conditions (0.186 for female flies and for 0.163 male flies) were only slightly above the mean CV (0.125) for all conditions (see Supplemental Figure 3):

We have added this analysis as Supplemental Figure 3 and added the following text to the paper:(

“The coefficients of variation were largely consistent for groups of TaG-EM barcodes pooled evenly or at different levels within the staggered pools (Supplemental Figure 3).”

(10) Barcode #14 cannot be reliably detected in oviposition experiment. This suggests that the BC 14 fly line might have additional mutations in the attp2 chromosome arm that affects this behavior. Perhaps other barcode lines also have unknown mutations and would cause issues for other untested behaviors. One possible solution is to backcross all 20 lines with the same genetic background wild-type flies for >7 generations to make all these lines to have the same (or very similar) genetic background. This strategy is common for aging and behavior assays.

See response to Reviewer #1 above on this topic.

Reviewer #3 (Public Review):

The work addresses challenges in linking anatomical information to transcriptomic data in single-cell sequencing. It proposes a method called Targeted Genetically-Encoded Multiplexing (TaG-EM), which uses genetic barcoding in Drosophila to label specific cell populations in vivo. By inserting a DNA barcode near the polyadenylation site in a UASGFP construct, cells of interest can be identified during single-cell sequencing. TaG-EM enables various applications, including cell type identification, multiplet droplet detection, and barcoding experimental parameters. The study demonstrates that TaGEM barcodes can be decoded using next-generation sequencing for large-scale behavioral measurements. Overall, the results are solid in supporting the claims and will be useful for a broader fly community. I have only a few comments below:

We thank the reviewer for these positive comments.

Specific comments:

(1) The authors mentioned that the results of structure pool tests in Fig. 2 showed a high level of quantitative accuracy in detecting the TaG-EM barcode abundance. Although the data were generally consistent with the input values in most cases, there were some obvious exceptions such as barcode 1 (under-represented) and barcodes 15, 20 (overrepresented). It would be great if the authors could comment on these and provide a guideline for choosing the appropriate barcode lines when implementing this TaG-EM method.

See the response to point 9 from Reviewer 2. Although there seem to be some systematic differences in barcode amplification, the coefficient of variation was relatively consistent across all of the barcode combinations and relative input levels that we examined. Our recommendation (described in the text) is to average across 3-4 independent barcodes (which yielded a R2 values of >0.99 with expected abundance in the structured pooled tests).  

(2) In Supplemental Figure 6, the authors showed GFP antibody staining data with 20 different TaG-EM barcode lines. The variability in GFP antibody staining results among these different TaG-EM barcode lines concerns the use of these TaG-EM barcode lines for sequencing followed by FACS sorting of native GFP. I expected the native GFP expression would be weaker and much more variable than the GFP antibody staining results shown in Supplemental Figure 6. If this is the case, variation of tissue-specific expression of TaG-EM barcode lines will likely be a confounding factor.

Aside from barcode 8, which had a mutation in the GFP coding sequence, we did not see significant variability in expression levels either in the wing disc. Subtle differences seen in this figure most likely result from differences in larval staging. Similar consistent native (unstained) GFP expression of the TaG-EM constructs was seen in crosses with Mhc-Gal4 (described above). 

(3) As the authors mentioned in the manuscript, multiple barcodes for one experimental condition would be a better experimental design. Could the authors suggest a recommended number of barcodes for each experiential condition? 3? 4? Or more? 

See response to Reviewer #3, point number 1 above.

(3b) Also, it would be great if the authors could provide a short discussion on the cost of such TaG-EM method. For example, for the phototaxis assay, if it is much more expensive to perform TaG-EM as compared to manually scoring the preference index by videotaping, what would be the practical considerations or benefits of doing TaG-EM over manual scoring?

While this will vary depending on the assay and the scale at which one is conducting experiments, we have added an analysis of labor savings for the larval gut motility assay (Supplemental Figure 8). We have also added the following text to the Discussion describing some of the trade-offs to consider in assessing the potential benefit of incorporating TaG-EM into behavioral measurements:

“While the utility of TaG-EM barcode-based quantification will vary based on the number of conditions being analyzed and the ease of quantifying the behavior or phenotype by other means, we demonstrate that TaG-EM can be employed to cost-effectively streamline labor-intensive assays and to quantify phenotypes with small effect sizes (Figure 4, Supplemental Figure 8).”

Recommendations for the authors:  

While recognising the potential of the TaG-EM methodology, we had a few major concerns that the authors might want to consider addressing:

As stated above, we are grateful to the reviewers and editor for their thoughtful comments. We have addressed many of the points below in our responses above, so we will briefly respond to these points and where relevant direct the reader to comments above.

(1) We were concerned about the efficacy of TaG-EM in assessing more complex behaviours than oviposition and phototaxis. We note that Barcode #14 cannot be reliably detected in oviposition experiment. This suggests that the BC 14 fly line might have additional mutations in the attp2 chromosome arm that affects this behavior. Perhaps other barcode lines also have unknown mutations and would cause issues for other untested behaviors. One possible solution is to back-cross all 20 lines with the same genetic background wild-type flies for >7 generations to make all these lines to have the same (or very similar) genetic background. This strategy is common for aging and behavior assays.

See response to Reviewer #1 and Reviewer #2, item 10, above.

(2) We were unable to assess the drop-out rates of the TaG-EM barcode from the sequencing. The barcode detection rate is low (Fig S9 and Fig 5F, J and N). This would be a considerable drawback (relating to both experimental design and cost), if a large proportion of the cells could not be assigned an identity.

See comments above addressing this point.

(3) The effectiveness of TaG-EM scRNA-seq on the larvae gut is not very effective - the cells are not well annotated, the barcodes seem not to have labelled expected cell types (ECs and EEs), and there is no validation of the Gal4 drivers in vivo.

See previous comments. We have addressed specific comments above on data processing and annotation, included a visualization of the overall effectiveness of labeling, added a protocol and data on enriched TaG-EM barcode libraries, and have added detailed characterization of the Gal4 drivers in the larval gut (Figure 6, Supplemental Figures 17-21).

(4) A formal assessment of the cost-effectiveness would be an important consideration in broad uptake of the methodology.

While this is difficult to do in a comprehensive manner given the breadth of potential applications, we have included estimates of labor savings for one of the behavioral assays that we tested (Supplemental Figure 8). We have also included a discussion of some of the factors that would make TaG-EM useful or cost-effective to apply for behavioral assays (see response to Reviewer #3, comment 3b, above). We have also added the following text to the discussion to address the cost considerations in applying TaG-EM for scRNA-Seq:

“For single cell RNA-Seq experiments, the cost savings of multiplexing is roughly the cost of a run divided by the number of independent lines multiplexed, plus labor savings by also being able to multiplex upstream flow cytometry, minus loss of unbarcoded cells. Our experiments indicated that for the specific drivers we tested TaG-EM barcodes are detected in around one quarter of the cells if relying on endogenous expression in the gene expression library, though this fraction was higher (~37%) if sequencing an enriched TaG-EM barcode library in parallel (Figure 6, Supplemental Figures 18-19).”

(5) Similarly, a formal assessment of the effect of the insertion on the variability in GFP expression and the behaviour needs to be documented.

See responses to Reviewer #1, Reviewer #2, item 9, and Reviewer #3, item 2 above.

Reviewer #1 (Recommendations For The Authors):

(in no particular order of importance)

• L84-85: the authors should either expand, or remove this statement. Indeed, lack of replicates is only true if one ignores that each cell in an atlas is indeed a replicate. Therefore, depending on the approach or question, this statement is inaccurate.

This sentence was meant to refer to experiments where different experimental conditions are being compared and not to more descriptive studies such as cell atlases. We have revised this sentence to clarify.

“Outside of descriptive studies, these costs are also a barrier to including replicates to assess biological variability; consequently, a lack of biological replicates derived from independent samples is a common shortcoming of single-cell sequencing experiments.”

• L103-104: this sentence is unclear.

We have revised this sentence as follows:

“Genetically barcoded fly lines can also be used to enable highly multiplexed behavioral assays which can be read out using high throughput sequencing.”

• In Fig S1 it is unclear why there are more than 20 different sequences in panel B where the text and panel A only mention the generation of 20 distinct constructs. This should be better explained.

The following text was added to the Figure legend to explain this discrepancy:

“Because the TaG-EM barcode constructs were injected as a pool of 29 purified plasmids, some of the transgenic lines had inserts of the same construct. In total 20 unique lines were recovered from this round of injection.”

• It would be interesting to compare the efficiency of TaG-EM driven doublet removal (Fig 5A) with standard doublet-removing software (e.g., DoubletFinder, McGinnis et al., 2019).

We have done this comparison, which is now shown in Supplemental Figure 15.

• I would encourage the authors to check whether barcode representation in Fig S13  can be correlated to average library size, as one would expect libraries with shorter reads to be more likely to include the 14-bp barcode and therefore more accurately recapitulate TaG-EM barcode expression.

These are not independent sequencing libraries, but rather data from barcodes that were multiplexed in a single flow sort, 10x droplet capture, and sequencing library. Thus, there must be some other variable that explains the differential recovery of these barcodes.

• Fig 4A should appear earlier in the paper.

We have moved Figure 4A from the previous manuscript (a schematic showing the detailed design of the TaG-EM construct) to Figure 1A in the revised version.

Reviewer #2 (Recommendations For The Authors):

Minor:

(1) There is a typo for Fig S13 figure legends: BC1, BC1, BC3... should be BC1, BC2, BC3.

Fixed.

Reviewer #3 (Recommendations For The Authors):

Comments to authors:

(1) It would be great if the authors could provide an additional explanation on how these 29 barcode sequences were determined.

Response: This information is in the Methods section. For the original cloned plasmids:

“Expected construct size was verified by diagnostic digest with _Eco_RI and _Apa_LI. DNA concentration was determined using a Quant-iT PicoGreen dsDNA assay (Thermo Fisher Scientific) and the randomer barcode for each of the constructs was determined by Sanger sequencing using the following primers:

SV40_post_R: GCCAGATCGATCCAGACATGA

SV40_5F: CTCCCCCTGAACCTGAAACA”

For transgenic flies, after DNA extraction and PCR enrichment (details also in the Methods section):

“The barcode sequence for each of the independent transgenic lines was determined by Sanger sequencing using the SV40_5F and SV40_PostR primers.”

(2) Why did the authors choose myr-GFP as the backbone instead of nls-GFP if the downstream application is to perform sequencing?

We initially chose myr::GFP as we planned to conduct single cell and not single nucleus sequencing and myr::GFP has the advantage of labeling cell membranes which could facilitate the characterization or confirmation of cell type-specific expression, particularly in the nervous system. However, we have considered making a version of the TaG-EM construct with a nuclear targeted GFP (thereby enabling “NucEM”). In the Discussion, we mention this possibility as well as the possibility of using a second nuclear-GFP construct in conjunction with TaG-EM lines is nuclear enrichment is desired:

“In addition, while the original TaG-EM lines were made using a membrane-localized myr::GFP construct, variants that express GFP in other cell compartments such as the cytoplasm or nucleus could be constructed to enable increased expression levels or purification of nuclei. Nuclear labeling could also be achieved by co-expressing a nuclear GFP construct with existing TaG-EM lines in analogy to the use of hexameric GFP described above.”

Minor comments:

(1) Line 193, Supplemental Figure 4 should be Supplemental Figure 5

Fixed.

(2) Scale bars should be added in Figure 4, Supplemental Figures 6, 7, and 8A.

We have added scale bars to these figures and also included scale bars in additional Supplemental Figures detailing characterization of the gut driver lines.

(3) Were Figure 4C and Supplemental Figure 7 data stained with a GFP antibody?

No, this is endogenous GFP signal. This is now noted in the Figure legends.

(4) Line 220, specify the three barcode lines (lines #7, 8, 9) in the text. 

Added this information.

Same for Lines 251-254. Line 258, which 8 barcode Gal4 line combinations?

(5) Line 994, typo: (BC1, BC1, BC3, and BC7)-> (BC1, BC2, BC3, and BC7)

Fixed.

(6) Figure 5 F, J and N, add EC-Gal4, EB-Gal4, and EE-Gal4 above each panel to improve readability.

We have added labels of the cell type being targeted (leftmost panels), the barcode, and the marker gene name to Figure 6 C-N.

Author Response

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

Thank you for the detailed and constructive reviews. We revised the paper accordingly, and a point-by-point reply appears below. The main changes are:

• An extended discussion section that places our work in context with other related developments in theory and modeling.

• A new results section that demonstrates a substantial improvement in performance from a non-linear activation function. This led to addition of a co-author.

• The mathematical proof that the resolvent of the adjacency matrix leads to the shortest path distances has been moved to a separate article, available as a preprint and attached to this resubmission. This allows us to present that work in the context of graph theory, and focus the present paper on neural modeling.

Reviewer #1 (Public Review):

This paper presents a highly compelling and novel hypothesis for how the brain could generate signals to guide navigation towards remembered goals. Under this hypothesis, which the authors call "Endotaxis", the brain co-opts its ancient ability to navigate up odor gradients (chemotaxis) by generating a "virtual odor" that grows stronger the closer the animal is to a goal location. This idea is compelling from an evolutionary perspective and a mechanistic perspective. The paper is well-written and delightful to read.

The authors develop a detailed model of how the brain may perform "Endotaxis", using a variety of interconnected cell types (point, map, and goal cells) to inform the chemotaxis system. They tested the ability of this model to navigate in several state spaces, representing both physical mazes and abstract cognitive tasks. The Endotaxis model performed reasonably well across different environments and different types of goals.

The authors further tested the model using parameter sweeps and discovered a critical level of network gain, beyond which task performance drops. This critical level approximately matched analytical derivations.

My main concern with this paper is that the analysis of the critical gain value (gamma_c) is incomplete, making the implications of these analyses unclear. There are several different reasonable ways in which the Endotaxis map cell representations might be normalized, which I suspect may lead to different results. Specifically, the recurrent connections between map cells may either be an adjacency matrix, or a normalized transition matrix. In the current submission, the recurrent connections are an unnormalized adjacency matrix. In a previous preprint version of the Endotaxis manuscript, the recurrent connections between the map cells were learned using Oja's rule, which results in a normalized state-transition matrix (see "Appendix 5: Endotaxis model and the successor representation" in "Neural learning rules for generating flexible predictions and computing the successor representation", your reference 17). The authors state "In summary, this sensitivity analysis shows that the optimal parameter set for endotaxis does depend on the environment". Is this statement, and the other conclusions of the sensitivity analysis, still true if the learned recurrent connections are a properly normalized state-transition matrix?

Yes, this is an interesting topic. In v.1 of our bioRxiv preprint we used Oja’s rule for learning, which will converge on a map connectivity that reflects the transition probabilities. The matrix M becomes a left-normalized or right-normalized stochastic matrix, depending on whether one uses the pre-synaptic or the post-synaptic version of Oja’s rule. This is explained well in Appendix 5 of Fang 2023.

In the present version of the model we use a rule that learns the adjacency matrix A, not the transition matrix T. The motivation is that we want to explain instances of oneshot learning, where an agent acquires a route after traversing it just once. For example, we had found experimentally that mice can execute a complex homing route on the first attempt.

An agent can establish whether two nodes are connected (adjacency) the very first time it travels from one node to the other. Whereas it can evaluate the transition probability for that link only after trying this and all the other available links on multiple occasions. Hence the normalization terms in Oja’s rule, or in the rule used by Fang 2023, all involve some time-averaging over multiple visits to the same node. This implements a gradual learning process over many experiences, rather than a one-shot acquisition on the first experience.

Still one may ask whether there are advantages to learning the transition matrix rather than the adjacency matrix. We looked into this with the following results:

• The result that (1/γ − A)−1 is monotonically related to the graph distances D in the limit of small γ (a proof now moved to the Meister 2023 preprint) , holds also for the transition matrix T. The proof follows the same steps. So in the small gain limit, the navigation model would work with T as well.

• If one uses the transition matrix to compute the network output (1/γ − T)-1 then the critical gain value is γc = 1. It is well known that the largest eigenvalue of any Markov transition matrix is 1, and the critical gain γc is the inverse of that. This result is independent of the graph. So this offers the promise that the network could use the same gain parameter γ regardless of the environment.

• In practice, however, the goal signal turned out to be less robust when based on T than when based on A. We illustrate this with the attached Author response image 1. This replicates the analysis in Figure 3 of the manuscript, using the transition matrix instead of the adjacency matrix. Some observations:

• Panel B: The goal signal follows an exponential dependence on graph distance much more robustly for the model with A than with T. This holds even for small gain values where the exponential decay is steep.

• Panel C: As one raises the gain closer to the critical value, the goal signal based on T scatters much more than when based on A.

• Panels D, E: Navigation based on A works better than based on T. For example, using the highest practical gain value, and a readout noise of ϵ = 0.01, navigation based on T has a range of only 8 steps on this graph, whereas navigation based on A ranges over 12 steps, the full size of this graph.

We have added a section “Choice of learning rule” to explain this. The Author response image 1 is part of the code notebook on Github.

Author response image 1.

Overall, this paper provides a very compelling model for how neural circuits may have evolved the ability to navigate towards remembered goals, using ancient chemotaxis circuits.

This framework will likely be very important for understanding how the hippocampus (and other memory/navigation-related circuits) interfaces with other processes in the brain, giving rise to memory-guided behavior.

Reviewer #2 (Public Review):

The manuscript presents a computational model of how an organism might learn a map of the structure of its environment and the location of valuable resources through synaptic plasticity, and how this map could subsequently be used for goal-directed navigation.

The model is composed of 'map cells', which learn the structure of the environment in their recurrent connections, and 'goal-cell' which stores the location of valued resources with respect to the map cell population. Each map cell corresponds to a particular location in the environment due to receiving external excitatory input at this location. The synaptic plasticity rule between map cells potentiates synapses when activity above a specified threshold at the pre-synaptic neuron is followed by above-threshold activity at the post-synaptic neuron. The threshold is set such that map neurons are only driven above this plasticity threshold by the external excitatory input, causing synapses to only be potentiated between a pair of map neurons when the organism moves directly between the locations they represent. This causes the weight matrix between the map neurons to learn the adjacency for the graph of locations in the environment, i.e. after learning the synaptic weight matrix matches the environment's adjacency matrix. Recurrent activity in the map neuron population then causes a bump of activity centred on the current location, which drops off exponentially with the diffusion distance on the graph. Each goal cell receives input from the map cells, and also from a 'resource cell' whose activity indicates the presence or absence of a given values resource at the current location. Synaptic plasticity potentiates map-cell to goal-cell synapses in proportion to the activity of the map cells at time points when the resource cell is active. This causes goal cell activity to increase when the activity of the map cell population is similar to the activity where the resource was obtained. The upshot of all this is that after learning the activity of goal cells decreases exponentially with the diffusion distance from the corresponding goal location. The organism can therefore navigate to a given goal by doing gradient ascent on the activity of the corresponding goal cell. The process of evaluating these gradients and using them to select actions is not modelled explicitly, but the authors point to the similarity of this mechanism to chemotaxis (ascending a gradient of odour concentration to reach the odour source), and the widespread capacity for chemotaxis in the animal kingdom, to argue for its biological plausibility.

The ideas are interesting and the presentation in the manuscript is generally clear. The two principle limitations of the manuscript are: i) Many of the ideas that the model implements have been explored in previous work. ii) The mapping of the circuit model onto real biological systems is pretty speculative, particularly with respect to the cerebellum.

Regarding the novelty of the work, the idea of flexibly navigating to goals by descending distance gradients dates back to at least Kaelbling (Learning to achieve goals, IJCAI, 1993), and is closely related to both the successor representation (cited in manuscript) and Linear Markov Decision Processes (LMDPs) (Piray and Daw, 2021, https://doi.org/ 10.1038/s41467-021-25123-3, Todorov, 2009 https://doi.org/10.1073/pnas.0710743106). The specific proposal of navigating to goals by doing gradient descent on diffusion distances, computed as powers of the adjacency matrix, is explored in Baram et al. 2018 (https://doi.org/10.1101/421461), and the idea that recurrent neural networks whose weights are the adjacency matrix can compute diffusion distances are explored in Fang et al. 2022 (https://doi.org/10.1101/2022.05.18.492543). Similar ideas about route planning using the spread of recurrent activity are also explored in Corneil and Gerstner (2015, cited in manuscript). Further exploration of this space of ideas is no bad thing, but it is important to be clear where prior literature has proposed closely related ideas.

We have added a discussion section on “Theories and models of spatial learning” with a survey of ideas in this domain and how they come together in the Endotaxis model.

Regarding whether the proposed circuit model might plausibly map onto a real biological system, I will focus on the mammalian brain as I don't know the relevant insect literature. It was not completely clear to me how the authors think their model corresponds to mammalian brain circuits. When they initially discuss brain circuits they point to the cerebellum as a plausible candidate structure (lines 520-546). Though the correspondence between cerebellar and model cell types is not very clearly outlined, my understanding is they propose that cerebellar granule cells are the 'map-cells' and Purkinje cells are the 'goal-cells'. I'm no cerebellum expert, but my understanding is that the granule cells do not have recurrent excitatory connections needed by the map cells. I am also not aware of reports of place-field-like firing in these cell populations that would be predicted by this correspondence. If the authors think the cerebellum is the substrate for the proposed mechanism they should clearly outline the proposed correspondence between cerebellar and model cell types and support the argument with reference to the circuit architecture, firing properties, lesion studies, etc.

On further thought we agree that the cerebellum-like circuits are not a plausible substrate for the endotaxis algorithm. The anatomy looks compelling, but plasticity at the synapse is anti-hebbian, and - as the reviewer points out - there is little evidence for recurrence among the inputs. We changed the discussion text accordingly.

The authors also discuss the possibility that the hippocampal formation might implement the proposed model, though confusingly they state 'we do not presume that endotaxis is localized to that structure' (line 564).

We have removed that confusing bit of text.

A correspondence with the hippocampus appears more plausible than the cerebellum, given the spatial tuning properties of hippocampal cells, and the profound effect of lesions on navigation behaviours. When discussing the possible relationship of the model to hippocampal circuits it would be useful to address internally generated sequential activity in the hippocampus. During active navigation, and when animals exhibit vicarious trial and error at decision points, internally generated sequential activity of hippocampal place cells appears to explore different possible routes ahead of the animal (Kay et al. 2020, https://doi.org/10.1016/j.cell.2020.01.014, Reddish 2016, https:// doi.org/10.1038/nrn.2015.30). Given the emphasis the model places on sampling possible future locations to evaluate goal-distance gradients, this seems highly relevant.

In our model, the possible future locations are sampled in real life, with the agent moving there or at least in that direction, e.g. via VTE movements. In this simple form the model has no provision for internal planning, and the animal never learns any specific route sequence. One can envision extending such a model with some form of sequence learning that would then support an internal planning mechanism. We mention this in the revised discussion section, along with citation of these relevant articles.

Also, given the strong emphasis the authors place on the relationship of their model to chemotaxis/odour-guided navigation, it would be useful to discuss brain circuits involved in chemotaxis, and whether/how these circuits relate to those involved in goal-directed navigation, and the proposed model.

The neural basis of goal-directed navigation is probably best understood in the insect brain. There the locomotor decisions seem to be initiated in the central complex, whose circuitry is getting revealed by the fly connectome projects. This area receives input from diverse sensory areas that deliver the signal on which the decisions are based. That includes the mushroom body, which we argue has the anatomical structure to implement the endotaxis algorithm. It remains a mystery how the insect chooses a particular goal for pursuit via its decisions. It could be revealing to force a change in goals (the mode switch in the endotaxis circuit) while recording from brain areas like the central complex. Our discussion now elaborates on this.

Finally, it would be useful to clarify two aspects of the behaviour of the proposed algorithm:

1) When discussing the relationship of the model to the successor representation (lines 620-627), the authors emphasise that learning in the model is independent of the policy followed by the agent during learning, while the successor representation is policy dependent. The policy independence of the model is achieved by making the synapses between map cells binary (0 or 1 weight) and setting them to 1 following a single transition between two locations. This makes the model unsuitable for learning the structure of graphs with probabilistic transitions, e.g. it would not behave adaptively in the widely used two-step task (Daw et al. 2011, https://doi.org/10.1016/ j.neuron.2011.02.027) as it would fail to differentiate between common and rare transitions. This limitation should be made clear and is particularly relevant to claims that the model can handle cognitive tasks in general. It is also worth noting that there are algorithms that are closely related to the successor representation, but which learn about the structure of the environment independent of the subjects policy, e.g. the work of Kaelbling which learns shortest path distances, and the default representation in the work of Piray and Daw (both referenced above). Both these approaches handle probabilistic transition structures.

Yes. Our problem statement assumes that the environment is a graph with fixed edge weights. The revised text mentions this and other assumptions in a new section “Choice of learning rule”.

2) As the model evaluates distances using powers of adjacency matrix, the resulting distances are diffusion distances not shortest path distances. Though diffusion and shortest path distances are usually closely correlated, they can differ systematically for some graphs (see Baram et al. ci:ted above).

The recurrent network of map cells implements a specific function of the adjacency matrix, namely the resolvent (Eqn 7). We have a mathematical proof that this function delivers the shortest graph distances exactly, in the limit of small gain (γ in Eqn 7), and that this holds true for all graphs. For practical navigation in the presence of noise, one needs to raise the gain to something finite. Figure 3 analyzes how this affects deviations from the shortest graph distance, and how nonetheless the model still supports effective navigation over a surprising range. The mathematical details of the proof and further exploration of the resolvent distance at finite gain have been moved to a separate article, which is cited from here, and attached to the submission. The preprint by Baram et al. is cited in that article.

Reviewer #3 (Public Review):

This paper argues that it has developed an algorithm conceptually related to chemotaxis that provides a general mechanism for goal-directed behaviour in a biologically plausible neural form.

The method depends on substantial simplifying assumptions. The simulated animal effectively moves through an environment consisting of discrete locations and can reliably detect when it is in each location. Whenever it moves from one location to an adjacent location, it perfectly learns the connectivity between these two locations (changes the value in an adjacency matrix to 1). This creates a graph of connections that reflects the explored environment. In this graph, the current location gets input activation and this spreads to all connected nodes multiplied by a constant decay (adjusted to the branching number of the graph) so that as the number of connection steps increases the activation decreases. Some locations will be marked as goals through experiencing a resource of a specific identity there, and subsequently will be activated by an amount proportional to their distance in the graph from the current location, i.e., their activation will increase if the agent moves a step closer and decrease if it moves a step further away. Hence by making such exploratory movements, the animal can decide which way to move to obtain a specified goal.

I note here that it was not clear what purpose, other than increasing the effective range of activation, is served by having the goal input weights set based on the activation levels when the goal is obtained. As demonstrated in the homing behaviour, it is sufficient to just have a goal connected to a single location for the mechanism to work (i.e., the activation at that location increases if the animal takes a step closer to it); and as demonstrated by adding a new graph connection, goal activation is immediately altered in an appropriate way to exploit a new shortcut, without the goal weights corresponding to this graph change needing to be relearnt.

As the reviewer states, allowing a graded strengthening of multiple synapses from the map cells increases the effective range of the goal signal. We have now confirmed this in simulations. For example, in the analysis of Fig 3E, a single goal synapse enables perfect navigation only over a range of 7 steps, whereas the distributed goal synapses allow perfect navigation over the full 12 steps. This analysis is included in the code notebook on Github.

Given the abstractions introduced, it is clear that the biological task here has been reduced to the general problem of calculating the shortest path in a graph. That is, no real-world complications such as how to reliably recognise the same location when deciding that a new node should be introduced for a new location, or how to reliably execute movements between locations are addressed. Noise is only introduced as a 1% variability in the goal signal. It is therefore surprising that the main text provides almost no discussion of the conceptual relationship of this work to decades of previous work in calculating the shortest path in graphs, including a wide range of neural- and hardwarebased algorithms, many of which have been presented in the context of brain circuits.

The connection to this work is briefly made in appendix A.1, where it is argued that the shortest path distance between two nodes in a directed graph can be calculated from equation 15, which depends only on the adjacency matrix and the decay parameter (provided the latter falls below a given value). It is not clear from the presentation whether this is a novel result. No direct reference is given for the derivation so I assume it is novel. But if this is a previously unknown solution to the general problem it deserves to be much more strongly featured and either way it needs to be appropriately set in the context of previous work.

As far as we know this proposal for computing all-pairs-shortest-path is novel. We could not find it in textbooks or an extended literature search. We have discussed it with two graph theorist colleagues, who could not recall seeing it before, although the proof of the relationship is elementary. Inspired by the present reviewer comment, we chose to publish the result in a separate article that can focus on the mathematics and place it in the appropriate context of prior work in graph theory. For related work in the area of neural modeling please see our revised discussion section.

Once this principle is grasped, the added value of the simulated results is somewhat limited. These show: 1) in practical terms, the spreading signal travels further for a smaller decay but becomes erratic as the decay parameter (map neuron gain) approaches its theoretical upper bound and decreases below noise levels beyond a certain distance. Both follow the theory. 2) that different graph structures can be acquired and used to approach goal locations (not surprising) .3) that simultaneous learning and exploitation of the graph only minimally affects the performance over starting with perfect knowledge of the graph. 4) that the parameters interact in expected ways. It might have been more impactful to explore whether the parameters could be dynamically tuned, based on the overall graph activity.

This is a good summary of our simulation results, but we differ in the assessment of their value. In our experience, simulations can easily demolish an idea that seemed wonderful before exposure to numerical reality. For example, it is well known that one can build a neural integrator from a recurrent network that has feedback gain of exactly 1. In practical simulations, though, these networks tend to be fickle and unstable, and require unrealistically accurate tuning of the feedback gain. In our case, the theory predicts that there is a limited range of gains that should work, below the critical value, but large enough to avoid excessive decay of the signal. Simulation was needed to test what this practical range was, and we were pleasantly surprised that it is not ridiculously small, with robust navigation over a 10-20% range. Similarly, we did not predict that the same parameters would allow for effective acquisition of a new graph, learning of targets within the graph, and shortest-route navigation to those targets, without requiring any change in the operation of the network.

Perhaps the most biologically interesting aspect of the work is to demonstrate the effectiveness, for flexible behaviour, of keeping separate the latent learning of environmental structure and the association of specific environmental states to goals or values. This contrasts (as the authors discuss) with the standard reinforcement learning approach, for example, that tries to learn the value of states that lead to reward. Examples of flexibility include the homing behaviour (a goal state is learned before any of the map is learned) and the patrolling behaviour (a goal cell that monitors all states for how recently they were visited). It is also interesting to link the mechanism of exploration of neighbouring states to observed scanning behaviours in navigating animals.

The mapping to brain circuits is less convincing. Specifically, for the analogy to the mushroom body, it is not clear what connectivity (in the MB) is supposed to underlie the graph structure which is crucial to the whole concept. Is it assumed that Kenyon cell connections perform the activation spreading function and that these connections are sufficiently adaptable to rapidly learn the adjacency matrix? Is there any evidence for this?

Yes, there is good evidence for recurrent synapses among Kenyon cells (map cells in the model), and for reward-gated synaptic plasticity at the synapses onto mushroom body output cells (goal cells in our model). We have expanded this material in the discussion section. Whether those functions are sufficient to learn the structure of a spatial environment has not been explored; we hope our paper might give an impetus, and are exploring behavioral experiments on flies with colleagues.

As discussed above, the possibility that an algorithm like 'endotaxis' could explain how the rodent place cell system could support trajectory planning has already been explored in previous work so it is not clear what additional insight is gained from the current model.

Please see our revised discussion section on “theories and models of spatial learning”. In short, some ingredients of the model have appeared in prior work, but we believe that the present formulation offers an unexpectedly simple end-to-end solution for all components of navigation: exploration, target learning, and goal seeking.

Reviewer #1 (Recommendations For The Authors):

Major concern:

See the public review. How do the results change depending on whether the recurrent connections between map cells are an adjacency matrix vs. a properly normalized statetransition matrix? I'm especially asking about results related to critical gain (gamma_c), and the dependence of the optimal parameter values on the environment.

Please see our response above including the attached reviewer figure.

Minor concerns:

It is not always clear when the learning rule is symmetric vs asymmetric (undirected vs directed graph), and it seems to switch back and forth. For example, line 127 refers to a directed graph; Fig 2B and the intro describe symmetric Hebbian learning. Most (all?) of the simulations use the symmetric rule. Please make sure it's clear.

For simplicity we now use a symmetric rule throughout, as is appropriate for undirected graphs. We mention that a directed learning rule could be used to learn directed graphs. See the section on “choice of learning rule”. M_ij is not defined when it's first introduced (eq 4). Consider labeling the M's and the G's in Fig 2.

Done.

The network gain factor (gamma, eq 4) is distributed over both external and recurrent inputs (v = gamma(u + Mv)), instead of local to the recurrent weights like in the Successor Representation. This notational choice is obviously up to the authors. I raise slight concern for two reasons -- first, distributing gamma may affect some of the parameter sweep results (see major concern), and second, it may be confusing in light of how gamma is used in the SR literature (see reviewer's paper for the derivation of how SR is computed by an RNN with gain gamma).

In our model, gamma represents the (linear) activation function of the map neuron, from synaptic input to firing output. Because the synaptic input comes from point cells and also from other map cells, the gain factor is applied to both. See for example the Dayan & Abbott book Eqn 7.11, which at steady state becomes our Eqn 4. In the formalism of Fang 2023 (Eqn 2), the factor γ is only applied to the recurrent synaptic input J ⋅ f, but somehow not to the place cell input ϕ. Biophysically, one could imagine applying the variable gain only to the recurrent synapses and not the feed-forward ones. Instead we prefer to think of it as modulating the gain of the neurons, rather than the synapses. The SR literature follows conventions from the early reinforcement learning papers, which were unconstrained by thinking about neurons and synapses. We have added a footnote pointing the reader to the uses of γ in different papers.

In eq 13, and simulations, noise is added to the output only, not to the activity of recurrently connected neurons. It is possible this underestimates the impact of noise since the same magnitude of noise in the recurrent network (map cells) could have a compounded effect on the output.

Certainly. The equivalent output noise represents the cumulative effect of noise everywhere in the network. We argue that a cumulative effect of 1% is reasonable given the overall ability of animals at stimulus discrimination, which is also limited by noise everywhere in the network. This has been clarified in the text.

Fig 3 E, F, it looks like the navigated distance may be capped. I ask because the error bars for graph distance = 12 are so small/nonexistent. If it's capped, this should be in the legend.

Correct. 12 is the largest distance on this graph. This has been added to the caption.

Fig 3D legend, what does "navigation failed" mean? These results are not shown.

On those occasions the agent gets trapped at a local maximum of the goal signal other than the intended goal. We have removed that line as it is not needed to interpret the data.

Line 446, typo (Lateron).

Fixed.

Line 475, I'm a bit confused by the discussion of birds and bats. Bird behavior in the real world does involve discrete paths between points. Even if they theoretically could fly between any points, there are costs to doing so, and in practice, they often choose discrete favorite paths. It is definitely plausible that animals that can fly could also employ Endotaxis, so it is confusing to suggest they don't have the right behavior for Endotaxis, especially given the focus on fruit flies later in the discussion.

Good points, we removed that remark. Regarding fruit flies, they handle much important business while walking, such as tracking a mate, fighting rivals over food, finding a good oviposition site.

Section 9.3, I'm a bit confused by the discussion of cerebellum-like structures, because I don't think they have as dense recurrent connections as needed for the map cells in Endotaxis. Are you suggesting they are analogous to the output part of Endotaxis only, not the whole thing?

Please see our reply in the public review. We have removed this discussion of cerebellar circuits.

Line 541, "After sufficient exploration...", clarify that this is describing learning of just the output synapses, not the recurrent connections between map cells?

We have revised this entire section on the arthropod mushroom body.

In lines 551-556, the discussion is confusing and possibly not consistent with current literature. How can a simulation prove that synapses in the hippocampus are only strengthened among immediately adjacent place fields? I'd suggest either removing this discussion or adding further clarification. More broadly, the connection between Endotaxis and the hippocampus is very compelling. This might also be a good point to bring up BTSP (though you do already bring it up later).

As suggested, we removed this section.

Line 621 "The successor representation (at least as currently discussed) is designed to improve learning under a particular policy" That's not actually accurate. Ref 17 (reviewer's manuscript, cited here) is not policy-specific, and instead just learns the transition statistics experienced by the animal, using a biologically plausible learning rule that is very similar to the Endotaxis map cell learning rule (see our Appendix 5, comparing to Endotaxis, though that was referencing the previous version of the Endotaxis preprint where Oja's rule was used).

We have edited this section in the discussion and removed the reference to policyspecific successor representations.

Line 636 "Endotaxis is always on" ... this was not clear earlier in the paper (e.g. line 268, and the separation of different algorithms, and "while learning do" in Algorithm 2).

The learning rules are suspended during some simulations so we can better measure the effects of different parts of endotaxis, in particular learning vs navigating. There is no interference between these two functions, and an agent benefits from having the learning rules on all the time. The text now clarifies this in the relevant sections.

Section 9.6, I like the idea of tracing different connected functions. But when you say "that could lead to the mode switch"... I'm a bit confused about what is meant here. A mode switch doesn't need to happen in a different brain area/network, because winnertake-all could be implemented by mutual inhibition between the different goal units.

This is an interesting suggestion for the high-level control algorithm. A Lorenzian view is that the animal’s choice of mode depends on internal states or drives, such as thirst vs hunger, that compete with each other. In that picture the goal cells represent options to be pursued, whereas the choice among the options occurs separately. But one could imagine that the arbitrage between drives happens through a competition at the level of goal cells: For example the consumption of water could lead to adaptation of the water cell, such that it loses out in the winner-take-all competition, the food cell takes over, and the mouse now navigates towards food. In this closed-loop picture, the animal doesn’t have to “know” what it wants at any given time, it just wants the right thing. This could eliminate the homunculus entirely! Of course this is all a bit speculative. We have edited the closing comments in a way that leaves open this possibility.

Line 697-704, I need more step-by-step explanation/derivation.

We now derive the properties of E step by step starting from Eqn (14). The proof that leads to Eqn 14 is now in a separate article (available as a preprint and attached to this submission).

Reviewer #3 (Recommendations For The Authors):

• Please include discussion and comparison to previous work of graph-based trajectory planning using spreading activation from the current node and/or the goal node. Here is a (far from comprehensive) list of papers that present similar algorithms:

Glasius, R., Komoda, A., & Gielen, S. C. (1996). A biologically inspired neural net for trajectory formation and obstacle avoidance. Biological Cybernetics, 74(6), 511-520.

Gaussier, P., Revel, A., Banquet, J. P., & Babeau, V. (2002). From view cells and place cells to cognitive map learning: processing stages of the hippocampal system. Biological cybernetics, 86(1), 15-28.

Gorchetchnikov A, Hasselmo ME. A biophysical implementation of a bidirectional graph search algorithm to solve multiple goal navigation tasks. Connection Science. 2005;17(1-2):145-166

Martinet, L. E., Sheynikhovich, D., Benchenane, K., & Arleo, A. (2011). Spatial learning and action planning in a prefrontal cortical network model. PLoS computational biology, 7(5), e1002045.

Ponulak, F., & Hopfield, J. J. (2013). Rapid, parallel path planning by propagating wavefronts of spiking neural activity. Frontiers in computational neuroscience, 7, 98.

Khajeh-Alijani, A., Urbanczik, R., & Senn, W. (2015). Scale-free navigational planning by neuronal traveling waves. PloS one, 10(7), e0127269.

Adamatzky, A. (2017). Physical maze solvers. All twelve prototypes implement 1961 Lee algorithm. In Emergent computation (pp. 489-504). Springer, Cham.

Please see our reply to the public review above, and the new discussion section on “Theories and models of spatial learning”, which cites most of these papers among others.

• Please explain, if it is the case, why the goal cell learning (other than a direct link between the goal and the corresponding map location) and calculation of the overlapping 'goal signal' is necessary, or at least advantageous.

Please see our reply in the public review above.

• Map cells are initially introduced (line 84) as getting input from "only one or a few point cells". The rest of the paper seems to assume only one. Does it work when this is 'a few'? Does it matter that 'a few' is an option?

We simplified the text here to “only one point cell”. A map cell with input from two distant locations creates problems. After learning the map synapses from adjacencies in the environment, the model now “believes” that those two locations are connected. This distorts the graph on which the graph distances are computed and introduces errors in the resulting goal signals. One can elaborate the present toy model with a much larger population of map cells that might convey more robustness, but that is beyond our current scope.

• (line 539 on) Please explain what feature in the mushroom body (or other cerebellumlike) circuits is proposed to correspond to the learning of connections in the adjacency matrix in the model.

Please see our response to this critique in the public review above. In the mushroom body, the Kenyon cells exhibit sparse responses and are recurrently connected. These would correspond to map cells in Endotaxis. For vertebrate cerebellum-like circuits, the correspondence is less compelling, and we have removed this topic from the discussion.

Author response:

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

Gating of Kv10 channels is unique because it involves coupling between non-domain swapped voltage sensing domains, a domain-swapped cytoplasmic ring assembly formed by the N- and C-termini, and the pore domain. Recent structural data suggests that activation of the voltage sensing domain relieves a steric hindrance to pore opening, but the contribution of the cytoplasmic domain to gating is still not well understood. This aspect is of particular importance because proteins like calmodulin interact with the cytoplasmic domain to regulate channel activity. The effects of calmodulin (CaM) in WT and mutant channels with disrupted cytoplasmic gating ring assemblies are contradictory, resulting in inhibition or activation, respectively. The underlying mechanism for these discrepancies is not understood. In the present manuscript, Reham Abdelaziz and collaborators use electrophysiology, biochemistry and mathematical modeling to describe how mutations and deletions that disrupt inter-subunit interactions at the cytoplasmic gating ring assembly affect Kv10.1 channel gating and modulation by CaM. In the revised manuscript, additional information is provided to allow readers to identify within the Kv10.1 channel structure the location of E600R, one of the key channel mutants analyzed in this study. However, the mechanistic role of the cytoplasmic domains that this study focuses on, as well as the location of the ΔPASCap deletion and other perturbations investigated in the study remain difficult to visualize without additional graphical information. This can make it challenging for readers to connect the findings presented in the study with a structural mechanism of channel function.

The authors focused mainly on two structural perturbations that disrupt interactions within the cytoplasmic domain, the E600R mutant and the ΔPASCap deletion. By expressing mutants in oocytes and recording currents using Two Electrode Voltage-Clamp (TEV), it is found that both ΔPASCap and E600R mutants have biphasic conductance-voltage (G-V) relations and exhibit activation and deactivation kinetics with multiple voltage-dependent components. Importantly, the mutant-specific component in the G-V relations is observed at negative voltages where WT channels remain closed. The authors argue that the biphasic behavior in the G-V relations is unlikely to result from two different populations of channels in the oocytes, because they found that the relative amplitude between the two components in the G-V relations was highly reproducible across individual oocytes that otherwise tend to show high variability in expression levels. Instead, the G-V relations for all mutant channels could be well described by an equation that considers two open states O1 and O2, and a transition between them; O1 appeared to be unaffected by any of the structural manipulations tested (i.e. E600R, ΔPASCap, and other deletions) whereas the parameters for O2 and the transition between the two open states were different between constructs. The O1 state is not observed in WT channels and is hypothesized to be associated with voltage sensor activation. O2 represents the open state that is normally observed in WT channels and is speculated to be associated with conformational changes within the cytoplasmic gating ring that follow voltage sensor activation, which could explain why the mutations and deletions disrupting cytoplasmic interactions affect primarily O2. 

Severing the covalent link between the voltage sensor and pore reduced O1 occupancy in one of the deletion constructs. Although this observation is consistent with the hypothesis that voltage-sensor activation drives entry into O1, this result is not conclusive. Structural as well as functional data has established that the coupling of the voltage sensor and pore does not entirely rely on the S4-S5 covalent linker between the sensor and the pore, and thus the severed construct could still retain coupling through other mechanisms, which is consistent with the prominent voltage dependence that is observed. If both states O1 and O2 require voltage sensor activation, it is unclear why the severed construct would affect state O1 primarily, as suggested in the manuscript, as opposed to decreasing occupancy of both open states. In line with this argument, the presence of Mg2+ in the extracellular solution affected both O1 and O2. This finding suggests that entry into both O1 and O2 requires voltage-sensor activation because Mg2+ ions are known to stabilize the voltage sensor in its most deactivated conformations. 

We agree with the reviewer that access to both states requires a conformational change in the voltage sensor. This was stated in our revised article: “In contrast, to enter O2, all subunits must complete both voltage sensor transitions and the collective gating ring transition.” We interpret the two gating steps as sequential; the effective rotation of the intracellular ring would happen only once the sensor is in its fully activated position.

We also agree that the S4-S5 segment cannot be the only interaction mechanism, as we demonstrated in our earlier work (Lörinczi et al., 2015; Tomczak et al., 2017).  

Activation towards and closure from O1 is slow, whereas channels close rapidly from O2. A rapid alternating pulse protocol was used to take advantage of the difference in activation and deactivation kinetics between the two open components in the mutants and thus drive an increasing number of channels towards state O1. Currents activated by the alternating protocol reached larger amplitudes than those elicited by a long depolarization to the same voltage. This finding is interpreted as an indication that O1 has a larger macroscopic conductance than O2. In the revised manuscript, the authors performed single-channel recordings to determine why O1 and O2 have different macroscopic conductance. The results show that at voltages where the state O1 predominates, channels exhibited longer open times and overall higher open probability, whereas at more depolarized voltages where occupancy of O2 increases, channels exhibited more flickery gating behavior and decreased open probability. These results are informative but not conclusive because additional details about how experiments were conducted, and group data analysis are missing. Importantly, results showing inhibition of single ΔPASCap channels by a Kv10-specific inhibitor are mentioned but not shown or quantitated - these data are essential to establish that the new O1 conductance indeed represents Kv10 channel activity.

We observed the activity of a channel compatible with Kv10.1 ΔPAS-Cap (long openings at low-moderate potentials, very short flickery activity at strong depolarizations) in 12 patches from oocytes obtained from different frog operations over a period of two and a half months once the experimental conditions could be established. As stated in the text, we did not proceed to generate amplitude histograms because we could not resolve clear single-channel events at strong depolarizations. Astemizole abolished the activity and (remarkably) strongly reduced the noise in traces at strong depolarizations, which we interpret as partially caused by flicker openings.

Author response image 1.

We include two example recordings of Astemizole application (100µM) on two different patches. Both recordings are performed at -60 mV (to decrease the likelihood that the channel visits O2) with 100 mM internal and 60 mM external K+. In both cases, the traces in Astemizole are presented in red.

It is shown that conditioning pulses to very negative voltages result in mutant channel currents that are larger and activate more slowly than those elicited at the same voltage but starting from less negative conditioning pulses. In voltage-activated curves, O1 occupancy is shown to be favored by increasingly negative conditioning voltages. This is interpreted as indicating that O1 is primarily accessed from deeply closed states in which voltage sensors are in their most deactivated position. Consistently, a mutation that destabilizes these deactivated states is shown to largely suppress the first component in voltage-activation curves for both ΔPASCap and E600R channels.

The authors then address the role of the hidden O1 state in channel regulation by calmodulation. Stimulating calcium entry into oocytes with ionomycin and thapsigarging, assumed to enhance CaM-dependent modulation, resulted in preferential potentiation of the first component in ΔPASCap and E600R channels. This potentiation was attenuated by including an additional mutation that disfavors deeply closed states. Together, these results are interpreted as an indication that calcium-CaM preferentially stabilizes deeply closed states from which O1 can be readily accessed in mutant channels, thus favoring current activation. In WT channels lacking a conducting O1 state, CaM stabilizes deeply closed states and is therefore inhibitory. It is found that the potentiation of ΔPASCap and E600R by CaM is more strongly attenuated by mutations in the channel that are assumed to disrupt interaction with the C-terminal lobe of CaM than mutations assumed to affect interaction with the N-terminal lobe. These results are intriguing but difficult to interpret in mechanistic terms. The strong effect that calcium-CaM had on the occupancy of the O1 state in the mutants raises the possibility that O1 can be only observed in channels that are constitutively associated with CaM. To address this, a biochemical pull-down assay was carried out to establish that only a small fraction of channels are associated with CaM under baseline conditions. These CaM experiments are potentially very interesting and could have wide physiological relevance. However, the approach utilized to activate CaM is indirect and could result in additional nonspecific effects on the oocytes that could affect the results.

Finally, a mathematical model is proposed consisting of two layers involving two activation steps for the voltage sensor, and one conformational change in the cytoplasmic gating ring - completion of both sets of conformational changes is required to access state O2, but accessing state O1 only requires completion of the first voltage-sensor activation step in the four subunits. The model qualitatively reproduces most major findings on the mutants. Although the model used is highly symmetric and appears simple, the mathematical form used for the rate constants in the model adds a layer of complexity to the model that makes mechanistic interpretations difficult. In addition, many transitions that from a mechanistic standpoint should not depend on voltage were assigned a voltage dependence in the model. These limitations diminish the overall usefulness of the model which is prominently presented in the manuscript. The most important mechanistic assumptions in the model are not addressed experimentally, such as the proposition that entry into O1 depends on the opening of the transmembrane pore gate, whereas entry into O2 involves gating ring transitions - it is unclear why O2 would require further gating ring transitions to conduct ions given that the gating ring can already support permeation by O1 without any additional conformational changes.

In essence, we agree with the reviewer; we already have addressed these points in our revised article:

Regarding the voltage dependence we write “the κ/λ transition could reasonably be expected to be voltage independent because we related it to ring reconfiguration, a process that should occur as a consequence of a prior VSD transition. We have made some attempts to treat this transition as voltage independent but state-specific with upper-layer bias for states on the right and lower-layer bias for states on the left. This is in principle possible, as can already be gleaned from the similar voltage ranges of the left-right transition (α/β) and the κL/λ transition. However, this approach leads to a much larger number of free, less well constrained kinetic parameters and drastically complicated the parameter search. ” As you can see, we also formulated a strategy to free the model of the potentially spurious voltage dependence and (in bold here) explained why we did not follow this route in this study. 

Regarding the need for gating ring transitions after O1, we wrote, “Thus, the underlying gating events can be separated into two steps: The first gating step involves only the voltage sensor without engaging the ring and leads to a pre-open state, which is non-conducting in the WT but conducting in our mutants. The second gating event operates at higher depolarizations, involves a change in the ring, and leads to an open state both in WT and in the mutants. ” 

We interpret your statements such that you expect the conducting state to remain available once O1 is reached. However, the experimental evidence speaks against that the pore availability remains regardless of the further gating steps beyond O1. The description of model construction is informative here: “... we could exclude many possible [sites at which O1 connects to closed states] because the attachment site must be sufficiently far away from the conventional open state [O2]. Otherwise, the transition from "O1 preferred" to "O2 preferred" via a few closed intermediate states is very gradual and never produces the biphasic GV curves [that we observed]. ” 

In other words, voltage-dependent gating steps beyond the state that offers access to O1 appear to close the pore, after it was open. That might occur because only then (for states in which at least one voltage sensor exceeded the intermediate position) the ring is fixed in a particular state until all sensors completed activation. In the WT, closing the pore in deactivated states might rely on an interaction that is absent in the mutant because, at least in HERG: “the interaction between the PAS domain and the C-terminus is more stable in closed than in open KV11.1 (HERG) channels, and a single chain antibody binding to the interface between PAS domain and CNBHD can access its epitope in open but not in closed channels, strongly supporting a change in conformation of the ring during gating ”

Reviewer #3 (Public Review):

In the present manuscript, Abdelaziz and colleagues interrogate the gating mechanisms of Kv10.1, an important voltage-gated K+ channel in cell cycle and cancer physiology. At the molecular level, Kv10.1 is regulated by voltage and Ca-CaM. Structures solved using CryoEM for Kv10.1 as well as other members of the KCNH family (Kv11 and Kv12) show channels that do not contain a structured S4-S5 linker imposing therefore a non-domain swapped architecture in the transmembrane region. However, the cytoplasmatic N- and C- terminal domains interact in a domain swapped manner forming a gating ring. The N-terminal domain (PAS domain) of one subunit is located close to the intracellular side of the voltage sensor domain and interacts with the C-terminal domain (CNBHD domain) of the neighbor subunit. Mutations in the intracellular domains has a profound effect in the channel gating. The complex network of interactions between the voltage-sensor and the intracellular domains makes the PAS domain a particularly interesting domain of the channel to study as responsible for the coupling between the voltage sensor domains and the intracellular gating ring.

The coupling between the voltage-sensor domain and the gating ring is not fully understood and the authors aim to shed light into the details of this mechanism. In order to do that, they use well established techniques such as site-directed mutagenesis, electrophysiology, biochemistry and mathematical modeling. In the present work, the authors propose a two open state model that arises from functional experiments after introducing a deletion on the PAS domain (ΔPAS Cap) or a point mutation (E600R) in the CNBHD domain. The authors measure a bi-phasic G-V curve with these mutations and assign each phase as two different open states, one of them not visible on the WT and only unveiled after introducing the mutations.

The hypothesis proposed by the authors could change the current paradigm in the current understanding for Kv10.1 and it is quite extraordinary; therefore, it requires extraordinary evidence to support it.

STRENGTHS: The authors use adequate techniques such as electrophysiology and sitedirected mutagenesis to address the gating changes introduced by the molecular manipulations. They also use appropriate mathematical modeling to build a Markov model and identify the mechanism behind the gating changes.

WEAKNESSES: The results presented by the authors do not fully support their conclusions since they could have alternative explanations. The authors base their primary hypothesis on the bi-phasic behavior of a calculated G-V curve that do not match the tail behavior, the experimental conditions used in the present manuscript introduce uncertainties, weakening their conclusions and complicating the interpretation of the results. Therefore, their experimental conditions need to be revisited. 

We respectfully disagree. We think that your suggestions for alternative explanations are addressed in the current version of the article. We will rebut them once more below, but we feel the need to point out that our arguments are already laid out in the revised article.

I have some concerns related to the following points:

(1) Biphasic gating behavior

The authors use the TEVC technique in oocytes extracted surgically from Xenopus Leavis frogs. The method is well established and is adequate to address ion channel behavior. The experiments are performed in chloride-based solutions which present a handicap when measuring outward rectifying currents at very depolarizing potentials due to the presence of calcium activated chloride channel expressed endogenously in the oocytes; these channels will open and rectify chloride intracellularly adding to the outward rectifying traces during the test pulse. The authors calculate their G-V curves from the test pulse steady-state current instead of using the tail currents. The conductance measurements are normally taken from the 'tail current' because tails are measured at a fix voltage hence maintaining the driving force constant. 

We respectfully disagree. In contrast to other channels, like HERG, a common practice for Kv10 is not to use tail currents. It is long known that in this channel, tail currents and test-pulse steady-state currents can appear to be at odds because the channels deactivate extremely rapidly, at the border of temporal resolution of the measurements and with intricate waveforms. This complicates the estimation of the instantaneous tail current. Therefore, the outward current is commonly used to estimate conductance (Terlau et al., 1996; Schönherr et al., 1999; Schönherr et al., 2002; Whicher and MacKinnon, 2019), while the latter authors also use the extreme of the tail for some mutants.

Due to their activation at very negative voltage, the reversal potential in our mutants can be measured directly; we are, therefore, more confident with this approach. Nevertheless, we have determined the initial tail current in some experiments. The behavior of these is very similar to the average that we present in Figure 1. The biphasic behavior is unequivocally present.

Author response image 2.

Calculating the conductance from the traces should not be a problem, however, in the present manuscript, the traces and the tail currents do not agree. 

The referee’s observation is perfectly in line with the long-standing experience of several labs working with KV10: tail current amplitudes in KV10 appear to be out of proportion for the WT open state (O2). Importantly, this is due to the rapid closure, which is not present in O1. As a consequence, the initial amplitude of tail currents from O1 are easier to estimate correctly, and they are much more obvious in the graphs. Taken together, these differences between O1 and O2 explain the misconception the reviewer describes next.

The tail traces shown in Fig1E do not show an increasing current amplitude in the voltage range from +50mV to +120mV, they seem to have reached a 'saturation state', suggesting that the traces from the test pulse contain an inward chloride current contamination. 

As stated in the text and indicated in Author response image 3, the tail currents In Figure 1E increase in amplitude between +50 and +120 mV, as can be seen in the examples below from different experiments (+50 is presented in black, +120 in red). As stated above, the increase is not as evident as in traces from other mutants because the predominance of O2 also implies a much faster deactivation.

Author response image 3. 

We are aware that Ca2+-activated Cl- currents can represent a problem when interpreting electrophysiological data in oocytes. In fact, we show in Supplement 1 to Figure 8 that this can be the case during the Ca2+-CaM experiments, where the increase in Ca2+ would certainly augment Cl- contribution to the outward current. This is why we performed these experiments in Cl--free solutions. As we show in Figure 8, the biphasic behavior was also present in those experiments. 

Importantly, Cl- free bath solutions would not correct contamination during the tail, since this would correspond to Cl- exiting the oocyte. Yet, if there would be contamination of the outward currents by Cl-, one would expect it to increase with larger depolarizations as the typical Ca2+activated Cl- current in oocytes does. As the reviewer states, this does not seem to be the case.

In addition, this second component identified by the authors as a second open state appears after +50mV and seems to never saturate. The normalization to the maximum current level during the test pulse, exaggerates this second component on the calculated G-V curve. 

We agree that this second component continues to increase; the reviewer brought this up in the first review, and we have already addressed this in our reply and in the discussion of the revised version: “This flicker block might also offer an explanation for a feature of the mutant channels, that is not explained in the current model version: the continued increase in current amplitude, hundreds of milliseconds into a strong depolarization (Supp. 4 to Fig. 9). If the relative stability of O2 and C2 continued to change throughout depolarization, such a current creep-up could be reproduced. However, this would require either the introduction of further layers of On ↔Cn states, or a non-Markovian modification of the model’s time evolution.” With non-Markovian, we mean a Langevin-type diffusive process. 

It's worth noticing that the ΔPASCap mutant experiments on Fig 5 in Mes based solutions do not show that second component on the G-V.

For the readers of this conversation, we would like to clarify that the reviewer likely refers to experiments shown in Fig. 5 of the initial submission but shown in Fig. 6 of the revised version (“Hyperpolarization promotes access to a large conductance, slowly activating open state.” Fig. 5 deals with single channels). We agree that these data look different, but this is because the voltage protocols are completely different (compare Fig. 6A (fixed test pulse, varied prepulse) and Fig. 2A (varied test pulse, fixed pre-pulse). Therefore, no biphasic behavior is expected. 

Because these results are the foundation for their two open state hypotheses, I will strongly suggest the authors to repeat all their Chloride-based experiments in Mes-based solutions to eliminate the undesired chloride contribution to the mutants current and clarify the contribution of the mutations to the Kv10.1 gating.

In summary, we respectfully disagree with all concerns raised in point (1). Our detailed arguments rebutting them are given above, but there is a more high-level concern about this entire exchange: the referee casts doubt on observations that are not new. Several labs have reported for a group of mutant KCNH channels: non-monotonic voltage dependence of activation (see, e.g., Fig. 6D in Zhao et al., 2017), multi-phasic tail currents (see e.g. Fig. 4A in Whicher and MacKinnon, 2019, in CHO cells where Cl- contamination is not a concern), and activation by high [Ca2+]i (Lörinczi et al., 2016). Our study replicates those observations and hypothesizes that the existence of an additional conducting state can alone explain all previously unexplained observations. We highlight the potency of this hypothesis with a Markov model that qualitatively reproduces all phenomena. We not only factually disagree with the individual points raised, but we also think that they don't touch on the core of our contribution

(2) Two step gating mechanism.

The authors interpret the results obtained with the ΔPASCap and the E600R as two step gating mechanisms containing two open states (O1 and O2) and assign them to the voltage sensor movement and gating ring rotation respectively. It is not clear, however how the authors assign the two open states.

The results show how the first component is conserved amongst mutations; however, the second one is not. The authors attribute the second component, hence the second open state to the movement of the gating ring. This scenario seems unlikely since there is a clear voltagedependence of the second component that will suggest an implication of a voltage-sensing current.

We do not suggest that the gating ring motion is not voltage dependent. We would like to point out that voltage dependence can be conveyed by voltage sensor coupling to the ring; this is the widely accepted theory of how the ring can be involved. Should the reviewer mean it in a narrow sense, that the model should be constructed such that all voltage-dependent steps occur before and independently of ring reconfiguration and that only then an additional step that reflects the (voltage-independent) reconfiguration solely, we would like to point the reviewer to the article, where we write: “the κ/λ transition could reasonably be expected to be voltage independent because we related it to ring reconfiguration, a process that should occur as a consequence of a prior VSD transition. We have made some attempts to treat this transition as voltage independent but state-specific with upper-layer bias for states on the right and lower-layer bias for states on the left. This is in principle possible, as can already be gleaned from the similar voltage ranges of the left-right transition (α/β) and the κL/λ transition. However, this approach leads to a much larger number of free, less well constrained kinetic parameters and drastically complicated the parameter search. ” As you can see, we also formulated a strategy to free the model from the potentially spurious voltage dependence and (in bold here) explained why we did not follow this route in this study. 

The split channel experiment is interesting but needs more explanation. I assume the authors expressed the 2 parts of the split channel (1-341 and 342-end), however Tomczak et al showed in 2017 how the split presents a constitutively activated function with inward currents that are not visible here, this point needs clarification.

As stated in the panel heading, the figure legend, and the main text, we did not use 1-341 and 342-end as done in Tomczak et al. Instead, “we compared the behavior of ∆2-10 and ∆210.L341Split,”. Evidently, the additional deletion (2-10) causes a shift in activation that explains the difference you point out. However, as we do not compare L341Split and ∆210.L341Split but ∆2-10 and ∆2-10.L341Split, our conclusion remains that “As predicted, compared to ∆2-10, ∆2-10.L341Split showed a significant reduction in the first component of the biphasic GV (Fig. 2C, D).” Remarkably, the behavior of the ∆3-9 L341Split described in Whicher and MacKinnon, 2019 (Figure 5) matches that of our ∆2-10 L341Split, which we think reinforces our case.

Moreover, the authors assume that the mutations introduced uncover a new open state, however the traces presented for the mutations suggest that other explanations are possible. Other gating mechanisms like inactivation from the closed state, can be introduced by the mutations. The traces presented for ΔPASCap but specially E600R present clear 'hooked tails', a direct indicator of a populations of inactive channels during the test pulse that recover from inactivation upon repolarization (Tristani-Firouzi M, Sanguinetti MC. J Physiol. 1998). 

There is a possibility that we are debating nomenclature here. In response to the suggestion that all our observations could be explained by inactivation, we attempted a disambiguation of terms in the reply and the article. As the argument is brought up again without reference to our clarification attempts, we will try to be more explicit here:

If, starting from deeply deactivated states, an open state is reached first, and then, following further activation steps, closed states are reached, this might be termed “inactivation”. In such a reading, our model features many inactivated states. The shortest version of such a model is C-O-I. It is for instance used by Raman and Bean (2001; DOI: 10.1016/S00063495(01)76052-3) to explain NaV gating in Purkinje neurons. If “inactivation” is meant in the sense that a gating transition exists, which is orthogonal to an activation/deactivation axis, and that after this orthogonal transition, an open state cannot be reached anymore, then all of the upper floor in our model is inactivated with respect to the open state O1. Finally, the state C2 is an inactivated state to O2. In this view, “inactivation” explains the observed phenomena. 

However, we must disagree if the referee means that a parsimonious explanation exists in which a single conducting state is the only source for all observed currents.   

There is a high-level reason: we found a single assumption that explains three different phenomena, while the inactivation hypothesis with one conducting state cannot explain one of them (the increase of the first component under raised CaM). But there is also a low-level reason: the tails in Tristani-Firouzi and Sanguinetti 1998 are fundamentally different from what we report herein in that they lack a third component. Thus, those tails are consistent with recovery from inactivation through a single open state, while a three-component tail is not. In the framework of a Markov model, the time constants of transitions from and to a given state (say O2), cannot change unless the voltage changes. During the tail current, the voltage does not change, yet we observe: 

i) a rapid decrease with a time constant of at most a few milliseconds (Fig 9 S2, 1-> 2),  ii) a slow increase in current, peaking after approximately 25 milliseconds and iii) a relaxation to zero current with a time constant of >50 ms. 

According to the reviewer’s suggestion, these processes on three timescales should all be explained by depopulating and repopulating the same open state while all rates are constant. There might well be a complicated multi-level state diagram with a single open state with different variants, like (open and open inactivated) that could produce triphasic tails with these properties if the system had not reached a steady state distribution at the end of the test pulse. It cannot, however, achieve it from an equilibrated system, and certainly, it cannot at the same time produce “biphasic activation” and “activation by CaM”. 

The results presented by the authors can be alternatively explained with a change in the equilibrium between the close to inactivated/recovery from inactivation to the open state. 

Again, we disagree. The model construction explains in detail that the transition from the first to the second phase is not gradual. Shifting equilibria cannot reproduce this. We have extensively tested that idea and can exclude this possibility.

Finally, the authors state that they do not detect "cumulative inactivation after repeated depolarization" but that is considering inactivation only from the open state and ignoring the possibility of the existence of close state inactivation or, that like in hERG, that the channel inactivates faster that what it activates (Smith PL, Yellen G. J Gen Physiol. 2002). 

We respectfully disagree. We explicitly model an open state that inactivates faster (O2->C2) than it activates. Once more, this is stated in the revised article, which we point to for details. Again, this alternative mechanism does not have the potential to explain all three effects. As discussed above about the chloride contamination concerns, this inactivation hypothesis was mentioned in the first review round and, therefore, addressed in our reply and the revised article. We also explained that “inactivation” has no specific meaning in Markov models. In the absence of O1, all transitions towards the lower layer are effectively “inactivation from closed states”, because they make access to the only remaining open state less likely”. But this is semantics. What is relevant is that no network of states around a single open state can reproduce the three effets in a more parsimonious way than the assumption of the second open state does.

(3) Single channel conductance.

The single channels experiments are a great way to assess the different conductance of single channel openings, unfortunately the authors cannot measure accurately different conductances for the two proposed open states. The Markov Model built by the authors, disagrees with their interpretation of the experimental results assigning the exact same conductance to the two modeled open states. To interpret the mutant data, it is needed to add data with the WT for comparison and in presence of specific blockers. 

We respectfully disagree. As previously shown, the conductance of the flickering wild-type open state is very difficult to resolve. Our recordings do not show that the two states have different single-channel conductances, and therefore the model assumes identical singlechannel conductance. 

The important point is that the single-channel recordings clearly show two different gating modes associated with the voltage ranges in which we predict the two open states. One has a smaller macroscopic current due to rapid flickering (aka “inactivation”). These recordings are another proof of the existence of two open states because the two gating modes occur.  Wild-type data can be found in Bauer and Schwarz, (2001, doi:10.1007/s00232-001-0031-3) or Pardo et al., (1998, doi:10.1083/jcb.143.3.767) for comparison.

We appreciate the effort editors and reviewers invested in assessing the revised manuscript. Yet, we think that the demanded revision of experimental conditions and quantification methods contradicts the commonly accepted practice for KV10 channels. Some of the reviewer comments are skeptical about the biphasic behavior, which is an established and replicated finding for many mutants and by many researchers. The alternative explanations for these disbelieved findings are either “semantics” or cannot quantitatively explain the measurements. Therefore, only the demand for more explanations and unprecedented resolution in singlechannel recordings remains. We share these sentiments.

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

(1) The authors must show that the second open state is not just an artifact of endogenous activity but represents the activity of the same EAG channels. I suggest that the authors repeat these experiments in Mes-based solutions. 

(2) Along the same lines, it is necessary to show that these currents can be blocked using known EAG channel blockers such as astemizole. Ultimately, it will be important to demonstrate using single-channel analysis that these do represent two distinct open states separated by a closed state. 

We have addressed these concerns using several approaches. The most substantial change is the addition of single-channel recordings on ΔPASCap. In those experiments, we could provide evidence of the two types of events in the same patch, and the presence of an outward current at -60 mV, 50 mV below the equilibrium potential for chloride. The channels were never detected in uninjected oocytes, and Astemizole silenced the activity in patches containing multiple channels. These observations, together with the maintenance of the biphasic behavior that we interpret as evidence of the presence of O1 in methanesulfonate-based solutions, strongly suggest that both O1 and O2 obey the expression of KV10.1 mutants.

(3) Currents should be measured by increasing the pulse lengths as needed in order to obtain the true steady-state G-V curves. 

We agree that the endpoint of activation is ill-defined in the cases where a steady-state is not reached. This does indeed hamper quantitative statements about the relative amplitude of the two components. However, while the overall shape does change, its position (voltage dependence) would not be affected by this shortcoming. The data, therefore, supports the claim of the “existence of mutant-specific O1 and its equal voltage dependence across mutants.”

(4) A more clear and thorough description should be provided for how the observations with the mutant channels apply to the behavior of WT channels. How exactly does state O1 relate to WT behavior, and how exactly do the parameters of the mathematical model differ between WT and mutants? How can this be interpreted at a structural level? What could be the structural mechanism through which ΔPASCap and E600R enable conduction through O1? It seems contradictory that O1 would be associated exclusively with voltage-sensor activation and not gating ring transitions, and yet the mutations that enable cation access through O1 localize at the gating ring - this needs to be better clarified. 

We have undertaken a thorough rewriting of all sections to clarify the structural correlates that may explain the behavior of the mutants. In brief, we propose that when all four voltage sensors move towards the extracellular side, the intracellular ring maintains the permeation path closed until it rotates. If the ring is altered, this “lock” is incompetent, and permeation can be detected (page 34). By fixing the position of the ring, calmodulin would preclude permeation in the WT and promote the population of O1 in the mutants.

(5) Rather than the t80% risetime, exponential fits should be performed to assess the kinetics of activation. 

We agree that the assessment of kinetics by a t80% is not ideal. We originally refrained from exponential fits because they introduce other issues when used for processes that are not truly exponential (as is the case here). We had planned to perform exponential fits in this revised version, but because the activation process is not exponential, the time constants we could provide would not be accurate, and the result would remain qualitative as it is now. In the experiments where we did perform the fits (Fig. 3), the values obtained support the statement made. 

(6) It is argued based on the G-V relations in Figure 2A that none of the mutations or deletions introduced have a major effect on state O1 properties, but rather affect state O2. However, the occupancy of state O2 is undetermined because activation curves do not reach saturation. It would be interesting to explore the fitting parameters on Fig.2B further to test whether the data on Fig 2A can indeed only be described by fits in which the parameters for O1 remain unchanged between constructs. 

We agree that the absolute occupancy of O2 cannot be properly determined if a steady state is not reached. This is, however, a feature of the channel. During very long depolarizations in WT, the current visually appears to reach a plateau, but a closer look reveals that the current keeps increasing after very long depolarizations (up to 10 seconds; see, e.g., Fig. 1B in Garg et al., 2013, Mol Pharmacol 83, 805-813. DOI: 10.1124/mol.112.084384). Interestingly, although the model presented here does not account for this behavior, we propose changes in the model that could. “If the relative stability of O2 and C2 continued to change throughout the depolarization such a current creep-up could be reproduced. However, this would require either the introduction of further layers of On↔Cn states or a non-Markovian modification of the model’s evolution.” Page 34.

(7) The authors interpret the results obtained with the mutants DPASCAP and E600R -tested before by Lorinczi et al. 2016, to disrupt the interactions between the PASCap and cNBHD domains- as a two-step gating mechanism with two open states. All the results obtained with the E600R mutant and DPASCap could also be explained by inactivation/recovery from inactivation behavior and a change in the equilibrium between the closed states closed/inactivated states and open states. Moreover, the small tails between +90 to +120 mV suggest channels accumulate in an inactive state (Fig 1E). It is not convincing that the two open-state model is the mechanism underlying the mutant's behavior.  

We respectfully disagree with the notion that a single open state can provide a plausible explanation for "All the results obtained with the E600R mutant and DPASCap". We think that our new single channel results settle the question, but even without this direct evidence, a quantitative assessment of the triphasic tail currents all but excludes the possibility of a single open state. We agree that it is, in principle, possible to obtain some form of a multiphasic tail with a single open state using the scheme suggested in this comment: at the end of the test pulse, a large fraction of the channels must be accumulated in inactive states, and a few are in the open state. The hyperpolarization to -100mV then induces a rapid depopulation of the open state, followed by slower replenishments from the inactive state. Exactly this process occurs in our model, when C2 empties through O2 (Supp. 5 to Fig 9, E600R model variant). However, this alone is highly unlikely to quantitatively explain the measured tail currents, because of the drastically different time scales of the initial current decay (submillisecond to at most a few milliseconds lifetime) and the much slower transient increase in current (several tens of milliseconds) and the final decay with time constants of >100 ms (see for instance data in Fig. 1 E for E600R +50 to +120mV test pulse). To sustain the substantial magnitude of slowly decaying current by slow replenishment of an open state with a lifetime of 1 ms requires vast amounts of inactivated channels. A rough estimation based on the current integral of the initial decay and the current integral of the slowly decaying current suggests that at the end of the test pulse, the ratio inactivated/open channels would have to be 500 to 1500 for this mechanism to quantitatively explain the observed tail currents. To put this in perspective: This would suggest that without inactivation all the expressed channels in an oocyte would provide 6 mA current during the +100 mV test pulse. While theoretically possible, we consider this a less likely explanation than a second open state.

(8) Different models should be evaluated to establish whether the results in Figure 4 can also be explained by a model in which states O1 and O2 have the same conductance. It would be desirable if the conductance of both states were experimentally determined - noise analysis could be applied to estimate the conductance of both states. 

In the modified model, O1 and O2 have the same single-channel conductance. The small conductance combined with the fast flickering did not allow an accurate determination, but we can state that there is no evidence that the single-channel conductance of the states is different.

(9) Although not included, it looks like the model predicts some "conventional inactivation" This can be appreciated in Fig 8, and in the traces at -60mV. Interestingly, the traces obtained in the absence of Cl- also undergo slow inactivation, or 'conventional inactivation' as referred to by the authors. Please revise the following statement "Conventional inactivation was never detected in any mutants after repeated or prolonged depolarization. In the absence of inactivation, the pre-pulse dependent current increase at +40 mV could be related to changes in the relative occupancy of the open states". 

We have carefully edited the manuscript to address this concern. The use of the term inactivation admittedly represents a challenge. We agree that the state that results from the flickering block (C2) could be defined as “inactivated” because it is preceded by an open state. Yet, in that case, the intermediate states that the channel travels between O1 and O2 would also be sensu stricto “inactivated”, but only in the mutants. We have made this clear in page 17.

Recommendations for improving the writing and presentation.

(1) Methods section: Please state the reversal potential calculated for the solution used. It looks like the authors used an Instantaneous I-V curve method to calculate the reversal potential; if that's correct, please show the I-V and the traces together with the protocol used. 

We have provided the calculated reversal potentials for excised patches. We cannot predict the reversal potential in whole oocytes because we have no control over the intracellular solution. The reversal potential was determined in the mutants through the current at the end of the stimulus because the mutants produced measurable inward currents. The differences in reversal potential were not significant among mutants.

Pulse protocols have been added to the figures.

(2) Figure 1 suggestion: Combine the two panels in panel D and move the F panel up so the figure gets aligned in the lower end.

Thank you, this has been done.

(3) Please clarify the rationale for using the E600R-specific mutant. I assume it is based on the Lorinzci et al. 2016 effect and how this is similar to the DPASCap phenotype, or is it due to the impact of this mutation in the interactions between the N-term and the cNBHD? 

We have explained the rationale for the use of E600R explicitly on page 6.

(4) Fig S1A is not present in the current version of the manuscript. Include a cartoon as well as a structural figure clearly depicting the perturbations introduced by E600R, ΔPASCap, and the other deletions that are tested. Additional structural information supporting the discussion would also be helpful to establish clearer mechanistic links between the experimental observations described here and the observed conformational changes between states in Kv10 channel structures. 

We have corrected this omission, thank you for pointing it out.

(5) It would be informative to see the traces corresponding to the I-V shown in Fig 7 A and B at the same indicated time points (0, 60, 150, and 300s). Did the authors monitor the Ca2+ signal rise after the I&T treatment to see if it coincides with the peak in the 60s? 

In Figure 7 (now Figure 8) we used voltage ramps instead of discrete I-V protocols because of the long time required for recording the latter. This is stated on page 19. Ca2+ was monitored through Cl- current after ionomycin/thapsigargin. The duration of the Ca2+ increase was reproducible among oocytes and in good agreement with the changes observed in the biphasic behavior of the mutants (Supplement 1 to Figure 8).

(6) Fig 4. Please state in the legend what the different color traces correspond to in E600R and DPASCap. Is there a reason to change the interpulse on DPASCap to -20mV and not allow this mutant to close? Please state. How do the authors decide the 10 ms interval for the experiments in Fig 2? 

Thank you for pointing this out, we have added the description. We have explained why we use a different protocol for ΔPASCap and the reason for using 10 ms interval (we believe the referee means Figure 4) on page 12.  

(7) Fig. 5. Since the pre-pulse is supposed to be 5s, but the time scale doesn't correspond with a pre-pulse of 5 s before the test pulse to +40mV. Has the pre-pulse been trimmed for representation purposes? If so, please state. 

The pre-pulse was 5s, but as the reviewer correctly supposed, the trace is trimmed to keep the +40 mV stimulus visible. This has now been clearly stated in the legend.

(8) The mutant L322H is located within the S4 helix according to the Kv10.1 structure (PDB 5K7L), not in the 'S3-S4 linker'; please correct. 

This has been done, thank you.

The introduction of this mutant should also shift the voltage dependence toward more hyperpolarizing potentials (around 30mV, according to Schoenherr et al. 1999). It looks like that shift is present within the first component of the G-V. Still, since the max amplitude from the second component could be contaminated by endogenous Cl- currents, this effect is minimized. Repeating these experiments in the no Cl- solutions will help clarify this point and see the effect of the DPASCap and E600R in the background of a mutation that accelerates the transitions between the closed states (see Major comment 1). Did the authors record L322H alone for control purposes? 

We have decided not to measure L322H alone or repeat the measurements in Cl--free solutions because we do not see a way to use the quantitative assessment of the voltage dependence of L322H and the L322H-variants of the eag domain mutants. Like in our answer to main point 3, we base our arguments not on the precise voltage dependence of the second component but on the shape of the G-V curves instead, specifically the consistent appearance of the first component and the local conductance minimum between the first and second components. After the introduction of L322H the first component is essentially absent.

We think that the measurements of the L322H mutants cannot be interpreted as a hyperpolarizing shift in the first component. The peak of the first conductance component occurs around -20 mV in ΔPASCap and E600R (Fig. 7 C, D). After a -30mV shift, in L322H+DPASCap and L322H+E600R, this first peak would still be detected within the voltage range in our experiments, but it is not. A contamination of the second component would have little impact on this observation, which is why we refrain from the suggested measurements.  

(9) The authors differentiate between an O1 vs. O2 state with different conductances, and maybe I missed it, but there's no quantitative distinction between the components; how are they different?

Please see the response to the main comments 1 and 2. This has been addressed in singlechannel recordings.

(10) Please state the voltage protocols, holding voltages, and the solutions (K+ concentration and Cl-presence/absence) used for the experiments presented in the legends on the figures. Hence, it's easier to interpret the experiments presented. 

Thank you, this has been done.

(11) The authors state on page 7 that "with further depolarizations, the conductance initially declined to rise again in response to strong depolarizations. This finding matches the changes in amplitude of the tail currents, which, therefore, probably reflect a true change in conductance" However, the tails in the strong voltage range (+50 to +120 mV) for the E600R mutant argue against this result. Please review.

The increase in the amplitude of the tail current is also present in E600R, but the relative increase is smaller. We have decided against rescaling these traces because the Figure is already rather complex. We indicated this fact with a smaller arrow and clarified it in the text (page 8).

(12) The authors mention that the threshold of activation for the WT is around -20mV; however, the foot of the G-V is more around -30 or -40mV. Please revise. 

Thank you. We have done this. 

(13) The authors state on page 9 that the 'second component occurs at progressively more depolarized potentials for increasingly larger N-terminal deletions" However E600R mutant that conserves the N-terminal intact has a shift as pronounced as the DPASCap and larger than the D2-10. How do the authors interpret this result? 

We have corrected this statement in page 10 : “…the second component occurs at progressively more depolarized potentials for increasingly larger N-terminal deletions and when the structure of the ring is altered through disruption of the interaction between N- and C-termini (E600R)”.

(14) The equation defined to fit the G-Vs, can also be used to describe the WT currents. If the O1 is conserved and present in the WT, this equation should also fit the WT data properly. The 1-W component shown could also be interpreted as an inactivating component that, in the WT, shifts the voltage-dependence of activation towards depolarizing potentials and is not visible. Still, the mutants do show it as if the transition from closed-inactivated states is controlled by interactions in the gating ring, and disturbing them does affect the transitions to the open state. 

Out of the two open states in the mutant, O2 is the one that shares properties with the WT (e.g. it is inaccessible during Ca2+-CaM binding) while O1 is the open state with the voltage dependence that is conserved across the mutants. We, therefore, believe that this question is based on a mix-up of the two open states. We appreciate the core of the question: does the pattern in the mutants’ G-V curves find a continuation in the WT channel? 

Firstly, the component that is conserved among mutants does not lead to current in the WT because the corresponding open state (O1) is not observed in WT. However, the gating event represented by this component should also occur in WT and –given its apparent insensitivity to eag domain mutations–  this gating step should occur in WT with the same voltage dependence as in all the mutants. This means that this first component sets a hard boundary for the most hyperpolarized G-V curve we can expect in the WT, based on our mutant measurements. Secondly, the second component shows a regular progression across mutants: The more intact the eag domain is, the more hyperpolarized the Vhalf values of transition term (1-W) and O2 activation. In Δ2-10, the transition term already almost coincides with O1 activation (estimated Vhalf values of -33.57 and -33.47 mV). A further shift of (1-W) in the WT is implausible because, if O1 activation is coupled to the earliest VSD displacement, the transition should not occur before O1 activation. Still, the second component might shift to more hyperpolarized values in the WT, depending on the impact of amino acids 2 to 10 on the second VSD transition.

In summary, in WT the G-V should not be more hyperpolarized than the first component of the mutants, and the (1-W)-component probably corresponds to the Δ2-10 (1-W)-component. In WT the second component should be no more depolarized than the second component of Δ2-10. The WT G-V (Fig.1B) meets all these predictions derived from the pattern in the mutant GVs: When we use Eq. 4 to fit the WT G-V with A1=0 (O1 is not present in WT) and the parameters of the transition term (1-W)  fixed to the values attained in Δ2-10, we obtain a fit for the O2 component with Vhalf\=+21mV. This value nicely falls into the succession of Vhalf values for Δeag, ΔPASCap, and Δ2-10 (+103mV,+80mV,+52mV) and, at the same time, it is not more hyperpolarized than the conserved first component (Vhalf -34mV). Our measurements therefore support that the O2 component in the mutants corresponds to the single open state in the WT. 

(15) Page 15, the authors state that 'The changes in amplitude and kinetics in response to rising intracellular Ca2+ support our hypothesis that Ca-CaM stabilized O1, possibly by driving the channels to deep closed states (Fig 5 and 6)' (pg 15). This statement seems contradictory; I can't quite follow the rationale since Ca2+ potentiates the current (Fig 7), and the addition of the L322H mutant in Fig 7 makes the shift of the first component to negative potentials visible.

Please check the rationale for this section. 

We have explained this more explicitly in the discussion (page 32). “Because access to O1 occurs from deep closed states, this could be explained by an increased occupancy of such deactivated states in response to CaM binding. This appears to be the case since CaM induces a biphasic behavior in the mutant channels that show reduced access to deep closed states; thus, L322H mutants behave like the parental variants in the presence of Ca2+-CaM. This implies a mechanistic explanation for the effect of Ca2+-CaM on WT since favoring entry into deep closed states would result in a decrease in current amplitude in the absence of (a permeable) O1”.

Also, Figs 5 and 6 seem miscited here. 

Thank you, we have corrected this.

(16) For Figure 5, it would be helpful if each of the current traces corresponding to a particular voltage had a different color. That way, it will be easier to see how the initial holding voltage modulates current. 

We have considered this suggestion, and we agree that it would make it easier to follow. Yet, since we have identified the mutants with different colors, it would be inconsistent if we used another color palette for this Figure. Supplement 3 to Figure 9 shows the differences in a clearer way.

(17) Add zero-current levels to all current traces.

We have done this.

(18) The mathematical model should be described better. Particularly, the states from which O1 can be accessed should be described more clearly, as well as whether the model considers any direct connectivity between states O1 and O2. The origin of the voltage-dependence for transitions that do not involve voltage-sensor movements should be discussed. Also, it separation of kappa into kappa-l and kappa-r should be described. 

We have extensively rewritten the description of the mathematical model to address these concerns.

(19) Page 4, "reveals a pre-open state in which the transmembrane regions of the channel are compatible with ion permeation, but is still a nonconducting state". Also, page 27, "renders a hydrophobic constriction wider than 8 Å, enough to allow K+ flow, but still corresponds to a non-conducting state". These sentences are confusing - how can the regions be compatible with ion permeation, and still not be conducting? Is cation conductance precluded by a change in the filter, or elsewhere? How is it established that it represents a non-conducting state? 

We have rephrased to clarify this apparent inconsistence. Page 4: “(…) in which the transmembrane regions of the channel are compatible with ion permeation (the permeation path is dilated, like in open states) but the intracellular gate is still in the same conformation as in closed states (Zhang et al., 2023).” Page 31: “The presence of an intact intracellular ring would preclude ionic flow in the WT, and its alteration would explain the permeability of this state in the mutants.”

Author response:

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

eLife Assessment

This is a useful report of a spatially-extended model to study the complex interactions between immune cells, fibroblasts, and cancer cells, providing insights into how fibroblast activation can influence tumor progression. The model opens up new possibilities for studying fibroblast-driven effects in diverse settings, which is crucial for understanding potential tumor microenvironment manipulations that could enhance immunotherapy efficacy. While the results presented are solid and follow logically from the model’s assumptions, some of these assumptions may require further validation, as they appear to oversimplify certain aspects in light of complex experimental findings, system geometry, and general principles of active matter research.

We thank the editor for recognizing the usefulness of our work. This work does not aim to precisely describe the complexity of the tumor microenvironment in lung cancer, but rather to classify and rigorously calibrate a minimum number of parameters to the clinical data we collect and generate, and reproduce the global structures of the microenvironment. We identify different scenarios, and show how they depend on the local interactions within this framework. Although we started in the first version with coalescence in the main text and anisotropic geometry in the supporting information, we realized that we needed to provide more directions to better show how our model can be extended. Thus, in Section III-4 we added an analysis of a microenvironment with blood vessels, and showed how to introduce anisotropic friction as a function of fiber orientation, as well as active stress, paving the way for further studies, that would make our model more complex. However, in a first step, it is crucial to start with a limited number of parameters that can be rigorously determined, and this is how this first work was conceived.

Public Reviews:

Reviewer #1 (Public review):

The authors present an important work where they model some of the complex interactions between immune cells, fibroblasts and cancer cells. The model takes into account the increased ECM production of cancer-associated fibroblasts. These fibres trap the cancer but also protect it from immune system cells. In this way, these fibroblasts’ actions both promote and hinder cancer growth. By exploring different scenarios, the authors can model different cancer fates depending on the parameters regulating cancer cells, immune system cells and fibroblasts. In this way, the model explores non-trivial scenarios. An important weakness of this study is that, though it is inspired by NSCLC tumors, it is restricted to modelling circular tumor lesions and does not explore the formation of ramified tumors, as in NSCLC. In this way, is only a general model and it is not clear how it can be adapted to simulate more realistic tumor morphologies.

We thank the reviewer for highligting the importance of our work. We acknowledge that although we provided anisotropic geometries and the study of the coalescence in the first version, more effort was needed to provide tools to extend our formalism to non-ideal cases. This is now added as Section III-4, where we analyze the impact of blood vessels, and the anisotropic friction due to the nematic order for the fibers; this nematic order can also be used to introduce active nematic stress.

Reviewer #2 (Public review):

Summary:

The authors develop a computational model (and a simplified version thereof) to treat an extremely important issue regarding tumor growth. Specifically, it has been argued that fibroblasts have the ability to support tumor growth by creating physical conditions in the tumor microenvironment that prevent the relevant immune cells from entering into contact with, and ultimately killing, the cancer cells. This inhibition is referred to as immune exclusion. The computational approach follows standard procedures in the formulation of models for mixtures of different material species, adapted to the problem at hand by making a variety of assumptions as to the activity of different types of fibroblasts, namely ”normal” versus ”cancer-associated”. The model itself is relatively complex, but the authors do a convincing job of analyzing possible behaviors and attempting to relate these to experimental observations.

Strengths:

As mentioned, the authors do an excellent job of analyzing the behavior of their model both in its full form (which includes spatial variation of the concentrations of the different cellular species) and in its simplified mean field form. The model itself is formulated based on established physical principles, although the extent to which some of these principles apply to active biological systems is not clear (see Weaknesses). The results of the model do offer some significant insights into the critical factors which determine how fibroblasts might affect tumor growth; these insights could lead to new experimental ways of unraveling these complex sets of issues and enhancing immunotherapy.

We thank the referee for this summary and for recognizing the strengths of our paper.

Weaknesses:

Models of the form being studied here rely on a large number of assumptions regarding cellular behavior. Some of these seemed questionable, based on what we have learned about active systems. The problem of T cell infiltration as well as the patterning of the extracellular matrix (ECM) by fibroblasts necessarily involve understanding cell motion and cell interactions due e.g. to cell signaling. Adopting an approach based purely on physical systems driven by free energies alone does not consider the special role that active processes can play, both in motility itself and in the type of self-organization that can occur due to these cell-cell interactions. This to me is the primary weakness of this paper.

We thank the referee for this important comment, that allows us to clarify this important point. Although biological materials are out of equilibrium, their behavior often resembles that dictated by thermodynamics. Hence the usefulness of constructing a free energy, in terms of these variables. In a first approach to decipher the complex interactions and describe the different and sometimes non-trivial outcomes in this system that involves many components, we must start by minimizing the number of parameters, and identifying those complex processes, that control the evolution of the system. The free energy that we build on this biological system contains therefore out-of-equilibrium processes that can be approximated by a ”close to equilibrium” description. Our approach is a classical one in statistical physics of active systems, namely in the effort to construct an equivalent free-energy for out-of-equilibrium systems. This allows to gain a clearer insight into those complex processes.

We have added a sentence in the main text, section III.1, to clarify this point:

“Building a free-energy density for a biological material is justified, because, although biological materials are out of equilibrium, their behavior often resembles that dictated by thermodynamics. It is therefore useful to write a free energy in terms of state variables.”

Nevertheless, we recognize that we should have provided more tools for using our formalism by making it active. This is why we introduced the nematic order in the fibers in Section III-4. This nematic order can be used to introduce active stress, and we have cited previous works by some of us see [?, ?, ?] as references for building active processes out of it.

We must also note that cell signaling has been introduced a minima in our system for providing the cue for the arrival of T-cells and NAFs from the boundaries. However, we found that although we had evoked the other role of the chemicals in the transformation from NAFs to CAFs in the text, details were not well explained. We have therefore corrected and added some explanations in the introduction of section III, and III.1, III.2.

A separate weakness concerns the assumption that fibroblasts affect T cell behavior primarily by just making a more dense ECM. There are a number of papers in the cancer literature (see, for some examples, Carstens, J., Correa de Sampaio, P., Yang, D. et al. Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer. Nat Commun 8, 15095 (2017);Sun, Xiujie, Bogang Wu, Huai-Chin Chiang, Hui Deng, Xiaowen Zhang, Wei Xiong, Junquan Liu et al. ” Tumour DDR1 promotes collagen fibre alignment to instigate immune exclusion.” Nature 599, no. 7886 (2021): 673-678) that seem to indicate that density alone is not a sufficient indicator of T cell behavior. Instead, the organization of the ECM (for example, its anisotropy) could be playing a much more essential role than is given credit for here. This possibility is hinted at in the Discussion section but deserves much more emphasis.

The referee is right in his comment, and we thank him for raising this issue. We have therefore introduced the anisotropic orientation of the fibers, which induces an anisotropic friction in a new section III-4. In addition, the references pointed out were included in this section. However, although the anisotropy strongly influences the fate of the tumor when the fibers are oriented perpendicular to the surface of the cancer nest, it is less effective when the fibroblasts are oriented in the direction of surface of the cancer nest. In the latter case, which is often the case before cancer cells reshape the tumor microenvironment, the matrix density should correlate with the friction.

Finally, the mixed version of the model is, from a general perspective, not very different from many other published models treating the ecology of the tumor microenvironment (for a survey, see Arabameri A, Asemani D, Hadjati J (2018), A structural methodology for modeling immune-tumor interactions including pro-and anti-tumor factors for clinical applications. Math Biosci 304:48-61). There are even papers in this literature that specifically investigate effects due to allowing cancer cells to instigate changes in other cells from being tumor-inhibiting to tumor-promoting. This feature occurs not only for fibroblasts but also for example for macrophages which can change their polarization from M1 to M2. There needed to be some more detailed comparison with this existing literature.

The referee is right that the first part of our approach, namely the dynamical system may be common in this kind of system, and it needs to be mentioned. So we added the following sentence in the discussion: ”This is in line with several similar mathematical models, that study through this lens the inhibition/activation of the immune system by cancer cells either by means of compartmental nonlinear models similar to our dynamical system, for instance regarding macrophage recruitment and cytokine signaling {arabameri2018structural} {li2019computational}, or mixture models {fotso2024mixture}. We combine the two approaches in order to rigorosly derive the parameters of the model and gain insights from both.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The authors should address the following points:

Major issues

(1) The shape of tumors simulated differs immensely from the observed tumors in Fig. 2. Here, the tumor is constituted by irregular domains, not dissimilar from domains in phase separating mixtures. The domains simulated are circular. Since the authors are using the space dependent model to model the increase in tumor cells with time in the different scenarios (immune-desert, immune-excluded, immune inflamed), it should explain how non-spherical tumor structures can be observed in these scenarios. The authors introduce tumor coalescence in page 28, however, it is not expected that the structures observed in Fig 2 are the result from different tumors merging and coalescing, because that would result from an unlikely large number of initial mutation events in the same region of the tissue. The authors should explain what mechanisms present in the model can lead to non-spherical forms.

We agree with the reviewer that real tumors are rarely round contrary to what our numerics suggests. In fact, only the last figure of our paper in the supporting information was more appropriate for such a discussion. We are now adding discussions and new figures to better illustrate our spatial model, see Figure 6 and section III-4. The in situ geometry of tumors depends on the shape of the host organ, the diffusive (chemical) or advected species such as T cells and fibroblasts, and on the nutrients. Thus, in our case, only cancer cells are produced locally, but during growth the tumor is strongly constrained by the microenvironment, and thus the geometry of the domain we model in the numerics and its boundary conditions. This is also true for the chemicals responsible for growth, cellular advection and phenotypic transformation. Their concentration depends on a convection-diffusion equation and boundary conditions. For a tumor in situ, such as in the lung, the available space is a constraint that will dominate the final geometry of the tumor nests. We do not think that coalescence is controlled by mutational events, but most likely by the search for space necessary for growth. Compared to the first version, we add new figures (Figure 6) that show that the geometry of the organ, as well as the localization of blood vessels, are a cause of the irregularity of the tumor shapes. We also introduce orientational order, which as suggested in section III-4, can induce anisotropic friction and stresses, as well as anisotropic growth. We cite (Ackermann, Joseph, and Martine Ben Amar. ”Onsager’s variational principle in proliferating biological tissues, in the presence of activity and anisotropy.” The European Physical Journal Plus 138.12 (2023): 1103.) where we described active stresses and coupling related to anisotropic growth.

(2) According to the authors, the model presented in equations (1) and onwards simulates the evolution of the fraction of tumor cells in the tissue. However the fraction of tumor cells, for example, depends itself on the variation of other cell types. For example, if fibroblasts were to proliferate with rate alpha, even without tumor cells proliferating, the fraction of tumor cells in the mixture should decrease as alpha times the tumor cells fraction. These terms are missing. The equations do not describe the evolution of the cells’ fractions but of the amount of cells of each type, normalised by the total carrying capacity of non-normal cells in the tissue. The text should be rewritten accordingly.

We agree with the referee: our definition of cell density was not precise enough and may appear misleading. In the paragraph II1, we more explictly introduce the word mass fraction which is the correct physical quantity to introduce into the spatial model.

”All these cells have the same mass density and the sum of their mass fraction satisfies the relationship S = C + T + F_NA + F_A = 1-N, where N is a healthy non active component as healthy cells, for example.”

It is less intuitive than ”number of cells per unit volume” but necessary for the following (III)

(3) The authors start by calculating fixed points of different versions of the dynamical system without spatial dependence. They should explain what is the relevance of these fixed points: in a real situation, where the concentration of tumor fibroblasts and T-cells depend on position, in which conditions are these fixed points relevant?

The referee is right and we will clarify this point: the dynamic analysis is a help for understanding and predicting the scenario occurring in the system. After all the steps of paragraph 2.2, we are faced with 11 independent parameters only for the dynamical system and without the parameters generated by the space modeling itself. Our estimation concerns only lung cancer. These parameters do not appear in the literature. The parameters introduced in Sec. III which are more related to physical interactions such as friction, cell-cell adhesion, etc. can be found in the literature or can be estimated and thus measured in in vitro experiments (see Ackermann and Ben Amar, EPJP 2023, P. Benaroch, J. Nikolic et al. 2024, biorxiv). So what are the fixed points for: they help to get the right numbers for spatial analysis. To recover special features of cancer evolution, we need a model, but also correct estimates of the data in a code that is quite technical and heavy, with each simulation taking a certain amount of time. For users who only need rough predictions, the analysis in section 2 is sufficient.

It is also important to note that the global result depends only on the source terms, and on the boundary conditions. This can be illustrated with a simple example: Consider the governing equation for the density of a component with velocity v and source term:

Integrating the equation over a fixed volume V of surface S gives:

. This integrated equation can then be approximated by the dynamical system that we write. Thus, while the dynamical system does not give any information about the local structure of the system, it may be indicative of its global outcome.

(4)   In page 15, the authors identify that α_NA is proportional to δ𝝐⁴. However, in equation (7), they replace α_NA by δ𝝐⁴ without the proportionality constant. This should be corrected.

Thank you for your remark. This typo is now corrected.

(5) The tumor cell movement should be much slower than the T-cells. Here, the authors assign a similar friction coefficient for the cancer cells and T-cells, for example. However, in lung cancer tumor cells are epithelial, and adhere to each other in the tissue. Their movement is very restricted by the basement membranes and by cell-cell adhesion. Immune cells and T-cells on the other hand move rapidly throughout the stroma. It is a gross simplification to not consider the low epitelial tissue mobility in the context of lung cancer.

It is possible to assume different friction coe cients for each phase pair. This has been done in a previous publication, Ackermann et al., Physics report 2021. It is also possible to play with the cell-cell adhesion in the energy density and on the diffusion coe cient introduced in the Flory-Higgins free energy. Cell-cell adhesion is taken into account in the energy, and this makes the tumor a more dense phase, while T-cells can move towards cancer cells to which they are attracted. In the last part of the paper, we show the role of an anisotropic friction due to a nematic order for activated fibroblasts and all the other cells

(6) What is the biological mechanism by which the T-cells form a colony with a surface tension? In the phase-field model, the authors have a surface tension assigned to the cancer cells, T-cells and fibroblasts. Can the authors justify biologically why do they consider these surface tensions?

The fact that T-cells form a colony is due to the accumulation of T-cells at the outer boundary of the tumor, as they are attracted to it but cannot penetrate due to the strong cell-cell adhesion of the tumor cells in the nest. Adding a gradient square is standard in continuous models to limit the sharp variations. In a continuous approach, the gradient square contribution limits the sharp variations in cell density which are not physical.

Minor issues

(a) Page 6 (end), characterisation of the fibre barrier produced by CAFs missing: what is the fibre density, how it can hinder the spread of cancer and T-cell motility? Is it so dense that it prevents ameboid movement? Can cells move through it using matrix degradation proteins?

The fiber density corresponds to the fibrous organic extracellular matrix secreted by cancer-associated fibroblasts. In desmotic (highly fibrous tumors such as PDAC or NSCLC), this extracellular matrix deposited around the tumor forms a physical barrier around the tumor nest, preventing both cell migration and capillary and immune cells penetration. In these cases, the fibrous belt actually prevents ameboid movement and cells must deform significantly to migrate. The role of this barrier was particularly demonstrated in the reference (Grout, John A., et al. ”Spatial positioning and matrix programs of cancer-associated fibroblasts promote T-cell exclusion in human lung tumors.” Cancer Discovery 12.11 (2022): 2606-2625.). In later stages of cancer, the tumor may adapt and develop strategies to metastasize, such as matrix degradation. This matrix can be oriented, organized or disordered. To build a minimal model, we first considered an isotropic friction and also an anisotropic friction of the nematic belt, due to the activated fibroblasts. In the case of T-cells, as mentioned in section I.1, it is true that the biological literature also considers a phenotypic transformation of the T cells by the activated fibroblasts: this concerns both their proliferative capacities, antigen recognition and also their cytotoxic function. To better document the different mechanisms, we add the following publication: Cancer associated fibroblasts-an impediment to effective anti-cancer T cell immunity, by Koppensteiner, Lilian and Mathieson, Layla and O’Connor, Richard A and Akram, Ahsan R, Frontiers in immunology (2022).

However, our goal is to build a minimal model and to characterize and quantify the physical process in which CAFs are involved, namely the role of a physical barrier, that has been documented, as documented above.

(b) Page 19 (Fig 3), in the figure legend it is written ”resting fibroblasts”, should be ”non-activated fibroblasts”.

The referee is right: it will be better to write non-activated fibroblasts. This is now changed in the main text.

(c) Page 21 (equation), what is dΩ? It is dr?

We thank the referee for raising this point. The text was indeed ambiguous as sometimes dΩ was replaced by dr. To be clearer, all the elements of volume are now noted dV , and the element of surface of the system are noted dS.

In the article the units are in italic and should be in roman.

Thank you for raising this point. It has been corrected.

(d) Page 25 (beginning section III.3), the authors mention that the simulation is 2D, however, the simulation has radial symmetry. A 1D simulation in radial coordinates could simulate a 3D spherical system. Is the simulation of this section equivalent to a 1D radial simulation (in 2D)?

The referee is right that in radial symmetry, a 1d equation may be written. We therefore present numerics with irregular shapes of the tumor nest in order to make the system fully 2d.

(e) Page 26 (Fig 4). Legends inside the plots of plates A, B, C and D are not clear. Colorbar range of plates A and D is different. Would facilitate if the ranges were the same.

The referee is right: the surface plots presented in figure 4 would be easier to compare with the same colorbar range for the legends. In fact, as the referee noted, figures in A, B and C have the same legends, while figure in D has a different one. This is due to the fact that D represents the case of the immune-inflamed tumor where the cancer mass fraction is quite vanishing, resulting in values that are of 3 orders of magnitude lower than those present in A, B and C. Therefore, they would disappear if the colorbar range were equal to the others.We insist more on the change of scale in the legend of Figure 4, in the new version.

(f) Page 29 (Fig 5), would facilitate if the order of immune-desert, immune-excluded, immune-inflamed was maintained throughout the document. In this figure the immune-inflamed case appears first.

We agree with the reviewer that following the same order in which the different cases are presented throughout the manuscript would be helpful in comparing the different figures. Therefore, we have modified Figure 5.

(g) Page 31, the authors indicate that pharmacodynamics and pharmacokinetics are highly dependent on tumour spatial structure. Can they provide examples and citations?

In the discussion, we have added references concerning pharmacodynamics.

(h) Page 33 (Fig Sup2), would facilitate if the order of immune-desert, immune-excluded, immune-inflamed was maintained throughout the document. ±±

We thank the reviewer for pointing this out, the order of the different scenarios in Fig Sup 2 has now been changed.

Reviewer #2 (Recommendations for the authors):

Major points

(1) Following on from the discussion in the public review, I feel that there are a number of critical issues that need to be addressed regarding modeling assumptions. I would like to understand why the authors believe it is possible to use a free energy-driven model of the microenvironment when many of the processes relevant for their study have an undeniably ”active media” flavor.

The referee is right that processes in biology are active processes. However, it is a classical approach to model physical interactions between biological components with a free-energy, especially cell adhesion, as they often lead to quasi-stationary equilibrium-like patterns. The free-energy approach has also the advantage to derive straight-forwardly complex phenomena involving many components. Activity can indeed be introduced in such a framework, if we know that the fibroblasts transform into myo-fibroblasts, see for example our previous publication Ackermann and Ben Amar, EPJP 2023. However, in the interest of simplification and reduction of the number of free parameters, we have not not considered further complication of the model here, as a minimal model allows to distinguish the main processes that occur. Nevertheless, introducing more precisely activity, in the nematic approach already achieved for the friction, is a natural continuation of our work: See the new Section III-4, where we introduce the nematic order, and we indicate that active nematic stresses can be written from it.

Next, I don’t understand the assumption that T cells do not proliferate once they detect neoantigens on the cancer cells; activation of T cells usually causes them to become more proliferative.

We thank the referee for this question. The T-cell fraction has two origins: proliferation of T-cells in situ in the stroma or inside tumor nest or external arrival from the sources that we privilege. We recognize that a full analysis of the tumor-microenvironment would require to consider proliferation near the tumor, as many more other processes which is do able but requires the knowledge of more biological date. In addition, besides, the proliferation of T-cells will be equivalent to increase the killing abilities of T-cells and these two effect overlapp in our approach.

In order to clarify this point, we modify the following sentence in Section II.2:

“Although proliferation of cytotoxic T-cells has been observed, we do not consider explicitly proliferation in our study as we focus on their ability to infiltrate the tumor.”

Rather, we consider that T-cells proliferate outside the domain boundaries, so that this proliferation is included in the boundary source contributions.

Finally, the issue of whether the density of fibers is sufficient to understand the role of fibroblasts is not at all settled. There should be a full discussion of this issue including mentioning of the Nature paper (cited in the public review) that argues that orientation (and not density) is the key to the role of fibers, as well as the earlier cited work of Kalluri and collaborators on the role of ECM density in pancreatic cancer.

We thank the referee for this remark. As we wrote above in the response to the public review, we introduced significant additions that aim to tackle this question in the article.

(2) The authors present a picture of a tumor cell with fibroblasts apparently arrayed circumferentially around the tumor boundary and therefore blocking infiltration. This type of tumor structure has been seen before, for example in ”On the mechanism of long-range orientational order of fibroblasts.” Proceedings of the National Academy of Sciences 114, no. 34 (2017): 8974-8979, which should be cited. More importantly, in that paper the argument is made that positive feedback between fibroblasts and ECM geometry can cause structures like this to form. If this is indeed what is occurring, this would indicate the crucial importance of a mechanism beyond what is contained in the current model. This issue should therefore be discussed within this paper. This issue is of course connected to the previous point regarding the role of ECM structure beyond density.

We completely agree that the interplay between the fibroblast layer and the tumor shapes the tumor boundary. One of the authors has worked recently on this precise topic (Aging and freezing of active nematic dynamics of cancer-associated fibroblasts by fibronectin matrix remodeling, C Jacques, J Ackermann, S Bell, C Hallopeau, CP Gonzalez, ... bioRxiv, 2023.11. 22.568216, Ordering, spontaneous flows and aging in active fluids depositing tracks S Bell, J Ackermann, A Maitra, R Voituriez arXiv preprint arXiv:2409.05195). Since the fibroblast layer is an active material, it contributes to an anisotropic stress that can be introduced into the model. Our first strategy was to present the simplest modeling in order to focus on the most important interactions as cell-cell adhesion and cell-tissue adhesion. However, we recognize that those questions should be discussed in the text, and we discuss it in the new section III-4

Minor points

There are also a number of more minor points to consider:

(1) Since the parameter is taken to be O(1), why exactly does it matter how the other parameters scale with it?

It is very important to compare the order of magnitude of the other parameters once the selected parameter of order O(1) is really the driving parameter of the coupling. It gives a first picture of the main interactions that has to consider.

(2) I didn’t understand the relevance of referring specifically to IL 6 among many other possibly relevant signals, as is currently done on page 7.

This corresponds to studies aiming to correlate lung cancer risks and the concentration of interleukin, mostly IL6 and IL8 (McKeown, D. J., et al. ”The relationship between circulating concentrations of C-reactive protein, inflammatory cytokines and cytokine receptors in patients with non-small-cell lung cancer.” British journal of cancer 91.12 (2004): 1993-1995.,Brenner, Darren R., et al. ”Inflammatory cytokines and lung cancer risk in 3 prospective studies.” American journal of epidemiology 185.2 (2017): 86-95. ) but in the absence of very detailed biological information, the modeling and its results are not modified if other chemicals intervene..We slightly modeified the following phrase in section I.1:

“In particular, in the family of inflammatory proteins, also called cytokines, Interlukin-6 (IL6) and (IL8) seem, among others to stimulate the infiltration of CD8⁺.

(3) The authors need to mention the possibility of T-cell chemotaxis to the tumor being ”self-amplified” in the T cell system, as put forth in Galeano Nin˜o, Jorge Luis, Sophie V. Pageon, Szun S. Tay, Feyza Colakoglu, Daryan Kempe, Jack Hywood, Jessica K. Mazalo et al. ”Cytotoxic T cells swarm by homotypic chemokine signalling.” eLife 9 (2020): e56554. This might again reveal a needed extension of the current modelling strategy.

We thank the referee for his/her comment on the self-amplification of T-cell population in the stroma and we mention the indicated reference in our paper. This auto-chemoatactic process which induces a dynamic of more e cient recruitment towards the tumor, may be important for immunotherapy. To have more e cient T-cell arriving at the site of the tumor, will lead a better issue for the patient, if the swarming organization is maintained in a desmoplastic nematic stroma.

(4) It is not obvious to me that in sub figures 3F and 3H the tumor is enroute to being totally eradicated, as is stated in the text. The blue lines seemed to asymptote at non-zero population values.

Looking at sub-figures 3F and 3H, we stated in the main text that the tumor is eradicated as the representative population approaches a 0 value fraction, or at least decays around the 0 (0.01/0.05 to be more precise). This is even more evident when compared with the other cases where the tumor mass fraction reaches values of a higher order (up to 0.6), thus leading us to dinstinguish between these different scenarios.

(5) The description of the interaction of cells with fibers as being increased friction might be misleading, as the real effect could be actual trapping in the network (as opposed to just slowing down the motion).

We thank the referee for this question as it allow us to make an important distinction. Indeed, what the referee describes seems to correspond to a discrete event, namely a cell trapped in a network. However, coarse-graining the dynamics to the continuous modeling seems to us as leading to an effective friction between the two phases. Moreover, we also now introduced an anisotropic friction which can represent a trapping. The velocities are not only directed around the tumor but can also be oriented towards the tumor, so that eventually the friction along the radius mimics a trapping (see Fig.4 on top). We have introduced this anisotropic friction via a nematic model, see the appendix.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Authors showed the presence of Mtb in human liver biopsy samples of TB patient and reported that chronic infection of Mtb causes immune-metabolic dysregulation. Authors showed that Mtb replicates in hepatocytes in a lipid rich environment created by up regulating transcription factor PPARγ. Authors also reported that Mtb protects itself from anti-TB drugs by inducing drug metabolising enzymes.

Strengths:

It has been shown that Mtb induces storage of triacylglycerol in macrophages by induction of WNT6/ACC2 which helps in its replication and intracellular survival, however, creation of favorable replicative niche in hepatocytes by Mtb is not reported. It is known that Mtb infect macrophages and induces formation of lipid-laden foamy macrophages which eventually causes tissue destruction in TB patient. In a recent article it has been reported that "A terpene nucleoside from M. tuberculosis induces lysosomal lipid storage in foamy macrophages" that shows how Mtb manipulates host defense mechanisms for its survival. In this manuscript, authors reported the enhancement of lipid droplets in Mtb infected hepatocytes and convincingly showed that fatty acid synthesis and triacylglycerol formation is important for growth of Mtb in hepatocytes. Authors also showed the molecular mechanism for accumulation of lipid and showed that the transcription factor associated with lipid biogenesis, PPARγ and adipogenic genes were upregulated in Mtb infected cells.

The comparison of gene expression data between macrophages and hepatocytes by authors is important which indicates that Mtb modulates different pathways in different cell type as in macrophages it is related to immune response whereas, in hepatocytes it is related to metabolic pathways.

Authors also reported that Mtb residing in hepatocytes showed drug tolerance phenotype due to up regulation of enzymes involved in drug metabolism and showed that cytochrome P450 monooxygenase that metabolize rifampicin and NAT2 gene responsible for N-acetylation of isoniazid were up regulated in Mtb infected cells.

Weaknesses:

There are reports of hepatic tuberculosis in pulmonary TB patients especially in immune-compromised patients, therefore finding granuloma in human liver biopsy samples is not surprising.

Mtb infected hepatic cells showed induced DME and NAT and this could lead to enhanced metabolism of drug by hepatic cells as a result Mtb in side HepG2 cells get exposed to reduced drug concentration and show higher tolerance to drug. Authors mentioned that " hepatocyte resident Mtb may display higher tolerance to rifampicin". In my opinion higher tolerance to drug is possible only when DME of Mtb inside is up regulated or target is modified. Although, in the end authors mentioned that drug tolerance phenotype can be better attributed to host intrinsic factors rather than Mtb efflux pumps. It may be better if Drug tolerant phenotype section can be rewritten to clarify the facts.

In the revised manuscript, by immune-staining authors convincingly showed that hepatocytes are a favourable niche for replication of MTb.

Authors have rewritten the drug tolerant phenotype section which reads better.

Overall, this paper has new and important information on how MTb establishes a favourable niche for growth in hepatocytes and creates a drug tolerant environment.

We thank the reviewer for the through and insightful review.

Reviewer #2 (Public review):

The manuscript by Sarkar et al has demonstrated the infection of liver cells/hepatocytes with Mtb and the significance of liver cells in the replication of Mtb by reprogramming lipid metabolism during tuberculosis. Besides, the present study shows that similar to Mtb infection of macrophages (reviewed in Chen et al., 2024; Toobian et al., 2021), Mtb infects liver cells but with a greater multiplication owing to consumption of enhanced lipid resources mediated by PPARg that could be cleared by its inhibitors. The strength of the study lies in clinical evaluation of the presence of Mtb in human autopsied liver samples from individuals with miliary tuberculosis and presence of a clear granuloma-like structure. The interesting observation is of granuloma-like structure in liver which prompts further investigations in the field.

The modulation of lipid synthesis during Mtb infection, such as PPARg upregulation, appears generic to different cell types including both liver cells and macrophage cells. It is also known that infection affect PPARγ expression and activity in hepatocytes. It is also known that this can lead to lipid droplet accumulation in the liver and the development of fatty liver disease (as shown for HCV). This study is in similar line for M.tb infection. As liver is the main site for lipid regulation, the availability of lipid resources is greater and higher is the replication rate. In short, the observations from the study confirm the earlier studies with these additional cell types. It is known that higher the lipid content, greater are Lipid Droplet-positive Mtb and higher is the drug resistance (Mekonnen et al., 2021). The DMEs of liver cells add further to the phenotype.

Comments on revised version:

The authors noted that even in experiments where mice were infected with lower CFUs, the presence of Mtb colonies could still be detected in the liver. It would be beneficial to include some experimental data related to this in the supplementary information, as it could provide valuable insights for the research field.

We thank the reviewer for the in depth evaluation of our manuscript and as suggested we will include the data where Mtb was detected in the liver at low CFUs

Reviewer #3 (Public review):

In this revised manuscript, the authors explore how Mtb can infect hepatocytes and create a favorable niche associated with upregulation of the transcription factor PPARγ which presumably allows the bacteria to scavenge lipids from lipid droplets in host cells and upregulate drug-metabolizing enzymes to protect against its elimination. In response to the review, the authors have performed some additional immunostaining of hepatocytes, added more detail to figure legends, added experiments somewhat showing improved colocalization and staining, clarified several points and paragraphs, and updated the referenced literature and discussion.

The current manuscript provides evidence that human miliary TB patients have infection of hepatocytes with Mtb, with evidence that the bacteria survive at least partially through upregulation of PPARγ, which significantly changes the lipid milieu of the cells. There is also an examination of transcriptomics and lipid metabolism in response to Mtb infection, as well as drug tolerance of Mtb inside hepatocytes. The current manuscript is an improvement over the previous one.

However, although the manuscript is improved, tissue immunophenotyping of the various cells in the liver remains weak and unconvincing. This is truly a missed opportunity and lessens the rigor of the central findings and conclusions. As pointed out by another reviewer, literature has described different fates of Mtb in the liver. Given the tissue available to the authors, carefully dissecting the various cells that the bacteria are in (esp. hepatocytes versus Kupffer cells) is critical. The authors use only 2 generic markers and do not distinguish among cell types within the tissue slices. A review of the literature shows a variety of both human and mouse antibody markers. In fact, a liver atlas based on immunophenotyping has been published. Likewise, the authors comment on liver granulomas, but this is not justified without immunophenotyping.

We would like to thank the reviewer for the in-depth and detailed suggestions. We would like to clarify that the primary aim of our study was to determine the localization of Mtb within hepatocytes and the downstream biological consequences. To this end, we employed two well-established and widely validated markers (ASPGR 1 and albumin) that are consistently used to identify hepatocytes in both human and murine liver tissue. While we acknowledge the broader potential of comprehensive immunophenotyping, our focused approach was designed to specifically address the question of hepatocyte involvement, which the selected markers effectively support, which was further reiterated by the Reviewer 1.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

In my opinion this paper contains important information and no further information is required for this manuscript.

We thank the reviewer for the insightful comments

Reviewer #2 (Recommendations for the authors):

The authors noted that even in experiments where mice were infected with lower CFUs, the presence of Mtb colonies could still be detected in the liver. It would be beneficial to include some experimental data related to this in the supplementary information, as it could provide valuable insights for the research field.

As suggested,  we will include the data with the low CFUs in the updated manuscript.

Reviewer #3 (Recommendations for the authors):

• Line 340, the fact that PPARγ inhibition decreases bacterial load should not be surprising, as the authors cite several papers where this is already shown.

• Line 379, the increased tolerance of Mtb to drugs in hepatocytes is only significant at the lower 2 concentrations, not at 5 ug/mL.

• Fig S4F-H, the y axis is inappropriately not set to zero on the lower limit.

• Fig S9B, the Y-axis states "relative" CFU, but there is no indication what the bars are normalized to, and the numbers are much more typical of standard CFU values. Was the "Relative" part left in by mistake?

• Double check the ending of the figure legend for Figure S10 and S11.

• Line 352, phenomenom [sic] is misspelled.

• On re-read, several sentences throughout this manuscript need improvement regarding structure and grammar. I suggest careful editorial review.

We thank the reviewer for pointing out the issues and these will be carefully modified in the next version.

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors showed the presence of Mtb in human liver biopsy samples of TB patients and reported that chronic infection of Mtb causes immune-metabolic dysregulation. Authors showed that Mtb replicates in hepatocytes in a lipid rich environment created by up regulating transcription factor PPARγ. Authors also reported that Mtb protects itself from anti-TB drugs by inducing drug metabolising enzymes.

Strengths:

It has been shown that Mtb induces storage of triacylglycerol in macrophages by induction of WNT6/ACC2 which helps in its replication and intracellular survival, however, creation of favorable replicative niche in hepatocytes by Mtb is not reported. It is known that Mtb infects macrophages and induces formation of lipid-laden foamy macrophages which eventually causes tissue destruction in TB patients. In a recent article it has been reported that "A terpene nucleoside from M. tuberculosis induces lysosomal lipid storage in foamy macrophages" that shows how Mtb manipulates host defense mechanisms for its survival. In this manuscript, authors reported the enhancement of lipid droplets in Mtb infected hepatocytes and convincingly showed that fatty acid synthesis and triacylglycerol formation is important for growth of Mtb in hepatocytes. The authors also showed the molecular mechanism for accumulation of lipid and showed that the transcription factor associated with lipid biogenesis, PPARγ and adipogenic genes were upregulated in Mtb infected cells.

The comparison of gene expression data between macrophages and hepatocytes by authors is important which indicates that Mtb modulates different pathways in different cell type as in macrophages it is related to immune response whereas, in hepatocytes it is related to metabolic pathways.

Authors also reported that Mtb residing in hepatocytes showed drug tolerance phenotype due to up regulation of enzymes involved in drug metabolism and showed that cytochrome P450 monooxygenase that metabolize rifampicin and NAT2 gene responsible for N-acetylation of isoniazid were up regulated in Mtb infected cells.

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

Weaknesses:

There are reports of hepatic tuberculosis in pulmonary TB patients especially in immune-compromised patients, therefore finding granuloma in human liver biopsy samples is not surprising.

Mtb infected hepatic cells showed induced DME and NAT and this could lead to enhanced metabolism of drug by hepatic cells as a result Mtb in side HepG2 cells get exposed to reduced drug concentration and show higher tolerance to drug. The authors mentioned that " hepatocyte resident Mtb may display higher tolerance to rifampicin". In my opinion higher tolerance to drugs is possible only when DME of Mtb inside is up regulated or the target is modified. Although, in the end authors mentioned that drug tolerance phenotype can be better attributed to host intrinsic factors rather than Mtb efflux pumps. It may be better if the Drug tolerant phenotype section can be rewritten to clarify the facts.

We agree that several case studies regarding liver infection in pulmonary TB patients have been reported in the literature, however this report is the first comprehensive study that establishes hepatocytes to be a favourable niche for Mtb survival and growth.

Drug tolerance is a phenomenon that is exhibited by the bacteria and during hostpathogen interactions, can be influenced by both intrinsic (bacterial) and extrinsic (host-mediated) factors. Multiple examples of tolerance being attributed to host driven factors can be found in literature (PMID 32546788, PMID: 28659799, PMID: 32846197). Our studies demonstrate that Mtb infected hepatocytes create a drug tolerant environment by modulating the expression of Drug modifying enzymes (DMEs) in the hepatocytes.

As suggested by the reviewer we will rewrite the drug tolerant phenotype section.

Reviewer #2 (Public review):

The manuscript by Sarkar et al has demonstrated the infection of liver cells/hepatocytes with Mtb and the significance of liver cells in the replication of Mtb by reprogramming lipid metabolism during tuberculosis. Besides, the present study shows that similar to Mtb infection of macrophages (reviewed in Chen et al., 2024; Toobian et al., 2021), Mtb infects liver cells but with a greater multiplication owing to consumption of enhanced lipid resources mediated by PPARg that could be cleared by its inhibitors. The strength of the study lies in the clinical evaluation of the presence of Mtb in human autopsied liver samples from individuals with miliary tuberculosis and the presence of a clear granuloma-like structure. The interesting observation is of granuloma-like structure in liver which prompts further investigations in the field.

The modulation of lipid synthesis during Mtb infection, such as PPARg upregulation, appears generic to different cell types including both liver cells and macrophage cells. It is also known that infection affect PPARγ expression and activity in hepatocytes. It is also known that this can lead to lipid droplet accumulation in the liver and the development of fatty liver disease (as shown for HCV). This study is in a similar line for M.tb infection. As the liver is the main site for lipid regulation, the availability of lipid resources is greater and higher is the replication rate. In short, the observations from the study confirm the earlier studies with these additional cell types. It is known that higher the lipid content, the greater are Lipid Droplet-positive Mtb and higher is the drug resistance (Mekonnen et al., 2021). The DMEs of liver cells add further to the phenotype.

We thank the reviewer for emphasizing on the strengths of our study and how it can lead to further investigations in the field.

Reviewer #3 (Public review):

This manuscript by Sarkar et al. examines the infection of the liver and hepatocytes during M. tuberculosis infection. They demonstrate that aerosol infection of mice and guinea pigs leads to appreciable infection of the liver as well as the lung. Transcriptomic analysis of HepG2 cells showed differential regulation of metabolic pathways including fatty acid metabolic processing. Hepatocyte infection is assisted by fatty acid synthesis in the liver and inhibiting this caused reduced Mtb growth. The nuclear receptor PPARg was upregulated by Mtb infection and inhibition or agonism of its activity caused a reduction or increase in Mtb growth, respectively, supporting data published elsewhere about the role of PPARg in lung macrophage Mtb infection. Finally, the authors show that Mtb infection of hepatocytes can cause upregulation of enzymes that metabolize antibiotics, resulting in increased tolerance of these drugs by Mtb in the liver.

Overall, this is an interesting paper on an area of TB research where we lack understanding. However, some additions to the experiments and figures are needed to improve the rigor of the paper and further support the findings. Most importantly, although the authors show that Mtb can infect hepatocytes in vitro, they fail to describe how bacteria get from the lungs to the liver in an aerosolized infection. They also claim that "PPARg activation resulting in lipid droplets formation by Mtb might be a mechanism of prolonging survival within hepatocytes" but do not show a direct interaction between PPARg activation and lipid droplet formation and lipid metabolism, only that PPARg promotes Mtb growth. Thus, the correlations with PPARg appear to be there but causation, implied in the abstract and discussion, is not proven.

The human photomicrographs are important and overall, well done (lung and liver from the same individuals is excellent). However, in lines 120-121, the authors comment on the absence of studies on the precise involvement of different cells in the liver. In this study there is no attempt to immunophenotype the nature of the cells harboring Mtb in these samples (esp. hepatocytes). Proving that hepatocytes specifically harbor the bacteria in these human samples would add significant rigor to the conclusions made.

We thank the reviewer for nicely summarizing our manuscript.

Our study establishes the involvement of liver and hepatocytes in pulmonary TB infection in mice. Understanding the mechanism of bacterial dissemination from the lung to the liver in aerosol infections demands a detailed separate study.

Figure 6E and 6F shows how PPARγ agonist and antagonist modulate (increase and decrease respectively) bacterial growth in hepatocytes (further supported by the CFU data in Supplementary Figure 9B). Again, the number of lipid droplets in hepatocytes increase and decrease with the treatment of PPARγ agonist and antagonist respectively as shown in Figure 6G and 6H. Collectively, these studies provide strong evidence that PPARγ activation leads to more lipid droplets that support better Mtb growth.

We thank the reviewer for finding our human photomicrographs convincing. In the manuscript, we provide evidence for the direct involvement of the hepatocytes (and liver) in Mtb infection. We have performed detailed immunophenotyping of hepatocyte cells in the mice model with ASPGR1 (asialoglycoprotein receptor 1) and in the revised version of record, we have further stained the infected hepatocytes with anti-albumin antibody.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

In my opinion drug tolerant phenotype section should be rewritten for better clarification. The manuscript contains important information about hepatic tuberculosis which are not reported yet.

We have rewritten the drug tolerant phenotype section for better clarity.

We appreciate the reviewer’s comments regarding important information about hepatic tuberculosis

Reviewer #2 (Recommendations for the authors):

The following are some observations and comments on the manuscript.

(1) The study delves into the mechanisms related to hepatic TB/miliary TB; however, the introduction and discussion only describe and discuss the data in the context of pulmonary TB giving a sense that the mandate of the MS is the exploration of the role of liver cells in pulmonary TB. There appears a gap in the connection of findings from the Miliary TB to the pulmonary TB. A discussion of the conversion of pulmonary TB to extrapulmonary /hepatic TB in the light of the findings may be helpful.

We have modified the discussion section to include possible mechanisms that convert pulmonary TB to hepatic TB in the light of findings. Briefly, Pulmonary tuberculosis (TB) can lead to miliary TB probably through hematogenous dissemination, where Mtb spreads from the infected lungs into blood vessels either from a primary lung focus, reactivated TB or caseous necrosis.  Once in blood vessels, the bacteria seed multiple organs, forming tiny granulomas, characteristic of miliary TB. The liver involvement could be either through direct hematogenous spread or extrusion from nearby infected lymph nodes, leading to hepatic TB, which presents with granulomas and liver dysfunction. This spread underscores the severity of untreated pulmonary TB and the need for early intervention. Our in vivo infection data clearly shows that pulmonary infection of Mtb in mice and guinea pigs can steadily leads to significant infection of the liver and metabolic abnormalities in the liver. The study further highlights the need for systemic studies to better understand the route and mode of dissemination from lungs to liver for better pathophysiological understanding of the disease and creating new therapeutic targets.  

(2) The authors show the presence of Mtb in the liver autopsies of miliary tuberculosis patients. It is well known that Mtb disseminates during the late stages to several organs and liver is a major site (Sharma et al. 2005; 10.1016/S1473-3099(05)70163-8). Other clinical observations also point to the fact that although Mtb infects liver cells, it is cleared (Thandi et al., 2018, https://doi.org/10.4049/jimmunol.200.Supp.173.20). As the samples are from miliary TB, it is expected that the bacterial load must have been very high before spreading to blood. It is known that once in blood, M.tb is expected to spread to various organs, especially highly vascular ones. Were any other tissues (especially with high vasculature) stained and verified? If yes, add to the supplementary data or discuss.

Other tissues were not collected and stained during this study. Studies are currently underway to understand whether other vasculated organs also harbour Mtb or not. Besides several studies have shown that Mtb can infect a wide range of organs like brain, kidney, bone marrow, etc (PMID: 33142108, PMID: 28046053, PMID: 34269789) during miliary conditions.

(3) It is not evident from this paper if hepatic infiltration occurs in pulmonary TB patients? It may therefore be important to discuss the status of liver infections in the primary pulmonary infection.

Based on the available data from human biopsied liver samples, there is an indication of liver involvement in systemic tuberculosis (TB). However, to gain a more comprehensive understanding of hepatic infiltration in pulmonary TB patients, it is essential to conduct well-organized clinical studies. These studies should specifically target pulmonary TB patients and explore the extent and nature of liver involvement in these individuals (discussion). As suggested by the reviewer it is in the discussion

(4) Similarly, in the mice model, M.tb was shown to localize to liver when aerosolic infection was given. Were any other tissues, such as kidney, bone marrow etc, checked? Is it because of the high dose of M.tb against the standard challenge dose of 50-100 CFU? Further, since the study in the mouse model is to mimic a miliary tuberculosis of liver, did the dissemination occur via bloodstream and if mycobacteremia could be observed in infected mice.

Currently studies are underway to understand the involvement of other organs like kidney, brain, bone marrow, in aerosol infection mice model and how dissemination occurs in those distant organs.

The focus of the current study was to understand the role of liver in systemic tuberculosis with emphasis on hepatocytes as a key cell type to be infected. We have also conducted the experiments with lower CFUs and could detect the presence of Mtb colonies in liver, so we do not think that the infection of liver is dependent on the dose of infection.

(5) There are studies in mouse model which infer that liver carried the lowest bacterial burden, was cleared the fastest, and it is established that as compared to sites persistently seeded by M. tuberculosis, in the liver the bacteria rarely infect cell types other than professional phagocytes. As the observations in this study are contrasting, the discussion section should include a critical comparative analysis to justify why in the conditions used in the study, the hepatocytes and not Kupffer cells are infected. Other than the morphological description to indicate M.tb infection of hepatocytes in the liver section (fig 1E), it will be good to show localization of M.tb specifically to hepatocytes by using hepatocyte specific marker. Unlike as reported, why was a clearance of M.tb not observed even after 10 weeks (figure 2B).

While some studies show that Mtb from the liver is cleared fast but there are several other studies that report Liver harbours Mtb even after 10 weeks postinfection (PMID: 22359543, PMID: 21533158, PMID: 29242198). We have consistently observed Mtb infection of liver post week 10 in our infection model. 

We have performed detailed immunophenotyping of hepatocyte cells in the mice model with ASPGR1 (asialoglycoprotein receptor 1) and in the revised version of record, we have further stained the isolated hepatocytes with anti-albumin antibody (albumin is a robust marker of hepatocyte identity) and have showed the presence of Mtb in it. The data has been included in the revised manuscript (Fig 2J)

(6) While the result section mentions that "individuals with miliary tuberculosis' (line 107), the legend of Figure 1 writes 'Presence of Mtb in human pulmonary tuberculosis patients'. This is confusing. Clarify

We thank the reviewer for pointing it out, we have changed the figure legends to miliary tuberculosis as most of the liver biopsy samples were obtained from military tuberculosis patients. 

(7) Supplementary Figure 2D: Corresponding control panel (uninfected) should be added, which will also verify the specificity of Ag85b. As it is known that Ag85B is secreted out from the bacteria and hence the detected signals may not confirm that Mtb is in hepatocytes. Ag85B per bacterium decreases by almost 10,000-fold at later stages of infection because of secretion (Ernst JD, Cornelius A, et al 2019 mBio). In Supl figure 2D, Ag85b signal seems to be present everywhere inside the cells. Hence, it is important that the control panel be added.

We have included a control image below which shows no staining of Ag85B in the uninfected sample.While we acknowledge with the reviewer’s comment, but Ag85B has been consistently used as a marker for Mtb presence in multiple studies. Nargan et al., uses Ag85B based staining to characterize infection both pulmonary and EPTB samples (PMID: 38880068). Jain et al., uses Ag85B to characterize Mtb infection of Mesenchymal stem cell in lung biopsy samples of pulmonary TB patients (PMID: 32546788)

Author response image 1.

Ag85B staining in uninfected mice shows no signals

(8) The kinetics experiments in Figure 3D-3G should have used time laps microscopy of a few of the infected cells or it should be represented in CFU. If we consider the doubling time of H37Rv is about 22h to 24h, the data showing that MFI increases dramatically from 5 HPI to 120 HPI, gives an impression that the bacterial number inside the cells increased more than its doubling time.

We have added the modified plot. As suggested, the CFU of Mtb within HepG2, PHCs, THP-1, RAW 264.7 and BMDMs have been included in the revised version (Supplementary Figure 4 D-H)

(9) What is the effect of C45 and T863 on Mtb growth invitro? The effect of C45 and T863 on Mtb growth invitro should be shown to be ruled out. The representative image in Figure 5F is DMSO or C45 treated cells panel? Please specify it.

As per the reviewer’s suggestion we have seen the effect of C45 (30 µM) and T863 (25 µM) on Mtb growth in vitro and did not find any difference in the growth kinetics. The representative image in Figure 5F is DMSO treated cells.

Author response image 2.

Growth kinetics of Mtb in 7H9 medium with DMSO, C75 and T863

(10) Supplementary Figure 6B: Correct the Y-axis label from mRNA levels to Fold change (normalised to control). Please do similar changes wherever required.

We have made the necessary changes as per the suggestion of the reviewer.

(11) Figure 7B and 7C: How was the normalization performed? Is the data normalized to the number of bacteria that entered the specific cell type or was normalized at 48hrs with respect to DMSO? DMSO alone data should be shown.

In the drug tolerance assays, we have calculated the ratio of the bacterial burden in hepatocytes treated with drugs compared to hepatocytes treated with DMSO. The infection was given for 48 hours post which the infected cells were treated with the mentioned concentrations of isoniazid and rifampicin for 24 hours. CFU enumeration was conducted after this 24 hour. Figure 7A gives a schematic of the experimental set up.

% Tolerant Bacterial population= [A/B X 100] % where A is the CFU of Mtb from infected hepatocytes treated with drug and B is the CFU of Mtb infected cells treated with DMSO.Thus the effect of MOI is negated.

To provide further credence to the CFU data, we have analysed these studies using microscopic studies as well, where no cell death was observed under the conditions. Mouse BMDMs were as a macrophage control. We have calculated the % tolerance as ratio by measuring the mean fluorescent intensity of GFP-Mtb per hepatocyte treated with drug to MFI of GFP-Mtb per hepatocyte treated with DMSO (control). More than 20 fields, each consisting of more than 4 infected cells have been used for analysis providing additional evidence of less killing of Mtb in hepatocytes compared to BMDMs with anti-TB drugs. All these details are included in the manuscript.

(12) While authors have shown the changes in mRNA levels of CYP3A4, CYP3A43, NAT2, the protein or activities of some of these should be measured to verify the effect.

Currently studies are underway to understand the activities of the key proteins involved in isoniazid and rifampicin metabolism and will be published as a separate manuscript.

Reviewer #3 (Recommendations for the authors):

Additional comments are:

• Figure 2D, the 20X and 40X magnifications do not look appreciably different in size. Please double-check that the correct images were used.

We thank the reviewer for pointing it out, we havecorrected it in the revised version.

• Lines 162-164: The authors state almost 100% purity. However, the contour plot in 2F appears to show 2 cell populations. Figure 2G is missing a legend of which colors correspond to which staining (and again there appears to be highly variable staining).

We agree with the reviewer that there are two contours observed in Figure 2F. Although both the contours are positive for ASPGR1 protein, but the level of expression of the ASPGR1 protein is variable. The corresponding confocal image (Nucleus stained by DAPI and ASPGR1 stained with ASPGR1 antibody with Alexa fluor 555 conjugated secondary antibody) also indicates a variable staining of isolated primary hepatocytes, where some cells give a stronger intensity signal than the other cells, further visually confirming our statement. Moreover, several studies show differential expression of ASPGR1 protein in hepatocyte like cells (PMID: 27143754)

To further clarify and be more specific with respect to the identity of the hepatocytes, we have stained primary hepatocytes from infected mouse livers with Albumin antibody (a stable marker for hepatocytes) and Ag85B (2J)

Multiple figures throughout the manuscript, including this one, would benefit from the use of arrows to depict what is described in the legend and text more clearly, and the use of higher power insets to better define cell architecture. Finally, some images appear blurry to the eye. Improvements are needed throughout.

As per the suggestion, we have modified the figures and figure legends for better clarity.

• Lines 153-155. Albumin, AST and GGT appear to be significantly up at week 8, contradicting the statement that there is no change until week 10.

We thank the reviewer for poiting it out and  have made suitable changes in the write up

• Lines 203-205: The authors state earlier that bacteria survive in macrophage phagosomes. Do the authors know the niche for bacteria in hepatocytes that enable them to continue to grow? Transcriptome data from HepG2 cells suggest perhaps a phagosomal pathway?

We thank the reviewer for this insightful question. As rightly pointed out by the reviewer, transcription data indeed suggests changes in several important pathways like macroautophagy, golgi vesicular transport and vacuolar transport, which can affect the subcellular localisation of Mtb within hepatocytes. High resolution microscopic studies with respect to the subcellular localisation of labelled Mtb within Primary hepatocytes, HepG2 and THP-1 has been conducted and the % colocalization within different intra-cellular compartments have been measured. The image of colocalization of labelled Mtb within PHCs is shown below along with the % colocalization within various compartments in PHCs, HepG2 and THP-1 is added. 

Author response image 3.

Colocalisation of Mtb-GFP with various intra-cellular markers within PHCs.

Author response image 4.

Percentage Colocalisation of Mtb-GFP with various intra-cellular markers within PHCs, HepG2 and THP-1.

• Validation of some critical genes found in the HepG2 cells should be done by qRTPCR in primary hepatocytes.

qRT-PCR analysis of some of the key genes in HepG2 have been validated in primary hepatocytes at 24 hours post infection. Majority of the genes show a similar trend.

Author response image 5.

Gene expression analysis of the mentioned genes in Mtb infected PHCs as compared to the uninfected control.

• Lines 259-260: The authors state a high degree of co-localization. The photomicrograph of a single cell in Fig. 5D is not convincing. I'm not even sure that they are really in the same subcellular compartment. Co-localization stated in Fig. S8B is also not convincing as shown.

The image currently shown in figure 3D is a maximum intensity projection image of multiple z-stacks encompassing the entire cell.

We agree with the reviewer with respect to figure Fig S8B and will modify the text and the figure legend accordingly.

Copywriting edits:

• It is difficult to see individual gene names in Figures 4D and 4E. A higher resolution or larger font would be appreciated for the reader.

An excel file with the top differentially regulated genes at both 0 hours post infection and 48 hours post infection has been added.

• Figure 5A has a shadow on the top right image.

We have changed the image in the revised manuscript

• Figure 5E is difficult to read the labels on the axes; it would be better in general to make the labels separately instead of relying on the graphing software, since these labels can get stretched when the size of the graph is modified.

We agree with the reviewer and have made necessary changes.

• Line 163: should be "percent" and not "perfect."

We thank the reviewer for pointing it out and have corrected it

• Line 190: is missing a period at the end of the sentence "...for further experiments"

We thank the reviewer for pointing it out and have corrected it

• Line 332: should be "hepatocytes" instead of "hepatoctyte" [sic]

We thank the reviewer for pointing it out and have corrected it

Author response:

Reviewer #1 (Public Review):

Summary:

Li et al investigated how adjuvants such as MPLA and CpG influence antigen presentation at the level of the Antigen-presenting cell and MHCII : peptide interaction. They found that the use of MPLA or CpG influences the exogenous peptide repertoire presented by MHC II molecules. Additionally, their observations included the finding that peptides with low-stability peptide:MHC interactions yielded more robust CD4+ T cell responses in mice. These phenomena were illustrated specifically for 2 pattern recognition receptor activating adjuvants. This work represents a step forward for how adjuvants program CD4+ Th responses and provides further evidence regarding the expected mechanisms of PRR adjuvants in enhancing CD4+ T cell responses in the setting of vaccination.

Strengths:

The authors use a variety of systems to analyze this question. Initial observations were collected in an H pylori model of vaccination with a demonstration of immunodominance differences simply by adjuvant type, followed by analysis of MHC:peptide as well as proteomic analysis with comparison by adjuvant group. Their analysis returns to peptide immunization and analysis of strength of relative CD4+ T cell responses, through calculation of IC:50 values and strength of binding. This is a comprehensive work. The logical sequence of experiments makes sense and follows an unexpected observation through to trying to understand that process further with peptide immunization and its impact on Th responses. This work will premise further studies into the mechanisms of adjuvants on T cells.

Weaknesses:

Comment 1. While MDP has a different manner of interaction as an adjuvant compared to CpG and MPLA, it is unclear why MDP has a different impact on peptide presentation and it should be further investigated, or at minimum highlighted in the discussion as an area that requires further investigation.

Thank you for the suggestion. We investigated the reasons for the different effects of MDP on peptide presentation compared with those of CpG and MPLA. We found that the expression of some proteins involved in antigen processing and presentation, such as CTSS, H2-DM, Ifi30, and CD74, was substantially lower in the MDP-treated group than in the CpG- and MPLA-treated groups. To further confirm whether these proteins play a key role during adjuvant modification of peptide presentation, we knocked down them using shRNA and then performed immunopeptidomics. The original mass spectra and peptide spectrum matches have been deposited in the public proteomics repository iProX (https://www.iprox.cn/page/home.html) under accession number IPX0007611000. Unfortunately, the expected results for peptide presentation repertoires were not observed. Thus, we hypothesized that the different effects of MDP on peptide presentation might not result from differences in protein expression. We cannot exclude the possibility that some other proteins that may be important in this process were overlooked. We are still working on the mechanisms and do not have an exact conclusion. Thus, we did not present related data in this manuscript.

The related statements were added in the Discussion section on page 13, lines 292–299: “In this study, we found that the peptide repertoires presented by APCs were significantly affected by the adjuvants CpG and MPLA, but not MDP. All three adjuvants belong to the PRR ligand adjuvant family. CpG and MPLA bind to TLRs and MDP is recognized by NOD2. Although the receptors are different, many common molecules are involved both in TLR and NLD pathway activation. Unfortunately, we did not demonstrate why the MDP had different impacts on peptide presentation compared with other adjuvants. Further investigation is required to clarify the mechanism by which MPLA, CpG, and MDP adjuvants modulate the presentation of peptides with different stabilities.”

Comment 2. It is alluded by the authors that TLR activating adjuvants mediate selective, low affinity, exogenous peptide binding onto MHC class II molecules. However, this was not demonstrated to be related specifically to TLR binding. I wonder if some work with TLR deficient mice (TLR 4KO for example) could evaluate this phenomenon more specifically.

Thank you for the suggestion. This is an important point that was overlooked in this study. Based on published research on the mechanisms of PRR adjuvants, CpG and MPLA, we believe that the effect of CpG and MPLA on APCs-selective epitope presentation needs to be bound to the corresponding receptor, although we did not give a definitive conclusion in the manuscript.

To confirm the TLR-activating adjuvants affecting peptides presented on MHC molecules specifically through TLR binding, we have used CRISPR-cas9 to knock out TLR4 and TLR9 of A20 cells and repeated the experiments, as suggested. We chose TLR4- and TLR9- knockout A20 cell lines instead of TLR-deficient mice because a large number of APCs are required for immunopeptidomics. Moreover, the data observed in this study were based on the A20 cell line. However, these experiments are time-consuming. Unfortunately, we were unable to provide timely data. In addition, we believe that elucidating the downstream molecular mechanisms of TLR activation is necessary, as mentioned in comment 1. All these data will be combined and reported in our upcoming publications.

Comment 3. It is unclear to me if this observation is H pylori model/antigen-specific. It may have been nice to characterize the phenomenon with a different set of antigens as supplemental. Lastly, it is unclear if the peptide immunization experiment reveals a clear pattern related to high and low-stability peptides among the peptides analyzed.

Q1: It is unclear to me if this observation is H. pylori model/antigen-specific. It may have been nice to characterize the phenomenon with a different set of antigens as supplemental.

Thank you for the comment. To confirm the effect of the adjuvant on the exogenous peptide repertoire presented by MHC II molecules, a set of antigens from another bacterium, Pseudomonas aeruginosa, was used, and the experiments were repeated. The A20 cells were treated with CpG and pulsed with Pseudomonas aeruginosa antigens. Twelve hours later, MHC-II–peptide complexes were immunoprecipitated, and immunopeptidomics were performed. The data are shown below (Author response image 1). Information on the MHC-peptides from Pseudomonas aeruginosa is given in the Supplementary Table named “Table S3 Response to comment3”. A total of 713 and 205 bacterial peptides were identified in the PBS and CpG groups (Author response image 1A). The number of exogenous peptides in the CpG-treated group was significantly lower than that in the PBS-treated control group (Author response image 1B). A total of 568 bacterial peptides were presented only in the PBS group; 60 bacterial peptides were presented in the CpG-treated group, and 145 bacterial peptides were presented in both groups (Author response image 1C). We then analyzed the MHC-binding stability of the peptides present in the adjuvant-treated group and that of the peptide-deficient after adjuvant stimulation using the IEDB website. We found that the IC50 of the peptides in the adjuvant-treated group were much higher than those of the deficient peptides, which indicated that the peptides presented in the CpG-treated groups have lower binding stability for MHC-II (Author response image 1D). These results indicate that CpG adjuvant affects the presentation of exogenous peptides with high binding stability, which is consistent with the data reported in our manuscript. Using another set of antigens, we confirmed that our observations were not H. pylori model- or antigen-specific.

Author response image 1.

MHC-II peptidome measurements in adjuvant-treated APCs pulsed with Pseudomonas aeruginosa antigens. (A) Total number of bacterial peptides identified in the PBS- and CpG-treated groups. (B) The number and length distribution of bacterial peptides in different groups were compared. (C) Venn diagrams showing the distribution of bacterial peptides in different groups. (D) IC50 of the presented, deficient, and co-presented peptides post-adjuvant stimulation from immunopeptidome binding to H2-IA and H2-IE were predicted using the IEDB website. High IC50 means low binding stability. *p<0.05, **p<0.01.

Q2: Lastly, it is unclear if the peptide immunization experiment reveals a clear pattern related to high and low-stability peptides among the peptides analyzed.

In this study, we used a peptide immunization experiment to evaluate the responses induced by the screened peptides with different stabilities. In addition to this method, tetramer staining and ELISA have been used to assess epitope-specific T-cell proliferation and cytokine secretion. Among these, tetramer staining is often used in studies involving model antigens. However, as many peptides were screened in our study, synthesizing a sufficient number of tetramers was difficult. However, we believe that the experimental data obtained in this study support the conclusion. Nevertheless, we agree that more methods applied will make the pattern more clearly.

Reviewer #2 (Public Review):

Adjuvants boost antigen-specific immune responses to vaccines. However, whether adjuvants modulate the epitope immunodominance and the mechanisms involved in adjuvant's effect on antigen processing and presentation are not fully characterized. In this manuscript, Li et al report that immunodominant epitopes recognized by antigen-specific T cells are altered by adjuvants.

Using MPLA, CpG, and MDP adjuvants and H. pylori antigens, the authors screened the dominant epitopes of Th1 responses in mice post-vaccination with different adjuvants and found that adjuvants altered antigen-specific CD4+ T cell immunodominant epitope hierarchy. They show that adjuvants, MPLA and CpG especially, modulate the peptide repertoires presented on the surface of APCs. Surprisingly, adjuvant favored the presentation of low-stability peptides rather than high-stability peptides by APCs. As a result, the low stability peptide presented in adjuvant groups elicits T cell response effectively.

Thanks a lot for your comments.

Reviewer #1 (Recommendations For The Authors):

Recommendation 1. Figure 6: The peptides considered low affinity- it would be helpful to specify from which adjuvant they were collected from. When they are pooled it is unclear if we are analyzing peptides collected from adjuvanting with any of the three adjuvants studied.

Thank you for the suggestion. The related description in Figure 6 has been modified in the revised manuscript. Data for the peptides identified from the adjuvants MPLA- and CpG-treated groups are shown separately.

Recommendation 2. It is unclear to me why the A20 cell line is less preferred to the J774 line for the immunopeptidome analysis - can the authors expand on this?

We apologize for not clearly explaining this in the original manuscript. In fact, the A20 cell line is better than J774A.1 cell line for immunopeptidomics experiments. Compared to J774A.1 cells, more MHC-II peptides were obtained from a smaller number of A20 cells using immunopeptidomics. At the beginning of this study, we chose the J774A.1 cell line as it is a macrophage cell line. J774A.1 cells (up to 5×108) were pulsed with the antigens, and MHC-II–peptide complexes were eluted from the cell surface for immunopeptidomics. Unfortunately, only a few hundred peptides from the host were detected and no exogenous peptides were detected. Next, we tested the A20 cell line. In total, 108 A20 cells were used in this study. More than 3500 host peptides and approximately 50 exogenous peptides have been identified. These data indicate that the A20 cell line was better.

To investigate the reasons for this, we detected MHC-II expression on cell surfaces using FACS. Our purpose was to elute peptides from MHC–peptide complexes present on the cell surface. Low MHC expression resulted in the elution of a few peptides. We found the MFI of MHC-II molecules on J774A.1 cell is about 500; however, the MFI of MHC-II molecules on A20 cells is more than 300,000. These data indicate that MHC-II expression on A20 cells was much higher than that on J774A.1 cells. J774A.1 cell is a macrophage cell line. Macrophages have excellent antigen phagocytic capabilities; however, their ability to present antigens is relatively weak. MHC molecules on the macrophage cell surface can be upregulated in the stimulation of some cytokines, for example, IFN-γ. In this study, we used adjuvants as stimulators and did not want to use additional cytokine stimulators. Thus, J774A.1 cells were not used in the present study.

The related statements are reflected on page 6 lines 120–128 “We also selected another H-2d cell J774A.1, a macrophage cell line, for immunopeptidome analysis in this study. Briefly, 5×108 J774A.1 cells were used for immunopeptidomics. Moreover, fewer than 350 peptides were observed at a peptide spectrum match (PSM) level of < 1.0% false discovery rate (FDR). However, more than 5500 peptides were detected in 108 A20 cells at FDR < 1.0% (Figure S2A). CD86 and MHC-II molecule expression on J774A.1 cells was substantially lower than that on A20 cells (Figure S2B). Low MHC-II expression on J774A.1 cells could be the reason for the lack of peptides identified by LC–MS/MS. Thus, A20 cells instead of J774A.1 cells were used for the subsequent experiments.”

Recommendation 3. Lines 172-177, can more details be provided about the whole proteome analysis? The plots are shown for relative representation of protein expression to PBS, but it is unclear to me what examples of these proteins are (IFN pathway, Ubiquitination pathway). Could these be confirmed by protein expression analyses in supplemental?

Thank you for the suggestion. In this study, we conducted whole proteome analysis to investigate changes in protein expression across different pathways in the adjuvant groups. Through KEGG enrichment analysis, we compared the differential expression of MHC presentation pathway proteins (such as H2-M, Ifi30, CD74, CTSS, proteasome, and peptidase subunits) between the PBS- and adjuvant-treated groups using our proteome data. In addition, we focused on IFN and ubiquitination pathways that play crucial roles in antigen presentation modification and immune response. The proteins and their relative expression in these pathways are shown in Figure S4B. Details regarding the protein names and expressions are provided in Supplemental Table S2 of the revised manuscript.

The original statements in the results “Then, we analyzed the whole proteome data to determine whether the proteins involved in antigen presentation and processing were altered. We found that proteins involved in antigen processing, peptidase function, ubiquitination pathway, and interferon (IFN) signaling were altered post adjuvants treatment, especially in MPLA and CpG groups (Figure 5C; Figure S4B and S4C). These data suggest that adjuvants MPLA and CpG may affect the antigen processing of APCs, resulting in fewer peptides presentation.” This has been revised on page 8 lines 172–182 as “We then investigated whole-proteome data to determine the evidence of adjuvant modification of antigen presentation. We focused on the proteins involved in antigen processing, peptidase function, ubiquitination pathway, and IFN signaling. The ubiquitination pathway and IFN signaling play crucial roles in the modification of antigen presentation and immune responses. Through KEGG enrichment analysis, we found that many proteins involved in antigen processing, peptidase function, ubiquitination pathways, and IFN signaling were altered after adjuvant treatment, particularly in the MPLA- and CpG-treated groups (Figure 5C; Figure S4B). The expression of each protein is shown in Figure S4C and Supplementary Table 2. These data suggest that MPLA and CpG adjuvants may affect the antigen processing of APCs, resulting in fewer peptide presentations.”

Recommendation 4. Lines 212-218: I think there needs to be more discussion of interpretation here. Only one of the low-stability peptides required low concentrations for CD4+ T cell responses in vitro. What about the other peptides in the analysis? Perhaps if the data is taken together there is not a clear pattern?

Thank you for the comment. In this study, epitope-specific CD4+ T-cells were expanded in vitro from the spleens of peptide-pool-immunized mice. T-cell responses to individual peptides were detected using ICS and FACS. Only one peptide, recA #23, with low binding stability, and one high-stability peptide, ureA #2, induced effective T-cell responses. Peptide ureA #3 with high stability induces low Th1 responses. The other peptides cannot induce CD4+ T-cell secreting IFN-γ (Data are shown in Author response image 2). Thus, we compared the strength of IFN-γ responses induced by these three peptides at a set of low concentrations. Data for other peptides without any response could not be taken together.

Author response image 2.

The expanded CD4+T cells from peptides immunized mice were screened for their response to the peptides in an ICS assay.

In this study, we used a peptide pool containing four low-stability peptides to vaccinate mice; however, only one peptide induced an effective CD4+ T-cell response. We speculate that the possible reasons are as follows. First, the number of peptides used for vaccination is too small. Only four low-stability peptides were synthesized and used to immunize mice. Three of these could not induce an effective T-cell response, possibly because of their low immunogenicity. If more peptides are synthesized and used, more peptides that induce T-cell responses may be observed. Second, epitope-specific T-cell responses are variable. Responses to the subdominant peptides can be inhibited by the dominant peptide. The subdominant peptide can become dominant by changing the peptide dose or in the absence of the dominant peptide. Thus, we believe that responses to the other three peptides may be detected if mice are immunized with a peptide pool that does not contain a response epitope.

The corresponding statements have been added to the Discussion section on page 13 lines 287–291 as “Unfortunately, only one peptide, recA #23, with low binding stability and induced significant Th1 responses, was identified in this study. To further confirm that low-stability peptides can induce stronger and higher TCR-affinity antigen-specific T-cell clonotype responses than high-stability peptides, further studies should monitor more peptides with different stabilities.”

Recommendation 5. There are some areas where additional editing to text would be beneficial due to grammar (eg lines 122-126; line 116, etc).

The manuscript has been edited by a professional language editing company.

Reviewer #2 (Recommendations For The Authors):

Recommendation 1. It is interesting that there was no difference in IFNg responses induced by different adjuvants.

Thank you for the comment. Possible reasons for the lack of difference in IFN-γ responses could be as follows. First, all adjuvants used in this study have been confirmed to effectively induce Th1 responses. Second, in this study, IFN-γ responses were examined using expanded antigen-specific T cells in vitro. The in vitro cell expansion efficiency may have affected these results.

Recommendation 2. The data to support the claim that changes in exogenous peptide presentation among adjuvant groups were not due to differences in antigen phagocytosis is insufficient.

Thank you for the comment. In this study, proteomics of A20 cells pulsed with antigens in different adjuvant-treated groups were used to determine exogenous antigens phagocytosed by cells. In addition, we used fluorescein isothiocyanate (FITC)-labeled OVA to pulse APCs and detected antigen phagocytosis by APCs after treatment with different adjuvants. The MFI of FITC was detected by FACS at different time points. The data are shown below (Author response image 3). No obvious differences in FITC MFI were detected after adjuvant stimulation, indicating that antigen phagocytosis among the adjuvant groups was almost the same.

A20 cells, used as APCs, are the B-cell line. Antigen recognition and phagocytosis by B-cells depends on the B-cell receptor (BCR) on the cell surface. The ability of BCRs to bind to different antigens varies, leading to significant differences in the phagocytosis of different antigens by B-cells. Therefore, detecting the phagocytosis of a single antigen may not reflect the overall phagocytic state of the B-cells. Thus, in this study, we used proteomics to detect exogenous proteins in B-cells pulsed with H. pylori antigens, which contain thousands of components, to evaluate their overall phagocytic capacity. Only the proteomic data are presented in our manuscript.

Author response image 3.

Antigen phagocytosis of A20 cells were measured using FITC-labeled OVA. (A) A20 cells were pulsed with FITC-labeled OVA. MFI of FITC was measured after 1 h. (B) MFI of FITC was examined post the stimulation of adjuvants at different time points.

Recommendation 3. It is not clear how MPLA, CpG, and MDP adjuvants modulate the presentation of low vs high stability peptides.

Thank you for pointing this out. We acknowledge that we did not clarify the mechanisms by which adjuvants affect the stability of the peptide presentations of APCs.

We performed experiments to detect the expression of proteins involved in antigen processing and presentation in the different adjuvant-treated groups. Furthermore, shRNAs were used to knock down the expression of key molecules. Immunopeptidomics was used to detect peptide presentation. Unfortunately, the expected results for peptide presentation repertoires were not observed. We are still working on the mechanisms.

Please also see our response to comment 1 of reviewer 1

The related statements were added in the Discussion section on page 13, lines 292–299: “In this study, we found that the peptide repertoires presented by APCs were significantly affected by the adjuvants CpG and MPLA, but not MDP. All three adjuvants belong to the PRR ligand adjuvant family. CpG and MPLA bind to TLRs and MDP is recognized by NOD2. Although the receptors are different, many common molecules are involved both in TLR and NLD pathway activation.  Unfortunately, we did not demonstrate why the MDP had different impacts on peptide presentation compared with other adjuvants. Further investigation is required to clarify the mechanism by which MPLA, CpG, and MDP adjuvants modulate the presentation of peptides with different stabilities.”

Author response:

The following is the authors’ response to the original reviews

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

Govindan and Conrad use a genome-wide CRISPR screen to identify genes regulating retention of intron 4 in OGT, leveraging an intron retention reporter system previously described (PMID: 35895270). Their OGT intron 4 reporter reliably responds to O-GlcNAc levels, mirroring the endogenous splicing event. Through a genome-wide CRISPR knockout library, they uncover a range of splicing-related genes, including multiple core spliceosome components, acting as negative regulators of OGT intron 4 retention. They choose to follow up on SFSWAP, a largely understudied splicing regulator shown to undergo rapid phosphorylation in response to O-GlcNAc level changes (PMID: 32329777). RNA-sequencing reveals that SFSWAP depletion not only promotes OGT intron 4 splicing but also broadly induces exon inclusion and intron splicing, affecting decoy exon usage. While this study offers interesting insights into intron retention and O-GlcNAc signaling regulation, the RNA sequencing experiments lack the essential controls needed to provide full confidence to the authors' conclusions. 

Strengths: 

(1) This study presents an elegant genetic screening approach to identify regulators of intron retention, uncovering core spliceosome genes as unexpected positive regulators of intron retention. 

(2) The work proposes a novel functional role for SFSWAP in splicing regulation, suggesting that it acts as a negative regulator of splicing and cassette exon inclusion, which contrasts with expected SR-related protein functions. 

(3) The authors suggest an intriguing model where SFSWAP, along with other spliceosome proteins, promotes intron retention by associating with decoy exons. 

We thank the reviewer for recognizing and detailing the strengths of our manuscript. 

Weaknesses: 

(1) The conclusions on SFSWAP impact on alternative splicing are based on cells treated with two pooled siRNAs for five days. This extended incubation time without independent siRNA treatments raises concerns about off-target effects and indirect effects from secondary gene expression changes, potentially limiting confidence in direct SFSWAP-dependent splicing regulation. Rescue experiments and shorter siRNA-treatment incubation times could address these issues. 

We repeated our SFSWAP knockdown analysis and analyzed both OGT e4-e5 junction splicing and SFSWAP transcript levels by RT-qPCR (now included in Sup. Fig. S4) from day 2 to day 5 post siRNA treatment. We observed that the time point at which OGT intron 4 removal increases (day 2) coincides with the time at which SFSWAP transcript levels start decrease, consistent with a direct effect of SFSWAP knockdown on OGT intron 4 splicing. Moreover, the effect of SFSWAP knockdown on OGT intron 4 splicing peaks between day 4-5, supporting our use of these longer time points to cast a wide net for SFSWAP targets.

(2) The mechanistic role of SFSWAP in splicing would benefit from further exploration. Key questions remain, such as whether SFSWAP directly binds RNA, specifically the introns and exons (including the decoy exons) it appears to regulate. Furthermore, given that SFSWAP phosphorylation is influenced by changes in O-GlcNAc signaling, it would be interesting to investigate this relationship further. While generating specific phosphomutants may not yield definitive insights due to redundancy and also beyond the scope of the study, the authors could examine whether distinct SFSWAP domains, such as the SR and SURP domains, which likely overlap with phosphorylation sites, are necessary for regulating OGT intron 4 splicing. 

We absolutely agree with the reviewer that the current work stops short of a detailed mechanistic study, and we have made every attempt to be circumspect in our interpretations to reflect that limitation. In addition, we are very interested in delving more deeply into the mechanistic aspects of this regulation. In fact, we have initiated many of the experiments suggested by the reviewer (and more), but in each case, rigorous interpretable results will require a minimum another year’s time. 

For example, we have used crosslinking and biotin labeling techniques (using previously available reagents from Eclipsebio) to test whether SFSWAP binds RNA. The results were negative, but the lack of strong SFSWAP antibodies required that we use a transiently expressed myc-tagged SFSWAP. Therefore, this negative result could be an artifact of the exogenous expression and/or tagging. Given the difficulties of “proving the negative”, considerably more work will be required to substantiate this finding. As another example, we intend to develop a complementation assay as suggested. For an essential gene, the ideal complementation system employs a degron system, and we have spent months attempting to generate a homozygous AID-tagged SFSWAP. Unfortunately, we so far have only found heterozygotes. Of course, this could be because the tag interferes with function, the insert was not efficiently incorporated by homologous repair, or that we simply haven’t yet screened a sufficient number of clones. We’re confident that these technical issues that can be addressed, but they will take a significant amount of time to resolve. While we would ideally define a mechanism, we think that the data reported here outlining functions for SFSWAP in splicing represent a body of work sufficient for publication. 

(3) Data presentation could be improved (specific suggestions are included in the recommendations section). Furthermore, Excel tables with gene expression and splicing analysis results should be provided as supplementary datasheets. Finally, a more detailed explanation of statistical analyses is necessary in certain sections. 

We have addressed all specific suggestions as detailed in the recommendations below.

Reviewer #2 (Public review): 

Summary: 

The paper describes an effort to identify the factors responsible for intron retention and alternate exon splicing in a complex system known to be regulated by the O-GlcNAc cycling system. The CRISPR/Cas9 system was used to identify potential factors. The bioinformatic analysis is sophisticated and compelling. The conclusions are of general interest and advance the field significantly. 

Strengths: 

(1) Exhaustive analysis of potential splicing factors in an unbiased screen. 

(2) Extensive genome wide bioinformatic analysis. 

(3) Thoughtful discussion and literature survey. 

We thank the reviewer for recognizing and detailing the strengths of our manuscript. 

Weaknesses: 

(1) No firm evidence linking SFSWAP to an O-GlcNAc specific mechanism. 

We couldn’t agree more with this critique. Indeed, our intention at the outset for the screen was to find an O-GlcNAc sensor linking OGT splicing with O-GlcNAc levels. As often occurs with high-throughput screens, we didn’t find exactly what we were looking for, but the screen nonetheless pointed us to interesting biology. Prompted by our screen, we describe new insights into the function of SFSWAP a relatively uncharacterized essential gene. Currently, we are testing other candidates from our screen, and we are performing additional studies to identify potential O-GlcNAc sensors.  

(2) Resulting model leaves many unanswered questions. 

We agree (see Reviewer 1, point 2 response).  

Reviewer #3 (Public review): 

Summary: 

The major novel finding in this study is that SFSWAP, a splicing factor containing an RS domain but no canonical RNA binding domain, functions as a negative regulator of splicing. More specifically, it promotes retention of specific introns in a wide variety of transcripts including transcripts from the OGT gene previously studied by the Conrad lab. The balance between OGT intron retention and OGT complete splicing is an important regulator of O-GlcNAc expression levels in cells. 

Strengths: 

An elegant CRISPR knockout screen employed a GFP reporter, in which GFP is efficiently expressed only when the OGT retained intron is removed (so that the transcript will be exported from the nucleus to allow for translation of GFP). Factors whose CRISPR knockdown causes decreased intron retention therefore increase GFP, and can be identified by sequencing RNA of GFP-sorted cells. SFSWAP was thus convincingly identified as a negative regulator of OGT retained intron splicing. More focused studies of OGT intron retention indicate that it may function by regulating a decoy exon previously identified in the intron, and that this may extend to other transcripts with decoy exons. 

We thank the reviewer for recognizing the strengths of our manuscript. 

Weaknesses: 

The mechanism by which SFSWAP represses retained introns is unclear, although some data suggests it can operate (in OGT) at the level of a recently reported decoy exon within that intron.

Interesting/appropriate speculation about possible mechanisms are provided and will likely be the subject of future studies. 

We completely agree that this is a limitation of the current study (see above). Now that we have a better understanding of SFSWAP functions, we will continue to explore SFSWAP mechanisms as suggested. 

Overall the study is well done and carefully described but some figures and some experiments should be described in more detail. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

(1) Clarify and add missing statistical details across the figures. For example, Figure S2 lacks statistical comparisons, and in Figures 4A and 4C the tests applied should be specified in the legend. 

We have added appropriate statistical analysis wherever missing and edited figure legends to specify the tests used.

(2) The authors are strongly encouraged to provide detailed tables of gene expression and alternative splicing analyses from RNA-Seq experiments (e.g., edgeR, rMATS, Whippet, and MAJIQ), as this would enhance transparency and facilitate data interpretation. 

We have added tables for gene expression and alternate splicing analysis as suggested (Suppl. tables 3-

6).

(3) Although the legend sometimes indicates differently (e.g., Figure 3b, 5a, 5c, etc), the volcano plots showing the splicing changes do not contain a cutoff for marginally differential percent spliced in or intron retention values. 

The legends have been edited to reflect the correct statistical and/or PSI cutoffs.

(4) For consistency, use a consistent volcano plot format across all relevant figures (Figures 3b, 5a-c, S3, S4, S7, and S8), including cutoffs for differential splicing and the total count of up- and down-regulated events. 

Due to different statistical frameworks and calculations employed by different alternate splicing pipelines, we could not use the same cutoffs for different pipelines.  However, we have now indicated the number of up- and down-regulated events for consistency among the volcano plots.

(5) What is the overlap of differentially regulated events between the different analytical methodologies applied? 

We analyzed the degree of overlap between the three pipelines used in the paper using a Venn diagram (added to Suppl. Fig. S7). However, as widely reported in literature (e.g., Olofsson et al., 2023; Biochem Biophys Res Commun. 2023; doi: 10.1016/j.bbrc.2023.02.053.), the degree of overlap between pipelines is quite low.

(6) To further substantiate your conclusions, additional validations of RNA-Seq splicing data, ideally visualized on an agarose gel, would be valuable, especially for exons and introns regulated by SFSWAP, and particularly for OGT decoy exons in Figure 4c. 

We have not included these experiments as we focused on other critiques for this resubmission. Because the RNA-seq, RT-PCR and RT-qPCR data all align, we are confident that the products we are seeing are correctly identified and orthogonally validated (Figs 2d, 4a, 4b, and 4c).  

(7) It would be more informative if the CRISPR screen data were presented in a format where both the adjusted p-value and LFC values of the hits are presented. Perhaps a volcano plot? 

We have now included these graphs in revised Supplementary Figure S2. 

(8) In Figure 2d, a cartoon showing primer binding sites for each panel could aid interpretation, particularly in explaining the unexpected simultaneous increase in OGT mRNA and intron retention upon SFSWAP knockdown. 

We have added a cartoon showing primer binding sites similar to that shown in Fig. 4a.

(9) Page 9, line 1, states that SFSWAP autoregulates its expression by controlling intron retention. Including a Sashimi plot would provide visual support for this claim. 

The data suggesting that SFSWAP autoregulates its own transcript abundance were reported in Zachar et al. (1994), not from our own studies. Validation of those data with our RNA-seq data is confounded by the fact that we are using siRNAs to knockdown the SFSWAP RNA at the transcript level (Fig. S15). 

(10) In the legend of Figure S2 the authors state that negative results are inconclusive because RNA knockdowns are not verified by western blotting or qRT-PCR. This is correct, but the reviewer would also argue that the positive results are also inconclusive as they are not supported by a rescue experiment to confirm that the effect is not due to off-target effects. 

This is a fair point with respect to the siRNA experiments on their own. However, the CRISPR screen was performed with sgRNAs, and MAGeCK RRA scores are high only for those genes that have multiple sgRNAs that up-regulate the gene. Examination of the SFSWAP sgRNAs individually shows that three of four SFSWAP sgRNAs had false discovery rates ≤10^-42 for GFP upregulation. Thus, the siRNAs provide an additional orthogonal approach. It seems unlikely that the siRNAs, and three independent sgRNAs will have the same off-target results. Thus, these combined observations support the conclusion that SFSWAP loss leads to decreased OGT intron retention.  

(11) For clarity in Figure 3a, consider using differential % spliced in or intron retention bar plots with directionality (positive and negative axis) and labeling siSFSWAP as the primary condition. 

(12) Consider presenting Figure 5D as a box plot with a Wilcoxon test for statistical comparison. 

For both points 11 and 12, we have tried the graphs as the reviewer suggested. While these were good suggestions, in both cases we felt that the original plots ended up presenting a clearer presentation of the data (see Author response image 1).

Author response image 1.

(13) Please expand the Methods section to detail the Whippet and MAJIQ analyses. 

We have expanded the methods section to include additional details of the alternate splicing analysis.

(14) Include coordinates for the four possible OGT decoy exon combinations analyzed in the Methods section. 

We have added the coordinates of all four decoy forms in the methods section.  

(15) A section on SFSWAP mass spectrometry is listed in Methods but is missing from the manuscript. 

This section has now been removed.

Reviewer #2 (Recommendations for the authors): 

This is an excellent contribution. The paper describes an effort to identify the factors responsible for intron retention and alternate exon splicing in a complex system known to be regulated by the O-GlcNAc cycling system. The CRISPR/Cas9 system was used to identify potential factors. The bioinformatic analysis is sophisticated and compelling. The conclusions are of general interest and advance the field significantly. 

Some specific recommendations. 

(1) The plots in Figure 3 describing SI and ES events are confusing to this reader. Perhaps the violin plot is not the best way to visualize these events. The same holds true for the histograms in the lower panel of Figure 3. Not sure what to make of these plots. 

For Figure 3b, we include both scatter and violin plots to represent the same data in two distinct ways. For Figure 3d, we agree that these are not the simplest plots to understand, and we have spent significant time trying to come up with a better way of displaying these trends in GC content as they relate to SE and RI events. Unfortunately, we were unable to identify a clearer way to present these data. 

(2) The model (Figure 6) is very useful but confusing. The legend and the Figure itself are somewhat inconsistent. The bottom line of the figure is apparent but I fear that the authors are trying to convey a more complete model than is apparent from this figure. Please revise. 

We have simplified the figure from the previous submission. As mentioned above, we admit that mechanistic details remain unknown. However, we have tried to generate a model that reflects our data, adds some speculative elements to be tested in the future, but remains as simple as possible. We are not quite sure what the reviewer was referring to as “somewhat inconsistent”, but we have attempted to clarify the model in the revised Discussion and Figure legend.  

(3) It is unclear how normalization of the RNA seq experiments was performed (eg. Figure S5 and 6).  

The normalization differences in Fig. S5 and S6 (now Fig S8 and S9) were due to scaling differences during the use of rmats2sashimiplot software. We have now replaced Fig. S5 to reflect correctly scaled images.

I am enthusiastic about the manuscript and feel that with some clarification it will be an important contribution. 

Thank you for these positive comments about our study!

Reviewer #3 (Recommendations for the authors): 

(1) In Figure 1f, it is clear that siRNA-mediated knockdown of OGT greatly increases spliced RNA as the cells attempt to compensate by more efficient intron removal (three left lanes). However, there is no discussion of the various treatments with TG or OSMI. Might quantitation of these lanes not also show the desired effects of TG and OSMI on spliced transcript levels? 

The strong effect of OGT knockdown masks the (comparatively modest) effects of subsequent inhibitor treatments on the reporter RNA. We have edited the results section to clarify this.

(2) In Figure 2c, why is the size difference between spliced RNA and intron-retained RNA so different in the GFP-probed gel (right) compared with the OGT-probed gel (left)? Even recognizing that the GFP probe is directed against reporter transcripts, and the OGT probe (I think) is directed against endogenous OGT transcripts, shouldn't the difference between spliced and unspliced bands be the same, i.e., +/- the intron 4 sequence. Also, why does the GFP probe detect the unspliced transcript so poorly? 

The fully spliced endogenous OGT mRNA is ~5.5 kb while the fully spliced reporter is only ~1.6kb, so the difference in size (the apparent shift relative to the mRNA) is quite different. Moreover, the two panels in Fig 2c are not precisely scaled to one another, so direct comparisons cannot be made. 

The intron retained isoform does not accumulate to high levels in this reporter, a phenotype that we also observed with our GFP reporter designed to probe the regulation of the MAT2A retained intron (Scarborough et al., 2021). We are not certain about the reason for these observations, but suspect that the reporter RNA’s retained intron isoforms are less stable in the nucleus than their endogenous counterparts. Alternatively, the lack of splicing may affect 3´ processing of the transcripts so that they do not accumulate to the high levels observed for the wild-type genes. 

(3) Please provide more information about the RNA-seq experiments. How many replicates were performed under each of the various conditions? The methods section says three replicates were performed for the UPF1/TG experiments; was this also true for the SFSWAP experiments?  

All RNA-seq experiments were performed in biological triplicates. We have edited the methods section to clarify this.

(4) Relatedly, the several IGV screenshots shown in Figure 3C presumably represent the triplicate RNA seq experiments. In part D, how many experiments does the data represent? Is it a compilation of three experiments? 

Fig. 3d is derived from alternate splicing analysis performed on three biological replicates. We have added the number of replicates (n=3) on the figure to clarify this. We have also noted that the three IGV tracks represent biological replicates in the Figure legend for 3c.  

(5) Please provide more details regarding the qRT-PCR experiments. 

We have provided the positions of primer sets used for RT-qPCR analysis and cartoon depictions of target sites below the data wherever appropriate.

(6) In the discussion of decoy exon function (in the Discussion section), several relevant observations are cited to support a model in which decoy exons promote assembly of splicing factors. One might also cite the finding that eCLIP profiling has found enriched binding of U2AF1 and U2AF2 at the 5' splice site region of decoy exons (reference 16). 

Excellent point. This has now been added to the Discussion. 

Minor corrections / clarifications: 

(1) In the Figure 2A legend, CRISPR is misspelled. 

Corrected.

(2) In the discussion, the phrase "indirectly inhibits splicing of exons 4 and 5, but promoting stable unproductive assembly of the spliceosome", the word "but" should probably be "by". 

Corrected.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

(1) Figure 2 and related text: it would be useful to explain more explicitly what is meant by "neurogenic" and "non-neurogenic" models. I presume that the total number of neurons in non-neurogenic models is lower than in neurogenic models because no new neurons are added. It would be useful to plot the number of GCs as a function of timesteps.

We have clarified the distinction between neurogenic and non-neurogenic models in the text (Lines 142-145), explicitly noting that in non-neurogenic models, no new GCs are added, resulting in a lower total neuron count over time. In response to the reviewer’s suggestion, we generated a plot showing the number of GCs over time (see below). Because the neurogenic model exhibits a simple linear increase, we found this plot not especially informative for inclusion in the manuscript. However, we agree with the reviewer’s later comments that similar plots are useful for interpreting specific results, and we have included those where appropriate.

Author response image 1.

Number of GCs over time for neurogenic (solid line) and non-neurogenic (dotted line) networks

(2) Figure 2F, G: memory declines dramatically when the number of GCs at enrichment onset increases beyond an optimum. Why?

We have explained the reasoning more thoroughly in the text (Lines 174-177) and added a new supplemental figure to support this reasoning (Figure S2). As the number of GCs increases, the network becomes overly inhibited and the response of abGCs to the stimuli decreases (Fig S2A). This leads to a smaller population of GCs being able to integrate with the stimulus (Fig S2B) which is expected given the activity-dependent plasticity rule. Moreover, it can be seen in Fig S2C that for networks with increasing size, the GCs that do learn only connect to MCs that are driven strongest by the stimuli until they struggle to connect to any MCs at all.

In principle, a homeostatic mechanism like synaptic scaling could reduce activity to restore balance, but such a mechanism would also likely disrupt existing memories. Alternatively, we suggest activity-dependent apoptosis as a superior homeostatic mechanism because it leads to a stable level of activity without substantially erasing existing memories.

(3) The paragraph describing synaptic connectivity of abGCs (related to Figure 2H) is confusing. What is the directionality of synapses considered here: mitral-to-granule, or granule-to-mitral? The text is opaque here. Connectivity matrix in Figure 2H: who is presynaptic, who is postsynaptic? If I understand correctly, these questions are actually irrelevant because all mitralgranule synapses in the network are reciprocal. This should be pointed out explicitly in the figure legend. Generally: the fact that the network is fully reciprocal (if I understand correctly) is very important but not stated with sufficient emphasis. It should be stated very explicitly in the text that connectivity matrices are fully reciprocal, and an equation clarifying this point should be included in Methods.

(6) Connectivity matrix: to what degree was connectivity between mitral and granule cells reciprocal (fraction of connections in either direction that were paired with a connection in the opposite direction between the same cell pair)? Was connectivity shaped by experience (enrichment) reciprocal?

(7) Directly related to the above: it would be useful to show the disynaptic connectivity matrix between mitral cells and analyze its symmetry. For the symmetric component, it should then be analyzed what fraction of this can be attributed to the reciprocal synapses, and what fraction is contributed by connectivity via different granule cells. This should then be compared to models with biologically realistic fractions of reciprocal connections. Is the model proposed here consistent with a biologically realistic fraction of reciprocal synapses between mitral-granule cell pairs?

We appreciate these insightful and detailed comments. We agree that the assumption that MC-GC synapses were fully reciprocal was not clearly stated. We now explicitly state this in the main text (lines 90-94, 369-370, Figure 2 caption) and methods (line 561), emphasize its importance. As the reviewer points out, this is a simplifying assumption and does not fully reflect the biology because not all synapses are reciprocal in the true system. We also note that our synaptic plasticity model does not break the reciprocity assumption: all connections added or pruned during learning remain reciprocal. As a result, the disynaptic connectivity matrix (Bottom panel below, MCs sorted by stimulus as shown in the top panel) is always symmetric.

We have now made these statements explicit in the main text and in the methods. Regarding functional consequences of this assumption, earlier work by our group has examined the impact of the degree of reciprocity of MC-GC synapses in a similar OB model (Chow, Wick & Riecke, Plos Comp Bio 2012). The study examined three different changes in reciprocity by (1) redirecting a fraction of the inhibitory connections of each GC to randomly chosen MCs instead of the MCs that drive that GC, (2) allowing heterogeneity in reciprocal weights so that there is no relationship between the strength of the MC -> GC synapse and the GC -> MC synapse, (3) reducing the level of self-inhibition a MC receives from the GCs that it excites. The model was found to be quite robust to each of these manipulations, suggesting that our present model likely remains functionally relevant even if biological reciprocity is partial. We reference this work now in the discussion, lines 490-492.

Author response image 2.

Disynaptic connectivity. Top: MC activity in response to the two stimuli, sorted by MC selectivity. Bottom: Disynaptic connectivity matrix (diagonal subtracted).

(4) How were mitral cells sorted in Figure 2H? This needs to be explained.

(5) Directly related to the point above: the text mentions that synaptic connectivity between GCs of the "learning cluster" and mitral cells (which direction?) is increased for mitral cells responding by enrichment odors, but this is not shown in the figure. This statement suggests that mitral cells sorted to the bottom of the y-axis respond more strongly to enrichment odors, but the information is not given directly. Please provide more information to back up your statements.

Indeed as the reviewer inferred, MCs in Figure 2H were sorted so that those that receive the strongest stimulation from the odor were at the bottom of the y-axis. We have clarified this in the Figure 2 caption and added a subplot to Figure 2H showing the average MC input to make this more explicit.

(8) Apoptosis (Figure 4 and related text): paragraph 231ff is somewhat difficult to comprehend because the "number" of enrichments should really be the "frequency" of enrichments. In Figure 4, it is not mentioned explicitly that each enrichment is with different random new odors.

We agree that the term “number” of enrichments was imprecise and have revised the text to refer instead to the frequency of enrichment events (Lines 255-267). We also clarified that in Figure 4, each enrichment corresponds to a different set of randomly sampled odors, and we now state this explicitly in both the Figure 4 legend and main text (Lines 260-261).

(9) Apoptosis: apoptosis improves memory but the underlying reason remains opaque. A simple prediction of the data in Figure 4D and 4E is that the number of GCs in 4E. It would be helpful to show this. Furthermore, an obvious question that arises is whether a higher frequency of enrichments improves memories because the total number of granule cells is kept low, or because granule cells are removed specifically based on their activity (or both). This could be addressed easily by artificially removing a random subset of granule cells in a simulation such as 4E to match granule cell numbers to the case in 4D.

Apoptosis improves learning is because it reduces the total inhibition in the network by removing GCs and thus prevents deficits in learning that occur in Fig. 2G as GCs accumulate in the network. As the reviewer inferred, the number of GCs in Figure 4D is lower than in 4E and this is now clarified in the text. This difference was shown implicitly in Supplementary Figure S4D (previously S3D), but we now explicitly reference this plot to support this point as well (Line 266).

As the reviewer notes, there is a question in whether increased enrichment frequency improves memory because it limits the total number of GCs, or because apoptosis selectively removes GCs based on their activity, or both. Our model supports both mechanisms. Importantly, simply reducing GC numbers through random deletion will degrade existing memories: random removal erodes memory representations encoded by those GCs. In contrast, our age and activity dependent apoptosis rule targets a specific cohort of adult-born GCs. This selective removal minimizes damage to existing memories encoded by GCs outside of this cohort while keeping GC numbers within a regime that supports robust learning (as shown in Figure 2G).

However, we note that if enrichment frequency becomes too high, even recent memories can be lost due to premature pruning of GCs that have not yet stabilized their synaptic connections. This tradeoff has been shown experimentally (Forest et al., Nat Comm 2019) which we reproduce in our model (Figure S4).

(10) Text related to Figure 5: "Learning flexibility...approached a steady state when the growth of the network started to saturate". Please show the growth (better: size) of the network (total number of GCs) for these simulations (and other panels in Figure 5). It would also be useful to show the total number of GCs in other figures (e.g. Figure 4; see above).

We have now added a supplementary figure (Figure S6) that shows the total number of GCs over time for the simulations presented. This confirms that the network size approaches a steady state around the same time that learning flexibility begins to plateau, as noted in the original text (now line 275), and highlights the large number of GCs without apoptosis as well as the slightly reduced number of GCs in the permanent encoding model (line 312).

(11) As much as I appreciate the comprehensive discussion of the results in a broader context, I feel that the discussion can be somewhat shortened. The section on lateral inhibition is not fully valid given that synaptic connectivity is reciprocal. I also feel that much of the final section (Model assumptions and outlook) can be dropped (except for the last paragraph), not because anything is irrelevant, but because these points have been made, onen repeatedly, in the text above.

We agree that the discussion could be streamlined and have revised the manuscript accordingly. Specifically, we have shortened the section on lateral inhibition and clarified that the OB relies predominantly on reciprocal connectivity (Line 370). We also agree that parts of the final section were repetitive and have removed these. However, to address comments by Reviewer 3, we also expanded on some of the model assumptions. We thank the reviewer for helping us improve the clarity and focus of the manuscript.

(12) Figure 5: bolding every 5th curve is confusing.

We have adjusted our figure accordingly.

(13) "...we biased the dendritic field...": it would be helpful to explain the idea of a "dendritic field" in a bit more detail prior to this sentence.

We have now noted that GC’s "dendritic field" refers to the subset of MCs with which it is capable of forming synaptic connections when we initially describe the model (Line 97).

Reviewer #3:

(1) The authors find that a network with age-dependent synaptic plasticity outperforms one with constant age-independent plasticity and that having more GC per se is not sufficient to explain this effect. In addition, having an initial higher excitability of GCs leads to increased performance. To what degree the increased excitability of abGCs is conceptually necessarily independent of them having higher synaptic plasticity rates / fast synapses?

We thank the reviewer for this question, as the difference between excitability and plasticity rate in memory formation is something we intended to highlight in this study. We have updated the (Lines 157-198) to clarify this.

At the cellular level, a neuron's excitability and its rate of synaptic plasticity are mechanistically distinct: excitability is governed by factors such as ion channel expression or membrane resistance, whereas plasticity rates are influenced by molecular pathways involved in synapse and dendritic spine formation and remodeling. While these are independent properties, they are functionally coupled: most synaptic plasticity rules are activity-dependent, so greater excitability can increase the likelihood of plasticity being induced but does not itself guarantee learning.

Our model reflects this distinction. Increased excitability biases which neurons become activated and thus eligible to undergo plasticity, but actual learning still depends on the plasticity rate itself. This can be seen by comparing the model constant plasticity and excitability (solid blue and green curves in Figure 2C) to the model with only transient excitability (solid blue and green lines in Figure 2E). In both cases, the strength and duration of the memory remain limited by the plasticity rate. We note additionally that, in this network, neurons compete to learn new stimuli: as GCs start to learn, they suppress MC activity through recurrent inhibition which suppresses learning in other GCs who otherwise would have been in position to learn the odor. As a result there is not a significant increase in the overall number of neurons recruited to learn (Figure 2J). In a different network architecture, such as a feedforward network, we would not expect this to be the case; greater excitability in a population of neurons would likely increase the memory by increasing the number of neurons recruited to learn. Transiently enhanced excitability biases which neurons join the memory engram (Figure 2J), but the extent and rate of learning still depend on the plasticity rates themselves. We did note in the original text (now lines 284-286) that this bias in recruitment subtly increases memory stability, but the extent is not great. In principle, a model can be engineered to rely on transiently increased excitability to encode memories in orthogonal subpopulations of neurons and that this could resolve the flexibility-stability dilemma. However, in that case, the number of memories that can be stored within a short time would be bounded by the size of this subpopulation such that even if a large number of odors are presented, mature GCs cannot become part of the engram and the network would likely fail to learn the stimuli. However, when this was tested experimentally (Forest et al. Cereb Cor. 2020), it was found that mature GCs participated in the engram when the number of odors was sufficiently high. Our results are consistent with these experiments: for complex odor environments, neonatal GCs, which are mature during odor exposure, and abGCs both participate in the engrams.

Author response image 3.

Simulating learning in more complex odor environments. Top: enrichment consisted of three odor pairs presented sequentially in a random order. Bottom: enrichment consisted of five odor pairs. Left: discriminability of the odor pairs over time. Middle: connectivity between MCs (sorted by odor selectivity) and GCs (sorted by age). In both cases AbGCs develop a clear connectivity structure. In more complex environments neonatal GCs also start to develop a clear connectivity structure. Right: combined engram membership across all stimuli by GC age.

In sum, transiently increased excitability alone will not make learning any faster, so a fast learning system must have a high plasticity rate. If this plasticity rate stays high, then memories stored in these neurons, even if no longer highly excitable, will be vulnerable as the neurons can still be driven above their plasticity threshold by moderately interfering stimuli and will thus be quickly forgotten. Conversely, if the reviewer is wondering if a greater increase in the plasticity rate of new neurons can compensate for a lack of excitability, this is not the case: if a newborn neuron is not sufficiently driven by the stimulus it will not learn regardless of how high its plasticity rate is.

(2) The authors do not mention previous theoretical work on the specificity of mitral to granule cell interactions from several groups (Koulakov & Rinberg - Neuron, 2011; Gilra & Bhalla, PLoSOne, 2015; Grabska-Bawinska...Mainen, Pouget, Latham, Nat. Neurosci. 2017; Tootoonian, Schaefer, Latham, PLoS Comput. Biol., 2022), nor work on the relevance of top-down feedback from the olfactory cortex on the abGC during odor discrimination tasks (Wu & Komiyama, Sci. Adv. 2020), or of top-down regulation from the olfactory cortex on regulating the activity of the mitral/tuned cells in task engaged mice (Lindeman et al., PLoS Comput. Biol., 2024), or in naïve mice that encounter odorants (in the absence of specific context; Boyd, et al., Cell Rep, 2015; Otazu et al., Neuron 2015, Chae et al., Neuron, 2022). In particular, the presence of rich topdown control of granule cell activity (including of abGCs) puts into question the plausibility of one of the opening statements of the authors with respect to relying solely on local circuit mechanisms to solve the flexibility-stability dilemma. I think the discussion of this work is important in order to put into context the idea of specific interactions between the abGCs and the mitral cells.

We thank the reviewer for these detailed and thorough comments, and whole-heartedly agree that it is important to discuss the listed studies in order to contextualize our work through the broader lens of how information is processed in the OB. We have expanded our discussion to further acknowledge and integrate insight from previous theoretical and experimental work cited by the reviewer. (Lines 361-366, 493-550)

Regarding the importance of top-down feedback, we of course recognize that in practice cortical inputs play a critical role in abGC survival and synaptic integration. However, its nature is not quite clear and is likely variable across behavioral seungs. In the paradigm that we study in the manuscript, there is likely no key reward value or contextual signal that is relayed to the OB. One plausible interpretation is that in this task, cortical feedback provides a random, variable baseline excitatory drive to GCs. This would likely be consistent with many of the listed studies, e.g.

(1) Glomerular layer targeting of feedback would be explicitly unrelated to glomerular odor specificity, as in Boyd et al.

(2) GC activity would decrease if these cortical inputs were silenced, resulting in stronger MC responses as in Otazu et al., Chae et al.

(3) Silencing PCx during learning would prevent GCs from reaching activity-dependent plasticity thresholds, resulting in decreased spine density as in Wu & Komiyama.

Likewise activating PCx would lead to increased spine density.

In this interpretation, the effect of top-down input could be captured implicitly by adjusting model parameters such as activity or plasticity thresholds. For the purposes of our study, we opted to neglect these inputs in favor of model simplicity.

Critically, even if top-down inputs play a substantially larger role, by perhaps even going as far as providing signals to abGCs to modulate their development, the core solution to the flexibility-stability dilemma that we describe stays local: we predict that the memory persists in the same network in which it was formed.

(3) To what the degree of specific connectivity reflects a specific stimulus configuration, and is a good proxy for determining the stimulus discriminability and memory capacity in terms of temporal activity patterns (difference in latency/phase with respect to the respiration cycle, etc.) which may account to a substantial fraction of ability to discriminate between stimuli? The authors mention in the discussion that this is, indeed, an upper bound and specific connectivity is necessary for different temporal activity patterns, but a further expansion on this topic would help in understanding the limitations of the model.

We thank the reviewer for raising this important point. Indeed, there have been several recent experimental studies indicating that much of the information needed for olfactory discrimination is encoded in the temporal activity patterns of mitral and tuned cells. Our model does not explicitly simulate these dynamics. It was for this reason that we defined memory in terms of the learned structure of the network rather than by firing rate activity. This is motivated by the idea that learned patterns of connectivity constrain the space of neural activity the network can support, and thus shape stimulus responses. We now make this limitation more explicit in the discussion and clarify that the specific MC–GC connectivity we analyze should be seen as a structural substrate that constrains the possible temporal transformations the network could support (Lines 492-506).

(4) Reward or reward prediction error signals are not considered in the model. They however are ubiquitous in nature and likely to be encountered and shape the connectivity and activity patterns of the abGC-mitral cell network. Including a discussion of how the model may be adjusted to incorporate reward/error signals would strengthen the manuscript.

We appreciate the reviewer’s suggestion and agree that reward and reward prediction error signals are critical components of many learning paradigms. We deliberately chose not to model associative learning, reward signals or top-down neuromodulation in this work. Our goal is to investigate the role of adult neurogenesis in a regime where its contribution has been shown to be experimentally necessary. Specifically, we focused on an unsupervised perceptual learning paradigm where adult neurogenesis is required for successful odor discrimination (Moreno et al. PNAS, 2008). In contrast, when the same odors are used in a rewarded learning paradigm, performance remains intact even when adult neurogenesis is ablated (Imayoshi et al., Nat. Neuro., 2008). This dissociation suggests that neurogenesis is dispensable in contexts where reward can guide learning. As such, we argue that isolating the contribution of local circuit dynamics in an unsupervised setting is critical to understanding what neurogenesis is uniquely enabling, especially given the evolutionary cost of maintaining it.

We agree that extending this work to incorporate reward-driven plasticity or neuromodulatory influences would be a valuable direction for future research. In particular, it could help clarify how different learning paradigms engage distinct abGC cohorts (e.g., Mandairon et al., eLife 2018; Wu & Komiyama, Sci. Adv. 2020), and how task structure shapes memory allocation and engram composition. We have incorporated this into the discussion regarding extending our model to include top down feedback (lines 539-553).

Specific comments

(1) Lines 84-86; 507-509; Eq(3): Sensory input is defined by a basal parameter of MCs spontaneous activity (Sspontaneus) and the odor stimuli input (Siodor) but is not clear from the main text or methods how sensory inputs (glomerular patterns) were modeled

We now clarify in the Methods section "Stimulus model" how the sensory inputs were modeled. Specifically, odor-evoked inputs to mitral cells (Siodor) were generated either as Gaussian profiles across the mitral cell population (Figs. 2,3) or as sparser random patterns (Figs. 4,5). In Figures 2 and 3, the denser Gaussian stimuli require more GCs to learn the odors, aiding in visualization of the connectivity matrix (Figure 2H) and abGC recruitment plots (Figure 2I,J; Figure 3C,E). However, real olfactory stimuli activate a sparse set of MCs, so in Figures 4 and 5 where we address learning of many stimuli, we utilize sparser, binary, stimuli delivered to only 10% of MCs, in range of experimental data (Wachowiak and Cohen, Neuron, 2001). The fact that the stimuli are binary, however, is not realistic and leads to denser representations. This leads to a worst-case scenario for the model as denser memory representations are easier to overwrite. These points has been added explicitly to the Methods section "Stimulus model" to improve clarity.

(2) Lines 118-122: The used perceptual learning task explanation is done only in the context of the discriminability of similar artificial stimuli using the Fisher discriminant and "Memory" metric. A detailed description of the logic of the perceptual learning task methods and objective, taking into account Comment 1, would help to better understand the model.

We thank the reviewer for pointing out had not adequately described the task and have updated the main text (lines 125-132) and included a new methods section "Perceptual learning task" to describe it more explicitly. The experiments that inspired the simulation followed an ecological model of discrimination learning (Moreno et al. PNAS 2009): For one hour a day over a ten day "enrichment period", two tea balls containing similar but distinct odors were suspended from the lid of each mouse's home cage. The mice engaged with the stimuli under self-directed conditions, therefore learning through natural experience. As a result the mice use olfactory information to discriminate between the similar stimuli, a skill potentially relevant for navigation or social behaviors.

In our simulations, we model these experiments as follows. During the enrichment period, the model is stimulated with a randomly selected stimulus chosen from a set of two similar stimuli, corresponding to a mouse choosing to sniff one of the tea balls. During enrichment, in between these bouts of "sniffing", the model only receives spontaneous activity, reflecting the temporal sparsity of sensory input even over the enrichment period. Outside of enrichment, the model again receives only spontaneous input.

(3) Rapid re-learning of forgotten odor pair is enabled by sensory-dependent dendritic elaboration of neurons that initially encoded the odors and the observed re-learning would occur even if neurogenesis was blocked following the first enrichment and even though the initial learning did require neurogenesis. When this would ever occur in nature? The re-learning of an odor period? Why is this highlighted in the study?

We believe that this sort of learning is certainly relevant in nature. To clarify: by “learning,” we do not refer to the memory of an entire “odor period”, but simply an altered mapping of specific stimuli. Therefore, forgeung could occur if these specific stimuli are absent from the environment for a period of time, and re-learning would occur when these stimuli are re-encountered. Natural odor environments are highly dynamic, as environmental conditions and social contexts change over time. The odors an animal encounters also depend strongly on its own behavior; as it explores different environments, it may be exposed to particular odors intermittently: it could encounter them in one location, then not return to that location for some time before returning again.

Such natural variability in odor exposure makes the ability to forget and re-learn especially valuable, allowing the animal to prioritize relevant information while maintaining flexibility. To this end, we show in Figure 5G that the synaptic forgetting of odors is beneficial to the performance of the model because it reduces interference in the network. Therefore we highlight that re-learning enabled by adult neurogenesis is a highly efficient strategy for memory storage and retrieval, which is why he emphasize it in this study.

(4) Figure 2A: I understand that the ages shown at the bottom of the colored boxes represent the GC age. If so, find a better way to express that to avoid confusing 'GC ages' from the days shown in the perceptual learning task description (Figure 2B).

We have updated the text in the figure to disambiguate the two and refer to the “days” shown in the perceptual learning task description now as “time relative to enrichment”

(5) Figure 2B: Clarify how the two-dimensional arrays are arranged to represent the patterns shown. Does each point of the array represent one neuron? If so, are these neurons re-arranged to help the readers visually differentiate patterns A and B? Are the patterns of activity of MCs in the model spatially and temporally sparse as observed in experimental work?

In Figure 2B, each point in the two-dimensional array represents the activity of a single mitral cell. The layout is purely for visualization—neurons are re-arranged to make the differences between odor patterns A and B visually apparent. This ordering does not reflect anatomical position or model architecture. We revised the Figure 2 caption to say this explicitly.

Regarding spatial sparseness, as we mentioned in the response to the reviewer’s comment (1), the activity of mitral cells in response to odors is spatially sparse in the model. Regarding temporal sparseness, while the model is not spiking and does not include temporal dynamics within the timescale of the breath, however, odor input is delivered in discrete, odorspecific epochs interleaved with periods of no input, which leads to temporally structured activity patterns. This information has been made explicit in the new methods sections "Stimulus model" and "Perceptual learning task"

(6) Figure 3C and Line 189: potential confusion between the color code mentioned in the legend for the enrichment and developing periods.

It appeared to be a confusion in the text and has been corrected (Lines 212-213).

(7) Figure 5F: For clarity, this would benefit from replacing the bold line with areas in the plot to depict the enrichment periods.

We agree that replacing the bolded line segments with shaded areas is more clear and have updated the figure accordingly, and appreciate the reviewer's suggestion to clarify the figure.

(8) Lines 380, 416: Potential role of cortical feedback and or neuromodulation depending on behavioral relevance or permanent exposure? Later mentioned in Lines 467 - 474.

We have updated the text to acknowledge the role of potential cortical feedback and neuromodulation, now in lines 403-407.

Author Response

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

Reviewer #1 (Public Review):

In this manuscript, Butkovic et al. perform a genome-wide association (GWA) study on Arabidopsis thaliana inoculated with the natural pathogen turnip mosaic virus (TuMV) in laboratory conditions, with the aim to identify genetic associations with virus infection-related parameters. For this purpose, they use a large panel of A. thaliana inbred lines and two strains of TuMV, one naïve and one pre-adapted through experimental evolution. A strong association is found between a region in chromosome 2 (1.5 Mb) and the risk of systemic necrosis upon viral infection, although the causative gene remains to be pinpointed.

This project is a remarkable tour de force, but the conclusions that can be reached from the results obtained are unfortunately underwhelming. Some aspects of the work could be clarified, and presentation modified, to help the reader.

(Recommendations For The Authors):

• It is important to note that viral accumulation and symptom development do not necessarily correlate, and that only the former is a proxy for "virus performance". These concepts need to be clear throughout the text, so as not to mislead the reader.

This has been explained better in line 118-120, “Virus performance has been removed.

• Sadly, only indirect measures of the viral infection (symptoms) are used, and not viral accumulation. It is important to note that viral accumulation and symptom development do not necessarily correlate and that only the former is a proxy for "virus performance". These concepts need to be clear throughout the text, so as not to mislead the reader. The mention of "virus performance" in line 143 is therefore not appropriate, nor is the reference to viral replication and movement in the Discussion section.

"Virus performance" was removed. Also, the reference to viral replication and movement in the Discussion section has been removed.

Now we mention: “We did not measure viral accumulation, but note this is significantly correlated with intensity of symptoms within the Col-0 line (Corrêa et al. 2020), although it is not clear if this correlation occurs in all lines.”

• Since symptoms are at the center of the screen, images representing the different scores in the arbitrary scales should ideally be shown.

Different Arabidopsis lines would look different and this could mislead a reader not familiar with the lines. In order to make a representation of our criteria to stablish the symptoms, we believe that a schematic representation is clearer to interpret. Here are some pictures of different lines showing variating symptoms:

Author response image 1.

• Statistical analyses could be added to the figures, to ease interpretation of the data presented.

Statistical analysis can be found in methods. We prefer to keep the figure legend as short as possible.

• The authors could include a table with the summary of the phenotypes measured in the panel of screened lines (mean values, range across the panel, heritability, etc.).

These data are plotted in Fig. 1. We believe that repeating this information in tabular form would not contribute to the main message of the work. Phenotype data and the code to reproduce figure 1 are available at GitHub (as stated in Data Availability), anyone interested can freely explore the phenotypes of the screened lines.

• The definition of the association peak found in chromosome 2 could be explained further: is the whole region (1.5 Mb) in linkage disequilibrium? How many genes are found within this interval, and how were the five strong candidates the authors mention in line 161 selected? It is also not clear which are these 5 candidates, apart from AT2G14080 and DRP3B - and among those in Table 1 (which, by the way, is cited only in the Discussion and not in the Results section)? Why were AT2G14080 and DRP3B in particular chosen?

We have replaced Table 1 with an updated Table S1 listing all genes found within the range of significant SNPs for each peak. We now highlight a subset of these genes as candidate genes if they have functions related to disease resistance or defence, and mentioned them explicitly in the text (lines 173-179. We have explicitly described how this table was constructed in the methods (lines 525-538).

• Concerning the validation of the association found in chromosome 2 (line 169 and onward): the two approaches followed cannot be considered independent validations; wouldn't using independent accessions, or an independent population (generated by the cross between two parental lines, showing contrasting phenotypes, for example) have been more convincing?

We aim to compare the hypothesis that the association is due to a causal locus to the null hypothesis that the observed association is a fluke due to, for example, the small number of lines showing necrosis. If this null hypothesis is true then we would not expect to see the association if we run the experiment again using the same lines. An alternative hypothesis is that the genotype at the QTL and disease phenotypes are not directly causally linked, but are both correlated with some other factor, such as another QTL, or maternal effects. We agree that an independent sample would be required to exclude the latter hypothesis, but argue that the former is the more pertinent. We have edited the text to be explicit about the hypothesis we are testing, and altered the language to shift the focus from ‘validation’ to ‘confirming the robustness’ of the association (line 182).

• Regarding the identification of the transposon element in the genomic region of AT2G14080: is the complementation of the knock-out mutant with the two alleles (presence/absence of the transposon) possible to confirm its potential role in the observed phenotype?

This could be feasible but we cannot do it as none of the researchers can continue this project.

• On the comparison between naïve and evolved viral strains: is the evolved TuMV more virulent in those accessions closer to Col-0?

This is not something we have looked at but would certainly be an interesting follow-up investigation.

• The Copia-element polymorphism is identified in an intron; the potential functional consequences of this insertion could be discussed. In the example the authors provide, the transposable element is inserted into the protein-coding sequence instead.

We now state explicitly that such insertions are expected to influence expression; beyond that we can only speculate. We have removed the reference to the insertion in the coding sequence.

• The authors state in line 398 that "susceptibility is unquestionably deleterious" - is this really the case? Are the authors considering susceptibility as the capacity to be infected, or to develop symptoms? Viral infections in nature are frequently asymptomatic, and plant viruses can confer tolerance to other stresses.

We have tone down the expression and clarify our wording: “Given that potyvirus outbreaks are common in nature (Pagán et al., 2010) and susceptibility to symptomatic infection can be deleterious”

Additional minor comments:

• In Table 1, Wu et al., 2018 should refer to DRP2A and 2B, not 3B.

We have removed Table 1 altogether.

• Line 126: a 23% increase in symptom severity is mentioned, but how is this calculated, considering that severity is measured in four different categories?

This is the change in mean severity of symptoms between the two categories.

• Figure 1F: "...symptoms"

Fixed.

• Line 179: "...suggesting an antiviral role..."

Changed.

• Lines 288-300: This paragraph does not fit into the narrative and could be omitted.

It has been removed and some of the info moved to the last paragraph of the Intro, when the two TuMV variants were presented.

• Lines 335-337: The rationale here is unclear since DRP2B will also be in the background - wouldn't DRPB2B and 3B be functionally redundant in the viral infection?

Our results suggest that DRPB3B is redundant with DRPB2B for the ancestral virus but not for the evolved viral strain. We speculate that the evolved viral isolate may have acquired the capacity to recruit DRPB3B for its replication and hence it produces less symptoms when the plant protein is missing.

We have spotted a mistake that may have add to the confusion. Originally the text said “In contrast, loss of function of DRP3B decreased symptoms relative to those in Col-0 in response to the ancestral, but not the evolved virus”. The correct statement is “In contrast, loss of function of DRP3B decreased symptoms relative to those in Col-0 in response to the evolved, but not the ancestral virus.”  

Reviewer #2 (Public Review):

The manuscript presents a valuable investigation of genetic associations related to plant resistance against the turnip mosaic virus (TuMV) using Arabidopsis thaliana as a model. The study infects over 1,000 A. thaliana inbred lines with both ancestral and evolved TuMV and assesses four disease-related traits: infectivity, disease progress, symptom severity, and necrosis. The findings reveal that plants infected with the evolved TuMV strain generally exhibited more severe disease symptoms than those infected with the ancestral strain. However, there was considerable variation among plant lines, highlighting the complexity of plant-virus interactions.

A major genetic locus on chromosome 2 was identified, strongly associated with symptom severity and necrosis. This region contained several candidate genes involved in plant defense against viruses. The study also identified additional genetic loci associated with necrosis, some common to both viral isolates and others specific to individual isolates. Structural variations, including transposable element insertions, were observed in the genomic region linked to disease traits.

Surprisingly, the minor allele associated with increased disease symptoms was geographically widespread among the studied plant lines, contrary to typical expectations of natural selection limiting the spread of deleterious alleles. Overall, this research provides valuable insights into the genetic basis of plant responses to TuMV, highlighting the complexity of these interactions and suggesting potential avenues for improving crop resilience against viral infections.

Overall, the manuscript is well-written, and the data are generally high-quality. The study is generally well-executed and contributes to our understanding of plant-virus interactions. I suggest that the authors consider the following points in future versions of this manuscript:

1. Major allele and minor allele definition: When these two concepts are mentioned in the figure, there is no clear definition of the two words in the text. Especially for major alleles, there is no clear definition in the whole text. It is recommended that the author further elaborate on these two concepts so that readers can more easily understand the text and figures.

We agree that the distinction between major/minor alleles and major/minor associations in our previous manuscript may have been confusing. In the current manuscript we now define the minor allele at a locus as the less-common allele in the population (line 167). We have removed references to major/minor associations, and instead refer to strong/weak associations.

1. Possible confusion caused by three words (Major focus / Major association and major allele): Because there is no explanation of the major allele in the text, it may cause readers to be confused with these two places in the text when trying to interpret the meaning of major allele: major locus (line 149)/ the major association with disease phenotypes (line 183).

See our response to the previous comment.

1. Discussion: The authors could provide a more detailed discussion of how the research findings might inform crop protection strategies or breeding programs.

We would prefer to restrain speculating about future applications in breeding programs.

(Recommendations For The Authors):

1. Stacked bar chart for the Fig 1F. It is recommended that the author use the form of a stacked bar chart to display the results of Fig 1F. On the one hand, it can fit in with the format of Fig 1D/E/G, on the other hand, it can also display the content more clearly.

We think the results are easier to interpret without the stacked bar chart.

1. Language Clarity: While there are no apparent spelling errors, some sentences could be rewritten for greater clarity, especially when explaining the results in Figure 1 and Figure 2.

We have reviewed these sections and attempted to improve clarity where that seemed appropriate.

There are some possibilities to explore in the future. For example: clarity of mechanisms for the future. While the study identifies genetic associations, it lacks an in-depth exploration of the underlying molecular mechanisms. Elaborating on the mechanistic aspects would enhance the scientific rigor and practical applicability of the findings.

Yes, digging into the molecular mechanisms is an ongoing task and will be published elsewhere. It was out of the scope of this already dense manuscript.  

Reviewer #3 (Public Review):

Summary of Work

This paper conducts the largest GWAS study of A. thaliana in response to a viral infection. The paper identifies a 1.5 MB region in the chromosome associated with disease, including SNPs, structural variation, and transposon insertions. Studies further validate the association experimentally with a separate experimental infection procedure with several lines and specific T-DNA mutants. Finally, the paper presents a geographic analysis of the minor disease allele and the major association. The major take-home message of the paper is that structural variants and not only SNPs are important changes associated with disease susceptibility. The manuscript also makes a strong case for negative frequency-dependent selection maintaining a disease susceptibility locus at low frequency.

Strengths and Weaknesses

A major strength of this manuscript is the large sample sizes, careful experimental design, and rigor in the follow-up experiments. For instance, mentioning non-infected controls and using methods to determine if geographic locus associations were due to chance. The strong result of a GWAS-detected locus is impressive given the complex interaction between plant genotypes and strains noted in the results. In addition to the follow-up experiments, the geographic analysis added important context and broadened the scope of the study beyond typical lab-based GWAS studies. I find very few weaknesses in this manuscript.

Support of Conclusions

The support for the conclusions is exceptional. This is due to the massive amount of evidence for each statement and also due to the careful consideration of alternative explanations for the data.

Significance of Work

This manuscript will be of great significance in plant disease research, both for its findings and its experimental approach. The study has very important implications for genetic associations with disease beyond plants.

(Recommendations For The Authors):

Line 41 - Rephrase, not clear "being the magnitude and sign of the difference dependent on the degree of adaptation of the viral isolate to A. thaliana."

Now it reads: “When inoculated with TuMV, loss-of-function mutant plants of this gene exhibited different symptoms than wild-type plants, where the scale of the difference and the direction of change between the symptomatology of mutant and wild-type plants depends on the degree of adaptation of the viral isolate to A. thaliana.”

Line 236 - typo should read: "and 21-fold"

Changed.

Author response:

The following is the authors’ response to the original reviews

Reviewer 1:

I would suggest that the authors focus on what I think is the main goal of the work, namely, to consider the whole cell contour when characterizing cell shape instead of only some points on the contour. A reference to the connection with Minkowski tensors and the biologically relevant mathematical consequences of this connection would suffice; a detailed definition of the Minkowski tensors does not seem to be necessary. Especially because you do not really use them. You could use the analysis of the simulation data to explain what the γ_p miss and for which statements they would be sufficient.

We argue that the explanation of Minkowski tensors is helpful and should remain in the Methods and materials section. There are two reasons: First, our argumentation relays on the robustness and stability properties of Minkowski tensors. Introducing q_p without the connection to Minkowski tensors would not allow us to make these statements. Second, Minkowski tensors seem not well known in the community, otherwise measures like γ_p would not have been introduced. Furthermore, readers not interested in the technical details could skip this part of the manuscript and directly go to the Results section. Concerning the questions, what the γ_p miss and for which statements they would be sufficient, the answer from a purly mathematical point of view is rather simple: As γ_p does not share robustness and stability it should not be used in any case! The provided results on computational and experimental data demonstrate the consequences of using such measures. In case of the proposed nematic-hexatic transition in Armengol-Collade et al. (2023) the consequence is severe, as this transition is specific only to the used method but not to the underlying physics. A second aspect which we now further highlight is the influence of approximating a cell by a polygon. We demonstrate that this approximation is responsible for a strong hexatic order on the cellular scale in the considered MDCK data from Armengol-Collade et al. (2023).

It is not clear to me what we should learn about the two tissue models by using q₂ and q₆ to quantify cell shape. Can you clearly formulate one or more conclusions?

What we can learn from the research is a dependence of q_p on model parameters in the two tissue models is

– increases with higher activity or deformability

– decreases with higher activity or deformability.

Furthermore, q₂ and q₆ are independent and describe distinct properties. Using these models as a basis to coarse-grain and derive continuous models on the tissue scale, these results indicate that more general p-atic liquid crystal theories should be used and the simplest nematic liquid crystal theories might not be sufficient.

The experimental data and their analysis does not seem to add anything to the work. Do you report only data from independent measurements, or did you consider all images of a monolayer?

As we now also analyze experimental data from Armengol-Collado et al. (2023) which confirm our findings on independency of q₂ and q₆ and also confirm that the proposed nematic-hexatic transition is only specific to the use of γ_p for characterizing the shape, additional experimental data are indeed no longer needed. We, therefore, skip the detailed analysis of this data and only keep the results in Fig 1 and Fig 2 and the corresponding figures in the appendix as illustrating examples.

L13: ”P-atic liquid crystal theories offer new perspectives on how cells self-organize (...)” This is a difficult entry, because the average reader of eLife might not be familiar with p-atic liquid crystals.

We agree that p-atic liquid crystals might not be familiar to the average reader. For this reason we introduce orientational order in the introduction with examples demonstrating that not only nematic, but also tetratic and hexatic order have been identified in tissue and introduce the different symmetries. Furthermore, we provide examples for p-atic liquid crystals from other fields and various references. In the conclusion, we also cite models for p-atic liquid crystal theories. Even if the average reader is not familiar with these theories, it should become evident that nematic order might not be sufficient to describe tissue as other symmetries are present as well.

L32: ”nematic” needs to be introduced.

Nematic order is already explained as rotational order with 180° degrees. The references cited discuss nematic liquid crystals in the context of morphological changes in tissue. We therefore only added a standard text book as reference for liquid crystal theories and refrain introducing it in more detail in the manuscript.

Figure 1: Why do you show the data for q₃, q₄, and q₅, which you do not really consider in this manuscript? Same for Figure 2. Why not combine the two figures? Furthermore, you show q_p without having defined them yet.

We consider all p \= 2,3,4,5,6, but focus on p = 2,6 in the main text and p = 3,4,5 in the appendix. Figures 1 and 2 essentially only introduce the subject and help to relate p-atic order to cell shapes and introduce the methodology to analyze the data. Our conclusion is that all p can be important and should be considered in continuous descriptions of tissue.

Equation 1: The notation is confusing: the domain of integration (C or ∂C) also appears as the variable you integrate.

The equation is correct. The variable of integration is 1 or H and the domain of integration is C (cell) or ∂C (cell contour).

L68: ”a snapshot of the considered monolayer of wild-type MDCK cells”. Did you analyse only one monolayer? Please, provide information about the number of monolayers that were imaged and how many cell shapes were analyzed.

We have analyzed one monolayer and have added the missing information.

L86: ”field-specific prefactors” I do not understand what is meant by these.

Different communities, e.g. physics, mathematics, cosmology, .... use different prefactors in the definition. We have removed this statement.

L89: ”Hadwiger’s characterization theorem”. What is this?

This mathematical result is important to claim robustness and stability, it can be found in the cited reference.

L104: ”the essential property is the continuity”. Essential for what?

Essential ”for our purpose” to characterize the shape of cells by a robust method.

L120: ”the theory also guarantees robust description of p-atic orientation for p = 3,4,5,6,...” I do not understand what you mean.

The previous examples only consider p \= 2. However, the cited theoretical results also hold for p = 3,4,5,6,..

Equations (5) and (6): You define ψ_p(C) twice. Are the definitions equivalent? Why do you need both?

This is not a different definition, equation (6) is a reformulation which is more useful for our purpose. But we indeed define ϑ_p twice. We now use a new symbol to distinguish ϑ_p in Equation 7 and 9.

Figure 4: ”The visualization uses rotationally-symmetric direction fields (known as p-RoSy fields in computer graphics (Vaxman et al., 2016)).” I guess that you have used these fields already in Figure 1, so why introduce them only now?

We have moved this comment to Figure 1.

Figure 6: Using a few discrete values cannot illustrate continuity. Also, the ”jump” in γ_p results from deleting a vertex, so I doubt that this is a fair comparison. Still, I think that it is important to point out to the reader that the value γ_p depends on the number of vertices (here, I allow that two edges connected by a vertex are aligned).

We adjusted the caption to make our point more clear. The last image is a triangle and according to the definition of γ_p is, therefore, described by only three vertices. So, it is indeed a fair comparison. The reviewer is right that the value of γ_p has a strong dependency of the number of used vertices, this is exactly the point that we are trying to make with this figure. Also, adding vertices artificially to make γ_p continuous leads to more problems, as the values for γ_p change if we change the number of vertices. But an equilateral triangle should be recognized as an equilateral triangle, no matter if there is an artificial fourth vertex or not. The triangle in our picture and the triangle that the reviewer mentioned (so our triangle with an artificial fourth vertex) both have the shape of an equilateral triangle, yet for one it is |γ₃| = 1.0 and for the other one it is |γ₃| = 0.935.

While we agree on the reviewers statement about continuity, we did not modify the sentence, as the meaning should be clear.

L160: The definition of the center of mass is incorrect as it is not that of an extended object whose contour is defined by a polygon, but only of the set of vertices. In Figure 6 you write ”the choice of the center of mass highly influences the value of γ_p” - is there really a choice of the center of mass? I thought that it was uniquely defined.

We here only repeat the definition from Armengol-Collado et al. (2023) in order to be able to directly compare our analyses with the results presented therein. We adjusted the caption to be more clear.

L166: What is the weighting you refer to in Equation 9?

We apologize, the reference is to Equation 8. We have modified this.

L312: ”Quantifying orientational order in biological tissues can be realized by Minkowsky tensors”. As mentioned above, you do not really use them, but use Equation (7), which can be defined without reference to Minkowski tensors.

Eq. (7) is part of the irreducible representations of the Minkowsky tensor. Therefore the sentence is correct.

L318: I do not quite understand the link between being able (or not) to compare q_p’s for different values of p and the interpretability of q₂ and q₆. Also, since you introduce q_p, how can the question about their comparability be a recurrent challenge? Finally, would you agree that even though a comparison between the absolute values of q₂ and q₆ is inappropriate, one can still meaningfully compare relative changes as a parameter is changed or when comparing cells in different conditions?

We have modified the sentence. Furthermore we agree that one can still meaningfully compare relative changes as a parameter is changed, as we do. However, our claim that q₂ and q₆ are independent, does not allow to conclude any kind of nematic-hexatic phase transition. We have now provided further evidence using the published data of Armengol-Collado et al. (2023), which unequivocally supports this statement. We would also like to remark that the detection of a phase-transition requires a single order parameter, which cannot exist as q₂ and q₆ are independent.

We have further explained this in the main text.

Figure 7: The axes are not labeled.

We added the labels.

L359: ”q₂ and q₆ values cluster tightly”, L362 ”q₂ and q₆ values become highly scattered” Please, quantify.

We kept these formulations but have added statistical measures to these qualitative descriptions, see Supplementary Figures to Fig 7 for the distance correlation and the P-values of the distance correlation. These data support our claim of independence.

L362: ”each q₂ value spans a broad range of q₆ values and vice versa, demonstrating their independence”. Please, use a quantitative test of statistical independence.

We have added statistical information by using the distance correlation and statistical tests, see Supplementary Figures to Fig 7. Similar results are obtained for the Pearson correlation and corresponding tests. However, they are not included as the distance correlation is more general.

L371: Please, define Q₂ and Q₆ in the main text.

We have now added the definition to the Materials and methods section.

L420: A reference seems to be missing.

Thanks for pointing this out. This was a formatting error, we only wanted to cite Balasubramaniam et al. (2021).

L425: ”strong dependence of cell shape on cell density”. But q₆ seems to be rather independent of density, see Figure 11. Also, what do you mean by ”strong”? Can you quantify?

The dependency of the cell shape on the cell density is shown in detail in (Eckert et al., 2023). Furthermore, to describe the cell shape the values for all p are needed. So the change in q₂ already indicates a change in the overall cell shape even as q₆ is barely changing. As we excluded these experimental results now in favor of the experimental data also used in Armengol-Collado et al. (2023), we did not add further evaluations regarding cell density.

L453 ”These divergences [nonmonotonic dependence of γ_p on activity or deformability] highlight the limitations of γ_p in capturing consistent patterns”. I am not sure to follow your argument here.

Besides the quantitative differences seen in comparing Fig. 1 and Fig 2 with the corresponding figures in the appendix, these results show qualitative differences. Using a method which is not robust and not continuous leads to qualitative different results. The nonmonotonic dependence of γ_p is specific to the method but not to the underlying physics.

Appendix 3 - Figure 20: It is not clear how to compare this figure to Figure 3e of Armengol-Collado et al 2023. Please, provide more details.

Appendix 3 - Figure 20 (Appendix 3 - Figure 25 in the revised version) and Figure 3e in Armengol-Collado et al. (2023) cannot be directly compared. Fig 3e shows results of experiments and multiphase field simulations for one parameter stetting and Fig 20 results of the active vertex model for various parameter settings. But both are considered using γ_p and Γ_p. We have added these computation, see Fig. 13, which indeed reproduces the results from Fig 3e. We refrain from considering corresponding plots to Fig 20 for the multiphase field model, as this first requires computing the vertices and no additional information can be expected.

Reviewer 2:

The manuscript lacks statistical information. The following should be addressed: How often have the experiments been performed? How many monolayers have been analyzed? How many time steps have been considered and in what duration? How many cells have been included in the analysis? What are the p-values to determine if q_p’s (Figure 2, panel a) and γ_p’s (Appendix 3-Figure 17, panel a) are significantly different? Same figures: How many cells and experiments have been considered here? Figure 11: What is the density of cells for each condition? Please provide the corresponding values. How significant are the differences? How many times has the experiment been repeated? Figure 12: Due to cell proliferation, the cell density changes over time. Does this need to be taken into account?

We agree, our information have only been qualitative. We have added the missing information. Especially we added statistical information by using the distance correlation and statistical tests, see Supplementary Figures to Fig. 7. Similar results are obtained for the Pearson correlation and corresponding tests (not included). As we excluded the experimental results previously shown in Figure 11 and Figure 12, in the revised version in favor of the experimental data that is already published in Armengol-Collado et al. (2023), we did not add further statistics regarding this. We added the number of frames and cells in the text.

The image analysis part of the Method section states that time-series were xy-drift corrected, and cells were tracked. However, the manuscript does not contain results of dynamical data, timedependent analyses, or discussions of how q_p changes over time. The authors mention that the fluidity of the tissue was confirmed by the MSD, neighbor number variance, and the self-intermediate scattering function, but none of the results are shown in the manuscript. I would like to ask the authors to provide the results and related content in the Method section.

We have modified the description and removed all parts related to dynamical data. Due to the heavy overload of images in the manuscript we refrain from providing all the results for the phase diagram to distinguish solid and fluid phase. These measures have been provided previously for the considered modeling approaches and provide here only a side remark. Our results do not depend on an exact localization of a solid-fluid phase boundary.

Additional information is missing in the Image analysis part of the Method section. Could the authors provide the information on the image analysis steps between obtaining the segmented image and inputting the parameters for the Minkowski tensor? This should include how the normal vectors have been determined and whether this has been done for all pixels along the contour.

We added further details in the section Extraction of the contour in Experimental setup in Methods and Materials and also provide the code to compute q_p for segmented images.

The authors have analyzed low-resolution phase contrast images acquired with a 10x objective to experimentally support their introduced Minkowski tensors. This may have decreased the resolution of the cell boundary detection and its curvature. I strongly suggest imaging the tissue with higher magnification (40x or 63x) and/or fluorescent markers to visualize the cell boundaries in high quality. This would allow the authors to distinguish between circles and circle-like shapes (lines 432-434) and to further investigate differences between MDCK wild-type and MDCK E-cad KO cells.

We agree that higher resolution of the images would be beneficial. However, we are convinced that this will not influence our findings. Instead of performing the experiments with higher magnification or using fluorescent markers, we have considered the experimental data from Armengol-Collado et al. (2023) to support our results.

The authors have coarse-grained the shape function, Γ_p, and have chosen the active vertex model (Appendix 3-Figure 20) for comparison with the Minkowski tensors, Q_p (Appendix 2 Figure 13). In both figures, the hexatic-nematic crossover does not occur. Armengol-Collado et al. have previously reported that the Voronoi model failed to achieve the hexatic-nematic crossover and argued that this is due to the artificial enhancement of the polygon’s hexagonality, leading to high hexatic order at the tissue scale. Since the authors have used the Voronoi-tailing method (line 196), I would like to ask the authors to compare the multiphase field models for Γ_p andQ_p instead.

We would like to mention that we do not consider a Voronoi model but an active vertex model. A Voronoi model is only used for initialization. Both models are certainly related but not identical and claims for a Voronoi model do not need to hold for an active vertex model. The suggested comparison for the multi phasefield model is not an easy task as it requires to compute the vertices from the phase field variables. There are gaps between cells and a reliable algorithm to identify the vertices is a task on its own. We, therefore, refrain from doing these calculations. Instead, we have used the experimental data from Armengol-Collado et al. (2023) for which the polygonal information are provided, see Figure 11. Especially for p \= 6, strong differences can be seen by comparing the PDF obtained by the full shape and the polygonal shape. Indeed, the strong hexatic order at the cellular scale is only a consequence of the approximation by polygons. With this result analysing the multi phasefield data by γ_p does not add any new information as this first requires an approximation by polygons.

The authors show the q_p distributions for the experimental systems (Figure 2, Figure 11). For completeness, I would like to ask the authors to also coarse-grain q_p and γ_p of the experimental data as shown for the computational models in Appendix 2 - Figure 13 and Appendix 2 - Figure 14. It would be interesting to see if the hexatic-nematic crossover appears. I would recommend that the authors avoid using the Voronoi tailing of the experimental system, as this may fail to obtain the crossover as explained in (5) above. Instead, I suggest using the real vertex positions for γ_p, which can be obtained from the segmented images.

It remains open what is meant by ”the real vertex positions for γ_p, which can be obtained from the segmented images”. Segmenting the images leads to smooth contours, partly even with gaps between cells. As the magnitude of γ_p depends on the number of points used in the calculation it is not meaningful to use all points of the contour for calculating γ_p, as this would lead to artificially low values for |γ_p|. Identifying the vertex positions for an approximating polygon is an issue of its own and the consequence of this approximation is already mentioned above. For a comparison we therefore added the experimental data from Armengol-Collado et al (2023) and used the provided vertex positions to compute q_p and γ_p as well as the raw data and performed the segmentation and used these data to compute q_p. See Figure 11. These results confirm our findings and show that the proposed nematic-hexatic phase transition is specific to γ_p to characterize shape.

In order to show that shape descriptors like the shape function, γ_p, introduced by Armengol-Collado et al., ’fail to capture the nuance of irregular shapes’ (line 445), the authors have compared γ_p with the Minkowski tensors, q_p, using the same dataset (Figure 1 with Appendix 3 - Figure 16, Figure 2 with Appendix 3 - Figure 17, and Figure 4 with Appendix 3 - Figure 15 Appendix 3). I agree that γ_p and q_p are different, not showing identical values. However, I see no evidence in these figures that q_p describes the symmetry of a cell better than γ_p, since the values are similar and vary quite similarly between different p-atic orders. What is the quantitative difference that shows the failure of the shape function to capture the nuance of irregular shapes?

The statement already follows from the mathematical properties of robustness and stability, which is illustrated in Fig. 6. The mentioned comparisons for simulation and experimental data only demonstrate that the lack of robustness and stability of γ_p also leads to different results if applied to averages of cell measures. The differences are twofold, first the approximation of cells by polygons leads to different results, and second even for polygons different results follow, as only one approach is continuous and the other not. This has strong consequences for the proposed nematic-hexatic phase transition if coarse-grained. Our added results for the experimental data from Armengo-Collado et al. (2023) show that this behavior is not a physical feature but only specific to the use of γ_p.

The authors claim that the Minkowski tensors provide a ’reliable framework’ and that this framework ’opens new pathways for understanding the role of orientational symmetries in tissue mechanics and development’ (line 78-79). However, the p-atic orders in the experimental systems peak at very low orders of q_p < 0.3, which may not allow conclusions about (non-)dominant orientational symmetry(ies) of cells. Can this framework be applied to experimental systems? Since the Minkowski tensors display the independence of the hexatic and nematic symmetry, the variations of cell shapes in experimental systems are too strong to provide any additional results (line 437), as stated by the authors, and no crossover was found, while the crossover was reported by Armengol-Collado et al., what new pathways can be opened to study tissues?

We have added a comparison with experimental data from Armengol-Collado et al. (2023) and demonstrate that the proposed nematic-hexatic transition is only specific to the use of γ_p for characterizing the shape. So our results first of all essentially close the ”pathway for understanding the role of orientational symmetries in tissue mechanics and development”, which was proposed on this nematic-hexatic transition. On the other side, even if q_p peaks at relatively low values, the results demonstrate independence of the measures for different p’s, for two different modeling approaches and two different sets of experimental data. This motivates to consider p-atic order for different p simultaneously. Such theories of ”multi”-p-atic liquid crystals, as proposed in the conclusions, are the mentioned new pathways.

In principle, the introduced Minkowski tensors integrate the orientation of the normal vectors (Equation 6) and consider the perimeter of the contour (Equation 1). Do the tensors distinguish between convex and concave curvature since both are present in tissues? Does a square with 4 concave and a square with 4 convex edges (same curvature) have the same q_p values?

For the specific situation of a square with 4 concave or 4 convex edges even p would lead to the same orientation and the same value for q_p, as even p have a 180 degree symmetry. Odd p would result in the same value for q_p but in a different orientation ϑ_p. In more general cases, e.g. shapes with concave and convex edges, no general statements can be made. In general the theoretical results on stability of q_p only hold for convex shapes. However, as discussed in Methods and materials the known counterexamples for concave shapes are not relevant for cell shapes.

In lines 169-172 and Figure 6, the authors report a jump in γ_p. Why has the fourth vertex in the last image been removed? The vertices are essential for the calculation of γ_p. If the fourth vertex is not removed, the following values result: γ₃ = 0.935 and γ₄ = 0.474, which leads to changes of the same order of magnitude as those of q_p. I think it is therefore not the choice of the center of mass that ’heavily influences the value of γ_p’, but the removal of the fourth vertex.

We adjusted the caption to make our point more clear. The last image is a triangle and according to the definition of γ_p is therefore described by only three vertices. The reviewer is right that the value of γ_p has a strong dependency of the number of used vertices, this is exactly the point that we are trying to make with this figure. An equilateral triangle should be recognized as an equilateral triangle, no matter if there is an artificial fourth vertex or not. The triangle in our picture and the triangle that the reviewer described (so our triangle with an artificial fourth vertex) both have the shape of an equilateral triangle, yet for one |γ₃| = 1.0 and for the other one it is |γ₃| = 0.935. This can be seen even more clearly if even more artificial vertices on the outline of the equilateral triangle are added, which will decrease |γ₃| even more. Furthermore, we think there was a misunderstanding regarding our statement about the center of mass. The general problem of γ_p - so the dependence of the values on the number of vertices - is independent of the calculation of the center of mass. The exact values of γ_p on the other hand depend on the choice of this. We follow Armengol-Collado et al. (2023) and use the mean of all vertex coordinates as center of mass. If the reviewer would use the center of mass of the equilateral triangle and do the same calculations the resulting values for γ_p would be different. This is what we meant with ’heavily influences the value of γ_p’.

In Appendix 3 - Figure 18, the authors show that the shape function, γ₆, exhibits a non-monotonic trend as a function of activity and deformability. I have no objection to this statement. However, I would like to ask the authors to check the values for γ₆. In the bottom-left corner, for example, γ₆ = 0.55. This value seems very low to me. In Appendix 3-Figure 20, |Q₆| for R/Rcell = 2 is already in this range, while |Q₆| for R/Rcell = 1 (not shown), corresponding to γ₆, must be even higher. Also, the parameters p₆ = 3.5 and v₀ = 0.1 should result in a nearly hexagonal lattice, which should be captured with high γ₆ values. I would expect γ₆ to be in the same range as q₆.

Many thanks for pointing this out. There are two different points addressed in this question: The first is if |Γ_p| is too high. We checked the values, |Γ_p| = 0.5075 for R/R_cell = 2, so it is lower than = 0.58. The second question is why γ_p and q_p are not in the same value range. You are right that for a perfectly hexagonal lattice both should give the same value, namely = = 1.0. However, even at p₆ = 3.5 and v₀ = 0.1 this is not a perfectly hexagonal lattice anymore and how fast the values of q₆ and |γ₆| drop if we move away from a perfect hexagon scales differently. As q_p is stable and only changes slightly for slight changes in the shape it makes sense, that q_p is still close to 1.0 . We included an image, see below, of one time step in said parameter to showcase that cells do not form a perfect hexagonal lattice anymore.

Reviewer 3:

Could the authors show why and how this method could bring new information which were missing so far in the understanding of morphogenesis in vitro and in vivo with the current quantification?

The introduction provides examples of how orientational order and its topological defects can be linked to morphological changes in tissues. The orientational order emerges from the shape of the cells. Most commonly nematic order has been considered, but more recently also hexatic order and even a nematic-hexactic crossover on larger scales. This suggests a mechanical mechanism for morphogenesis, like a phase transition from hexatic to nematic, which would have consequences on the evolution of shape. We demonstrate that the measures q₂ and q₆ are independent. Furthermore the proposed nematic-hexatic transition is only specific to the use of γ_p for characterizing the shape and coarse-graining of the associated order. These measures are not robust and therefore should not be used. Results for the robust measures q_p suggest to consider all p for a coarse-grained theory to model morphological changes in tissues.

Could authors show quantitative comparisons between available methods with the same sets of data and highlight pros and cons?

Author response image 1.

Screenshot from p₆ = 3.5 and v₀ = 0.1

In addition to what was already done for the simulation data we have added data from Armengol-Collado et al. (2023) and compared the results for q_p and Q<sub>p<sub> and γ_p and Γ_p. The theoretical results and the illustrating example in Fig. 6 already show that there are no pros for γ_p. Other methods belong to the class of bond-order methods and measure neighbor relations instead of shape. We already comment that these methods are inappropriate to classify shape, see Methods and materials, last sentence and Mickel et al. (2013) for a detailed discussion why these methods are not robust.

Instead of using phase contrast images, which exhibit curved cell-cell contours, could authors use data with E-cadherin staining instead - as used in many epithelial studies in vitro and in vivo? Could they show both images for wild type and for the E-cadherin KO cell lines with fluorescent readout?

We are convinced that our results do not depend on the way to visualize the cell contours. Furthermore the images do not provide additional information. To further strengthen the experimental part of the manuscript, we instead analyzed data from Armengol-Collado et al. (2023).

They confirm our findings.

The authors acknowledge differences in density between cell lines p. 13 so this calls for new experiments with solid readouts and analysis using comparable experimental conditions.

Additionally, we analyzed data from Armengol-Collado et al. (2023) which confirm our findings. Our results are now supported by two different modeling approaches and two different experimental settings. Because of redundancy we removed the original experimental data from the revised manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors employed direct RNA sequencing with nanopores, enhanced by 5' end adaptor ligation, to comprehensively interrogate the human transcriptome at singlemolecule and nucleotide resolution. They conclude that cellular stress induces prevalent 5' end RNA decay that is coupled to translation and ribosome occupancy. Contrary to the literature, they found that, unlike typical RNA decay models in normal conditions, stress-induced RNA decay is dependent on XRN1 but does not depend on the removal of the poly(A) tail. The findings presented are interesting but a substantial amount of work is needed to fully establish these paradigm-shifting findings.

Strengths:

These are paradigm-shifting observations using cutting-edge technologies.

Weaknesses:

The conclusions do not appear to be fully supported by the data presented.

Our response to the reviewer comments is provided at the end of this document in the section "Recommendations For The Authors"

Reviewer #2 (Public Review):

In the manuscript "Full-length direct RNA sequencing uncovers stress-granule dependent RNA decay upon cellular stress", Dar, Malla, and colleagues use direct RNA sequencing on nanopores to characterize the transcriptome after arsenite and oxidative stress. They observe a population of transcripts that are shortened during stress. The authors hypothesize that this shortening is mediated by the 5'-3' exonuclease XRN1, as XRN1 knockdown results in longer transcripts. Interestingly, the authors do not observe a polyA-tail shortening, which is typically thought to precede decapping and XRN1-mediated transcript decay. Finally, the authors use G3BP1 knockout cells to demonstrate that stress granule formation is required for the observed transcript shortening.

The manuscript contains intriguing findings of interest to the mRNA decay community. That said, it appears that the authors at times overinterpret the data they get from a handful of direct RNA sequencing experiments. To bolster some of the statements additional experiments might be desirable.

A selection of comments:

(1) Considering that the authors compare the effects of stress, stress granule formation, and XRN1 loss on transcriptome profiles, it would be desirable to use a single-cell system (and validated in a few more). Most of the direct RNAseq is performed in HeLa cells, but the experiments showing that stress granule formation is required come from U2OS cells, while short RNAseq data showing loss of coverage on mRNA 5'ends is reanalyzed from HEK293 cells. It may be plausible that the same pathways operate in all those cells, but it is not rigorously demonstrated.

We agree with the reviewer that performing all experiments in a single cell system would be desirable. Presently, our core findings on 5’ RNA shortening are all performed in HeLa cells: the identification of 5’ RNA shortening, the reliance of shortening through XRN1 silencing, suppression of shortening by translation inhibition, and now the relationship between 5’ shortening and deadenylation/decapping through experiments described further below. Our use of other cell lines is primarily to show that 5’ shortening is a general phenomenon, and we have now done this for U20S cells, HEK293 cells, and primary 3T3 cells from mouse. 

Regarding stress granule formation, we are unfortunately restricted by the lack of available wellcharacterized resources. The DDG3BP1/2 U2OS is a well characterized cell line that has been extensively used for stress granule-related experiments. We have therefore opted to use it and performed experiments to verify both the occurrence of stress-induced RNA shortening as well as the rescue in the absence of stress granules. The reproducibility and breadth of the cell lines used in our analysis makes us confident on the generality of our findings.

(2) An interesting finding of the manuscript is that polyA tail shortening is not observed prior to transcript shortening. The authors would need to demonstrate that their approach is capable of detecting shortened polyA tails. Using polyA purified RNA to look at the status of polyA tail length may not be ideal (as avidity to oligodT beads may increase with polyA tail length and therefore the authors bias themselves to longer tails anyway). At the very least, the use of positive controls would be desirable; e.g. knockdown of CCR4/NOT.

We thank the reviewer for their comment. Previous studies, using in vitro transcribed RNA molecules, have shown that direct RNA sequencing can capture and quantify poly(A) tails of varying lengths (Krause et al. 2019). Specifically, a range of 10 to 150 nt has been tested and a high concordance between known and dRNA-Seq determined values was observed. Both tailfindR and nanopolish (used in this work) showed high poly(A) tail estimation accuracy.

Regardless, we agree with the reviewer that our method depends on poly(A) tail capture and thus may be incomplete for fully quantifying poly(A) length changes. We therefore opted to replace these data and instead follow this and other reviewers’ suggestions and perform experiments following knockdown of CCR4/NOT using cells expressing a catalytically inactive CNOT8 (CNOT8*) dominant negative mutant (Chang et al. 2019). Our new data show that stress-induced 5’ end decay is indeed not dependent on prior removal of the poly(A) tail. Specifically, we find that transcript shortening is still observed upon oxidative stress in cells expressing CNOT8* compared to control cells. We present these new results in Fig. 3 and Sup. Fig 3. 

(3) The authors use a strategy of ligating an adapter to 5' phosphorylated RNA (presumably the breakdown fragments) to be able to distinguish true mRNA fragments from artifacts of abortive nanopore sequencing. This is a fantastic approach to curating a clean dataset. Unfortunately, the authors don't appear to go through with discarding fragments that are not adapter-ligated (presumably to increase the depth of analysis; they do offer Figure 1e that shows similar changes in transcript length for fragments with adapter, compared to Figure 1d). It would be good to know how many reads in total had the adapter. Furthermore, it would be good to know what percentage of reads without adapters are products of abortive sequencing. What percentage of reads had 5'OH ends (could be answered by ligating a different adapter to kinasetreated transcripts). More read curation would also be desirable when building the metagene analysis - why do the authors include every 3'end of sequenced reads (their RNA purification scheme requires a polyA tail, so non-polyadenylated fragments are recovered in a nonquantitative manner and should be discarded).

We thank the reviewer for appreciating our approach. The reviewer is correct that we do not discard reads that are not adapter-ligated. As the reviewer correctly mentions this is to increase the sequencing depth. We have found that the ligation efficiency is very low, ~1-2 % of total reads (now in Sup. Table. 1), across all libraries, and so the percentage of REL5-ligated reads does not directly infer the total amount of non-artifactual 5’ ends. Instead, we use these REL5ligated reads as a subset of our data for which we have extremely high confidence in the true 5’end. Our results show that non-ligated reads display the same length distribution as ligated ones, and that the results are reproducible regardless of read selection (e.g. Fig. 1c, e, Sup. Fig. 1k, l, Fig. 3b, c). This strong concordance between REL5-ligated and non-ligated reads suggests that our conclusions on 5’ end shortening are not substantially influenced by abortive sequencing or other artefactual creation of 5’ shortening. We have modified the text to clarify these points and have added plots using only ligated molecules for relevant figures that this was not previously done (Sup. Fig 1l, 3c)

We agree with the reviewer that non-polyadenylated reads could be discarded from metagene analysis and we have performed this change in the revised version. Our conclusions following removal of non-polyadenylated reads remain unchanged (Sup. Fig. 1g).

(4) The authors should come to a clear conclusion about what "transcript shortening" means. Is it exonucleolytic shortening from the 5'end? They cannot say much about the 3'ends anyway (see above). Or are we talking about endonucleolytic cuts leaving 5'P that then can be attached by XRN1 (again, what is the ratio of 5'P and 5'OH fragments; also, what is the ratio of shortened to full-length RNA)?

We thank the reviewer for their suggestion. We have performed additional experiments to investigate the role of deadenylation and decapping by expressing dominant negative forms of the NOT8 deadenylase (NOT8*) and DCP2 decapping (DCP2*) enzyme in HeLa cells. Our results show that neither expression of NOT8* nor DCP2* can inhibit stress-induced transcript shortening following arsenite treatment (Fig. 3e-f). These new data suggest that neither deadenylation nor decapping are required for stress-induced RNA decay. Instead, our data are more compatible with endonucleolytic cleavage as the most likely mechanism for stressinduced RNA decay. We have incorporated these results in the text and present them in Fig. 3 and Sup. Fig. 3.

(5) The authors should clearly explain how they think the transcript shortening comes about. They claim it does not need polyA shortening, but then do not explain where the XRN1 substrate comes from. Does their effect require decapping? Or endonucleolytic attacks?

Please also refer to our answer to the previous comment (#4). Collectively, our results from a) the dominant negative expression of NOT8* and DCP2* that show no effect on stress-induced shortening and b) the rescue of transcript length upon translation initiation inhibition, indicate a potential endonucleolytic mechanism as a mediator of stress-induced RNA decay. However, we believe that extensive, further studies currently beyond the scope of this work, will be required to discover the nuclease and to dissect the exact molecular mechanisms that define the 5' ends of mRNAs upon stress-induced decay. We now discuss these points in the discussion.

(6) XRN1 KD results in lengthened transcripts. That is not surprising as XRN1 is an exonuclease - and XRN1 does not merely rescue arsenite stress-mediated transcript shortening, but results in a dramatic transcript lengthening.

The reviewer raises an intriguing point. Additional analysis of data has showed that in fact, in unstressed cells, XRN1 KD leads to modestly significant reduction in overall transcript length (Fig. 3b, c). This could possibly be the result of an accumulation of intermediate cleavage products normally expected to be degraded by XRN1 as previously described (Pelechano, Wei, and Steinmetz 2015; Ibrahim et al. 2018).

Instead, we find that under stress, XRN1 KD shows an almost identical transcript length distribution to unstressed cells and significantly higher than siCTRL stressed cells (Fig. 3b, c). These results indicate that in the absence of XRN1, stress-induced decay is largely abolished. As the reviewer correctly points out, this seems to affect the majority of RNAs which we believe is evidence of the general lack of specificity in the mechanism. Nevertheless, we find that transcripts that are the primary substrates to stress-induced shortening are substantially more lengthened than all other transcripts (Fig. 3e). This indicates that transcripts primarily affected by stress-induced decay are also lengthened the most in the absence of XRN1 and at an even higher level than expected by general XRN1 KD effects.

Reviewer #3 (Public Review):

The work by Dar et al. examines RNA metabolism under cellular stress, focusing on stressgranule-dependent RNA decay. It employs direct RNA sequencing with a Nanopore-based method, revealing that cellular stress induces prevalent 5' end RNA decay that is coupled to translation and ribosome occupancy but is independent of the shortening of the poly(A) tail. This decay, however, is dependent on XRN1 and enriched in the stress granule transcriptome. Notably, inhibiting stress granule formation in G3BP1/2-null cells restores the RNA length to the same level as wild-type. It suppresses stress-induced decay, identifying RNA decay as a critical determinant of RNA metabolism during cellular stress and highlighting its dependence on stress-granule formation.

This is an exciting and novel discovery. I am not an expert in sequencing technologies or sequencing data analysis, so I will limit my comments purely to biology and not technical points. The PI is a leader in applying innovative sequencing methods to studying mRNA decay.

One aspect that appeared overlooked is that poly(A) tail shortening per se does lead to decapping. It is shortening below a certain threshold of 8-10 As that triggers decapping. Therefore, I found the conclusion that poly(A) tail shortening is not required for stress-induced decay to be somewhat premature. For a robust test of this hypothesis, the authors should consider performing their analysis in conditions where CNOT7/8 is knocked down with siRNA.

We agree with the reviewer. We have now performed experiments in cells expressing a well characterized catalytically inactive dominant negative NOT8 isoform (NOT8*) (Chang et al.

2019). Our new data show that stress-induced decay still occurs in cells expressing NOT8*.

These results confirm our findings that stress-induced decay does not require deadenylation. We present these new results in Fig. 3 and Sup. Fig. 3. 

Similarly, as XRN1 requires decapping to take place, it necessitates the experiment where a dominant-negative DCP2 mutant is over-expressed.

We agree with the reviewer and have performed this experiment as requested. Expression of a dominant negative DCP2 (DCP2*) isoform (Loh, Jonas, and Izaurralde 2013) in HeLa cells showed that decapping is also not required for stress-induced decay. We present these new results in Fig. 3 and Sup. Fig. 3.

Are G3BP1/2 stress granules required for stress-induced decay or simply sites for storage? This part seems unclear. A very worthwhile test here would be to assess in XRN1-null background.

We thank the reviewer for their comment. Our data show that stress-induced decay is not observed in DDG3BP1/2 U2OS cells, unable to form stress granules (Fig. 6). This result suggests that G3BP1/2 SGs are either a) required for 5’ RNA shortening or b) preserve partially fragmented RNAs that would otherwise be rapidly degraded. We find the second option unlikely for two reasons. First, even if the fragments were rapidly degraded, we would still expect to find evidence of their presence in our data. However, Fig. 6f shows that the length distribution of DDG3BP1/2 U2OS cells, with and without arsenite, are almost identical, thus arguing against the presence of such a pool of rapidly degrading RNAs. Second, if these RNAs were protected by SGs, then they would be expected to be downregulated in the absence of SGs in DDG3BP1/2 U2OS cells treated with arsenite. Our results contradict this hypothesis as no association is found between the level of downregulation in arsenite-treated DDG3BP1/2 U2OS cells and the observed stress-induced fragmentation in WT. Collectively our results point towards G3BP1/2 stress granules being required for stress-induced decay. We have expanded on these points in the manuscript to clarify.

Finally, the authors speculate that the mechanism of stress-induced decay may have evolved to relieve translational load during stress. But why degrade the 5' end when removing the cap may be sufficient? This returns to the question of assessing the role of decapping in this mechanism.

The reviewer raises a very interesting point. Our new results, following expression of dominant negative DCP2, show that stress-induced decay does not require decapping. It is therefore plausible that a stress-induced co-translational mechanism cleaves mRNAs endonucleolyticaly to reduce the translational load. Such a mechanism would have many functional benefits as it would acutely reduce the translational load, degrade non-essential RNAs, preserve energy and release ribosomes for translation of the stress response program. We have expanded the discussion to mention these points.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

As you can see from the comments, although the reviewers appreciate the novelty of your findings, there was a consensus opinion from all reviewers that the authors overinterpreted their data, since they only have one assay and did not fully analyze it, as laid out in one of the reviewer's critiques. Some orthogonal validation of the "groundbreaking" claims is necessary. Examination of the effects of upstream events in 5'-to-3' decay, namely deadenylation, and decapping, would be necessary for a better understanding of the phenomena the authors describe. Many tools and approaches for studying this are described well in the literature (CNOT7-KD, dominant negative DCP2 E148Q, XRN1-null cell lines), so it is well within the authors' reach. Overall, while some of the evidence presented is novel and solid, for some of the claims there is only incomplete evidence.

We thank the reviewers and the editor for their comments and suggestions. We have performed several additional experiments to further support our conclusions. We have notably investigated the role of deadenylation and decapping in the stress-induced decay by expressing dominant negative NOT8 and DCP2, respectively, as suggested. Our results show that neither deadenylation nor decapping is necessary for stress-induced transcript shortening, suggesting an endonucleolytic event. We believe that these additional experiments strengthen the main conclusions of our work. 

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1) The experiments were conducted in two unrelated cell lines, HeLa and U2OS. The authors should determine if the 5'end RNA decay in response to stress is also observed in normal human cells such as normal human diploid fibroblasts. Furthermore, it would be important to know if this mechanism is conserved between human and mouse cells. This can be tested in mouse embryonic fibroblasts.

We thank the reviewer for their suggestion. We have now also performed experiments in the mouse embryonic fibroblast NIH 3T3 cell line. Our new results confirm that stress-induced 5’ end RNA decay is also observed in this primary cell line and is conserved between human and mouse (Sup. Fig. 1k, I). 

(2) The authors state that they monitored cell viability up to 24 hours after Arsenite treatment, but the data is shown up to 240 min (Suppl. 1a). Also, the Y-axis label of this Figure is "Active cells (%)". This should be changed to "Live cells (%)" if this is what they are referring to.

We thank the reviewer for identifying this mistake. Cell viability was monitored up to 4 hours after arsenite treatment. We have corrected the text and modified the figure according to the reviewer’s suggestion.

(3) Based on direct Nanopore-based RNA-seq the authors surprisingly found that RNAs in oxidative stress were globally shorter than unstressed cells. Since Nanopore-based RNA-seq will not detect RNAs that lack a poly A-tail, are they not missing out on RNAs that have already started getting degraded due to the loss of a poly A-tail? Also, I am not sure if they used a spikein control which would be critical to claim global changes in RNA expression.

We agree with the reviewer that our strategy does not capture RNA molecules without a poly(A) tail. Nevertheless, our data do identify shortening upon stress at the 5’ end of RNAs that include poly(A) tails. We considered this as direct evidence that decay at the 5’ end does not require prior removal of the poly(A) tail. Otherwise, these molecules would not have been captured and observed. Indeed, our newly added data from cells expressing a well characterized catalytically inactive dominant negative NOT8 isoform (Chang et al. 2019) show that stress-induced decay occurs even upon silencing of the CCR4-NOT deadenylation complex. We present these results in Fig. 3 and Sup. Fig 3.

We would like to clarify that in our results we did not use a spike-in control and thus refrain from claiming global changes in RNA expression. Instead, we compare relative ratios of groups of molecules within libraries that are internally normalized, we perform correlative comparisons that are invariant to normalization and we perform differential gene expression using established normalization schemes such as DESeq2 (Love, Huber, and Anders 2014). 

(4) Many graphs are confusing and inconsistent. For example, samples for Nanopore RNA-seq were prepared in triplicates. Biological or technical? The schematic in Figure 1a shows ISRIB but it appears from Figure 4 onwards. It is missing in the Figure 1 results and the Figure legend. The X-axis labels of many graphs are confusing. For example, Supplementary Figure 1d, 1e, 1g and 1h. It says transcript length but are these nucleotides? P-values are missing from many of these graphs. For some graphs, the authors compared Unstressed vs Arsenite (Figure 1), but in other panels they state No Ars vs 0.5 mM Ars (Fig. 3a) or Control vs Ars (Figure 5c). Likewise, in Figure 1b, Expression change (log2) is unstressed vs Arsenite or Arsenite vs unstressed?

We thank the reviewer identifying these inconsistencies in the presentation of our results. The replicates for nanopore RNA-seq experiments were biological. We have now clarified this point in the text. Furthermore, we have removed “ISRIB” from Fig. 1a to avoid any confusion. We have also made our labelling across all figures more consistent using ‘unstressed’ for NO arsenite treatment vs “arsenite” or ‘+ Ars’ for arsenite treatment. 

(5) The authors transfected cells with siCTRL or siXRN1 using electroporation and treated the cells 72 hours after transfection. Since XRN1 is an essential gene, it would be important to determine the viability of cells 72 hours after transfection. Along these lines, in Figure 3b, it would be important to determine the effect of XRN1 knockdown in unstressed cells. Currently, there are only 3 comparisons in Figure 3b - unstressed, siCTRL + Ars and siXRN1 + Ars, and this is insufficient to conclude the effects of XRN1 knockdown in the presence of Arsenite.

We thank the reviewer for their suggestion. We have updated Fig. 3b and the text to show the requested conditions: siCTRL and siXRN1 with and without arsenite. While XRN2 is an essential gene for many organisms, XRN1 is not essential in mammalian cells and no increased cell death has been reported for XRN1-KO or –KD cells (Brothers et al. 2023). We have also tested different concentration (up to 40 nM) of siRNA and monitored the cells up to five days after transfection without observing any cell toxicity, as previously reported.

(6) More broadly, the whole study is somewhat descriptive. The biological effect of 5'end mRNA shortening on gene expression is unclear. There is no data indicating how these changes in RNA lengths impact protein expression. Global quantitative proteomics would be critical to determine this.

We thank the reviewer for their suggestion. To address this concern we have performed additional experiments using cells expressing catalytically inactive forms of NOT8 (Chang et al. 2019) and DCP2 (Loh, Jonas, and Izaurralde 2013) to inhibit deadenylation and decapping.

These experiments provide additional mechanistic details for 5’ shortening and suggest endonucleolytic cleavage as a critical step (Fig. 3 and Sup. Fig. 3). We agree that it would be interesting to study the fate of these shortened transcripts notably regarding translation. However, given the complexity of the expected proteome changes also following global translation arrest under stress (Harding et al., 2003; Pakos-Zebrucka et al., 2016), we think that this work is beyond the scope of this manuscript and will be the subject of future studies. 

Minor comments:

(1) Some of the affected RNAs can be validated in HeLa and other cell lines.

We thank the reviewer for their suggestion. We have performed RT-qPCR on 3 different mRNAs that present 5’ shortening upon oxidative stress using different primers located along the mRNA. We hypothesized that the closer the primer set is located to the 5’ end, the less abundant the corresponding region would be for arsenite-treated compared to untreated cells. Our results show indeed that the measured level of these mRNAs depends on the location of the primer sets used for the qPCR, the closer to the 5’end it is, the less abundant the mRNA is upon oxidative stress compared to control cells. We present these data as well as a schematic representing the positions of the primers in Sup. Fig. 2d. 

(2) The authors should check whether XRN1 also co-localizes in SGs.

We thank the reviewer for their suggestion. We have performed immunofluorescence on U2OS and HeLa upon oxidative stress and did not observe a co-localization of XRN1 with TIA-1, a marker of stress granules (see below). These results are consistent with (Kedersha et al. 2005) that have shown that XRN1 mainly co-localizes to processing bodies and are very weakly detectable in SGs in DU145 cells. We think that this result is beyond the scope of this study and thus decided to only include it for the reviewers.

Author response image 1.

Representative immunofluorescence merged image of HeLa (left panel) and U2OS (right panel) cells treated with sodium arsenite and labelled with anti-TIA1 (red), anti-XRN1 (green) antibodies and DAPI (blue). Scale bar 50 µm.

(3) XRN1 should be knocked down with more than one siRNA.

We thank the reviewer for this suggestion. Our results show that our XRN1 KD specifically rescues the length of the most shortened mRNAs (Fig. 3e). This is a highly specific effect that makes us confident it is not mediated by non-specific siRNA binding; thus, we do not consider it necessary to repeat the experiment.

(4) There are typos in the text regarding Figure 6d, e, and f. Also, Supplementary Figure 4a.

We thank the reviewer for identifying these mistakes. We have corrected the typos. 

Reviewer #3 (Recommendations For The Authors):

The authors should consider testing their hypotheses by arresting the decay pathway using the approaches I mentioned previously. As it stands, some conclusions are somewhat speculative.

We have replied to the reviewer comments in the public review section. 

References:

• Brothers, William R., Farah Ali, Sam Kajjo, and Marc R. Fabian. 2023. “The EDC4-XRN1 Interaction Controls P-Body Dynamics to Link MRNA Decapping with Decay.” The EMBO Journal, August, e113933.

• Chang, Chung-Te, Sowndarya Muthukumar, Ramona Weber, Yevgen Levdansky, Ying Chen, Dipankar Bhandari, Catia Igreja, Lara Wohlbold, Eugene Valkov, and Elisa Izaurralde. 2019. “A Low-Complexity Region in Human XRN1 Directly Recruits Deadenylation and Decapping Factors in 5’-3’ Messenger RNA Decay.” Nucleic Acids Research 47 (17): 9282–95.

• Harding, Heather P., Yuhong Zhang, Huiquing Zeng, Isabel Novoa, Phoebe D. Lu, Marcella Calfon, Navid Sadri, et al. 2003. “An Integrated Stress Response Regulates Amino Acid Metabolism and Resistance to Oxidative Stress.” Molecular Cell 11 (3): 619–33.

• Ibrahim, Fadia, Manolis Maragkakis, Panagiotis Alexiou, and Zissimos Mourelatos. 2018. “Ribothrypsis, a Novel Process of Canonical MRNA Decay, Mediates Ribosome-Phased MRNA Endonucleolysis.” Nature Structural & Molecular Biology 25 (4): 302–10.

• Kedersha, Nancy, Georg Stoecklin, Maranatha Ayodele, Patrick Yacono, Jens Lykke-Andersen, Marvin J. Fritzler, Donalyn Scheuner, Randal J. Kaufman, David E. Golan, and Paul Anderson. 2005. “Stress Granules and Processing Bodies Are Dynamically Linked Sites of MRNP Remodeling.” The Journal of Cell Biology 169 (6): 871–84.

• Krause, Maximilian, Adnan M. Niazi, Kornel Labun, Yamila N. Torres Cleuren, Florian S. Müller, and Eivind Valen. 2019. “Tailfindr: Alignment-Free Poly(A) Length Measurement for Oxford Nanopore RNA and DNA Sequencing.” RNA  25 (10): 1229–41.

• Loh, Belinda, Stefanie Jonas, and Elisa Izaurralde. 2013. “The SMG5-SMG7 Heterodimer Directly Recruits the CCR4-NOT Deadenylase Complex to MRNAs Containing Nonsense Codons via Interaction with POP2.” Genes & Development 27 (19): 2125–38.

• Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15 (12): 550.

• Pakos-Zebrucka, Karolina, Izabela Koryga, Katarzyna Mnich, Mila Ljujic, Afshin Samali, and Adrienne M. Gorman. 2016. “The Integrated Stress Response.” EMBO Reports 17 (10): 1374–95.

• Pelechano, Vicent, Wu Wei, and Lars M. Steinmetz. 2015. “Widespread Co-Translational RNA Decay Reveals Ribosome Dynamics.” Cell 161 (6): 1400–1412.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

The manuscript discusses the role of phosphorylated ubiquitin (pUb) by PINK1 kinase in neurodegenerative diseases. It reveals that elevated levels of pUb are observed in aged human brains and those affected by Parkinson's disease (PD), as well as in Alzheimer's disease (AD), aging, and ischemic injury. The study shows that increased pUb impairs proteasomal degradation, leading to protein aggregation and neurodegeneration. The authors also demonstrate that PINK1 knockout can mitigate protein aggregation in aging and ischemic mouse brains, as well as in cells treated with a proteasome inhibitor. While this study provided some interesting data, several important points should be addressed before being further considered.

Strengths:

(1) Reveals a novel pathological mechanism of neurodegeneration mediated by pUb, providing a new perspective on understanding neurodegenerative diseases.

(2) The study covers not only a single disease model but also various neurodegenerative diseases such as Alzheimer's disease, aging, and ischemic injury, enhancing the breadth and applicability of the research findings.

Weaknesses:

(1) PINK1 has been reported as a kinase capable of phosphorylating Ubiquitin, hence the expected outcome of increased p-Ub levels upon PINK1 overexpression. Figures 5E-F do not demonstrate a significant increase in Ub levels upon overexpression of PINK1 alone, whereas the evident increase in Ub expression upon overexpression of S65A is apparent. Therefore, the notion that increased Ub phosphorylation leads to protein aggregation in mouse hippocampal neurons is not yet convincingly supported.

Indeed, overexpression of sPINK1 alone resulted in minimal changes in Ub levels in the soluble fraction (Figure 5E), which is expected given that the soluble Ub pool remains relatively stable and buffered. However, sPINK1* overexpression led to a marked increase in Ub levels in the insoluble fraction, indicative of increased protein aggregation (Figure 5F). The molecular weight distribution of Ub in the insoluble fraction was predominantly below 70 kDa, suggesting that phosphorylation inhibits Ub chain elongation.

To further validate this mechanism, we utilized the Ub/S65A mutant to antagonize Ub phosphorylation and observed a significant reduction in the intensity of aggregated bands at low molecular weights, indicating restored proteasomal activity. The observed increase in Ub levels in the soluble fraction upon Ub/S65A overexpression is likely due to enhanced ubiquitination driven by elevated Ub-S65A, and notably, Ub/S65A was also detectable using an antibody against wild-type Ub.

Consistent with these findings, overexpression of Ub/S65E resulted in a further increase in Ub levels in the insoluble fraction, with intensified low molecular weight bands. The effect was even more pronounced than that observed with sPINK1 transfection, likely resulting from the complete phosphorylation mimicry achieved by Ub/S65E, compared to the relatively low levels of phosphorylation by PINK1.

These findings collectively support the conclusion that sPINK1 promotes protein aggregation via Ub phosphorylation. We have updated the Results and Discussion sections to more clearly present the data and explain the various controls.

(2) The specificity of PINK1 and p-Ub antibodies requires further validation, as a series of literature indicate that the expression of the PINK1 protein is relatively low and difficult to detect under physiological conditions.

We acknowledge the challenges in achieving high specificity with commercially available and customgenerated antibodies targeting PINK1 and pUb, particularly given their low endogenous expression under physiological conditions. However, in our study, we observed robust immunofluorescent staining for PINK1 (Figures 1A, 1C, and 1G) and pUb (Figures 1B, 1D, and 1G) in human brain samples from Alzheimer's disease (AD) patients, as well as in mouse models of AD and cerebral ischemia. The clear visualization can be partly attributed to the pathological upregulation of PINK1 and pUb under disease conditions. Importantly, the images from pink1^-/- mice exhibit much weaker staining.

Additionally, we detected a significant elevation in the pUb levels in aged mouse brains compared to younger ones (Figures 1E and 1F). In contrast, pink1^-/- mice showed no change in pUb levels with aging, despite some background signals, demonstrating that pUb accumulation during aging is PINK1dependent. Collectively, these results support the specificity of the antibodies used in detecting pathophysiological changes in PINK1 and pUb levels.

For cultured cells, pink1^-/- cells served as a negative control for both PINK1 (Figures 2B and 2C) and pUb (Figures 2D and 2E). While the pUb Western blot exhibited some nonspecific background, pUb levels in pink1^-/- cells remained unchanged across all MG132 treatment conditions (Figures 2D and 2E), further attesting the usability of the antibodies in conjunction with appropriated controls.

We have updated the manuscript with higher-resolution images; individual image files have been uploaded separately.

(3) In Figure 6, relying solely on Western blot staining and Golgi staining under high magnification is insufficient to prove the impact of PINK1 overexpression on neuronal integrity and cognitive function. The authors should supplement their findings with immunostaining results for MAP2 or NeuN to demonstrate whether neuronal cells are affected.

We included NeuN immunofluorescent staining at 10, 30, and 70 days post transfection in Figure 5— figure supplement 2. The results clearly demonstrate a significant loss of NeuN-positive cells in the hippocampus following Ub/S65E overexpression, while no apparent reduction was observed with sPINK1 transfection alone. 

We have also quantified MAP2 protein levels via Western blotting and examined morphology of neuronal dendrite and synaptic structure using Golgi staining. These analyses revealed a significant reduction in MAP2 levels and synaptic damage upon sPINK1 or Ub/S65E overexpression (Figures 6F and 6H), consistent with the proteomics analysis (Figure 5—figure supplementary 5). Notably, these detrimental effects could be rescued by co-expression of Ub/S65A, reinforcing the role of pUb in mediating these structural changes.

Together, our findings from NeuN immunostaining, MAP2 protein analysis, proteomics analysis, and Golgi staining provide strong evidence for the impact of PINK1 overexpression and pUb elevation on neuronal integrity and synaptic structure.

(4) The authors should provide more detailed figure captions to facilitate the understanding of the results depicted in the figures.

Figure captions have been updated with more details incorporated in the revised manuscript.

(5) While the study proposes that pUb promotes neurodegeneration by affecting proteasomal function, the specific molecular mechanisms and signaling pathways remain to be elucidated.

The molecular mechanisms and signaling pathways through which pUb promotes neurodegeneration are likely multifaceted and interconnected. Our findings suggest that mitochondrial dysfunction plays a central role following sPINK1* overexpression. This is supported by (1) an observed increase in full-length PINK1, indicative of impaired mitochondrial quality control, and (2) proteomic data showing enhanced mitophagy at 30 days post-transfection, followed by substantial mitochondrial injuries at 70 days post-transfection (Figure 5—figure supplement 5 and Supplementary Data). The progressive mitochondrial damage caused by protein aggregates would exacerbate neuronal injury and degeneration.

Additionally, reduced proteasomal activity may lead to the accumulation of inhibitory proteins that are normally degraded by the ubiquitin-proteasome system. Our proteomics analysis identified a >50fold increase in CamK2n1 (UniProt ID: Q6QWF9), an endogenous inhibitor of CaMKII activation, following sPINK1* overexpression. The accumulation of CamK2n1 suppresses CaMKII activation, thereby inhibiting the CREB signaling pathway (Figure 7), which is essential for synaptic plasticity and neuronal survival. This disruption can further contribute to neurodegenerative processes.

Thus, our findings underscore the complexity of pUb-mediated neurodegeneration and call for further investigation into downstream consequences.

Reviewer #1 (Recommendations for the authors):

Suggestions for improved or additional experiments, data or analyses.

We have performed additional experiments to investigate how the impairment of ubiquitinproteasomal activity contributes to neurodegeneration. Specifically, we investigated CamK2n1, an endogenous inhibitor of CaMKII, which is normally degraded by the proteasome to allow CaMKII activation. Our proteomics analysis revealed a significant (>50-fold) elevation of CamKI2n1 following sPINK1 overexpression (Figure 5—figure supplement 5 and Supplementary Data).

To validate this mechanism, we conducted immunofluorescence and Western blot analyses, demonstrating reduced levels of phosphorylated CaMKII (pCaMKII) and phosphorylated CREB (pCREB), as well as reduced levels of downstream proteins such as BDNF and ERK. These results have been incorporated into the revised manuscript (Figure 7).

As the proteasome is crucial in maintaining proteostasis, its dysregulation would trigger neurodegeneration through multiple pathways, contributing to a broad cascade of pathological events.

Reviewer #2 (Public review):

Summary:

The manuscript makes the claim that pUb is elevated in a number of degenerative conditions including Alzheimer's Disease and cerebral ischemia. Some of this is based on antibody staining which is poorly controlled and difficult to accept at this point. They confirm previous results that a cytosolic form of PINK1 accumulates following proteasome inhibition and that this can be active. Accumulation of pUb is proposed to interfere with proteostasis through inhibition of the proteasome. Much of the data relies on over-expression and there is little support for this reflecting physiological mechanisms.

Weaknesses:

The manuscript is poorly written. I appreciate this may be difficult in a non-native tongue, but felt that many of the problems are organizational. Less data of higher quality, better controls and incision would be preferable. Overall the referencing of past work is lamentable. Methods are also very poor and difficult to follow.

Until technical issues are addressed I think this would represent an unreliable contribution to the field.

(1) Antibody specificity and detection under pathological conditions

We recognize the limitations of commercially available antibodies for detecting PINK1 and pUb. Nevertheless, our findings reveal a significant elevation in PINK1 and pUb levels under pathological conditions, such as Alzheimer's disease (AD) and ischemia. Additionally, we observed an increase in pUb level during brain aging, further demonstrating its relevance and a potentially causative role for this special pathological condition. Similarly, elevated pUb levels were observed for cultured cells following pharmacological treatment or oxygen-glucose deprivation (OGD).

In contrast, in pink1^-/- mice and HEK293 cells used as negative controls, PINK1 and pUb levels remained consistently low. Therefore, the observed elevation of PINK1 and pUb are associated with special pathological conditions, rather than an antibody-detection anomaly.

(2) Overexpression as a model for pathological conditions

To investigate whether the inhibitory effects of sPINK1 on the ubiquitin-proteasome system (UPS) depend on its kinase activity, we employed a kinase-dead version of sPINK1* as a negative control. Given that PINK1 targets multiple substrates, we also investigated whether its effects on UPS inhibition were specifically mediated by ubiquitin phosphorylation. To this end, we used Ub/S65A (a phospho-null mutant) to block Ub phosphorylation by sPINK1, and Ub/S65E (a phospho-mimetic mutant) to mimic phosphorylated Ub. These well-defined controls ensured the robustness of our conclusions.

Although overexpression does not perfectly replicate physiological conditions, it provides a valuable model for studying pathological scenarios such as neurodegeneration and brain aging, where pUb levels are elevated. For example, we observed a 30.4% increase in pUb levels in aged mouse brains compared to young brains (Figure 1F). Similarly, in our sPINK1 overexpression model, pUb levels increased by 43.8% and 59.9% at 30- and 70-days post-transfection, respectively, compared to controls (Figures 5A and 5C). Notably, co-expression of sPINK1* with Ub/S65A almost entirely prevented sPINK1* accumulation (Figure 5B), indicating that an active UPS can efficiently degrade this otherwise stable variant of sPINK1.

Together, our findings demonstrate that sPINK1 accumulation inhibits UPS activity, an effect that can be reversed by the phospho-null Ub mutant. The overexpression model mimics pathological conditions and provides valuable insights into pUb-mediated proteasomal dysfunction.

(3) Organization of the manuscript

Following your suggestion, we have restructured the manuscript to present the key findings in a more logical and cohesive sequence:

(a) Evidence for elevated PINK1 and pUb levels across a broad spectrum of pathological and neurodegenerative conditions;

(b) The effects of pUb elevation in cultured cells, focusing on the proteasome;

(c) Mechanistic insights into how pUb elevation inhibits proteasomal activity;

(d) The absence of PINK1 and pUb alleviates protein aggregation;

(e) Evidence for the causative relationship between elevated pUb levels and proteasomal inhibition;

(f) Demonstration that pUb elevation directly contributes to neuronal degeneration;

(g) Give an additional evidence to explain the mechanism of neuronal degeneration post sPINK1* over-expression. The downstream effects of elevated CamK2n1, an inhibitor of CaMKII, resulting from proteasomal inhibition.

This reorganization should ensure a clear and progressive narrative, and enhance the overall coherence and impact of the revised manuscript.

(4) Revisions to writing, referencing, and methodology

We have made a great effort to enhance the clarity and flow of the manuscript, including the addition of references to appropriately acknowledge prior work. We have also expanded the Methods section with additional details to improve readability and ensure reproducibility. We believe these revisions effectively address the concerns raised and strengthen the overall quality of the manuscript.

Reviewer #2 (Recommendations for the authors):

Figure 1: PINK1 is a poorly expressed protein and difficult to detect by Western blot let alone by immunofluorescence. I have direct experience of the antibody used in this study and do not consider it reliable. There are much cleaner reagents out there, although they still have many challenges. The minimal requirement here is for the PINK1 antibody staining to be compared in wild-type and knockout mice. One would also expect to see a mitochondrial staining which would require higher magnification to be definitive, but it does not look like it to me. This is a key foundational figure and is unreliable. The pUb antibody also has a high background, see for example figure 2E.

Under physiological conditions, PINK1 and pUb levels are indeed low, making their detection challenging. However, under pathological conditions, their expression is significantly elevated, correlating with disease severity. Given the limitations of available reagents, using appropriate controls is a standard approach in biological research.

Nevertheless, we observed robust immunofluorescent staining for PINK1 (Figures 1A, 1C, and 1G) and pUb (Figures 1B, 1D, and 1G) in human brain samples from Alzheimer’s disease (AD) patients and mouse models of AD and cerebral ischemia. Compared to healthy controls, the significant elevation of PINK1 and pUb under these pathological conditions accounts for their clear visualization. To validate antibody specificity, we have included images from pink1^-/- mice as negative controls (Figure 1C and 1D, third panel).

Furthermore, we analyzed pUb levels in both young and aged mice, using pink1^-/- mice as controls.

Our results revealed a significant increase in pUb levels in aged wild-type mice (Figures 1E and 1F), In contrast, pink1^-/- mice exhibited relatively low pUb levels, with no notable change between young and aged groups. These findings reinforce the conclusion that pUb accumulation during aging is dependent on PINK1.Furthermore, we analyzed pUb levels in both young and aged mice, using pink1^-/- mice as controls.

For HEK293 cells, pink1^-/- cells were used as a negative control for assessing PINK1 (Figures 2B and 2C) and pUb levels (Figures 2D and 2E). While the pUb Western blot did show some nonspecific background, as you have noted, pUb levels significantly increased following MG132 treatment of the wildtype cells. In contrast, no such increase was observed in pink1^-/- cells (Figure 2D and 2E). These results further validate the reliability of our findings.

Regarding mitochondrial staining, we recognize that PINK1 localization can vary depending on the pathological context. For example, in Alzheimer’s disease, PINK1 exhibits relatively high nuclear staining, while in cerebral ischemia and brain aging, it is predominantly cytoplasmic and punctate. In contrast, in young, healthy mouse brains, PINK1 is more uniformly distributed. The observed elevation in pUb levels could arise from mitochondrial PINK1 or soluble sPINK1 in the cytoplasm, and it remains unclear whether nuclear PINK1 contributes to pUb accumulation. Investigating the role of PINK1 in different forms and subcellular localizations will be an important avenue for future research.

To enhance clarity, we have updated our images and replaced them with higher-resolution versions in the revised manuscript.

Please also confirm that the GAPDH loading controls represent the same gels, to my eye they do not match.

We have reviewed all the bands, and confirmed that the GAPDH loading controls correspond to the same gels. For different gels, we use separate GAPDH loading controls. There are two experimental scenarios to consider:

(1) When there is a large difference in molecular weight between target proteins, we cut the gel into sections and incubate each section with different antibodies separately.

(2) When the molecular weight difference is small and cutting is not feasible, we first probe the membrane with one antibody, strip it, and then re-incubate the membrane with a second antibody.

These approaches ensure accurate and reliable detection of target proteins with various molecular weights relative to GAPDH.

1H. Ponceau.

We have corrected the spelling.

Figure 2 many elements are confirmation of work already reported and this must be made clearer in the text. 

Indeed, the elevation of sPINK1 and pUb upon proteasomal inhibition has been previously reported, and these studies have been acknowledged (Gao, et al, 2016; Dantuma, et al, 2000). In the present study, we expand on these findings by conducting a detailed analysis of the time- and concentrationdependent effects of MG132 on sPINK1 and pUb levels, establishing a causative relationship between pUb accumulation and proteasomal inhibition. Furthermore, we demonstrate that sPINK1 overexpression and MG132-induced proteasomal inhibition exhibit no additive effect, indicating that both converge on the same pathway, resulting in the impairment of proteasomal activity.

It has been established that ubiquitin phosphorylation inhibits Ub chain elongation (Wauer, et al, 2015). However, our study provides novel insights by identifying an additional mechanism: phosphorylated Ub also interferes with the noncovalent interactions between Ub chain and Ub receptors in the proteasome, which further contributes to the impairment of UPS function.

The PINK1 kinase-dead mutant construction (Figure 2F) and the use of Ub-GFP as a proteasomal substrate were based on established methodologies, which have been appropriately cited in the manuscript (Beilina, etal 2005 for KD sPINK1; Yamano, et al for endogenous PINK1; Samant, et al, 2018 and Dantuma, et al, 2000 for Ub-GFP probe). Similarly, our use of puromycin and BALA treatments follows previously reported protocols (Gao, et al, 2016), which allowed us to dissect the relative contributions of sPINK1* overexpression to proteasomal vs. autophagic dysfunction.

As you have noted, our study has built upon prior findings while introducing new mechanistic insights into sPINK1 and pUb-mediated proteasomal dysfunction.

2C 24h MG132 not recommended, most cells are dead by then.

We used MG132 treatment for 24 hours to evaluate the time-course effects of proteasomal inhibition on PINK1 and pUb levels in HEK293 cells (Figures 2C and 2E). We did observe some decrease in both PINK1 and pUb levels at 24 hours compared to 12 hours, which may result from some extend of cell death at the longer treatment duration.

In SH-SY5Y cells, we collected cells at 24 hours after MG132 administration (Figure 5—figure supplementary 1). Though protein aggregation was evident in these cells, we did not observe pronounced cell death under these conditions, justifying our treatment.

Our findings are consistent with previous studies demonstrating that MG132 at 5 µM for 24 hours effectively induces proteasomal inhibition without substantial cytotoxicity. For example, studies using human esophageal squamous cancer cells have reported that this treatment condition inhibits cell proliferation while maintaining cell viability, with cell viability >70% after 24-hour treatment with 5 µM MG132 (Int J Mol Med 33: 1083-1088, 2014). 

MG132 has been commonly used at concentrations ranging from 5 to 50 µM for durations of 1 to 24 hours, as stated at the vendor’s website （https://www.cellsignal.com/products/activatorsinhibitors/mg-132/2194）.

2I what is BALA do they mean bafilomycin. This is a v-ATPase inhibitor, not just an autophagy inhibitor.

We appreciate the reviewer’s comment regarding the use of BALA in Figure 2I. To clarify, BALA refers to bafilomycin A1, a well-established v-ATPase inhibitor that blocks lysosomal acidification. While bafilomycin A1 is commonly used as an autophagy inhibitor, its primary mechanism involves inhibiting lysosomal function, which is critical for autophagosome-lysosome fusion and subsequent degradation of autophagic cargo.

In our study, we used bafilomycin A1 in conjunction with puromycin to dissect the relative contributions of sPINK1 overexpression on proteasomal and autophagic activities. Puromycin induces protein misfolding and aggregation, causing stress on both degradation pathways. By inhibiting lysosomal function with bafilomycin A1 and blocking the protein degradation load at various stages, we can tell the relative contributions of autophagy and UPS pathways.

We acknowledge that bafilomycin A1’s effects extend beyond autophagy, as it also inhibits v-ATPase activity. However, its inhibition of lysosomal degradation is integral to distinguishing autophagy’s contribution under the experimental conditions, and BALA treatment has been used in extensively in previous studies (Mauvezin and Neufeld, 2015). 

We have further clarified this treatment in the revised manuscript.

Figure 3. Legend or text needs to be more explicit about how chains have been produced. From what I can gather from methods only a single E2 has been trialed. Authors should use at least one of the criteria used by Wauer et al. (2014) to confirm the stoichiometry of phosphorylation. The concept that pUb can interfere with E2 discharging is not new, but not universal across E2s.

We have cited in the manuscript that PINK1-mediated ubiquitin phosphorylation can interfere with ubiquitin chain elongation for certain E2 enzymes (Wauer et al., 2015). 

To clarify, the focus of our current work is on how elevation of Ub phosphorylation impacts UPS activity, rather than exploring the broader effects of Ub phosphorylation on Ub chain elongation. For this reason, we have used the standard E2 that is well-established for generating K48-linked polyUb chain (Pickart CM, 2005). Moreover, our findings go further and by demonstrate that phosphorylated K48-linked polyubiquitin exhibits weaker non-covalent interactions with proteasomal ubiquitin receptors. This dual effect—on both covalent chain elongation and non-covalent interactions— contributes to the observed reduction in ubiquitin-proteasome activity, a novel aspect of our study.

To address the reviewer’s concerns, we have added details in the Methods section and figure legends regarding the generation of ubiquitin chains. Specifically, we used ubiquitin-activating enzyme E1 (UniProt ID: P22314) and ubiquitin-conjugating enzyme E2-25K (UniProt ID: P61086) to generate K48-linked ubiquitin chains. 

Our ESI-MS analysis showed that only 1–2 phosphoryl groups were incorporated into the K48-linked tetra-ubiquitin chains (Figure 3—figure supplement 2). This is consistent with our in vivo findings, where pUb levels increased by 30.4% in aged mouse brains compared to young brains (Figure 1F). Notably, even sub-stoichiometric phosphorylation onto the K48-linked ubiquitin chain significantly weakens the non-covalent interactions with the proteasome (Figures 3E and 3H).

Figure 4. I could find no definition of the insoluble fraction, nor details on how it is prepared.

The insoluble fraction primarily contains proteins that are aggregated or associated with hydrophobic interactions and cannot be solubilized by RIPA buffer. We have provided more details in the Methods of the revised manuscript about how the insoluble fraction was prepared. Our approach was based on established protocols for fractionating soluble and insoluble proteins from brain tissues (Wirths, 2017). Here is an outline of the procedure, which enables the separation and subsequent analysis of distinct protein populations:

• Lysis and preparation of soluble fraction: Cells and brain tissues were lysed using RIPA buffer (Beyotime Biotechnology, cat# P0013B) containing protease (P1005) and phosphatase inhibitors (P1081) on ice for 30 minutes, with gentle vortexing every 10 minutes. Brain samples were homogenized using a precooled TissuePrep instrument (TP-24, Gering Instrument Company). Lysates were centrifuged at 12,000 rpm for 30 minutes at 4°C. The supernatant was collected as the soluble protein fraction.

• Preparation of insoluble fraction: The pellet was resuspended in 20 µl of SDS buffer (2% SDS, 50 mM Tris-HCl, pH 7.5) and subjected to ultrasonic pyrolysis at 4°C for 8 cycles (10 seconds ultrasound, 30 seconds interval). The samples were then centrifuged at 12,000 rpm for 30 minutes at 4°C. The supernatant obtained after this step was designated as the insoluble protein fraction.

• Protein quantification: Protein concentrations for both soluble and insoluble fractions were determined using the BCA Protein Assay Kit (Beyotime Biotechnology, cat# P0009).

Figure 5. What is the transfection efficiency? How many folds is sPINK1 over-expressed? Typically, a neuron will have only a few hundred copies of PINK1 at the basal state. How much mutant ubiquitin is expressed relative to wild type, seeing the free ubiquitin signals on the gels might be helpful here, but they seem to have been cut off. 

We appreciate the reviewer's insightful comments regarding transfection efficiency, the extent of sPINK1 overexpression, and the expression levels of mutant ubiquitin relative to wild-type ubiquitin. Below, we provide detailed responses to each point:

Transfection Efficiency: Our immunofluorescent staining for NeuN, a neuronal marker, demonstrated that over 90% of NeuN-positive cells were co-localized with GFP (Figure 5—figure supplement 2), indicating a high transfection efficiency in our neuronal cultures.

Extent of sPINK1 Overexpression: Quantifying the exact fold increase of sPINK1 upon overexpression is inherently difficult due to its low basal expression under physiological conditions, making the relative increase difficult to measure (small denominator effect). However, our Western blot analysis shows that ischemic events can cause a substantial elevation of PINK1 levels, including both full-length and cleaved forms (Figure 1H). This suggests that our overexpression model recapitulates the pathological increase in PINK1, making it a relevant system for studying disease mechanisms.

From Figure 5B, it is evident that sPINK1 levels differ significantly between neurons overexpressing sPINK1 alone and those co-expressing sPINK1 + Ub/S65A (70 days post-transfection). Overexpression of sPINK1 alone results in multiple PINK1 bands, consistent with sPINK1, endogenous PINK1 (induced by mitochondrial damage), and ubiquitinated sPINK1. In comparison, co-expressing Ub/S65A leads to faint PINK1 bands, suggesting that in the presence of a functionally restored proteasome, overexpressed sPINK1 is rapidly degraded. Therefore, actual accumulation of sPINK1 depends on proteasomal activity, and the “over-expressed” PINK1 level can be comparable to levels observed under native, pathological conditions.

Expression Levels of Mutant Ubiquitin Relative to Wild-Type: Assessing the expression levels of mutant versus wild-type ubiquitin is indeed valuable. In Figure 5E, we observed a 38.9% increase in high-molecular-weight ubiquitin conjugates in the soluble fraction when comparing the sPINK1+Ub/S65A group to the control. This increase suggests that mutant ubiquitin is actively incorporated into polyubiquitin chains.

Regarding free monomeric ubiquitin, its low abundance and rapid incorporation into polyubiquitin chains make it difficult to visualize in Western blots. Additionally, its low molecular weight and lower antibody binding valency further reduce its visibility.

General: a number of effects are shown following over-expression but no case is made that these levels of pUb are ever attained physiologically. I am very unconvinced by these findings and think the manuscript needs to be improved at multiple levels before being added to the record.

We understand the reviewer’s concerns regarding the relevance of pUb levels observed in our overexpression model. To clarify, our study is not focused on physiological levels of pUb, but rather on pathologically elevated levels, which have been documented in various neurodegenerative conditions. While overexpression is not a perfect replication of pathological states, it provides a valuable tool to investigate mechanisms that become relevant under disease conditions. Moreover, we have taken steps to ensure the validity of our findings and to address potential limitations associated with overexpression models:

Pathological Relevance: Besides several reported literatures, we observed significant increases in PINK1 and pUb levels in human brain samples from Alzheimer's disease (AD) patients, as well as in mouse models of AD, cerebral ischemia (including mouse middle cerebral artery occlusion ischemic model and oxygen glucose deprivation cell model), and aging (e.g., Figures 1E, 1F, and 1H). All these data show that pUb levels are elevated under pathological conditions. Our overexpression model mimics these pathological scenarios by recreating the high levels of pUb, which lead to the impairment of proteasomal activity and subsequent disruption of proteostasis.

Use of Robust Controls: To ensure the reliability of our results and interpretations, we employed multiple controls for our experiments. We have used pink1^-/- mice and cells to confirm that pUb accumulation is PINK1-dependent (Figures 1C and 2C). We have also included kinase-dead sPINK1 mutant and Ub/S65A phospho-null mutants to negate/counteract the specific roles of PINK1 activity and pUb in proteasomal dysfunction. On the other hand, we have used Ub/S65E for phosphomimetic mutant, corresponding to a 100% Ub phosphorylation.

Importantly, we have compared sPINK1 overexpression with both baseline and disease-mimicking conditions, thus to ensure that the observed effects are consistent with pathological changes. Furthermore, our findings are supported by complementary evidences from human brain samples, model animals, cell cultures, and molecular assays. Integrating the different controls and various approaches, we have provided mechanistic insights into how elevated pUb levels causes proteasomal impairment and contributes to neurodegeneration.

Our findings elucidate how elevated pUb level contributes to the disruption of proteostasis in neurodegenerative conditions. While overexpression may have limitations, it remains a powerful tool for dissecting pathological mechanisms and testing hypotheses. Our results align with and expand upon previous studies suggesting pUb as a biomarker of neurodegeneration (Hou, et al, 2018; Fiesel, et al, 2015), and provide mechanistic insights into how elevated pUb and sPINK1 drive a viscous feedforward cycle, ultimately leading to proteasomal dysfunction and neurodegeneration. 

We hope these clarifications highlight the relevance and rigor of our study, and welcome additional suggestions to improve the manuscript.

Reviewer #3 (Public review):

Summary:

This study aims to explore the role of phosphorylated ubiquitin (pUb) in proteostasis and its impact on neurodegeneration. By employing a combination of molecular, cellular, and in vivo approaches, the authors demonstrate that elevated pUb levels contribute to both protective and neurotoxic effects, depending on the context. The research integrates proteasomal inhibition, mitochondrial dysfunction, and protein aggregation, providing new insights into the pathology of neurodegenerative diseases.

Strengths:

- The integration of proteomics, molecular biology, and animal models provides comprehensive insights.

- The use of phospho-null and phospho-mimetic ubiquitin mutants elegantly demonstrates the dual effects of pUb.

- Data on behavioral changes and cognitive impairments establish a clear link between cellular mechanisms and functional outcomes.

Weaknesses:

- While the study discusses the reciprocal relationship between proteasomal inhibition and pUb elevation, causality remains partially inferred.

It has been well-established that protein aggregates, particularly neurodegenerative fibrils, can impair proteasomal activity (McDade, et al., 2024; Kinger, et al., 2024; Tseng, et al., 2008). Other contributing factors, including ATP depletion, reduced proteasome component expression, and covalent modifications of proteasomal subunits, can also lead to declined proteasomal function. Additionally, mitochondrial injury serves as an important source of elevated PINK1 and pUb levels. Recent studies have demonstrated that efficient mitophagy is essential to prevent pUb accumulation, whereas partial mitophagy failure results in elevated PINK1 levels (Chin, et al, 2023; Pollock, et al. 2024).

While pathological conditions can impair proteasomal function and slow sPINK1 degradation, leading to its accumulation, our results demonstrate that overexpression of sPINK1 or PINK1 can initiate this cycle as well. Once this cycle is initiated, it becomes self-perpetuating, as sPINK1 and pUb accumulation progressively impair proteasomal function, leading to more protein aggregates and mitochondrial damages.

Importantly, we show that co-expression of Ub/S65A effectively rescues cells from this cycle, which further illustrates the pivotal role of pUb in driving proteasomal inhibition and the causality between pUb elevation and proteasomal inhibition. At the animal level, pink1 knockout prevents protein aggregation under aging and cerebral ischemia conditions (Figures 1E and 1G). 

Together, by controlling at protein, cell, and animal levels, our findings support this self-reinforcing and self-amplifying cycle of pUb elevation, proteasomal inhibition, protein aggregation, mitochondrial damage, and ultimately, neurodegeneration.

- The role of alternative pathways, such as autophagy, in compensating for proteasomal dysfunction is underexplored.

Indeed, previous studies have shown that elevated sPINK1 can enhance autophagy (Gao, et al., 2016,), potentially compensating for impaired UPS function. One mechanism involves PINK1mediated phosphorylation of p62, which enhances autophagic activity.

In our study, we observed increased autophagic activity upon sPINK1 overexpression, as shown in Figure 2I (middle panel, without BALA). This increase in autophagy may facilitate the degradation of ubiquitinated proteins induced by puromycin, partially mitigating proteasomal dysfunction. This compensation might also explain why protein aggregation, though statistically significant, increased only slightly at 70 days post-sPINK1 transfection (Figure 5F). Additionally, we detected a mild but statistically insignificant increase in LC3II levels in the hippocampus of mouse brains at 70 days postsPINK1 transfection (Figure 5—figure supplement 6), further supporting the notion of autophagy activation.

However, while autophagy may provide some compensation, its effect is likely limited. The UPS and autophagy serve distinct roles in protein degradation:

• Autophagy is a bulk degradation pathway, primarily targeting damaged organelles, intracellular pathogens, and protein aggregates, often in a non-selective manner.

• The UPS, in contrast, is highly selective, degrading short-lived regulatory proteins, misfolded proteins, and proteins tagged for degradation via ubiquitination.

Thus, while sPINK1 overexpression enhances autophagy-mediated degradation, it simultaneously impairs UPS-mediated degradation. This suggests that autophagy partially compensates for proteasomal dysfunction but is insufficient to counterbalance the UPS's selective degradation function. We have incorporated additional discussion in the revised manuscript.

- The immunofluorescence images in Figure 1A-D lack clarity and transparency. It is not clear whether the images represent human brain tissue, mouse brain tissue, or cultured cells. Additionally, the DAPI staining is not well-defined, making it difficult to discern cell nuclei or staging. To address these issues, lower-magnification images that clearly show the brain region should be provided, along with improved DAPI staining for better visualization. Furthermore, the Results section and Figure legends should explicitly indicate which brain region is being presented. These concerns raise questions about the reliability of the reported pUb levels in AD, which is a critical aspect of the study's findings.

We have taken steps to address the concerns regarding clarity and transparency in Figure 1A-D. We have already addressed the source of tissues at the left of each images. For example, we have written “human brain with AD” at the left side of Figure 1A, and “mouse brains with AD” at the left side of Figure 1C.

Briefly, the human brain samples in Figure 1 originate from the cingulate gyrus of Alzheimer’s disease (AD) patients. Our analysis revealed that PINK1 is primarily localized within cell bodies, whereas pUb is more abundant around Aβ plaques, likely in nerve terminals. For the mouse brain samples, we have now explicitly indicated in the figure legends and Results section that the images represent the neocortex of APP/PS1 mice, a mouse model relevant to AD pathology, as well as the corresponding regions in wild-type and pink1^-/- mice. We have ensured that the brain regions and sources are clearly stated throughout the manuscript.

Regarding image clarity, we have uploaded higher-resolution versions of the images in the revised manuscript to improve visualization of key features, including DAPI staining. We believe these revisions enhance the reliability and interpretability of our findings, particularly in relation to the reported pUb levels in AD. 

- Figure 4B should also indicate which brain region is being presented.

The images were taken for layer III-IV in the neocortex of mouse brains. We have included this information in the figure legend of the revised manuscript.

Reviewer #3 (Recommendations for the authors):

- Expand on the potential compensatory role of autophagy in response to proteasomal dysfunction.

Upon proteasomal inhibition, cells may activate autophagy as an alternative pathway of degradation to help clear damaged or misfolded proteins. Autophagy is a bulk degradation process that targets long-lived proteins, damaged organelles, and aggregated proteins for lysosomal degradation. While this pathway can provide some compensation, it is distinct from the ubiquitin-proteasome system (UPS), which specializes in the selective degradation of short-lived regulatory proteins and misfolded proteins.

In our study, we observed increased autophagic activity following sPINK1 overexpression (Figure 2J, middle panel, without BALA) and a slight, though statistically insignificant, increase in LC3II levels in the hippocampus of mouse brains at 70 days post-sPINK1 transfection (Figure 5—figure supplement 6). These findings suggest that autophagy is indeed upregulated as a compensatory response to proteasomal dysfunction, potentially facilitating the degradation of aggregated ubiquitinated proteins. Additionally, gene set enrichment analysis (GSEA) revealed similar enrichment of autophagy pathways at 30 and 70 days post-sPINK1 overexpression (Figure 5—figure supplement 5).

However, the compensatory capacity of autophagy is likely limited. While autophagy can reduce protein aggregation, it is an inherently non-selective process and cannot fully replace the targeted functions of the UPS. Moreover, as we illustrate in Figure 7 of the revised manuscript, UPS is essential for degrading specific regulatory and inhibitory proteins and plays a critical role in cellular proteostasis, particularly in signaling regulation, cell cycle control, and stress responses.

Together, while autophagy activation provides some degree of compensation, it cannot fully restore cellular proteostasis. The interplay between these two degradation pathways is an important area for future investigation. For the present study, our focus is on how pUb elevations impact proteasomal activity and elicits downstream effects.

We have incorporated these additional discussions on this topic in the revised manuscript.

- Simplify the discussion of complex mechanisms to improve accessibility for readers.

We have revised the Discussion to present the mechanisms in a more coherent and accessible manner, ensuring clarity for a broader readership. These revisions should make the discussion more intuitive while preserving the depth of our findings.

- Statistical analyses could benefit from clarifying how technical replicates and biological replicates were accounted for across experiments.

We have clarified our statistical analysis in the Methods section and figure legends, explicitly detailing how many biological replicates were accounted for across experiments. These revisions should enhance transparency and clarity, ensuring that our findings are robust and reproducible.

- The image in Figure 3D is too small to distinguish any signals. A larger and clearer image should be presented.

We have expanded the images in Figure 3D. Additionally, we have replaced figures with version of better resolutions throughout the manuscript.

- NeuN expression in Figure 4B differs between wildtype and pink-/- mice. Additional validation is needed to determine whether pink-/- enhances NeuN expression.

The difference in NeuN immunofluorescence intensity between wild-type and pink1^-/- mice in Figure 4B may simply result from variations in image acquisition rather than an actual difference in NeuN expression.

Our single nuclei RNA-seq analyses of wild-type and pink1^-/- mice at 3 and 18 months of age reveal no significant differences in NeuN expression at the transcript level (data provided below). This confirms that the observed variation in fluorescence intensity is unlikely to reflect an authentic upregulation of NeuN expression. Thus, factors like the concentration of antibody, image exposure and processing may contribute to differences in staining intensity.

Author response image 1.

Author response:

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

eLife assessment

This valuable work analyzes how specialized cells in the auditory cells, known as the octopus cells, can detect coincidences in their inputs at the submillisecond time scale. While previous work indicated that these cells receive no inhibitory inputs, the present study unambiguously demonstrates that these cells receive inhibitory glycinergic inputs. The physiologic impact of these inputs needs to be studied further. It remains incomplete at present but could be made solid by addressing caveats related to similar sizes of excitatory postsynaptic potentials and spikes in the octopus neurons.

We apologize for not explicitly describing our experimental methods and analyses procedures that ensure the discrimination between action potentials and EPSPs. This has been addressed in responses to reviewer comments and amended in the manuscript.

Reviewer #1 (Public Review):

Kreeger and colleagues have explored the balance of excitation and inhibition in the cochlear nucleus octopus cells of mice using morphological, electrophysiological, and computational methods. On the surface, the conclusion, that synaptic inhibition is present, does not seem like a leap. However, the octopus cells have been in the past portrayed as devoid of inhibition. This view was supported by the seeming lack of glycinergic fibers in the octopus cell area and the lack of apparent IPSPs. Here, Kreeger et al. used beautiful immunohistochemical and mouse genetic methods to quantify the inhibitory and excitatory boutons over the complete surface of individual octopus cells and further analyzed the proportions of the different subtypes of spiral ganglion cell inputs. I think the analysis stands as one of the most complete descriptions of any neuron, leaving little doubt about the presence of glycinergic boutons.

Kreeger et al then examined inhibition physiologically, but here I felt that the study was incomplete. Specifically, no attempt was made to assess the actual, biological values of synaptic conductance for AMPAR and GlyR. Thus, we don't really know how potent the GlyR could be in mediating inhibition. Here are some numbered comments:

(1) "EPSPs" were evoked either optogenetically or with electrical stimulation. The resulting depolarizations are interpreted to be EPSPs. However previous studies from Oertel show that octopus cells have tiny spikes, and distinguishing them from EPSPs is tricky. No mention is made here about how or whether that was done. Thus, the analysis of EPSP amplitude is ambiguous.

We agree that large EPSPs can be difficult to distinguish from an octopus cell’s short spikes during experiments. During analysis, we distinguished spikes from EPSPs by generating phase plots, which allow us to visualize the first derivative of the voltage trace on the y-axis and the value of the voltage on the x-axis at each moment in time. In the example shown below, four depolarizing events were electrically evoked in an octopus cell (panel A). The largest of these events (shown in orange in panels B-D) has an amplitude of ~9mV and could be a small spike. The first derivative of the voltage (panel C) reveals a bi-phasic response in the larger orange trace, where during the rising phase (mV/ms > 0) of the EPSP there is a second, sharper rising phase for the spike. Like more traditionally sized action potentials, phase plots for octopus cell spikes also reveal a sharp change in the rate of voltage change over time (Author response image 1 panel D, ✱) after the rising action of the EPSP begins to slow. EPSPs (shown in blue in panels B-D) lack the deflection in the phase plot. Not all cases were as unambiguous as this example. Therefore, our analysis only included subthreshold stimulation that unambiguously evoked EPSPs, not spikes. A brief description of this analysis has been added to the methods text (lines 625-627) and we have noted in the results section that both ChR2-evoked and electrically-evoked stimulation can produce small action potentials, which were excluded from analysis (lines 156-158).

Author response image 1.

(2) For this and later analysis, a voltage clamp of synaptic inputs would have been a simple alternative to avoid contaminating spikes or shunts by background or voltage-gated conductances. Yet only the current clamp was employed. I can understand that the authors might feel that the voltage clamp is 'flawed' because of the failure to clamp dendrites. But that may have been a good price to pay in this case. The authors should have at least justified their choice of method and detailed its caveats.

We agree that data collected using voltage-clamp would have eliminated the confound of short action potentials and avoided the influence of voltage-gated conductances. The large-diameter, and comparatively simple dendritic trees of octopus cells make them good morphological candidates for reliable voltage clamp. However, as suggested, we were concerned that the abundance of channels open at the neuron’s resting potential would make it difficult to sufficiently clamp dendrites. Ultimately, given the low input resistances of octopus cells and the fast kinetics of excitatory inputs, we determined that bad voltage clamp conditions were likely to result in unclamped synaptic events with unpredicted distortions in kinetics and attenuation (To et al. 2022; PMID: 34480986; DOI: 10.1016/j.neuroscience.2021.08.024). We therefore chose to focus our efforts on current-clamp.

Beyond the limits of both current-clamp and voltage-clamp, we chose to leave all conductances that influence EPSP dendritic propagation intact because our model demonstrates that active Kv and leak conductances shape and attenuate synaptic inputs as they travel through the dendritic tree (Supp. Fig. 4F-G). The addition of voltage-clamp recordings would not impact the conclusions we make about EPSP summation at the soma. Future studies will need to focus on a dendrite-centric view of local excitatory and inhibitory summation. For dendrite-centric experiments, dendritic voltage-clamp recordings are well suited to answer that set of questions.

(3) The modeling raised several concerns. First, there is little presentation of assumptions, and of course, a model is entirely about its assumptions. For example, what excitatory conductance amplitudes were used? The same for inhibitory conductance? How were these values arrived at? The authors note that EPSGs and IPSGs had peaks at 0.3 and 3 ms. On what basis were these numbers obtained? The model's conclusions entirely depend on these values, and no measurements were made here that could have provided them. Parenthetical reference is made to Figure S5 where a range of values are tested, but with little explanation or justification.

We apologize for not providing this information. We used our octopus neuron model to fit both EPSP and IPSP parameters to match experimental data. We have expanded the methods to include final values for the conductances (lines 649-651), which were adjusted to match experimental values seen in current-clamp recordings. We have also expanded the results section to describe each of the parameters we tuned (lines 203-222). An example of these adjustments is illustrated in Fig. 4F where the magnitude of inhibitory potentials at different conductances (100nS and 1nS) was compared to experimental data over a range of octopus cell input resistance conditions. Kinetic parameters were determined by aligning modeled PSPs to the rise times and full width at half maximum (FWHM) measurements from experiments under control and Kv block conditions. The experimental data for EPSPs and IPSPs that was used to fit the model is shown in Author response image 2 below.

Author response image 2.

(4) In experiments that combined E and I stimulation, what exactly were time courses of the conductance changes, and how 'synchronous' were they, given the different methods to evoke them? (had the authors done voltage clamp they would know the answers).

We chose to focus data collection on voltage changes at the soma under physiological conditions to better understand how excitation and inhibition integrate at the somatic compartment. Our conclusions in the combined E and I stimulation experiments require the resting membrane properties of octopus cells to be intact to make physiologically-relevant conclusions. Our current-clamp data includes the critical impact of leak, Kv, and HCN conductances on this computation. Reliable voltage-clamp would necessitate the removal of the Kv and HCN conductances that shape PSP magnitude, shape, and speed. Because it was not necessary to measure the conductances and kinetics of specific channels, we chose to use current-clamp.

Evoked IPSPs and EPSPs had cell-to-cell variability in their latencies to onset. Somatically-recorded optically-evoked inhibition under pharmacological conditions that changed cable properties had onset latencies between 2.5 and 4.3ms; electrically-evoked excitation under control conditions had latencies between 0.8 and 1.4ms. To overcome cell-to-cell timing variabilities, we presented a shuffled set of stimulation pairings that had a 3ms range of timings with 200µs intervals. As the evoked excitation and inhibition become more ‘synchronous’, the impact on EPSP magnitude and timing is greatest. Data presented in this paper was for the stimulation pairings that evokes a maximal shift in EPSP timing. On average, this occurred when the optical stimulation began ~1.2ms before electrical stimulation. Stimulation pairing times ranged between a 0ms offset and a 1.8ms offset at the extremes. An example of the shuffled stimulation pairings is shown in Author response image 3 below, and we have included information about the shuffled stimulus in the methods (lines 627-630)

Author response image 3.

(5) Figure 4G is confusing to me. Its point, according to the text, is to show that changes in membrane properties induced by a block of Kv and HCN channels would not be expected to alter the amplitudes of EPSCs and IPSCs across the dendritic expanse. Now we are talking about currents (not shunting effects), and the presumption is that the blockers would alter the resting potential and thus the driving force for the currents. But what was the measured membrane potential change in the blockers? Surely that was documented. To me, the bigger concern (stated in the text) is whether the blockers altered exocytosis, and thus the increase in IPSP amplitude in blockers is due BOTH to loss of shunting and increase in presynaptic spike width. Added to this is that 4AP will reduce the spike threshold, thus allowing more ChR2-expressing axons to reach the threshold. Figure 4G does not address this point.

These are valuable points that motivated us to improve the clarity of this figure and the corresponding text. We discussed two separate points in this paragraph and were not clear. Our intention with Figure 4G was to address concerns that using pharmacological blockers changes driving forces and may confound the measured change in magnitude of postsynaptic potentials. Membrane potentials hyperpolarized by approximately 8-10 mV after application of blockers. We corrected for this effect by adding a holding current to depolarize the neuron to its baseline resting potential. Text in the results (lines 187-190) and figure legends have been changed to clarify these points.

We also removed any discussion of presynaptic effects from this portion of the text because our description was incomplete and we did not directly collect data related to these claims. We originally wrote, “While blocking Kv and HCN allowed us to reveal IPSPs at the soma, 4-AP increases the duration of the already unphysiological ChR2-evoked presynaptic action potential (Jackman et al., 2014; DOI: 10.1523/jneurosci.4694-13.2014), resulting in altered release probabilities and synaptic properties, amongst other caveats (Mathie et al., 1998; DOI: 10.1016/S0306-3623(97)00034-7)”. Ultimately, effects on exocytosis, presynaptic excitability, or release probability are only relevant for the experiments presented in Figure 4. Figure 4 serves as evidence that synaptic release of glycine elicits strychnine-sensitive inhibitory postsynaptic potentials in octopus cells. Concerns of presynaptic effects do not carry over to the data presented in Figure 5, as Kv and HCN were not blocked in these experiments. Therefore, we have removed this portion of the text.

(6) Figure 5F is striking as the key piece of biological data that shows that inhibition does reduce the amplitude of "EPSPs" in octopus cells. Given the other uncertainties mentioned, I wondered if it makes sense as an example of shunting inhibition. Specifically, what are the relative synaptic conductances, and would you predict a 25% reduction given the actual (not modeled) values?

We agree that both shunting and hyperpolarizing inhibition could play a role in the measured EPSP changes. Because we focused data collection on voltage changes at the soma under physiological conditions, we cannot calculate the relative synaptic conductances. Together, our experimental current-clamp results paired with estimates from the model provide compelling evidence for the change we observe in EPSPs. Regardless, the relative weights of the synaptic conductances is a very interesting question, but this information is not necessary to answer the questions posed in this study, namely the impact of dendritic inhibition on the arrival of EPSPs in the soma.

(7) Some of the supplemental figures, like 4 and 5, are hardly mentioned. Few will glean anything from them unless the authors direct attention to them and explain them better. In general, the readers would benefit from more complete explanations of what was done.

We apologize for not fully discussing these figures in the results text. We have fully expanded the results section to detail the experiments and results presented in the supplement (lines 203-238).

Reviewer #2 (Public Review):

Summary:

Kreeger et.al provided mechanistic evidence for flexible coincidence detection of auditory nerve synaptic inputs by octopus cells in the mouse cochlear nucleus. The octopus cells are specialized neurons that can fire repetitively at very high rates (> 800 Hz in vivo), yield responses dominated by the onset of sound for simple stimuli, and integrate auditory nerve inputs over a wide frequency span. Previously, it was thought that octopus cells received little inhibitory input, and their integration of auditory input depended principally on temporally precise coincidence detection of excitatory auditory nerve inputs, coupled with a low input resistance established by high levels of expression of certain potassium channels and hyperpolarization-activated channels.

In this study, the authors used a combination of numerous genetic mouse models to characterize synaptic inputs and enable optogenetic stimulation of subsets of afferents, fluorescent microscopy, detailed reconstructions of the location of inhibitory synapses on the soma and dendrites of octopus cells, and computational modeling, to explore the importance of inhibitory inputs to the cells. They determined through assessment of excitatory and inhibitory synaptic densities that spiral ganglion neuron synapses are densest on the soma and proximal dendrite, while glycinergic inhibitory synaptic density is greater on the dendrites compared to the soma of octopus cells. Using different genetic lines, the authors further elucidated that the majority of excitatory synapses on the octopus cells are from type 1a spiral ganglion neurons, which have low response thresholds and high rates of spontaneous activity. In the second half of the paper, the authors employed electrophysiology to uncover the physiological response of octopus cells to excitatory and inhibitory inputs. Using a combination of pharmacological blockers in vitro cellular and computational modeling, the authors conclude that glycine in fact evokes IPSPs in octopus cells; these IPSPs are largely shunted by the high membrane conductance of the cells under normal conditions and thus were not clearly evident in prior studies. Pharmacological experiments point towards a specific glycine receptor subunit composition. Lastly, Kreeger et. al demonstrated with in vitro recordings and computational modeling that octopus cell inhibition modulates the amplitude and timing of dendritic spiral ganglion inputs to octopus cells, allowing for flexible coincidence detection.

Strengths:

The work combines a number of approaches and complementary observations to characterize the spatial patterns of excitatory and inhibitory synaptic input, and the type of auditory nerve input to the octopus cells. The combination of multiple mouse lines enables a better understanding of and helps to define, the pattern of synaptic convergence onto these cells. The electrophysiology provides excellent functional evidence for the presence of the inhibitory inputs, and the modeling helps to interpret the likely functional role of inhibition. The work is technically well done and adds an interesting dimension related to the processing of sound by these neurons. The paper is overall well written, the experimental tests are well-motivated and easy to follow. The discussion is reasonable and touches on both the potential implications of the work as well as some caveats.

Weaknesses:

While the conclusions presented by the authors are solid, a prominent question remains regarding the source of the glycinergic input onto octopus cells. In the discussion, the authors claim that there is no evidence for D-stellate, L-stellate, and tuberculoventral cell (all local inhibitory neurons of the ventral and dorsal cochlear nucleus) connections to octopus cells, and cite the relevant literature. An experimental approach will be necessary to properly rule out (or rule in) these cell types and others that may arise from other auditory brainstem nuclei. Understanding which cells provide the inhibitory input will be an essential step in clarifying its roles in the processing of sound by octopus cells.

We are glad that the reviewer agrees with the conclusions we have made and is interested in learning more about how these findings impact sound processing. We agree that defining the source of inhibition will dramatically shape our understanding of the computation octopus cells are making. However, this is not an easy task, given the small size of the octopus cell area, and will involve considerable additional work. Since the overall findings do not depend on knowing the source of inhibition, we have instead re-written the discussion to clarify the lack of evidence for intrinsic inhibitory inputs to octopus cells, in addition to presenting likely candidates. As genetic profiles of cochlear nucleus and other auditory brainstem neurons become available, we intend to make and utilize genetic mouse models to answer questions like this.

The authors showed that type 1a SGNs are the most abundant inputs to octopus cells via microscopy. However, in Figure 3 they compare optical stimulation of all classes of ANFs, then compare this against stimulation of type 1b/c ANFs. While a difference in the paired-pulse ratio (and therefore, likely release probability) can be inferred by the difference between Foxg1-ChR2 and Ntng1-ChR2, it would have been preferable to have specific data with selective stimulation of type 1a neurons.

We agree that complete genetic access to only the Ia population would have been the preferable approach, but we did not have an appropriate line when beginning these experiments. Because our results did not suggest a meaningful difference between the populations, we did not pursue further investigation once a line was available.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Besides the points mentioned in the main review:

Minor

(1) I really like the graphics and the immunohistological presentation.

(2) Lines 316-319 say that octopus cells lack things like back-propagating spikes and dendritic Ca spikes. How do you know this?

This statement was intended to be a summary of suggestions from the literature and lacked references and context as written. We have rewritten this section and clarified that our hypothesis was formed from data found in the literature (lines 334-337).

(3) Spectrograms of Figure 6A...where were these data obtained?

We recorded and visualized human-generated rhythmic tapping and high-frequency squeaking sounds using Audacity. The visualizations of rhythmic tapping and imitated vocalizations are meant to show two different types of multi-frequency stimuli we hypothesize would result in somatic summation within an octopus cell’s spike integration window, despite differences in timing. We rewrote the figure legend to explain more clearly what is shown and how it relates to the model in Figure 6.

(4) 'on-path' and 'off-path' seem like jargon that may not be clear to the average reader.

Thank you for pointing out our use of unapproachable jargon. We have replaced the term from the figure with “proximal” and “distal” inhibition. In the main text, we now describe on-path and off-path together as the effect of location of dendritic inhibition on somatically recorded EPSPs.

(5) The paper could benefit from a table of modeled values.

We have added specific details about the modelling in the text and clarified which modeled values were referenced from previous computational models and which were tuned to fit experimental data. Since most values were taken from a referenced publication, we did not add a table and instead point readers towards that source.

(6) Figure S4A-C what currents were delivered to the modeled cells?

The model cells were injected with a -0.8 nA DC current for 300 ms in current clamp mode. This information has been added to the figure legend.

(7) In that figure "scaling factors" scale exactly which channels?

Scaling factor is used to scale low-voltage activated K⁺ (ḡ_KLT), high threshold K⁺ (ḡ_KHT), fast transient K⁺ (ḡ_KA), hyperpolarization-activated cyclic nucleotide-gated HCN (ḡ_h) but not fast Na⁺ (ḡ_Na) and leak K⁺ (ḡ_leak). This information has been added to the text (lines 205-208 and 646-653).

(8) In performing and modeling Kv/HCN block, do you know how complete the level of the block is?

Since we cannot assess how complete the level of block is, we have changed the language in the text to clarify that we are reducing Kv and HCN channel conductance to the degree needed to increase resistance of the neuron (line 185).

(9) More on this Figure S4. It is hardly referred to in the text except to say that it supports that blocking the Kv/HCN channels will enhance the IPSP. Given how large the figure is, can you offer more of a conclusion than that? Also, in the synaptic model in that figure, the IPSCs are presumably happening in current-clamp conditions, and the reduction in amplitude of the IPSC (as opposed to the increase in IPSP) is due to hyperpolarization. Can you simply state that so readers can track what this figure is showing? Other similar things: what is a transfer impedance? How is it measured? What do we take from the analysis?

We have elaborated on our description of both Supp. Fig. 4 and Supp. Fig. 5 in the results section of the text (lines 203-238).

(10) Figure S5 also needs a better explanation. E.g., in C-D, what does 'average' mean? The gray is an SD of this average? You modeled a range of values...but which ones are physiological? To me, this is a key point.

We have elaborated on our description of both Supp. Fig. 4 and Supp. Fig. 5 in the results section of the text (lines 203-238).

Reviewer #2 (Recommendations For The Authors):

General:

The images and 3-D reconstructions are visually stunning, but they are not colorblind-friendly and in some cases, hard to distinguish. This shows up particularly in the green and blue colors used in Figure 1. Also, better representative images could be used for Figure 1B.

Thank you for pointing out that blue and green were difficult to distinguish in Figure 1H. We have outlined the green inhibitory puncta in this image to make them more distinguishable. We have also increased the resolution of the image in Figure 1B for better clarity. All other colors are selected from Wong, 2011 (PMID: 21850730; DOI: https://doi.org/10.1038/nmeth.1618).

Supplemental Figure 1D: The low-power view is good to have, but the CN is too small and the image appears a bit noisy. An inset showing the CN on a larger scale (higher resolution image?) would be more convincing. In this image, I see what appear to be cells in the DCN labeled, which calls into question the purity of the source of optogenetic synaptic activation. It is also difficult to tell whether there are other cells labeled in the VCN. Such inputs would still be minor, but it would be good to be very clear about the expression pattern.

To offer more information about the activity of the Ntng1^Cre line in other regions of the auditory system, we increased the resolution of the image included in Supp. Fig. 1D and have also included an additional image (Supp. Fig. 1E) of a coronal section of the cochlear nucleus complex with Ntng1-tdT labelling. This image provides additional context for the cells labeled in the DCN. The text in the figure legend has been changed to clarify that some cells in the DCN were labeled (lines 118-120).

We agree that in the Ntng1^Cre experiments, there is the possibility of minor contamination from excitatory cells that express ChR2 outside of the spiral ganglion. This is also true for our Foxg1^Cre and Foxg1^Flp experiments, because these lines label cortical cells in addition to cochlear cells. However, we do not observe direct descending inputs from the cortex into the PVCN, making contamination from other Foxg1^Cre-positive neurons unlikely. While non-cochlear inputs from the Ntng1^Cre line are possible, evidence from both lines gives us confidence that we are not capturing inputs to octopus cells outside the cochlea. Central axons from Type I spiral ganglion neurons have VGLUT1+ synaptic terminals. When comparing the overlap between VGLUT1+ terminals and Foxg1-tdT labelling, we see full coverage. That is, all VGLUT+ terminals on octopus cells are co-labelled by Foxg1^Cre-mediated expression of tdTomato. An example image is shown below. Here, an octopus cell soma is labeled with blue fluorescent Nissl stain and inputs to the cochlear nucleus complex are labeled with Foxg1^Cre-dependent tdTomato (Foxg1-tdT; magenta). We have also immunolabeled for VGLUT1 puncta in green. This eliminates the possibility that VGLUT+ cells from outside the cochlea and cortex are sources of excitation to octopus cells.

Author response image 4.

Further, we have looked at expression of Ntng1-tdT and Foxg1-EYFP together in the octopus cell area.  An example image is shown below. All Ntng1-tdT+ fibers (magenta) are also Foxg1-EYFP+ (green), suggesting that all Ntng1^Cre-targeted inputs to octopus cells are a part of the Foxg1^Cre-targeted input population, which are very likely to only be from the cochlea. We have expanded the results section to include information about the overlap in expression driven by the Ntng1^Cre and Foxg1^Flp lines.

Author response image 5.

Supplemental Figure 2 G: These are a bit hard to read. Perhaps use a different image, or provide a reference outline drawing telling us what is what.

We have used a different image with a Thy1-YFP labeled octopus cell for clarity.

In some places, the term "SGN" is used when referencing the axons and terminals within the CN, and without some context, this was occasionally confusing (SGN would seem to refer to the cell bodies). In some places in the text, it may be preferable to separate SGN, auditory nerve fibers (ANFs), and terminals, as entities for clarity.

In order to make the study accessible to a broad neuroscience audience, we refer to the neurons of the spiral ganglion and their central axon projections using one name. We understand why, for those well acquainted with the auditory periphery, condensing terminology may feel awkward. However, for those readers unfamiliar with the anatomy of the cochlea and auditory nerve, we feel that the use of “SGN central axon” makes it clear that the “auditory nerve fibers” come from neurons in the spiral ganglion. This is clarified in the first paragraph of the introduction (lines 29-31) and in the methods (line 533).

Specific: Numbers refer to the line numbers on the manuscript.

L29-31: Cochlear nucleus neurons are more general in their responses than this sentence indicates. While we can all agree that they are specialized to carry (or improve upon) the representation of these specific features of sound, they also respond more generally to sounds that might not have specific information in any of these domains. They are not silos of neural computation, and their outputs become mixed and "re-represented" well before they reach the auditory cortex. Octopus cells are no exception to this. I suggest striking most of the first paragraph, and instead using the first sentence to lead into the second paragraph, and putting the last sentence (of the current first paragraph) at the end of the second (now first) paragraph.

We agree with this assessment and have made major changes to the introduction in line with these suggestions.

L33-46: A number of points in this paragraph need references (exp. line 41).

We agree and have added references accordingly.

L43: Not sure what is meant by "fire at the onset of the sound, breaking it up into its frequency components"?

We changed this text as part of a major reworking of the introduction.

L47-66: Again more citations are needed (at the end of sentence at line 55, probably moving some of the citations from the next sentence up).

We agree and have added references accordingly.

L51: The consistent orientation of octopus cell dendrites across the ANFs has been claimed in the literature (as mentioned here), but there are some (perhaps problematic - plane of sectioning?) counterexamples from the older Golgi-stained images, and even amongst intracellularly stained cells (for example see Reccio-Spinoza and Rhode, 2020). This is important with regards to the broader hypothesis regarding traveling-wave compensation (e.g., McGinley et al; but also many others); if the cells are not all in the appropriate orientation then such compensation may be problematic. Likewise, the data from Lu et al., 2022, points towards a range of sensitivity to frequency-swept stimuli, some of which work in opposition to the traveling wave compensation hypothesis. It would seem that with the Thy1 mice, you have an opportunity to clarify the orientation. Figures 1A and 2A show a consistent dendritic orientation, assuming that these drawings are reconstructions of the cells as they were actually oriented in the tissue. Can you either comment on this or provide clearer evidence?

We are happy to offer more information about the appearances of octopus cells in our preparations. In our hands, sparsely labeled octopus cells in Thy1-YFP-H mice show consistent dendritic orientation when visualized in a 15 degree parasaggital plane, with the most diversity apparent in cells with somas located more dorsally in the octopus cell area. We hypothesize that this is due to the limited area through which the central projections of spiral ganglion neurons (i.e. ANFs) must pass through before they enter the dorsal cochlear nucleus and continue their tonotopic organization in that area.

A caveat to studies without physiological or genetic identification of octopus cells is the assumption that all neurons in the octopus cell area are octopus cells. We find, especially along the borders of the octopus cell area, that stellate cells can be seen amongst octopus cells. Because stellate cell dendrites are not oriented like octopus cell dendrites, any stellate cells misidentified as octopus cells would appear to have poorly-oriented dendrites. This may explain why some studies report this finding. In addition, it can be difficult to assess tonotopic organization because of the 3D trajectory of tightly bundled axons, which is not capturable by a single section plane. Although a parasaggital plane of sectioning captures the tonotopic axis in one part of the octopus cell area, that same plane may be perpendicular at the opposing end.

L67: canonical -> exceptional.

Thank you for the suggestion. We have made this change in the introduction.

L127: This paragraph was confusing on first reading. I don't think Supplemental Figure 1D shows the restricted pattern of expression very clearly. The "restricted to SGNs" might be better as "restricted to auditory nerve fibers" (except in the DCN, where there seem to be some scattered small cells?). A higher magnification image of the CN, but lower magnification than in panel E, would be helpful here.

To avoid confusion, we have re-written this paragraph (lines 117-127) and included a higher magnification image of the CN in a revised Supp. Fig. 1.

L168: Here, perhaps say ANFs instead of SGNs.

As above, we have decided to describe ANFs as SGN central axons to make the anatomy more accessible to people unfamiliar with cochlear anatomy.

L201-204: The IPSPs are surprisingly slow (Figures 5B, C), especially given the speed of the EPSPs/EPSCs in these cells. This is reminiscent of the asymmetry between EPSC and IPSC kinetics in bushy cells (Xie and Manis, 2014). The kinetics used in the model (3 ms; mentioned on line 624) however seem a bit arbitrary and no data is provided for the selection of that value. Were there any direct measurements of the IPSC kinetics (all of the traces in the paper are in the current clamp) that were used to justify this value?

The kinetics of the somatically-recorded IPSPs are subject to the effects of our pharmacological manipulations. EPSPs measured at the soma under control conditions are small amplitude and rapid. With pharmacological reduction of HCN and Kv channels, EPSPs are larger and slower (please see figure in response to a similar question posed by Reviewer #1). We expect that this change also occurs with the IPSP kinetics under pharmacological conditions. Our justification of kinetics has been expanded and justified in the methods section (lines 641-661).

L594: Technically, this is a -11 mV junction potential, but thanks for including the information.

We have corrected this in the text (line 618). Thank you for the close reading of all experimental and methodological details.

L595: The estimated power of the LED illumination at the focal plane should be measured and indicated here.

We measured the power of the LED illumination at the focal plane using a PM100D Compact Power and Energy Meter Console (Thorlabs), a S120C Photodiode Power Sensor (Thorlabs), and a 1000µm diameter Circular Precision Pinhole (Thorlabs). Light intensity at the focal plane ranged between 1.9 and 4.1mW/mm², corresponding to 6% and 10% intensity on the Colibri5 system. We have reported these measurements in the results section (Lines 621-622).

L609: One concern about the model is that the integration time of 25 microseconds is rather close to the relative shifts in latency. While I doubt it will make a difference (except in the number), it may be worth verifying (spot checks, at least) that running the model with a 5 or 10-microsecond step yields a similar pattern of latency shifts (e.g., Supplementary Figure 5, Figure 5).

Also, it is not clear what temperature the model was executed at (I would presume 35C); this needs to be given, and channel Q10's listed.

We realize that additional information is needed to fully understand the model and have added this to the results and the methods. The synaptic mechanism (.mod) files were obtained from Manis and Campagnola (2018) (PMID: 29331233; DOI: https://doi.org/10.1016/j.heares.2017.12.017). Q10 (3) and temperature (22°C) were also matched to parameters from Manis and Campagnola (2018). Because temperature is a critical factor for channel kinetics, we verified that our primary results remain consistent under conditions using a temperature of 35°C and a time step of 5µs, depicted below. Panel A illustrates the increase in IPSP as a function of glycine conductance under Kv+HCN block conditions at 35°C. As at 22°C, an increase in IPSP magnitude is absent in the control condition at 35°C. Panels B and C provide a direct comparison between the initial (i.e. 22°C) and suggested (i.e. 35°C) simulation conditions. Again we found that temperature does not have a major impact on the amplitude of IPSPs. Thus, results at 35°C do not change the conclusions we make from the model.

Author response image 6.

The nominal conductance densities should at least be provided in a table (supplemental, in addition to including them in the deposited code). The method for "optimization" of the conductance densities to match the experimental recordings needs to be described; the parameter space can be quite large in a model such as this. The McGinley reference needs a number.

We added a more thorough description of modeling parameters and justification of choices in the methods section of the text (lines 641-661). We have also added a reference number to the McGinley 2012 reference in the text.

I think this is required by the journal:

The model code, test results, and simulation results should be deposited in a public resource (Github would be preferable, but dryad, Zenodo, or Figshare could work), and the URL/doi for the resource provided in the manuscript. This includes the morphology swc/hoc file. The code should be in a form, and with a description, that readily allows an interested party with appropriate skills to download it and run it to generate the figures.

We will upload the code and all associated simulation files to the ModelDB repository upon publication.

Author response:

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

Response to Reviewer #1:

Thank you for the careful reading and the positive evaluation of our manuscript. As you mentioned, the present study tried to address the question of how the lost genomic functions could be compensated by evolutionary adaptation, indicating the potential mechanism of "constructive" rather than "destructive" evolution. Thank you for the instructive comments that helped us to improve the manuscript. We sincerely hope the revised manuscript and the following point-to-point response meet your concerns.

• Line 80 "Growth Fitness" is this growth rate?

Yes. The sentence was revised as follows.

(L87-88) “The results demonstrated that most evolved populations (Evos) showed improved growth rates, in which eight out of nine Evos were highly significant (Fig. 1B, upper).”

• Line 94 a more nuanced understanding of r/K selection theory, allows for trade-ups between R and K, as well as trade-offs. This may explain why you did not see a trade-off between growth and carrying capacity in this study. See this paper https://doi.org/10.1038/s41396-023-01543-5. Overall, your evos lineages evolved higher growth rates and lower carrying capacity (Figures 1B, C, E). If selection was driving the evolution of higher growth rates, it may have been that there was no selective pressure to maintain high carrying capacity. This means that the evolutionary change you observed in carrying capacity may have been neutral "drift" of the carrying capacity trait, during selection for growth rate, not because of a trade-off between R and K. This is especially likely since carrying capacity declined during evolution. Unless the authors have convincing evidence for a tradeoff, I suggest they remove this claim.

• Line 96 the authors introduce a previous result where they use colony size to measure growth rate, this finding needs to be properly introduced and explained so that we can understand the context of the conclusion.

• Line 97 This sentence "the collapse of the trade-off law likely resulted from genome reduction." I am not sure how the authors can draw this conclusion, what is the evidence supporting that the genome size reduction causes the breakdown of the tradeoff between R and K (if there was a tradeoff)?

Thank you for the reference information and the thoughtful comments. The recommended paper was newly cited, and the description of the trade-off collapse was deleted. Accordingly, the corresponding paragraph was rewritten as follows.

(L100-115) “Intriguingly, a positive correlation was observed between the growth fitness and the carrying capacity of the Evos (Fig. 1D). It was somehow consistent with the positive correlations between the colony growth rate and the colony size of a genome-reduced strain 11 and between the growth rates and the saturated population size of an assortment of genome reduced strains 13. Nevertheless, the negative correlation between growth rate and carrying capacity, known as the r/K selection30,31 was often observed as the trade-off relationship between r and K in the evolution and ecology studies 32 33,34. As the r/K trade-off was proposed to balance the cellular metabolism that resulted from the cost of enzymes involved 34, the deleted genes might play a role in maintaining the metabolism balance for the r/K correlation. On the other hand, the experimental evolution (i.e., serial transfer) was strictly performed within the exponential growth phase; thus, the evolutionary selection was supposed to be driven by the growth rate without selective pressure to maintain the carrying capacity. The declined carrying capacity might have been its neutral "drift" but not a trade-off to the growth rate. Independent and parallel experimental evolution of the reduced genomes selecting either r or K is required to clarify the actual mechanisms.”

• Line 103 Genome mutations. The authors claim that there are no mutations in parallel but I see that there is a 1199 base pair deletion in eight of the nine evo strains (Table S3). I would like the author to mention this and I'm actually curious about why the authors don't consider this parallel evolution.

Thank you for your careful reading. According to your comment, we added a brief description of the 1199-bp deletion detected in the Evos as follows.

(L119-122) “The number of mutations largely varied among the nine Evos, from two to 13, and no common mutation was detected in all nine Evos (Table S3). A 1,199-bp deletion of insH was frequently found in the Evos (Table S3, highlighted), which well agreed with its function as a transposable sequence.”

• Line 297 Please describe the media in full here - this is an important detail for the evolution experiment. Very frustrating to go to reference 13 and find another reference, but no details of the method. Looked online for the M63 growth media and the carbon source is not specified. This is critical for working out what selection pressures might have driven the genetic and transcriptional changes that you have measured. For example, the parallel genetic change in 8/9 populations is a deletion of insH and tdcD (according to Table S3). This is acetate kinase, essential for the final step in the overflow metabolism of glucose into acetate. If you have a very low glucose concentration, then it could be that there was selection to avoid fermentation and devote all the pyruvate that results from glycolysis into the TCA cycle (which is more efficient than fermentation in terms of ATP produced per pyruvate).

Sorry for the missing information on the medium composition, which was additionally described in the Materials and Methods. The glucose concentration in M63 was 22 mM, which was supposed to be enough for bacterial growth. Thank you for your intriguing thinking about linking the medium component to the genome mutation-mediated metabolic changes. As there was no experimental result regarding the biological function of gene mutation in the present study, please allow us to address this issue in our future work.

(L334-337) “In brief, the medium contains 62 mM dipotassium hydrogen phosphate, 39 mM potassium dihydrogen phosphate, 15 mM ammonium sulfate, 15 μM thiamine hydrochloride, 1.8 μM Iron (II) sulfate, 0.2 mM magnesium sulfate, and 22 mM glucose.”

• Line 115. I do not understand this argument "They seemed highly related to essentiality, as 11 out of 49 mutated genes were essential (Table S3)." Is this a significant enrichment compared to the expectation, i.e. the number of essential genes in the genome? This enrichment needs to be tested with a Hypergeometric test or something similar.

• Also, "As the essential genes were known to be more conserved than nonessential ones, the high frequency of the mutations fixed in the essential genes suggested the mutation in essentiality for fitness increase was the evolutionary strategy for reduced genome." I do not think that there is enough evidence to support this claim, and it should be removed.

Sorry for the unclear description. Yes, the mutations were significantly enriched in the essential genes (11 out of 45 genes) compared to the essential genes in the whole genome (286 out of 3290 genes). The improper description linking the mutation in essential genes to the fitness increase was removed, and an additional explanation on the ratio of essential genes was newly supplied as follows.

(L139-143) “The ratio of essential genes in the mutated genes was significantly higher than in the total genes (286 out of 3290 genes, Chi-square test p=0.008). As the essential genes were determined according to the growth35 and were known to be more conserved than nonessential ones 36,37, the high frequency of the mutations fixed in the essential genes was highly intriguing and reasonable.”

• Line 124 Regarding the mutation simulations, I do not understand how the observed data were compared to the simulated data, and how conclusions were drawn. Can the authors please explain the motivation for carrying out this analysis, and clearly explain the conclusions?

Random simulation was additionally explained in the Materials and Methods and the conclusion of the random simulation was revised in the Results, as follows.

(L392-401) “The mutation simulation was performed with Python in the following steps. A total of 65 mutations were randomly generated on the reduced genome, and the distances from the mutated genomic locations to the nearest genomic scars caused by genome reduction were calculated. Subsequently, Welch's t-test was performed to evaluate whether the distances calculated from the random mutations were significantly longer or shorter than those calculated from the mutations that occurred in Evos. The random simulation, distance calculation, and statistic test were performed 1,000 times, which resulted in 1,000 p values. Finally, the mean of p values (μp) was calculated, and a 95% reliable region was applied. It was used to evaluate whether the 65 mutations in the Evos were significantly close to the genomic scars, i.e., the locational bias.”

(L148-157) “Random simulation was performed to verify whether there was any bias or hotspot in the genomic location for mutation accumulation due to the genome reduction. A total of 65 mutations were randomly generated on the reduced genome (Fig. 2B), and the genomic distances from the mutations to the nearest genome reduction-mediated scars were calculated. Welch's t-test was performed to evaluate whether the genomic distances calculated from random mutations significantly differed from those from the mutations accumulated in the Evos. As the mean of p values (1,000 times of random simulations) was insignificant (Fig. 2C, μp > 0.05), the mutations fixed on the reduced genome were either closer or farther to the genomic scars, indicating there was no locational bias for mutation accumulation caused by genome reduction.”

• Line 140 The authors should give some background here - explain the idea underlying chromosomal periodicity of the transcriptome, to help the reader understand this analysis.

• Line 142 Here and elsewhere, when referring to a method, do not just give the citation, but also refer to the methods section or relevant supplementary material.

The analytical process (references and methods) was described in the Materials and Methods, and the reason we performed the chromosomal periodicity was added in the Results as follows.

(L165-172) “As the E. coli chromosome was structured, whether the genome reduction caused the changes in its architecture, which led to the differentiated transcriptome reorganization in the Evos, was investigated. The chromosomal periodicity of gene expression was analyzed to determine the structural feature of genome-wide pattern, as previously described 28,38. The analytical results showed that the transcriptomes of all Evos presented a common six-period with statistical significance, equivalent to those of the wild-type and ancestral reduced genomes (Fig. 3A, Table S4).”

• Line 151 "The expression levels of the mutated genes were higher than those of the remaining genes (Figure 3B)"- did this depend on the type of mutation? There were quite a few early stops in genes, were these also more likely to be expressed? And how about the transcriptional regulators, can you see evidence of their downstream impact?

Sorry, we didn't investigate the detailed regulatory mechanisms of 49 mutated genes, which was supposed to be out of the scope of the present study. Fig. 3B was the statistical comparison between 3225 and 49 genes. It didn't mean that all mutated genes expressed higher than the others. The following sentences were added to address your concern.

(L181-185) “As the regulatory mechanisms or the gene functions were supposed to be disturbed by the mutations, the expression levels of individual genes might have been either up- or down-regulated. Nevertheless, the overall expression levels of all mutated genes tended to be increased. One of the reasons was assumed to be the mutation essentiality, which remained to be experimentally verified.”

• Line 199 onward. The authors used WGCNA to analyze the gene expression data of evolved organisms. They identified distinct gene modules in the reduced genome, and through further analysis, they found that specific modules were strongly associated with key biological traits like growth fitness, gene expression changes, and mutation rates. Did the authors expect that there was variation in mutation rate across their populations? Is variation from 3-16 mutations that they observed beyond the expectation for the wt mutation rate? The genetic causes of mutation rate variation are well understood, but I could not see any dinB, mutT,Y, rad, or pol genes among the discovered mutations. I would like the authors to justify the claim that there was mutation rate variation in the evolved populations.

Thank you for the intriguing thinking. We don't think the mutation rates were significantly varied across the nine populations, as no mutation occurred in the MMR genes, as you noticed. Our previous study showed that the spontaneous mutation rate of the reduced genome was higher than that of the wild-type genome (Nishimura et al., 2017, mBio). As nonsynonymous mutations were not detected in all nine Evos, the spontaneous mutation rate couldn't be calculated (because it should be evaluated according to the ratio of nonsynonymous and synonymous single-nucleotide substitutions in molecular evolution). Therefore, discussing the mutation rate in the present study was unavailable. The following sentence was added for a better understanding of the gene modules.

(L242-245) “These modules M2, M10 and M16 might be considered as the hotspots for the genes responsible for growth fitness, transcriptional reorganization, and mutation accumulation of the reduced genome in evolution, respectively.”

• Line 254 I get the idea of all roads leading to Rome, which is very fitting. However, describing the various evolutionary strategies and homeostatic and variable consequence does not sound correct - although I am not sure exactly what is meant here. Looking at Figure 7, I will call strategy I "parallel evolution", that is following the same or similar genetic pathways to adaptation and strategy ii I would call divergent evolution. I am not sure what strategy iii is. I don't want the authors to use the terms parallel and divergent if that's not what they mean. My request here would be that the authors clearly describe these strategies, but then show how their results fit in with the results, and if possible, fit with the naming conventions, of evolutionary biology.

Thank you for your kind consideration and excellent suggestion. It's our pleasure to adopt your idea in tour study. The evolutionary strategies were renamed according to your recommendation. Both the main text and Fig. 7 were revised as follows.

(L285-293) “Common mutations22,44 or identical genetic functions45 were reported in the experimental evolution with different reduced genomes, commonly known as parallel evolution (Fig. 7, i). In addition, as not all mutations contribute to the evolved fitness 22,45, another strategy for varied phenotypes was known as divergent evolution (Fig. 7, ii). The present study accentuated the variety of mutations fixed during evolution. Considering the high essentiality of the mutated genes (Table S3), most or all mutations were assumed to benefit the fitness increase, partially demonstrated previously 20. Nevertheless, the evolved transcriptomes presented a homeostatic architecture, revealing the divergent to convergent evolutionary strategy (Fig. 7, iii).”

Author response image 1.

• Line 327 Growth rates/fitness. I don't think this should be called growth fitness- a rate is being calculated. I would like the authors to explain how the times were chosen - do the three points have to be during the log phase? Can you also explain what you mean by choosing three ri that have the largest mean and minor variance?

Sorry for the confusing term usage. The fitness assay was changed to the growth assay. Choosing three ri that have the largest mean and minor variance was to avoid the occasional large values (blue circle), as shown in the following figure. In addition, the details of the growth analysis can be found at https://doi.org/10.3791/56197 (ref. 59), where the video of experimental manipulation, protocol, and data analysis is deposited. The following sentence was added in accordance.

Author response image 2.

(L369-371) “The growth rate was determined as the average of three consecutive ri, showing the largest mean and minor variance to avoid the unreliable calculation caused by the occasionally occurring values. The details of the experimental and analytical processes can be found at https://doi.org/10.3791/56197.”

• Line 403 Chromosomal periodicity analysis. The windows chosen for smoothing (100kb) seem big. Large windows make sense for some things - for example looking at how transcription relates to DNA replication timing, which is a whole-genome scale trend. However, here the authors are looking for the differences after evolution, which will be local trends dependent on specific genes and transcription factors. 100kb of the genome would carry on the order of one hundred genes and might be too coarse-grained to see differences between evos lineages.

Thank you for the advice. We agree that the present analysis focused on the global trend of gene expression. Varying the sizes may lead to different patterns. Additional analysis was performed according to your comment. The results showed that changes in window size (1, 10, 50, 100, and 200 kb) didn't alter the periodicity of the reduced genome, which agreed with the previous study on a different reduced genome MDS42 of a conserved periodicity (Ying et al., 2013, BMC Genomics). The following sentence was added in the Materials and Methods.

(L460-461) “Note that altering the moving average did not change the max peak.”

• Figures - the figures look great. Figure 7 needs a legend.

Thank you. The following legend was added.

(L774-777) “Three evolutionary strategies are proposed. Pink and blue arrowed lines indicate experimental evolution and genome reduction, respectively. The size of the open cycles represents the genome size. Black and grey indicate the ancestor and evolved genomes, respectively.”

Response to Reviewer #2:

Thank you for reviewing our manuscript and for your fruitful comments. We agree that our study leaned towards elaborating observed findings rather than explaining the detailed biological mechanisms. We focused on the genome-wide biological features rather than the specific biological functions. The underlying mechanisms indeed remained unknown, leaving the questions as you commented. We didn't perform the fitness assay on reconstituted (single and combinatorial) mutants because the research purpose was not to clarify the regulatory or metabolic mechanisms. It's why the RNA-Seq analysis provided the findings on genome-wide patterns and chromosomal view, which were supposed to be biologically valuable. We did understand your comments and complaints that the conclusions were biologically meaningless, as ALE studies that found the specific gene regulation or improved pathway was the preferred story in common, which was not the flow of the present study.

For this reason, our revision may not address all these concerns. Considering your comments, we tried our best to revise the manuscript. The changes made were highlighted. We sincerely hope the revision and the following point-to-point response are acceptable.

Major remarks:

(1) The authors outlined the significance of ALE in genome-reduced organisms and important findings from published literature throughout the Introduction section. The description in L65-69, which I believe pertains to the motivation of this study, seems vague and insufficient to convey the novelty or necessity of this study i.e. it is difficult to grasp what aspects of genome-reduced biology that this manuscript intends to focus/find/address.

Sorry for the unclear writing. The sentences were rewritten for clarity as follows.

(L64-70) “Although the reduced growth rate caused by genome reduction could be recovered by experimental evolution, it remains unclear whether such an evolutionary improvement in growth fitness was a general feature of the reduced genome and how the genome-wide changes occurred to match the growth fitness increase. In the present study, we performed the experimental evolution with a reduced genome in multiple lineages and analyzed the evolutionary changes of the genome and transcriptome.”

(2) What is the rationale behind the lineage selection described in Figure S1 legend "Only one of the four overnight cultures in the exponential growth phase (OD600 = 0.01~0.1) was chosen for the following serial transfer, highlighted in red."?

The four wells (cultures of different initial cell concentrations) were measured every day, and only the well that showed OD600=0.01~0.1 (red) was transferred with four different dilution rates (e.g., 10, 100, 1000, and 10000 dilution rates). It resulted in four wells of different initial cell concentrations. Multiple dilutions promised that at least one of the wells would show the OD600 within the range of 0.01 to 0.1 after the overnight culture. They were then used for the next serial transfer. Fig. S1 provides the details of the experimental records. The experimental evolution was strictly controlled within the exponential phase, quite different from the commonly conducted ALE that transferred a single culture in a fixed dilution rate. Serial transfer with multiple dilution rates was previously applied in our evolution experiments and well described in Nishimura et al., 2017, mBio; Lu et al., 2022, Comm Biol; Kurokawa et al., 2022, Front Microbiol, etc. The following sentence was added in the Materials and Methods.

(L344-345) “Multiple dilutions changing in order promised at least one of the wells within the exponential growth phase after the overnight culture.”

(3) The measured growth rate of the end-point 'F2 lineage' shown in Figure S2 seemed comparable to the rest of the lineages (A1 to H2), but the growth rate of 'F2' illustrated in Figure 1B indicates otherwise (L83-84). What is the reason for the incongruence between the two datasets?

Sorry for the unclear description. The growth rates shown in Fig. S2 were obtained during the evolution experiment using the daily transfer's initial and final OD600 values. The growth rates shown in Fig. 1B were obtained from the final population (Evos) growth assay and calculated from the growth curves (biological replication, N=4). Fig. 1B shows the precisely evaluated growth rates, and Fig. S2 shows the evolutionary changes in growth rates. Accordingly, the following sentence was added to the Results.

(L84-87) “As the growth increases were calculated according to the initial and final records, the exponential growth rates of the ancestor and evolved populations were obtained according to the growth curves for a precise evaluation of the evolutionary changes in growth.”

(4) Are the differences in growth rate statistically significant in Figure 1B?

Eight out of nine Evos were significant, except F2. The sentences were rewritten and associated with the revised Fig. 1B, indicating significance.

(L87-90) “The results demonstrated that most evolved populations (Evos) showed improved growth rates, in which eight out of nine Evos were highly significant (Fig. 1B, upper). However, the magnitudes of growth improvement were considerably varied, and the evolutionary dynamics of the nine lineages were somehow divergent (Fig. S2).”

(5) The evolved lineages showed a decrease in their maximal optical densities (OD600) compared to the ancestral strain (L85-86). ALE could accompany changes in cell size and morphologies, (doi: 10.1038/s41586-023-06288-x; 10.1128/AEM.01120-17), which may render OD600 relatively inaccurate for cell density comparison. I suggest using CFU/mL metrics for the sake of a fair comparison between Anc and Evo.

The methods evaluating the carrying capacity (i.e., cell density, population size, etc.) do not change the results. Even using CFU is unfair for the living cells that can not form colonies and unfair if the cell size changes. Optical density (OD600) provides us with the temporal changes of cell growth in a 15-minute interval, which results in an exact evaluation of the growth rate in the exponential phase. CFU is poor at recording the temporal changes of population changes, which tend to result in an inappropriate growth rate. Taken together, we believe that our method was reasonable and reliable. We hope you can accept the different way of study.

(6) Please provide evidence in support of the statement in L115-119. i.e. statistical analysis supporting that the observed ratio of essential genes in the mutant pool is not random.

The statistic test was performed, and the following sentence was added.

(L139-141) “The ratio of essential genes in the mutated genes was significantly higher than in the total genes (286 out of 3290 genes, Chi-square test p=0.008).”

(7) The assumption that "mutation abundance would correlate to fitness improvement" described in L120-122: "The large variety in genome mutations and no correlation of mutation abundance to fitness improvement strongly suggested that no mutations were specifically responsible or crucially essential for recovering the growth rate of the reduced genome" is not easy to digest, in the sense that (i) the effect of multiple beneficial mutations are not necessarily summative, but are riddled with various epistatic interactions (doi: 10.1016/j.mec.2023.e00227); (ii) neutral hitchhikers are of common presence (you could easily find reference on this one); (iii) hypermutators that accumulate greater number of mutations in a given time are not always the eventual winners in competition games (doi: 10.1126/science.1056421). In this sense, the notion that "mutation abundance correlates to fitness improvement" in L120-122 seems flawed (for your perusal, doi: 10.1186/gb-2009-10-10-r118).

Sorry for the improper description and confusing writing, and thank you for the fruitful knowledge on molecular evolution. The sentence was deleted, and the following one was added.

(L145-146) “Nevertheless, it was unclear whether and how these mutations were explicitly responsible for recovering the growth rate of the reduced genome.”

(8) Could it be possible that the large variation in genome mutations in independent lineages results from a highly rugged fitness landscape characterized by multiple fitness optima (doi: 10.1073/pnas.1507916112)? If this is the case, I disagree with the notion in L121-122 "that no mutations were specifically responsible or crucially essential" It does seem to me that, for example, the mutations in evo A2 are specifically responsible and essential for the fitness improvement of evo A2 in the evolutionary condition (M63 medium). Fitness assessment of individual (or combinatorial) mutants reconstituted in the Ancestral background would be a bonus.

Thank you for the intriguing thinking. The sentence was deleted. Please allow us to adapt your comment to the manuscript as follows.

(L143-145) “The large variety of genome mutations fixed in the independent lineages might result from a highly rugged fitness landscape 38.”

(9) L121-122: "...no mutations were specifically responsible or crucially essential for recovering the growth rate of the reduced genome". Strictly speaking, the authors should provide a reference case of wild-type E. coli ALE in order to reach definitive conclusions that the observed mutation events are exclusive to the genome-reduced strain. It is strongly recommended that the authors perform comparative analysis with an ALEed non-genome-reduced control for a more definitive characterization of the evolutionary biology in a genome-reduced organism, as it was done for "JCVI-syn3.0B vs non-minimal M. mycoides" (doi: 10.1038/s41586-023-06288-x) and "E. coli eMS57 vs MG1655" (doi: 10.1038/s41467-019-08888-6).

The improper description was deleted in response to comments 7 and 8. The mentioned references were cited in the manuscript (refs 21 and 23). Thank you for the experimental advice. We are sorry that the comparison of wild-type and reduced genomes was not in the scope of the present study and will probably be reported soon in our future work.

(10) L146-148: "The homeostatic periodicity was consistent with our previous findings that the chromosomal periodicity of the transcriptome was independent of genomic or environmental variation" A Previous study also suggested that the amplitudes of the periodic transcriptomes were significantly correlated with the growth rates (doi: 10.1093/dnares/dsaa018). Growth rates of 8/9 Evos were higher compared to Anc, while that of Evo F2 remained similar. Please comment on the changes in amplitudes of the periodic transcriptomes between Anc and each Evo.

Thank you for the suggestion. The correlation between the growth rates and the amplitudes of chromosomal periodicity was statistically insignificant (p>0.05). It might be a result of the limited data points. Compared with the only nine data points in the present study, the previous study analyzed hundreds of transcriptomes associated with the corresponding growth rates, which are suitable for statistical evaluation. In addition, the changes in growth rates were more significant in the previous study than in the present study, which might influence the significance. It's why we did not discuss the periodic amplitude.

(11) Please elaborate on L159-161: "It strongly suggested the essentiality mutation for homeostatic transcriptome architecture happened in the reduced genome.".

Sorry for the improper description. The sentence was rewritten as follows.

(L191-193) “The essentiality of the mutations might have participated in maintaining the homeostatic transcriptome architecture of the reduced genome.”

(12) Is FPKM a valid metric for between-sample comparison? The growing consensus in the community adopts Transcripts Per Kilobase Million (TPM) for comparing gene expression levels between different samples (Figure 3B; L372-379).

Sorry for the unclear description. The FPKM indicated here was globally normalized, statistically equivalent to TPM. The following sentence was added to the Materials and Methods.

(L421-422) “The resulting normalized FPKM values were statistically equivalent to TPM.”

(13) Please provide % mapped frequency of mutations in Table S3.

They were all 100%. The partially fixed mutations were excluded in the present study. The following sentence was added to the caption of Table S3.

(Supplementary file, p 9) “Note that the entire population held the mutations, i.e., 100% frequency in DNA sequencing.”

(14) To my knowledge, M63 medium contains glucose and glycerol as carbon sources. The manuscript would benefit from discussing the elements that impose selection pressure in the M63 culture condition.

Sorry for the missing information on M63, which contains 22 mM glucose as the only carbon source. The medium composition was added in the Materials and Methods, as follows.

(L334-337) “In brief, the medium contains 62 mM dipotassium hydrogen phosphate, 39 mM potassium dihydrogen phosphate, 15 mM ammonium sulfate, 15 μM thiamine hydrochloride, 1.8 μM Iron (II) sulfate, 0.2 mM magnesium sulfate, and 22 mM glucose.”

(15) The RNA-Seq datasets for Evo strains seemed equally heterogenous, just as their mutation profiles. However, the missing element in their analysis is the directionality of gene expression changes. I wonder what sort of biological significance can be derived from grouping expression changes based solely on DEGs, without considering the magnitude and the direction (up- and down-regulation) of changes? RNA-seq analysis in its current form seems superficial to derive biologically meaningful interpretations.

We agree that most studies often discuss the direction of transcriptional changes. The present study aimed to capture a global view of the magnitude of transcriptome reorganization. Thus, the analyses focused on the overall features, such as the abundance of DEGs, instead of the details of the changes, e.g., the up- and down-regulation of DEGs. The biological meaning of the DEGs' overview was how significantly the genome-wide gene expression fluctuated, which might be short of an in-depth view of individual gene expression. The following sentence was added to indicate the limitation of the present analysis.

(L199-202) “Instead of an in-depth survey on the directional changes of the DEGs, the abundance and functional enrichment of DEGs were investigated to achieve an overview of how significant the genome-wide fluctuation in gene expression, which ignored the details of individual genes.”

Minor remarks

(1) L41: brackets italicized "(E. coli)".

It was fixed as follows.

(L40) “… Escherichia coli (E. coli) cells …”

(2) Figure S1. It is suggested that the x-axis of ALE monitor be set to 'generations' or 'cumulative generations', rather than 'days'.

Thank you for the suggestion. Fig. S1 describes the experimental procedure, so the" day" was used. Fig. S2 presents the evolutionary process, so the "generation" was used, as you recommended here.

(3) I found it difficult to digest through L61-64. Although it is not within the job scope of reviewers to comment on the language style, I must point out that the manuscript would benefit from professional language editing services.

Sorry for the unclear writing. The sentences were revised as follows.

(L60-64) “Previous studies have identified conserved features in transcriptome reorganization, despite significant disruption to gene expression patterns resulting from either genome reduction or experimental evolution 27-29. The findings indicated that experimental evolution might reinstate growth rates that have been disrupted by genome reduction to maintain homeostasis in growing cells.”

(4) Duplicate references (No. 21, 42).

Sorry for the mistake. It was fixed (leaving ref. 21).

(5) Inconsistency in L105-106: "from two to 13".

"From two to 13" was adopted from the language editing. It was changed as follows.

(L119) “… from 2 to 13, …”

Response to Reviewer #3:

Thank you for reviewing our manuscript and for the helpful comments, which improved the strength of the manuscript. The recommended statistical analyses essentially supported the statement in the manuscript were performed, and those supposed to be the new results in the scope of further studies remained unconducted. The changes made in the revision were highlighted. We sincerely hope the revised manuscript and the following point-to-point response meet your concerns. You will find all your suggested statistic tests in our future work that report an extensive study on the experimental evolution of an assortment of reduced genomes.

(1) Line 106 - "As 36 out of 45 SNPs were nonsynonymous, the mutated genes might benefit the fitness increase." This argument can be strengthened. For example, the null expectation of nonsynonymous SNPs should be discussed. Is the number of observed nonsynonymous SNPs significantly higher than the expected one?

(2) Line 107 - "In addition, the abundance of mutations was unlikely to be related to the magnitude of fitness increase." Instead of just listing examples, a regression analysis can be added.

Yes, it's significant. Random mutations lead to ~33% of nonsynonymous SNP in a rough estimation. Additionally, the regression is unreliable because there's no statistical significance between the number of mutations and the magnitude of fitness increase. Accordingly, the corresponding sentences were revised with additional statistical tests.

(L123-129) “As 36 out of 45 SNPs were nonsynonymous, which was highly significant compared to random mutations (p < 0.01), the mutated genes might benefit fitness increase. In addition, the abundance of mutations was unlikely to be related to the magnitude of fitness increase. There was no significant correlation between the number of mutations and the growth rate in a statistical view (p > 0.1). Even from an individual close-up viewpoint, the abundance of mutations poorly explained the fitness increase.”

(3) Line 114 - "They seemed highly related to essentiality, as 11 out of 49 mutated genes were essential (Table S3)." Here, the information mentioned in line 153 ("the ratio of essential to all genes (302 out of 3,290) in the reduced genome.") can be used. Then a statistical test for a contingency table can be used.

(4) Line 117 - "the high frequency of the mutations fixed in the essential genes suggested the mutation in essentiality for fitness increase was the evolutionary strategy for reduced genome." What is the expected number of fixed mutations in essential genes vs non-essential genes? Is the observed number statistically significantly higher?

Sorry for the improper and insufficient information on the essential genes. Yes, it's significant. The statistical test was additionally performed. The corresponding part was revised as follows.

(L134-146) “They seemed highly related to essentiality7 (https://shigen.nig.ac.jp/ecoli/pec/genes.jsp), as 11 out of 49 mutated genes were essential (Table S3). Although the essentiality of genes might differ between the wild-type and reduced genomes, the experimentally determined 302 essential genes in the wild-type E. coli strain were used for the analysis, of which 286 were annotated in the reduced genome. The ratio of essential genes in the mutated genes was significantly higher than in the total genes (286 out of 3290 genes, Chi-square test p=0.008). As the essential genes were determined according to the growth35 and were known to be more conserved than nonessential ones 36,37, the high frequency of the mutations fixed in the essential genes was highly intriguing and reasonable. The large variety of genome mutations fixed in the independent lineages might result from a highly rugged fitness landscape 38. Nevertheless, it was unclear whether and how these mutations were explicitly responsible for recovering the growth rate of the reduced genome.”

(5) The authors mentioned no overlapping in the single mutation level. Is that statistically significant? The authors can bring up what the no-overlap probability is given that there are in total x number of fixed mutations observed (either theory or simulation is good).

Sorry, we feel confused about this comment. It's unclear to us why it needs to be statistically simulated. Firstly, the mutations were experimentally observed. The result that no overlapped mutated genes were detected was an Experimental Fact but not a Computational Prediction. We feel sorry that you may over-interpret our finding as an evolutionary rule, which always requires testing its reliability statistically. We didn't conclude that the evolution had no overlapped mutations. Secondly, considering 65 times random mutations happened to a ~3.9 Mb sequence, the statistical test was meaningful only if the experimental results found the overlapped mutations. It is interesting how often the random mutations cause the overlapped mutations in parallel evolutionary lineages while increasing the evolutionary lineages, which seems to be out of the scope of the present study. We are happy to include the analysis in our ongoing study on the experimental evolution of reduced genomes.

(6) The authors mentioned no overlapping in the single mutation level. How about at the genetic level? Some fixed mutations occur in the same coding gene. Is there any gene with a significantly enriched number of mutations?

No mutations were fixed in the same gene of biological function, as shown in Table S3. If we say the coding region, the only exception is the IS sequences, well known as the transposable sequences without genetic function. The following description was added.

(L119-122) “The number of mutations largely varied among the nine Evos, from 2 to 13, and no common mutation was detected in all nine Evos (Table S3). A 1,199-bp deletion of insH was frequently found in the Evos (Table S3, highlighted), which well agreed with its function as a transposable sequence.”

(7) Line 151-156- It seems like the authors argue that the expression level differences can be just explained by the percentage of essential genes that get fixed mutations. One further step for the argument could be to compare the expression level of essential genes with vs without fixed mutations. Also, the authors can compare the expression level of non-essential genes with vs without fixed mutations. And the authors can report whether the differences in expression level became insignificant after the control of the essentiality.

It's our pleasure that the essentiality intrigued you. Thank you for the analytical suggestion, which is exciting and valuable for our studies. As only 11 essential genes were detected here and "Mutation in essentiality" was an indication but not the conclusion of the present study, we would like to apply the recommended analysis to the datasets of our ongoing study to demonstrate this statement. Thank you again for your fruitful analytical advice.

(8) Line 169- "The number of DEGs partially overlapped among the Evos declined significantly along with the increased lineages of Evos (Figure 4B). " There is a lack of statistical significance here while the word "significantly" is used. One statistical test that can be done is to use re-sampling/simulation to generate a null expectation of the overlapping numbers given the DEGs for each Evo line and the total number of genes in the genome. The observed number can then be compared to the distribution of the simulated numbers.

Sorry for the inappropriate usage of the term. Whether it's statistically significant didn't matter here. The word "significant" was deleted as follows.

(L205--206) “The number of DEGs partially overlapped among the Evos declined along with the increased lineages of Evos (Fig. 4B).”

(9) Line 177-179- "In comparison,1,226 DEGs were induced by genome reduction. The common DEGs 177 of genome reduction and evolution varied from 168 to 540, fewer than half of the DEGs 178 responsible for genome reduction in all Evos" Is the overlapping number significantly lower than the expectation? The hypergeometric test can be used for testing the overlap between two gene sets.

There's no expectation for how many DEGs were reasonable. Not all numbers experimentally obtained are required to be statistically meaningful, which is commonly essential in computational and data science.

(10) The authors should give more information about the ancestral line used at the beginning of experimental evolution. I guess it is one of the KHK collection lines, but I can not find more details. There are many genome-reduced lines. Why is this certain one picked?

Sorry for the insufficient information on the reduced genome used for the experimental evolution. The following descriptions were added in the Results and the Materials and Methods, respectively.

(L75-79) “The E. coli strain carrying a reduced genome, derived from the wild-type genome W3110, showed a significant decline in its growth rate in the minimal medium compared to the wild-type strain 13. To improve the genome reduction-mediated decreased growth rate, the serial transfer of the genome-reduced strain was performed with multiple dilution rates to keep the bacterial growth within the exponential phase (Fig. S1), as described 17,20.”

(L331-334) “The reduced genome has been constructed by multiple deletions of large genomic fragments 58, which led to an approximately 21% smaller size than its parent wild-type genome W3110.”

(11) How was the saturated density in Figure 1 actually determined? In particular, the fitness assay of growth curves is 48h. But it seems like the experimental evolution is done for ~24 h cycles. If the Evos never experienced a situation like a stationary phase between 24-48h, and if the author reported the saturated density 48 h in Figure 1, the explanation of the lower saturated density can be just relaxation from selection and may have nothing to do with the increase of growth rate.

Sorry for the unclear description. Yes, you are right. The evolution was performed within the exponential growth phase (keeping cell division constant), which means the Evos never experienced the stationary phase (saturation). The final evolved populations were subjected to the growth assay to obtain the entire growth curves for calculating the growth rate and the saturated density. Whether the decreased saturated density and the increased growth rate were in a trade-off relationship remained unclear. The corresponding paragraph was revised as follows.

(L100-115) “Intriguingly, a positive correlation was observed between the growth fitness and the carrying capacity of the Evos (Fig. 1D). It was somehow consistent with the positive correlations between the colony growth rate and the colony size of a genome-reduced strain 11 and between the growth rates and the saturated population size of an assortment of genome reduced strains 13. Nevertheless, the negative correlation between growth rate and carrying capacity, known as the r/K selection30,31 was often observed as the trade-off relationship between r and K in the evolution and ecology studies 32 33,34. As the r/K trade-off was proposed to balance the cellular metabolism that resulted from the cost of enzymes involved 34, the deleted genes might play a role in maintaining the metabolism balance for the r/K correlation. On the other hand, the experimental evolution (i.e., serial transfer) was strictly performed within the exponential growth phase; thus, the evolutionary selection was supposed to be driven by the growth rate without selective pressure to maintain the carrying capacity. The declined carrying capacity might have been its neutral "drift" but not a trade-off to the growth rate. Independent and parallel experimental evolution of the reduced genomes selecting either r or K is required to clarify the actual mechanisms.”

(12) What annotation of essentiality was used in this paper? In particular, the essentiality can be different in the reduced genome background compared to the WT background.

Sorry for the unclear definition of the essential genes. They are strictly limited to the 302 essential genes experimentally determined in the wild-type E coli strain. Detailed information can be found at the following website: https://shigen.nig.ac.jp/ecoli/pec/genes.jsp. We agree that the essentiality could differ between the WT and reduced genomes. Identifying the essential genes in the reduced genome will be an exhaustedly vast work. The information on the essential genes defined in the present study was added as follows.

(L134-139) “They seemed highly related to essentiality7 (https://shigen.nig.ac.jp/ecoli/pec/genes.jsp), as 11 out of 49 mutated genes were essential (Table S3). Although the essentiality of genes might differ between the wild-type and reduced genomes, the experimentally determined 302 essential genes in the wild-type E. coli strain were used for the analysis, of which 286 were annotated in the reduced genome.”

(13) The fixed mutations in essential genes are probably not rarely observed in experimental evolution. For example, fixed mutations related to RNA polymerase can be frequently seen when evolving to stressful environments. I think the author can discuss this more and elaborate more on whether they think these mutations in essential genes are important in adaptation or not.

Thank you for your careful reading and the suggestion. As you mentioned, we noticed that the mutations in RNA polymerases (rpoA, rpoB, and rpoD) were identified in three Evos. As they were not shared across all Evos, we didn't discuss the contribution of these mutations to evolution. Instead of the individual functions of the mutated essential gene functions, we focused on the enriched gene functions related to the transcriptome reorganization because they were the common feature observed across all Evos and linked to the whole metabolic or regulatory pathways, which are supposed to be more biologically reasonable and interpretable. The following sentence was added to clarify our thinking.

(L268-273) “In particular, mutations in the essential genes, such as RNA polymerases (rpoA, rpoB, rpoD) identified in three Evos (Table S3), were supposed to participate in the global regulation for improved growth. Nevertheless, the considerable variation in the fixed mutations without overlaps among the nine Evos (Table 1) implied no common mutagenetic strategy for the evolutionary improvement of growth fitness.”

(14) In experimental evolution to new environments, several previous literature also show that long-term experimental evolution in transcriptome is not consistent or even reverts the short-term response; short-term responses were just rather considered as an emergency plan. They seem to echo what the authors found in this manuscript. I think the author can refer to some of those studies more and make a more throughput discussion on short-term vs long-term responses in evolution.

Thank you for the advice. It's unclear to us what the short-term and long-term responses referred to mentioned in this comment. The "Response" is usually used as the phenotypic or transcriptional changes within a few hours after environmental fluctuation, generally non-genetic (no mutation). In comparison, long-term or short-term experimental "Evolution" is associated with genetic changes (mutations). Concerning the Evolution (not the Response), the long-term experimental evolution (>10,000 generations) was performed only with the wild-type genome, and the short-term experimental evolution (500~2,000 generations) was more often conducted with both wild-type and reduced genomes, to our knowledge. Previous landmark studies have intensively discussed comparing the wild-type and reduced genomes. Our study was restricted to the reduced genome, which was constructed differently from those reduced genomes used in the reported studies. The experimental evolution of the reduced genomes has been performed in the presence of additional additives, e.g., antibiotics, alternative carbon sources, etc. That is, neither the genomic backgrounds nor the evolutionary conditions were comparable. Comparison of nothing common seems to be unproductive. We sincerely hope the recommended topics can be applied in our future work.

Some minor suggestions

• Figures S3 & Table S2 need an explanation of the abbreviations of gene categories.

Sorry for the missing information. Figure S3 and Table S3 were revised to include the names of gene categories. The figure was pasted followingly for a quick reference.

Author response image 3.

• I hope the authors can re-consider the title; "Diversity for commonality" does not make much sense to me. For example, it can be simply just "Diversity and commonality."

Thank you for the suggestion. The title was simplified as follows.

(L1) “Experimental evolution for the recovery of growth loss due to genome reduction.”

• It is not easy for me to locate and distinguish the RNA-seq vs DNA-seq files in DRA013662 at DDBJ. Could you make some notes on what RNA-seq actually are, vs what DNA-seq files actually are?

Sorry for the mistakes in the DRA number of DNA-seq. DNA-seq and RNA-seq were deposited separately with the accession IDs of DRA013661 and DRA013662, respectively. The following correction was made in the revision.

(L382-383) “The raw datasets of DNA-seq were deposited in the DDBJ Sequence Read Archive under the accession number DRA013661.”

Author Response

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

Reviewer 1 (Public Review):

1. The name of the new method "inter-haplotype distance" is more confusing than helpful, as the haplotype information is not critical for implementing this method. First, the mutation spectrum is aggregated genome-wide regardless of the haplotypes where the mutations are found. Second, the only critical haplotype information is that at the focal site (i.e., the locus that is tested for association): individuals are aggregated together when they belong to the same "haplotype group" at the focal site. However, for the classification step, haplotype information is not really necessary: individuals can be grouped based on their genotypes at the given locus (e.g., AA vs AB). As the authors mentioned, this method can be potentially applied to other mutation datasets, where haplotype information may well be unavailable. I hope the authors can reconsider the name and remove the term "haplotype" (perhaps something like "inter-genotype distance"?) to avoid giving the wrong impression that haplotype information is critical for applying this method.

We appreciate the reviewer's concern about the name of our method. The reviewer is correct that haplotype information is not critical for our method to work, and as a result we've decided to simply rename the approach to "aggregate mutation spectrum distance" (abbreviated AMSD). For simplicity, we refer to the method as IHD throughout our responses to reviewers, but the revised manuscript now refers to AMSD.

1. The biggest advantage of the IHD method over QTL mapping is alleviation of the multiple testing burden, as one comparison tests for any changes in the mutation spectrum, including simultaneous, small changes in the relative abundance of multiple mutation types. Based on this, the authors claim that IHD is more powerful to detect a mutator allele that affects multiple mutation types. Although logically plausible, it is unclear under what quantitative conditions IHD can actually have greater power over QTL. It will be helpful to support this claim by providing some simulation results.

This comment prompted us to do a more detailed comparison of IHD vs. QTL power under conditions that are more similar to those observed in the BXD cohort. While preparing the original manuscript, we assumed that IHD might have greater power than QTL mapping in a population like the BXDs because some recombinant inbred lines have accumulated many more germline mutations than others (see Figure 1 in Sasani et al. 2022, Nature). In a quantitative trait locus scan (say, for the fraction of C>A mutations in each line) each BXD's mutation data would be weighted equally, even if a variable number of mutations was used to generate the phenotype point estimate in each line.

To address this, we performed a new series of simulations in which the average number of mutations per haplotype was allowed to vary. At the low end, some BXDs accumulated as few as 100 total germline mutations, while others have accumulated as many as 2,000. Thus, instead of simulating a mean number of mutations on each simulated haplotype, we allowed the mean number of mutations per haplotype to vary from N to 20N. By simulating a variable count of mutations on each haplotype, we could more easily test the benefits of comparing aggregate, rather than individual, mutation spectra between BXDs.

In these updated simulations, we find that IHD routinely outperforms QTL mapping under a range of parameter choices (see Author Response image 1). Since IHD aggregates the mutation spectra of all haplotypes with either B or D alleles at each locus in the genome, the method is much less sensitive to individual haplotypes with low mutation counts. We include a mention of these updated simulations on lines 135-138 and describe the updated simulations in greater detail in the Materials and Methods (lines 705-715).

Author response image 1.

Power of IHD and QTL mapping on simulated haplotypes with variable counts of mutations. We simulated germline mutations on the specified number of haplotypes (as described in the manuscript) but allowed the total number of mutations per haplotype to vary by a factor of 20.

1. The flip side of this advantage of IHD is that, when a significant association is detected, it is not immediately clear which mutation type is driving the signal. Related to this, it is unclear how the authors reached the point that "...the C>A mutator phenotype associated with the locus on chromosome 6", when they only detected significant IHD signal at rs46276051 (on Chr6), when conditioning on D genotypes at the rs27509845 (on Chr4) and no significant signal for any 1-mer mutation type by traditional mapping. The authors need to explain how they deduced that C>A mutation is the major source of the signal. In addition, beyond C>A mutations, can mutation types other than C>A contribute to the IHD signal at rs46276051? More generally, I hope the authors can provide some guidelines on how to narrow a significant IHD signal to specific candidate mutation type(s) affected, which will make the method more useful to other researchers.

We thank the reviewer for pointing out this gap in our logic. We omitted specific instructions for narrowing down an IHD signal to specific mutation type(s) for a few reasons. First, this can be addressed using mutational signature analysis methods that are in widespread use. For example, upon identifying one or more candidate mutator loci, we can enter the mutation spectra of samples with each possible mutator genotype into a program (e.g., SigProfilerExtractor) to determine which combinations of mutation types occur proportionally more often in the genomes that harbor mutators (see Figure 3c in our manuscript). A second approach for narrowing down an IHD signal, highlighted in Figure 3a (and now described in the text of the Results section at lines 256-261), is to simply test which mutation type proportion(s) differ significantly between groups of samples with and without a candidate mutator (for example, with a Chi-square test of independence for each mutation type).

Although this second approach incurs a multiple testing burden, the burden is offset somewhat by using IHD to identify mutator loci, rather than performing association tests for every possible mutation type to begin with. Although Figure 3a only shows the significant difference in C>A fraction among BXDs with different mutator locus genotypes, Figure 3-figure supplement 1 shows the complete set of 1-mer spectrum comparisons. It is possible that this second approach would not prove very useful in the case of a mutator with a “flat” signature (i.e., a mutator that slightly perturbs the rates of many different mutation types), but in our case it clearly shows which mutation type is affected.

1. To account for differential relatedness between the inbred lines, the authors regressed the cosine distance between the two aggregate mutation spectra on the genome-wide genetic similarity and took the residual as the adjusted test metric. What is the value of the slope from this regression? If significantly non-zero, this would support a polygenic architecture of the mutation spectrum phenotype, which could be interesting. If not, is this adjustment really necessary? In addition, is the intercept assumed to be zero for this regression, and does such an assumption matter? I would appreciate seeing a supplemental figure on this regression.

The reviewer raises a good question. We find that the slope of the "distance vs. genetic similarity" regression is significantly non-zero, though the slope estimate itself is small. A plot of cosine distance vs. genome-wide genetic similarity (using all BXDs) is shown below in Author response image 2:

Author response image 2.

Relationship between cosine distance and genetic similarity in the BXDs. As described in the Materials and Methods, we computed two values at each marker in the BXDs: 1) the cosine distance between the aggregate mutation spectra of BXDs with either B or D genotypes at the marker, and 2) the correlation between genome-wide D allele frequencies in BXDs with either B or D genotypes at the marker. We then regressed these two values across all genome-wide markers.

This result indicates that if two groups of BXDs (one with D genotypes and one with B genotypes at a given locus) are more genetically similar, their mutation spectra are also more similar. Since the regression slope estimate is significantly non-zero (p < 2.2e-16), we believe that it's still worth using residuals as opposed to raw cosine distance values. This result also suggests that there may be a polygenic effect on the mutation spectrum in the BXDs.

We have also generated a plot showing the cosine distance between the mutation spectra of every possible pair of BXDs, regressed against the genetic similarity between each of those pairs (Author Response image 3). Here, the potential polygenic effects on mutation spectra similarity are perhaps more obvious.

Author response image 3.

Pairwise cosine distance between BXD mutation spectra as a function of genetic similarity. We computed two values for every possible pair of n = 117 BXDs: 1) the cosine distance between the samples' individual 1-mer mutation spectra and 2) the correlation coefficient between the samples' genome-wide counts of D alleles.

Private Comments

1. It will also be useful to see how the power of IHD and QTL mapping depend on the allele frequency of the mutator allele and the sample size, as mutator alleles are likely rare or semi-rare in natural populations (such as the human de novo mutation dataset that the authors mentioned).

This is another good suggestion. In general, we'd expect the power of both IHD and QTL mapping to decrease as a function of mutator allele frequency. At the same time, we note that the power of these scans should mostly depend on the absolute number of carriers of the mutator allele and less on its frequency. In the BXD mouse study design, we observe high frequency mutators but also a relatively small sample size of just over 100 individuals. In natural human populations, mutator frequencies might be orders of magnitude smaller, but sample sizes may be orders of magnitude larger, especially as new cohorts of human genomes are routinely being sequenced. So, we expect to have similar power to detect a mutator segregating at, say, 0.5% frequency in a cohort of 20,000 individuals, as we would to detect a mutator segregating at 50% frequency in a dataset of 200 individuals.

To more formally address the reviewer's concern, we performed a series of simulations in which we simulated a population of 100 haplotypes. We assigned the same average number of mutations to each haplotype but allowed the allele frequency of the mutator allele to vary between 0.1, 0.25, and 0.5. The results of these simulations are shown in Author response image 4 and reveal that AMSD tends to have greater power than QTL mapping at lower mutator allele frequencies. We now mention these simulations in the text at lines 135-138 and include the simulation results in Figure 1-figure supplement 4.

Author response image 4.

Power of AMSD and QTL mapping on simulated haplotypes with variable marker allele frequencies. We simulated germline mutations on the specified number of haplotypes (as described in the manuscript), but simulated genotypes at the mutator allele such that "A" alleles were at the specified allele frequency.

1. In the Methods section of "testing for epistasis between the two mutator loci", it will be helpful to explicitly lay out the model and assumptions in mathematical formulae, in addition to the R scripts. For example, are the two loci considered independent when their effects on mutation rate is multiplicative or additive? Given the R scripts provided, it seems that the two loci are assumed to have multiplicative effects on the mutation rate, and that the mutation count follows a Poisson distribution with mean being the mutation rate times ADJ_AGE (i.e., the mutation opportunity times the number of generations of an inbred line). However, this is not easily understandable for readers who are not familiar with R language. In addition, I hope the authors can be more specific when discussing the epistatic interaction between the two loci by explicitly saying "synergistic effects beyond multiplicative effects on the C>A mutation rate".

The reviewer raises a good point about the clarity of our descriptions of tests for epistasis. We have now added a more detailed description of these tests in the section of the Materials and Methods beginning at line 875. We have also added a statement to the text at lines 289-291: “the combined effects of D genotypes at both loci exceed the sum of marginal effects of D genotypes at either locus alone.” We hope that this will help clarify the results of our tests for statistical epistasis.

Reviewer 2 (Public Review):

1. The main limitation of the approach is that it is difficult to see how it might be applied beyond the context of mutation accumulation experiments using recombinant inbred lines. This is because the signal it detects, and hence its power, is based on the number of extra accumulated mutations linked to (i.e. on the same chromosome as) the mutator allele. In germline mutation studies of wild populations the number of generations involved (and hence the total number of mutations) is typically small, or else the mutator allele becomes unlinked from the mutations it has caused (due to recombination), or is lost from the population altogether (due to chance or perhaps selection against its deleterious consequences).

The reviewer is correct that as it currently exists, IHD is mostly limited to applications in recombinant inbred lines (RILs) like the BXDs. This is due to the fact that IHD assumes that each diploid sample harbors one of two possible genotypes at a particular locus and ignores the possibility of heterozygous genotypes for simplicity. In natural, outbreeding populations, this assumption will obviously not hold. However, as we plan to further iterate on and improve the IHD method, we hope that it will be applicable to a wider variety of experimental systems in the future. We have added additional caveats about the applicability of our method to other systems in the text at lines 545-550.

Private Comments

1. On p. 8, perhaps I've misunderstood but it's not clear in what way the SVs identified were relevant to the samples used in this dataset - were the founder strains assembled? Is there any chance that additional SVs were present, e.g. de novo early in the accumulation line?

Our description of this structural variation resource could have been clearer. The referenced SVs were identified in Ferraj et al. (2023) by generating high-quality long read assemblies of inbred laboratory mice. Both DBA/2J and C57BL/6J (the founder strains for the BXD resource) were included in the Ferraj et al. SV callset. We have clarified our description of the callset at lines 247-248.

It is certainly possible that individual BXD lines have accumulated de novo structural variants during inbreeding. However, these "private" SVs are unlikely to produce a strong IHD association signal (via linkage to one of the ~7,000 markers) at either the chromosome 4 or chromosome 6 locus, since we only tested markers that were at approximately 50% D allele frequency among the BXDs.

1. On p. 13, comparing the IHD and QTL approaches, regarding the advantage of the former in that it detects the combined effect of multiple k-mer mutation types, would it not be straightforward to aggregate counts for different types in a QTL setting as well?

The mutation spectrum is a multi-dimensional phenotype (6-dimensional if using the 1-mer spectrum, 96-dimensional if using the 3-mer spectrum, etc.). Most QTL mapping methods use linear models to test for associations between genotypes and a 1-dimensional phenotype (e.g., body weight, litter size). In the past, we used QTL mapping to test for associations between genotypes and a single element of the mutation spectrum (e.g., the rate of C>A mutations), but there isn't a straightforward way to aggregate or collapse the mutation spectrum into a 1dimensional phenotype that retains the information contained within the full 1-mer or 3-mer spectrum. For that reason, we developed the "aggregate mutation spectrum" approach, as it preserves information about the complete mutation spectrum in each group of strains.

The reviewer is correct that we could also aggregate counts of different mutation types to, say, perform a QTL scan for the load of a specific mutational signature. For example, we could first perform standard mutational signature analysis on our dataset and then test for QTLs associated with each signature that is discovered. However, this approach would not solve the second problem that our method is designed to solve: the appropriate weighting of samples based on how many mutations they contain.

1. pp. 15-16: In the discussion of how you account for relatedness between strains, I found the second explanation (on p. 16) much clearer. It would be interesting to know how much variance was typically accounted for by this regression?

As shown in the response to Reviewer 1, genotype similarity between genotype groups (i.e., those with either D or B genotypes at a marker) generally explains a small amount of variance in the cosine distance between those groups (R2 ~= 0.007). However, since the slope term in that regression is significantly non-zero, correcting for this relationship should still improve our power relative to using raw cosine distance values that are slightly confounded by this relationship.

1. Similarly, in the section on Applying the IHD method to the BXDs (pp. 18-19), I think this description was very useful, and some or all of this description of the experiment (and how the DNMs in it arise) could profitably be moved to the introduction.

We appreciate the reviewer’s feedback about the details of the BXD cohort. Overall, we feel the description of the BXDs in the Introduction (at lines 65-73) is sufficient to introduce the cohort, though we now add some additional detail about variability in BXD inbreeding duration (at lines 89-93) to the Introduction as well, since it is quite relevant to some of the new simulation results presented in the manuscript.

1. A really minor one, not sure if this is for the journal or the authors, but it would be much better to include both page and line numbers in any version of an article for review. My pdf had neither!

We apologize for the lack of page/line numbers in the submitted PDF. We have now added line numbers to the revised version of the manuscript.

Reviewer 3 (Public Review):

1. Under simulated scenarios, the authors' new IHD method is not appreciably more powerful than conventional QTL mapping methods. While this does not diminish the rigor or novelty of the authors findings, it does temper enthusiasm for the IHD method's potential to uncover new mutators in other populations or datasets. Further, adaptation of this methodology to other datasets, including human trios or multigenerational families, will require some modification, which could present a barrier to broader community uptake. Notably, BXD mice are (mostly) inbred, justifying the authors consideration of just two genotype states at each locus, but this decision prevents out-of-the-box application to outbred populations and human genomic datasets. Lastly, some details of the IHD method are not clearly spelled out in the paper. In particular, it is unclear whether differences in BXD strain relatedness due to the breeding epoch structure are fully accounted for in permutations. The method's name - inter-haplotype distance - is also somewhat misleading, as it seems to imply that de novo mutations are aggregated at the scale of sub-chromosomal haplotype blocks, rather than across the whole genome.

The reviewer raises very fair concerns. As mentioned in response to a question from Reviewer 1, we performed additional simulation experiments that demonstrate the improved power of IHD (as compared to QTL mapping) in situations where mutation counts are variable across haplotypes or when mutator alleles are present at allele frequencies <50% (see Author response image 2 and 3, as well as new supplements to Figure 1 in the manuscript). However, the reviewer is correct that the IHD method is not applicable to collections of outbred individuals (that is, individuals with both heterozygous and homozygous genotypes), which will limit its current applications to datasets other than recombinant inbred lines. We have added a mention of these limitations to the Results at lines 138-141 and the Discussion at lines 545-550, but plan to iterate on the IHD method and introduce new features that enable its application to other datasets. We have also explicitly stated that we account for breeding epochs in our permutation tests in the Materials and Methods at lines 670-671. Both Reviewer 1 and Reviewer 3 raised concerns about the name of our method, and we have therefore changed “inter-haplotype distance” to “aggregate mutation spectrum distance” throughout the manuscript.

1. Nominating candidates within the chr6 mutator locus requires an approach for defining a credible interval and excluding/including specific genes within that interval as candidates. Sasani et al. delimit their focal window to 5Mb on either side of the SNP with the most extreme P-value in their IHD scan. This strategy suffers from several weaknesses. First, no justification for using 10 Mb window, as opposed to, e.g., a 5 Mb window or a window size delimited by a specific threshold of P-value drop, is given, rendering the approach rather ad hoc. Second, within their focal 10Mb window, the authors prioritize genes with annotated functions in DNA repair that harbor protein coding variants between the B6 and D2 founder strains. While the logic for focusing on known DNA repair genes is sensible, this locus also houses an appreciable number of genes that are not functionally annotated, but could, conceivably, perform relevant biological roles. These genes should not be excluded outright, especially if they are expressed in the germline. Further, the vast majority of functional SNPs are non-coding, (including the likely causal variant at the chr4 mutator previously identified in the BXD population). Thus, the author's decision to focus most heavily on coding variants is not well-justified. Sasani et al. dedicate considerable speculation in the manuscript to the likely identity of the causal variant, ultimately favoring the conclusion that the causal variant is a predicted deleterious missense variant in Mbd4. However, using a 5Mb window centered on the peak IHD scan SNP, rather than a 10Mb window, Mbd4 would be excluded. Further, SNP functional prediction accuracy is modest [e.g., PMID 28511696], and exclusion of the missense variant in Ogg1 due its benign prediction is potentially premature, especially given the wealth of functional data implicating Ogg1 in C>A mutations in house mice. Finally, the DNA repair gene closest to the peak IHD SNP is Rad18, which the authors largely exclude as a candidate.

We agree that the use of a 10 Mb window, rather than an empirically derived confidence interval, is a bit arbitrary and ad hoc. To address this concern, we have implemented a bootstrap resampling approach (Visscher et al. 1996, Genetics) to define confidence intervals surrounding IHD peaks. We have added a description of the approach to the Materials and Methods at lines 609-622, but a brief description follows. In each of N trials (here, N = 10,000), we take a bootstrap sample of the BXD phenotype and genotype data with replacement. We then perform an IHD scan on the chromosome of interest using the bootstrap sample and record the position of the marker with the largest cosine distance value (i.e., the "peak" marker). After N trials, we calculate the 90% confidence interval of bootstrapped peak marker locations; in other words, we identify the locations of two genotyped markers, between which 90% of all bootstrap trials produced an IHD peak. We note that bootstrap confidence intervals can exhibit poor "coverage" (a measure of how often the confidence intervals include the "true" QTL location) in QTL mapping studies (see Manichaikul et al. 2006, Genetics), but feel that the bootstrap is more reasonable than simply defining an ad hoc interval around an IHD peak.

The new 90% confidence interval surrounding the IHD peak on chromosome 6 is larger than the original (ad hoc) 10 Mbp window, now extending from around 95 Mbp to 114 Mbp. Notably, the new empirical confidence interval excludes Mbd4. We have accordingly updated our Results and Discussion sections to acknowledge the fact that Mbd4 no longer resides within the confidence interval surrounding the IHD peak on chromosome 6 and have added additional descriptions of genes that are now implicated by the 90% confidence interval. Given the uncertainties associated with using bootstrap confidence intervals, we have retained a brief discussion of the evidence supporting Mbd4 in the Discussion but focus primarily on Ogg1 as the most plausible candidate.

The reviewer raises a valid concern about our treatment of non-DNA repair genes within the interval surrounding the peak on chromosome 6. We have added more careful language to the text at lines 219-223 to acknowledge the fact that non-annotated genes in the confidence interval surrounding the chromosome 6 peak may play a role in the epistatic interaction we observed.

The reviewer also raises a reasonable concern about our discussions of both Mbd4 and Ogg1 as candidate genes in the Discussion. Since Mbd4 does not reside within the new empirical bootstrap confidence interval on chromosome 6 and given the strong prior evidence that Ogg1 is involved in C>A mutator phenotypes (and is in the same gene network as Mutyh), we have reframed the Discussion to focus on Ogg1 as the most plausible candidate gene (see lines 357360).

Using the GeneNetwork resource, we also more carefully explored the potential effects of noncoding variants on the C>A mutator phenotype we observed on chromosome 6. We have updated the Results at lines 240-246 and the Discussion at line 439-447 to provide more evidence for regulatory variants that may contribute to the C>A mutator phenotype. Specifically, we discovered a number of strong-effect cis-eQTLs for Ogg1 in a number of tissues, at which D genotypes are associated with decreased Ogg1 expression. Given new evidence that the original mutator locus we discovered on chromosome 4 harbors an intronic mobile element insertion that significantly affects Mutyh expression (see Ferraj et al. 2023, Cell Genomics), it is certainly possible that the mutator phenotype associated with genotypes on chromosome 6 may also be mediated by regulatory, rather than coding, variation.

1. Additionally, some claims in the paper are not well-supported by the author's data. For example, in the Discussion, the authors assert that "multiple mutator alleles have spontaneously arisen during the evolutionary history of inbred laboratory mice" and that "... mutational pressure can cause mutation rates to rise in just a few generations of relaxed selection in captivity". However, these statements are undercut by data in this paper and the authors' prior publication demonstrating that a number of candidate variants are segregating in natural mouse populations. These variants almost certainly did not emerge de novo in laboratory colonies, but were inherited from their wild mouse ancestors. Further, the wild mouse population genomic dataset used by the authors falls far short of comprehensively sampling wild mouse diversity; variants in laboratory populations could derive from unsampled wild populations.

The reviewer raises a good point. In our previous publication (Sasani et al. 2022, Nature), we hypothesized that Mutyh mutator alleles had arisen in wild, outbreeding populations of Mus musculus, and later became fixed in inbred strains like DBA/2J and C57BL/6J. However, in the current manuscript, we included a statement about mutator alleles "spontaneously arising during the evolutionary history of inbred laboratory mice" to reflect new evidence (from Ferraj et al. 2023, Cell Genomics) that the mutator allele we originally identified in Mutyh may not be wild derived after all. Instead, Ferraj et al. suggest that the C>A mutator phenotype we originally identified is caused by an intronic mobile element insertion (MEI) that is present in DBA/2J and a handful of other inbred laboratory strains. Although this MEI may have originally occurred in a wild population of mice, we wanted to acknowledge the possibility that both the original Mutyh mutator allele, as well as the new mutator allele(s) we discovered in this manuscript, could have arisen during the production and inbreeding of inbred laboratory lines. We have also added language to the Discussion at lines 325-327 to acknowledge that the 67 wild mice we analyzed do not comprise a comprehensive picture of the genetic diversity present in wild-derived samples.

We have added additional language to the Discussion at lines 349-357 in which we acknowledge that the chromosome 6 mutator allele might have originated in either laboratory or wild mice and elaborate on the possibility that mutator alleles with deleterious fitness consequences may be more likely to persist in inbred laboratory colonies.

1. Finally, the implications of a discovering a mutator whose expression is potentially conditional on the genotype at a second locus are not raised in the Discussion. While not a weakness per se, this omission is perceived to be a missed opportunity to emphasize what, to this reviewer, is one of the most exciting impacts of this work. The potential background dependence of mutator expression could partially shelter it from the action of selection, allowing the allele persist in populations. This finding bears on theoretical models of mutation rate evolution and may have important implications for efforts to map additional mutator loci. It seems unfortunate to not elevate these points.

We agree and have added additional discussion of the possibility that the C>A mutator phenotypes in the BXDs are a result of interactions between the expression of two DNA repair genes in the same base-excision network to the Discussion section at lines 447-449.

Private comments

1. The criteria used to determine or specify haplotype size are not specified in the manuscript. I mention this above but reiterate here as this was a big point of confusion for me when reading the paper. Haplotype length is important consideration for overall power and for proper extension of this method to other systems/populations.

We may not have been clear enough in our description of our method, and as suggested by Reviewer 1, the name "inter-haplotype distance" may also have been a source of confusion. At a given marker, we compute the aggregate mutation spectrum in BXDs with either B or D genotypes using all genome-wide de novo mutations observed in those BXDs. Since the BXDs were inbred for many generations, we expect that almost all de novo germline mutations observed in an RIL are in near-perfect linkage with the informative genotypes used for distance scans. Thus, the "haplotypes" used in the inter-haplotype distance scans are essentially the lengths of entire genomes.

1. Results, first paragraph, final sentence. I found the language here confusing. I don't understand how one can compute the cosine distance at single markers, as stated. I'm assuming cosine distance is computed from variants residing on haplotypes delimited by some defined window surrounding the focal marker?

As discussed above, we aggregate all genome-wide de novo mutations in each group of BXDs at a given marker, rather than only considering DNMs within a particular window surrounding the marker. The approach is discussed in greater detail in the caption of Figure 1.

1. Nominating candidates for the chr6 locus, Table 1. It would be worth confirming that the three prioritized candidates (Setmar, Ogg1, and Mbd4) all show germline expression.

Using the Mouse Genome Informatics online resource, we confirmed that all prioritized candidate genes (now including Setmar and Ogg1, but not Mbd4) are expressed in the male and female gonads, and mention this in the Results at lines 228 and 233-234.

1. Does the chr6 peak on the C>A LOD plot (Figure 2- figure supplement 1) overlap the same peak identified in the IHD scan? And, does this peak rise to significance when using alpha = 0.05? Given that the goal of these QTL scans is to identify loci that interact with the C>A mutator on chr4, it is reasonable to hypothesize that the mutation impact of epistatic loci will also be restricted to C>A mutations. Therefore, I am not fully convinced that the conservative alpha = 0.05/7 threshold is necessary.

The chromosome 6 peak in Figure 2-figure supplement 1 does, in fact, overlap the peak marker we identified on chromosome 6 using IHD. One reason we decided to use a more conservative alpha of (0.05 / 7) is that we wanted these results to be analogous to the ones we performed in a previous paper (Sasani et al. 2022, Nature), in which we first identified the mutator locus on chromosome 4. However, the C>A peak does not rise to genome-wide significance if we use a less conservative alpha value of 0.05 (see Author response image 5). As discussed in our response to Reviewer 1, we find that QTL mapping is not as powerful as IHD when haplotypes have accumulated variable numbers of germline mutations (as in the BXDs), which likely explains the fact that the peak on chromosome 6 is not genome-wide significant using QTL mapping.

Author response image 5.

QTL scan for the fraction of C>A mutations in BXDs harboring D alleles at the locus near Myth QTL scan was performed at a genome-wide significance alpha of 0.05, rather than 0.05/7.

1. Is there significant LD between the IHD peaks on chr6 and chr4 across the BXD? If so, it could suggest that the signal is driven by cryptic population structure that is not fully accounted for in the author's regression based approach. If not, this point may merit an explicit mention in the text as an additional validation for the authenticity of the chr6 mutator finding.

This is a good question. We used the scikit-allel Python package to calculate linkage disequilibrium (LD) between all pairs of genotyped markers in the BXD cohort, and found that the two peak loci (on chromosomes 4 and 6) exhibit weak LD (r2 = 4e-5). We have added a mention of this to the main text of the Results at lines 212-213. That being said, we do not think the chromosome 6 mutator association (or the apparent epistasis between the alleles on chromosomes 4 and 6) could be driven by cryptic population structure. Unlike in human GWAS and other association studies in natural populations, there is no heterogeneity in the environmental exposures experienced by different BXD subpopulations. In humans, population structure can create spurious associations (e.g., between height and variants that are in LD and are most common in Northern Europe), but this requires the existence of a phenotypic gradient caused by genetic or environmental heterogeneity that is not likely to exist in the context of inbred laboratory mice that are all the progeny of the same two founder strains.

1. Discussion, last sentence of the "Possible causal alleles..." section: I don't understand how the absence of the Mariner-family domain leads the authors to this conclusion. Setmar is involved in NHEJ, which to my knowledge is not a repair process that is expected to have a specific C>A mutation bias. I think this is grounds enough for ruling out its potential contributions, in favor of focusing on other candidates, (e.g., Mbd4 and Ogg1).

The reviewer raises a good point. Our main reason for mentioning the absence of the Marinerfamily domain is that even if NHEJ were responsible for the C>A mutator phenotype, it likely wouldn't be possible for Setmar to participate in NHEJ without the domain. However, the reviewer is correct that NHEJ is not expected to cause a C>A mutation bias, and we have added a mention of this to the text as well at lines 379-382.

1. Discussion, second to last paragraph of section "Mbd4 may buffer...": The authors speculate that reduced activity of Mbd4 could modulate rates of apoptosis in response to DNA damage. This leads to the prediction that mice with mutator alleles at both Mutyh and Mbd4 should exhibit higher overall mutation rates compared to mice with other genotypes. This possibility could be tested with the authors' data.

The reviewer raises a good question. As mentioned above, however, we implemented a new approach to calculate confidence intervals surrounding distance peaks and found that this empirical approach (rather than the ad hoc 10-Mbp window approach we used previously) excluded Mbd4 from the credible interval. Although we still mention Mbd4 as a possible candidate (since it still resides within the 10 Mbp window), we have refactored the Discussion section to focus primarily on the evidence for Ogg1 as a candidate gene on chromosome 6.

In any case, we do not observe that mice with mutator alleles at both the chromosome 4 and chromosome 6 loci have higher overall mutation rates compared to mice with other genotype combinations. This may not be terribly surprising, however, since C>A mutations only comprise about 10% of all possible mutations. Thus, given the variance in other 1-mer mutation counts, even a substantial increase in the C>A mutation rate might not have a detectable effect on the overall mutation rate. Indeed, in our original paper describing the Mutyh mutator allele (Sasani et al. 2022, Nature), we did not identify any QTL for the overall mutation rate in the BXDs and found that mice with the chromosome 4 mutator allele only exhibited a 1.11X increase in their overall mutation rates relative to mice without the mutator allele.

1. Methods, "Accounting for BXD population structure": An "epoch-aware" permutation strategy is described here, but it is not clear when (and whether) this strategy is used to determine significance of IHD P-values.

We have added a more explicit mention of this to the Methods section at lines 670-671, as we do, in fact, use the epoch-aware permutation strategy when calculating empirical distance thresholds.

1. The simulation scheme employed for power calculations is highly specific to the BXD population. This is not a weakness, and perfectly appropriate to the study population used here. However, it does limit the transferability of the power analyses presented in this manuscript to other populations. This limitation may merit an explicit cautionary mention to readers who may aspire to port the IHD method over to their study system.

This is true. Our simulation strategy is relatively simple and makes a number of assumptions about the simulated population of haplotypes (allele frequencies normally distributed around 0.5, expected rates of each mutation type, etc.). In response to concerns from Reviewer 1, we performed an updated series of simulations in which we varied some of these parameters (mutator allele frequencies, mean numbers of mutations on haplotypes, etc.). However, we have added a mention of the simulation approach's limitations and specificity to the BXDs to the text at lines 545-550.

Author response:

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

eLife assessment

This valuable study uses a novel experimental design to elegantly demonstrate how we exploit stimulus structure to overcome working memory capacity limits. While the behavioural evidence is convincing, the neural evidence is incomplete, as it only provides partial support for the proposed information compression mechanism. This study will be of interest to cognitive neuroscientists studying structure learning and memory.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Huang and Luo investigated whether regularities between stimulus features can be exploited to facilitate the encoding of each set of stimuli in visual working memory, improving performance. They recorded both behavioural and neural (EEG) data from human participants during a sequential delayed response task involving three items with two properties: location and colour. In the key condition ('aligned trajectory'), the distance between locations of successively presented stimuli was identical to their 'distance' in colour space, permitting a compression strategy of encoding only the location and colour of the first stimulus and the relative distance of the second and third stimulus (as opposed to remembering 3 locations and 3 colours, this would only require remembering 1 location, 1 colour, and 2 distances). Participants recalled the location and colour of each item after a delay.

Consistent with the compression account, participants' location and colour recall errors were correlated and were overall lower compared to a non-compressible condition ('misaligned trajectory'). Multivariate analysis of the neural data permitted decoding of the locations and colours during encoding. Crucially, the relative distance could also be decoded - a necessary ingredient for the compression strategy.

Strengths:

The main strength of this study is a novel experimental design that elegantly demonstrates how we exploit stimulus structure to overcome working memory capacity limits. The behavioural results are robust and support the main hypothesis of compressed encoding across a number of analyses. The simple and well-controlled design is suited to neuroimaging studies and paves the way for investigating the neural basis of how environmental structure is detected and represented in memory. Prior studies on this topic have primarily studied behaviour only (e.g., Brady & Tenenbaum, 2013).

Thanks for the positive comments and excellent summary.

Weaknesses:

The main weakness of the study is that the EEG results do not make a clear case for compression or demonstrate its neural basis. If the main aim of this strategy is to improve memory maintenance, it seems that it should be employed during the encoding phase. From then on, the neural representation in memory should be in the compressed format. The only positive evidence for this occurs in the late encoding phase (the re-activation of decoding of the distance between items 1 and 2, Fig. 5A), but the link to behaviour seems fairly weak (p=0.068).

Thanks for raising this important concern. The reviewer is correct that in principle subjects should employ the compression strategy during the encoding phase when sequence stimuli are presented, yet our results show that the 1-2 trajectory could only be decoded during the late encoding phase.

Meanwhile, subjects could not get enough information to form the compressed strategy for the location and color sequences until the appearance of the 3rd item. Specifically, based on the first two items, the 1st and 2nd item, they only learn whether the 1st-2nd trajectories are congruent between location and color features. However, they could not predict whether it would also apply to the incoming 2nd-3rd trajectory. This is exactly what we found in neural decoding results. The 1st-2nd trajectory could be decoded after the 2nd item presentation, and the 2nd-3rd trajectory appears after the 3rd item onset. Most critically, the 1st-2nd trajectory is reactivated after the 3rd item but only for alignment condition, implicating formation of the full-sequence compression strategy wherein the previously formed 1st-2nd trajectory is reactivated to be connected to the 2nd-3rd trajectory.

Regarding the difference between higher- and lower-correlation groups, previously we used the time window based on the overall 2nd-3rd neural reactivations, which might not be sensitive to reactivation strength. We now re-chose the time window based on the higher-correlation group (bootstrap test, p = 0.037, two sides).

Results have been updated (Figure 5; Results, Page 16). Interpretations about the formation of compression strategy during encoding phase have been added to Results (Page 15-16) and Discussion (Page 18).

Stronger evidence would be showing decoding of the compressed code during memory maintenance or recall, but this is not presented. On the contrary, during location recall (after the majority of memory maintenance is already over), colour decoding re-emerges, but in the un-compressed item-by-item code (Fig. 4B). The authors suggest that compression is consolidated at this point, but its utility at this late stage is not obvious.

Thank you for the important question we apologize for omitting previously - neural evidence for the compressive account.

The reason we did not perform neural decoding during maintenance is that previous EEG/MEG studies including our own failed to reveal robust and sustained time-resolved memory decoding during this period. This is posited to arise from “activity-silent” WM states, wherein memories are not necessarily retained in sustained firing but silently stored within connection weights of WM networks (Stokes, Trends Cogn. Sci., 2015; Rose, Curr Dir Psychol Sci, 2020). Our previous work showed that by transiently perturbing the 'activity-silent' WM using a retrocue or neutral impulse, memories could be reactivated and robustly decoded from neural activities (Huang et al., eLife, 2021). However, due to the lack of transient events during retention in the current design, we do not expect robust decoding results during maintenance. As shown below (AB), this is indeed what we have observed, i.e., no robust neural decoding of trajectories during retention.

We further used alpha-band (8-11 Hz) neural activities, which have been shown to carry WM information (de Vries et al., Trends Cogn. Sci, 2020; Foster et al., Curr. Biol, 2016; Fukuda et al., J. Neurophysiol, 2016; Sutterer et al., PLOS Biol., 2019) to perform decoding analysis of compression trajectories during maintenance. As shown below, the alpha-band decoding results are indeed stronger than raw activities. Importantly, as shown below (CD), the aligned condition indeed showed significant and long-lasting decoding of compression trajectories (1st-2nd, 2nd-3rd) during retention, while the misaligned condition only showed decoding at the beginning (GH), which might be due to the non-specific offset response of the 3rd item. The results, although not as clear as those during encoding and recalling periods, support the reviewer’s hypothesis that the compressive strategy, if exploited, would be demonstrated during both encoding and maintenance periods. New results and related discussion have been added (Page 16, Supplementary Figure 4).

With regards to the observed item-by-item color replay during location recall, the reviewer was concerned that this was not consistent with the compressive account, given the lack of trajectory decoding.

First, item sequences stored in compressive formats need to be converted to sequences during serial recall. In other words, even though color and location sequences are retained in a compressive format (i.e., common 1st-2nd, 2nd-3rd trajectories) throughout the encoding and retention phases, they should be transferred to two sequences as outputs. This is exactly why we performed decoding analysis on individual color and location items rather than trajectories.

Second and most importantly, we observed serial replay of color sequences when recalling locations. In our view, these results constitute strong evidence for common structure, since the spontaneous color replay during location recall for aligned condition highlights the close bound between color and location sequences stored in WM. In fact, item-by-item serial replay has been well acknowledged as a critical neural index of cognitive maps, not only for spatial navigation but also for higher-order tasks (e.g., Liu et al., Cell, 2019; Liu et al., Science, 2021). Therefore, spontaneous color sequence replay during location sequence recall supports their shared underlying cognitive map.

Finally, spontaneous serial replay is also correlated with the reactivation of compressive trajectories during encoding (Supplementary Figure 3). This further indicates that serial replay during recalling is associated with memory reorganization formed during encoding.

Taken together, we posit that memories need to be converted to sequences as outputs, which leads to serial reactivations during recalling. Importantly, the observed spontaneous replay of color sequences for the aligned condition provides strong evidence supporting the associations between color and location sequences in WM.

We have now added relevant interpretations and discussions (Page 11&13).

Reviewer #2 (Public Review):

Summary:

In this study, the authors wanted to test if using a shared relational structure by a sequence of colors in locations can be leveraged to reorganize and compress information.

Strength:

They applied machine learning to EEG data to decode the neural mechanism of reinstatement of visual stimuli at recall. They were able to show that when the location of colors is congruent with the semantically expected location (for example, green is closer to blue-green than purple) the related color information is reinstated at the probed location. This reinstatement was not present when the location and color were not semantically congruent (meaning that x displacement in color ring location did not displace colors in the color space to the same extent) and semantic knowledge of color relationship could not be used for reducing the working memory load or to benefit encoding and retrieval in short term memory.

Weakness:

The experiment and results did not address any reorganization of information or neural mechanism of working memory (that would be during the gap between encoding and retrieval).

We apologize for not presenting clear neural evidence for memory reorganization, particularly neural decoding during WM maintenance and retrieval, in the previous version. As below, we explain why the findings provide converging neural evidence for WM reorganization based on a shared cognitive map.

First, during the encoding phase when location and color sequences are serially presented, our results reveal reactivation of the 1st-2nd trajectories upon the onset of the 3rd item when location and color sequences are aligned with each other. The reactivation of 1st-2nd trajectory right after the emergence of 2nd-3rd trajectory for aligned but not for misaligned sequences strongly supports WM reorganization, since only stimulus sequences that could be compressed based on shared trajectories (aligned condition) show the co-occurrence of 1st-2nd and 2nd-3rd trajectories. Moreover, the relevance of 1st-2nd reactivation to behavioral measurements of color-location reorganization (i.e., behavioral trajectory correlation, Figure 5D) further indicates its link to WM reorganization.

Second, the reason we originally did not perform neural decoding during maintenance is that previous EEG/MEG studies including our own failed to reveal robust and sustained time-resolved memory decoding during this period. This is posited to arise from “activity-silent” WM states, wherein memories are not necessarily retained in sustained firing but silently stored within connection weights of WM networks (Stokes, Trends Cogn. Sci., 2015; Wolff et al., Nat. Neurosci, 2017; Rose et al., Curr Dir Psychol Sci, 2020). Our previous work showed that by transiently perturbing the 'activity-silent' WM using a retrocue or neutral impulse, memories could be reactivated and robustly decoded from neural activities (Huang et al., eLife, 2021). However, due to the lack of transient events during retention in the current design, we do not expect robust decoding results during maintenance. As shown in Supplementary Figure 4(AB), this is indeed what we have observed, i.e., no robust neural decoding of trajectories during retention.

We then used alpha-band (8-11 Hz) neural activities, which have been found to carry WM information (de Vries et al., Trends Cogn. Sci, 2020; Foster et al., Curr. Biol, 2016; Fukuda et al., J. Neurophysiol, 2016; Sutterer et al., PLOS Biol., 2019) to perform decoding analysis of compression trajectories during maintenance. As shown below, the alpha-band decoding results are indeed stronger than raw activities. Importantly, as shown in Supplementary Figure 4(CD), the aligned condition indeed showed significant and long-lasting decoding of compression trajectories (1st-2nd, 2nd-3rd) during retention, while the misaligned condition only showed decoding at the beginning (GH), which might be due to the non-specific offset response of the 3rd item. The results, although not as clear as those during encoding and recalling periods, thus also support WM reorganization.

Finally, during the recalling period, we observed automatic serial replay of color sequences when recalling locations. In our view, these results constitute strong evidence for common structure, since the spontaneous color replay during location recall for aligned condition highlights the close bound between color and location sequences stored in WM. In fact, item-by-item serial replay has been well acknowledged as a critical neural index of cognitive maps, not only for spatial navigation but also for higher-order tasks (e.g., Liu et al., Cell, 2019; Liu et al., Science, 2021). Therefore, spontaneous replay of color sequence during location recall supports their shared underlying cognitive map. Moreover, the spontaneous serial replay is correlated with the reactivation of compressive trajectories during encoding (Supplementary Figure 3). This further indicates that serial replay during recalling is associated with memory reorganization formed during encoding.

Taken together, we have added updated results about the maintenance period (Page 16, Supplementary Figure 4) and included clarifications and interpretations about why the findings during the encoding and retrieval periods support the WM reorganization view (Page 15-16).

There was also a lack of evidence to rule out that the current observation can be addressed by schematic abstraction instead of the utilization of a cognitive map.

The likely impact of the initial submission of the study would be in the utility of the methods that would be helpful for studying a sequence of stimuli at recall. The paper was discussed in a narrow and focused context, referring to limited studies on cognitive maps and replay. The bigger picture and long history of studying encoding and retrieval of schema-congruent and schema-incongruent events is not discussed.

We agree with the reviewer that cognitive map referred here could be understood as schematic abstraction. Cognitive map refers to the internal representation of spatial relations in a specific environment (Tolman 1948). Schematic abstraction denotes a more broad range of circumstances, whereby the gist or structure of multiple environments or episodes can be integrated (Bartlett, 1932; Farzanfar et al., Nat. Rev. Neurosci, 2023).

In other words, schema refers to highly abstract framework of prior knowledge that captures common patterns across related experiences, which does not necessarily occur in a spatial framework as cognitive maps do. Meanwhile, in the current design, we specifically manipulate the consistency of spatial trajectory distance between color and location sequences. Therefore, we would argue that cognitive map is a more conservative and appropriate term to frame our findings.

Relevant discussions have been added (Page 3&19).

We apologize for the lack of more generalized discussion and have added schema-related literatures. Thanks for the suggestion.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Do time-frequency-domain data (e.g., alpha-band power) in the delay provide evidence for delay-period decoding of trajectory lengths? This might strengthen the case for compression.

Thanks for the suggestion. We now performed decoding analysis of the delay period based on alpha-band power. As shown in supplementary figure 4, both the 1st-2nd and 2nd-3rd trajectories could be decoded for the aligned condition.

Added in supplementary figure 4 and Page 16.  

(2) Do participants erroneously apply the compression strategy in the misaligned condition? This would not show up in the trajectory error correlation analysis, but might be visible when examining correlations between raw trajectory lengths.

Thanks for raising this interesting suggestion. To test the hypothesis, we chose a typical misaligned condition where 1st-2nd trajectory distances are same between location and color sequences, while the 2nd-3rd trajectory distances are different between the two features.

In this case, participants might exploit the compression strategy for the first two items and erroneously apply the strategy to the 3rd item. If so, we would expect better memory performance for the first two items but worse memory for the 3rd item, compared to the rest of misaligned trials. As shown below, the 1st-2nd aligned trials showed marginally significant higher performance than misaligned trials for the first two items (t(32) = 1.907, p = 0.066, Cohen’s d = 0.332) . Unfortunately, we did not find significant worse performance for the 3rd item between the two conditions (t(32) = -0.4847, p = 0.631, Cohen’s d = -0.084). We observed significant interactions between the last two items and the alignment effect (t(32) = 2.082, p = 0.045, Cohen’s d = 0.362), indicating a trend of applying wrong compression strategy to the 3nd item.

Author response image 1.

(3a) Some more detail on some of the methods might help readers. For instance, did trajectories always move in a clockwise direction? Could the direction reverse on the third item? If not, did this induce a response bias? Could such a bias possibly account for the trajectory error correlations

Sorry for the unclear statement. For individual trial, both the color and location features of the three items are randomly selected from nine possible values without any constraint about the directions. That is to say, the trajectories can move in a clockwise or anticlockwise direction, and the direction can also reverse on the third item in some trials. Thus, we think the current design can actually help us to reduce the influence of response bias. Taking a step back, if trajectory error correlations are due to response bias, we should expect consistent significant correlation for all conditions, instead of only observing significant correlation for 1st-2nd and 2nd-3rd trajectories but not for 1st-3rd trajectory and only in aligned trajectory condition but not in misaligned condition. Therefore, we think the trajectory error correlations cannot be simply explained by response bias.

Details have been added (Page 23).

(3b) Is the colour wheel always oriented the same way for a participant? If so, given there are only nine colors, it seems possible that colors are mapped to locations and remembered in a location code instead. This does not seem to be a problem in principle for the behavioural findings, but might change the interpretation of what is being decoded from the EEG. If this is a possibility then this might be acknowledged.

The color wheel is always oriented the same way for each participant. We agree with the reviewer that it is possible that participants tend to map colors to locations and remembered in a location code. We don’t have sufficient evidence to rule out this possibility. One possible way could be running another experiment with varied color wheel during response period. Meanwhile, we would like to point out that the underlying logic of the current design is based on the facts that thinking spatially is intuitive and spatial metaphors like “location” and “distance” is commonly used to describe world, e.g., the well-known mental number line (Dehaene et al., JEP: General, 1993). Therefore, we expected participants to associate or integrate location and color maps based on trajectory distance.

The reviewer is correct that the color decoding would reflect spatial location rather than the genuine color feature. This is actually the point of the experimental design, whereby two irrelevant features could be possibly combined within a common cognitive map. Without the realignment of the two feature maps defined in space, subjects could not at all form the strategy to compress the two sequences. In other words, decoding of color sequences could be understood as neural representation of a series of corresponding locations along the ring that are independent of the physical locations of the items.

Interpretations and clarifications have been added (Page 23&26).

(4) Does the discretisation of the stimulus distribution (to only 9 possible locations) make the compression strategy easier to use? If the features had been continuously distributed across the location/colour circle, would participants still pick up on and use the shared trajectory structure?

Thanks for the question. Without further data, it’s hard to say whether the discretization of the stimulus distribution would make the compression strategy easier to use or not, compared to continuous distribution. Both outcomes seem possible. On the one hand, discrete stimulus distribution would result in discrete trajectory distribution, which helps participants to realize the common trajectory strategy. On the other hand, discrete stimulus distribution would result in category or label representation, which may weaken the effectiveness of structure compression strategy. We postulate that our findings could be generalized to continuous trajectories in a cognitive map within certain resolution.

(5a) Minor point: I disagree that avoiding the same points for location and colour for a given item allows them to be independently decoded. I would argue the contrary - this kind of constraint should create a small anti-correlation that in principle could lead to spurious decoding of one variable (although this seems unlikely here).

We appreciate the concern. As mentioned above, with discrete stimulus distribution (9 possible values for both color and location domains), it is quite possible that a fraction of trials would share same values in location and color. Therefore, the neural decoding for one domain might be confounded by another domain. To dissociate their neural representations, we imposed constraints that color and location could not occupy the same value for a given item.

We agree that this kind of constraint might create a small anti-correlation, even though it is not observed here. Future studies using continuous stimulus distribution would reduce the correlation or anti-correlation between stimuli.

(5b) Very minor point: 1,000 permutations for significance testing seems on the low side. Since some of the p-values are close to 0.05 it may be worth running more permutations.

Thanks for this suggestion. We got similar results using 1000 or 10000 permutations.

(6) Missing reference: H. H. Li et al., 2021 (line 213) seems not to be on the list of references.

Sorry for the mistake. Added.

Reviewer #2 (Recommendations For The Authors):

The study aimed to discuss the working memory mechanism, instead, it seems to be focused on the encoding and recall strategies after a short while, I recommend updating the manuscript to refer to the relevant cognitive mechanism.

There was a strong voice on the effect of using the cognitive map in working memory, without any tests on if indeed a cognitive map was used (for example the novel link between stimuli and how a cognitive map can be used to infer shortcuts). Was the participant required to have any mental map beyond the schema of the shown color ring?

In the current experiment, to discuss if the effect is driven by utilizing a cognitive map or schematic abstraction of color-relatedness, further analysis is required to possibly assess the effects of schema on neural activity and behavior. Namely,<br /> (1) Was there any reinstatement of schematically congruent (expected) colors that were probed by location 1, at locations 2 and 3 in the MAT condition?

Thanks for pointing out this possibility. However, we don’t think there will be stable color expectations given location information under the MAT condition. First, as the trajectory distance varied on a trial-by-trial basis, no prior common trajectory knowledge could be used to make inference about the current stimuli in individual trial. Second, the starting points for color and location (1st item) were randomly and independently selected, such that color sequence could not be predicted based on the location sequence for both aligned and misaligned conditions.

(2) Given that response time can be a behavioral marker of schematic conflict, was the response time faster for congruent than incongruent conditions?

Thanks for this question. Unfortunately, due to the experimental design, the response time could not be used as a behavioral marker to infer mental conflicts, since participants were not required to respond as fast as possible. Instead, they took their own pace to reproduce sequences without time limit. They could even take a short break before submitting their response to initiate the next trial.

(3) In case you cannot rule out that utilizing schema is the cognitive mechanism that supports working memory performance (the behavior), please add the classical literature (on the memory of schematically congruent and incongruent events) to the discussion.

Thanks for this suggestion and we have added relevant literatures now (Page 3&19).

(4) On page 6, 'common structure in the cognitive map' is the schema, isn't it?

Correct. Based on our understanding, ‘common structure in the cognitive map’ is a spatial schema.

(5) In Figure 2 EFG, would you please use a mixed effect model or show evidence that all participants demonstrated a correlation between the location trajectory error and color trajectory error?

Thanks for the suggestion. We have added the mixed effect model results, which are consistent with Figure 2EFG (AT: 1st-2nd trajectory, β = 0.071, t = 4.215, p < 0.001; 2nd-3rd trajectory, β = 0.077, t = 3.570, p < 0.001; 1st-3rd trajectory, β = 0.019, t = 1.118, p = 0.264; MAT: 1st-2nd trajectory, β = 0.031, t = 1.572, p = 0.116; 2nd-3rd trajectory, β = 0.002, t = 0.128 , p = 0.898; 1st-3rd trajectory, β = -0.017, t = -1.024, p = 0.306).

In general, doesn't such correlation just show that good participants/trials were good (some did well in the study and some did poorly throughout?)

We don’t think the trajectory error correlation results just reveal that some participants did well and some participants did poorly. If that is the case, we shouldn’t observe significant correlation in Figure 2D, where we first run correlation for each participant and then test correlation significance at group level. Indeed, trajectory error correlation between color and location domains characterizes the consistent changes between the two domains.

It is worth to note that the correlation was estimated with signed trajectory errors in color and location domains, which meant that we indeed cared about whether the errors in the two domains were consistently varied in the same direction, i.e., whether longer trajectory memory compared to the actual trajectory in location domain would predict longer trajectory memory in color domain.

Moreover, as shown in Figure 2EFG, by dividing trials into 4 bins according to the location trajectory error for each participant and pooling the data across participants, we observed 4 clusters along x-axis (location trajectory error). This suggests that participants’ memory performance is rather consistent instead of being extremely good or bad. Besides, if trajectory error correlation is due to different overall memory performance between participants, we should observe significant trajectory error correlations both in AT and MAT conditions, instead of only under AT condition and for 1st-2nd and 2nd-3rd trajectories but not for 1st-3rd trajectory.

In Figure 2 G, is the marginal error just too big to be sensitive? I am not sure what we are learning here, please clarify.

Sorry for the confusion. To examine this possibility, we excluded errors which are beyond 2.5 * σ, and still observed non-significant 1st-3rd trajectory error correlation between color and location domains (r = 0.119, p = 0.167).

The 1st-3rd trajectory showed nonsignificant behavioral correlation and neural representation, which suggests that the current sequential memory task would encourage participants to organize all information by relying more on the adjacent items and their distance. Thus, we think the 1st-3rd trajectory would serve as a control trajectory, which helps us not only exclude other possible explanation (e.g., systematic response bias), but also validate current findings both in behavioral and neural level.

Results and statements (Page 10-11) added now.

Author response image 2.

(6) Regarding the first lines on page 11, did you do qualitative research to know if less information was encoded in congruent conditions?

The current experimental design is inspired by the mental compression of spatial sequence studies from Dehaene’s lab (Amalric er al., 2017; Roumi et al., 2021), in which they propose that human brain compresses spatial sequence using an abstract language and formalize minimal description length of a sequence as the “language-of-thought complexity.” Based on this evidence, we think less information is required to describe congruent condition compared to incongruent condition. This idea is supported by better memory performance for congruent condition. Unfortunately, we couldn’t manage to quantify how less information was encoded in congruent condition.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work by Ding et al uses agent-based simulations to explore the role of the structure of molecular motor myosin filaments in force generation in cytoskeletal structures. The focus of the study is on disordered actin bundles which can occur in the cell cytoskeleton and have also been investigated with in vitro purified protein experiments.

Strengths:

The key finding is that cooperative effects between multiple myosin filaments can enhance both total force and the efficiency of force generation (force per myosin). These trends were possible to obtain only because the detailed structure of the motor filaments with multiple heads is represented in the model.

We appreciate your comments about the strength of our study. 

Weaknesses:

It is not clearly described what scientific/biological questions about cellular force production the work answers. There should be more discussion of how their simulation results compare with existing experiments or can be tested in future experiments.

Please see our response to the comment (1) below.

The model assumptions and scientific context need to be described better.

We apologize for the insufficient descriptions about the model and the scientific context. We revised the manuscript to better explain model assumptions and scientific context as described in our responses below.

The network contractility seems to be a mere appendix to the bundle contractility which is presented in much more detail.

Please see our response to the comment (6) below.

Reviewer #1 (Recommendations for the authors):

(1) It is not clearly described what scientific/biological questions about cellular force production the work answers. There should be more discussion of how their simulation results compare with existing experiments, or can be tested in future experiments. The authors do briefly mention Reference 4 where different myosin isoforms were used, but it is not clear that these experiments support the scalings predicted in this work in Figures 3-6. Also, the experiments in Ref. 4 apparently did not involve passive crosslinkers (ACPs) which are key in this study.

Thank you for the comment. In the 5th paragraph of the discussion section of the original manuscript, we applied our findings to understand how structural differences between ventral stress fibers and actin arcs could affect force generation. In addition, at the end of the discussion section, we mentioned that experiments with artificially-made myosin thick filaments could be used for verifying our results. 

The experiments in Ref. 4 were only ones that we could directly compare our results with. In previous study, actomyosin bundles were experimentally created with ACPs (K.L. Weirich et al., Biophys J, 2021, 120: 1957-1970), but the motions of myosin thick filaments were only quantities measured in the experiments. In general, measuring forces generated by in vitro actomyosin bundles is very challenging. This is why the predictions from our model are particularly valuable for understanding the force generation of actomyosin structures. 

(2) The architecture of the bundles seems to be prescribed by hand in these simulations. Several well-known stochastic aspects of the dynamics of actin and actin-binding proteins are not included in the model. For example, there is no remodeling of the actin structures through actin polymerization and depolymerization, or crosslink (ACP) binding and unbinding. Can the authors comment on why these effects could be neglected for the questions they want to address?

Thank you for the comment. We previously showed that the force generation process in actomyosin networks and bundles is affected by actin dynamics (Q. Yu et al., Biophys J, 2018, 115: 2003-2013) and the unbinding of ACPs (T. Kim, Biomech Model Mechanobiol, 2015, 14(2): 345-355 and W. Jung et al., Comput Part Mech, 2015, 2(4): 317-327). 

However, we did not include the actin dynamics and the ACP unbinding in the current study to clearly understand the effects of the structural properties of thick filaments on the force generation process. We have learned that the stochastic behaviors of cytoskeletal components lead to noisier results, which requires us to run a much larger number of simulations to obtain statistically convincing data. We added the following paragraph in the discussion section of the revised manuscript:

“Although this study focused mainly on parameters related to motor structures, we expect that other parameters would affect the force generation process. For example, as we showed before, a decrease in ACP density would reduce forces by deteriorating connectivity between filaments. With very low ACP density, some of neighboring motors may not have ACPs between them, thus adding up their forces as shown in Fig. 2. However, such low ACP density may not maintain the structure of bundles or cross-linked networks well. In addition, the force-dependent unbinding of ACPs could change the spatial distribution of ACPs during force generation. If they behave as a slip bond which unbinds more frequently with higher forces, ACPs may not stay between two motors for long time due to high tension. Then, forces generated by two motors may have a higher chance to add up. By contrast, if they behave as a catch bond which unbinds less frequently with larger forces, more ACPs will be recruited between two motors, reducing a chance to add up

forces. The length of actin filaments is unlikely to affect the force generation process significantly unless filaments are very short. Additionally, as we showed before, actin turnover would reduce forces by competing with motor activities, change connectivity between filaments over time, and prevent motors from being stalled for long time, all of which could affect force generation.”

(3) The present study is confined to the fixed density of motors and ACPs. However, these can be easily varied in in vitro experiments. Works such as Reference 4 show an optimum in contractility vs myosin concentration. Myosins act not only to slide actin filaments but also crosslink them.

Can the authors vary myosin concentration to demonstrate such effects in their model?

As the reviewer pointed out, there is a belief that myosin thick filaments can serve as crosslinkers as well. However, unless there are a fraction of dead myosins (which remain bound on filaments without walking) or myosins dwell at the barbed ends filaments for very long time, it looks very hard for bundles or networks to generate large forces. A former experiment showed that active myosins increases the viscosity of actin networks, not elasticity (D. Humphrey et al., Nature, 2002, 416: 413-416) Computer simulations with reasonable assumptions did not show significant force generation without cross-linkers. We have tested systems with a large number of motors and a few cross-linkers in previous studies (T. Kim, Biomech Model Mechanobiol, 2015, 14(2): 345-355 and W. Jung et al., Comput Part Mech, 2015, 2(4): 317-327). We observed that large force/stress was generated momentarily, but it was relaxed very fast. It is expected that there will be similar outcomes if we try such conditions in the current study.

(4) Why is there a (factor of 1.5-2) discrepancy in the measured (Ftot) and estimated (Fest) force values in Figure 4-6? How can the authors improve their scaling arguments to capture this? What about the estimated efficiency?

Thank you for the comment. Indeed, there was a discrepancy between the actual and estimated forces. When the estimated force was calculated, we used the z positions of motors without consideration of the actual bundle geometry with multiple filaments. For example, if two motors are located on the opposite sides of the bundle (i.e., if they are located far from each other in x or y direction), forces generated by them may not counterbalance each other. Then, the estimated force can be smaller than the actual force because counterbalance between motors can be overcounted. The original manuscript had the following sentences to clarify this point: “F</sub>est</sub> was generally smaller than F_tot because this analysis does not account for actual bundle geometry consisting of multiple F-actins; if two motors are located far from each other in x or y direction, they may not counterbalance or add up forces. Nevertheless, we found that F_est captures the overall dependence of F_tot on parameters well.”

(5) Several choices of parameter values used in the simulations are not clear:

a) Why consider F actin of 140 nm specifically? Actin can come in a range of lengths. How do their results depend upon the length scale of actin?

It seems that there is a misunderstanding. 140 nm is the equilibrium length of one actin segment in our model. The actual F-actin consists of multiple actin segments. The length of Factin was 9 μm in bundle simulations and 10 μm (average) in network simulations. We expect that the general tendency of our results would not change with different filament length. However, if filament length becomes too short, the force generation process would be impaired due to lack of connectivity between filaments. 

b) Similarly, very specific values of myosin backbone length (42 nm), number of myosin heads (8), number of arms (24), and Actin Cross-linking Proteins (ACPs). What informs these values and how will the results change if they are different? It is not especially clear how an "Arm" differs from "heads" and what kind of coarse-graining is involved.

In the “model overview” section of the original manuscript, we mentioned the following to clarify the definitions of motor arms and motor heads: 

“To mimic the structure of bipolar filaments, each motor has a backbone, consisting of serially linked segments, and two arms on each endpoint of the backbone segments that represent 8 myosin heads (N_h = 8).”

We devised this coarse-graining scheme of myosin thick filaments in our previous work (T. Kim, Biomech Model Mechanobiol, 2015, 14(5): 1143-1155). Through extensive tests, we showed that force generation and motor behaviors are largely independent of coarse-graining level. In other words, a motor with the same value of N_hN_a leads to similar outcomes regardless of the value of N_a. However, in a bundle with multiple filaments, each motor has a sufficient number of arms to ensure simultaneous interactions with those filaments. This is why we decided to useN_h = 8 and N_a = 24. 

To match the length of thick filaments and the total number of heads (N_hN_a) in the model with real myosin thick filaments, we have used 42 nm for each backbone length. Varying this length is equivalent to a variation in L_sp that we did for Fig. 6.

We used high ACP density to ensure connections between all neighboring pairs of actin filaments. We already showed how the presence of ACPs affects the force generation process in Fig. 2 using two actin filaments. It is expected that a variation of ACP density would affect our results to some extent. Since the main focus of the current study is the structural properties of motors, we did not explore the effects of ACP density. I hope that the reviewer would understand our intention. 

(6) The manuscript focuses on disordered bundles with only one figure on networks. However, actin fibers also ubiquitously exist as disordered networks, and it is important to explore in more detail the contractile forces in such network arrangements.

We appreciate the comment. Because we plan to delve into the effects of motor structures on the force generation in networks as a follow-up study, we showed the minimal results in the current study to prove the generality of our findings. I hope that the reviewer would understand our intention and plan.

It is not described very clearly how these networks were generated.

We apologize for lack of explanation about how the networks were generated. We added the following section in Supplementary Text of the revised manuscript:

“Network assembly

Unlike F-actin in bundle simulations, F-actin in network simulations is formed by stochastic processes as in our previous studies. The formation of F-actin is initiated from a nucleation event with a constant rate constant, k_n,A, with the appearance of one cylindrical segment in a random position with a random orientation perpendicular to the z direction. The polymerization of F-actin is simulated by adding cylindrical segments at the barbed end of existing filaments with a rate constant, k_p,A. The ratio of k_n,Ato k_p,A is adjusted to result in the average filament length of ~10 μm. The rest of the assembly process is identical to that described in the main text.”

Crosslinked biopolymers like actin typically form disordered elastic networks with their coordination number below rigidity percolation threshold (z=4 in 2D), see for example review by Broedersz and Mackintosh Rev. Mod, Phys. 2013. Such networks should exist in the bendingdominated regime, where bending forces play a vital role in force propagation. Was that observed in the simulations? Why or why not?

We appreciate the comment. We are aware of the bending-dominated regime and indeed showed the importance of the bending stiffness of actin filaments at low shear strain level in our previous work (T. Kim et al., PLOS Comput Biol, 2009, 5(7): e1000439). In case of active networks with motors, such a bending-dominated regime has not been observed without external shear strain. Instead, buckling of actin filaments was found to be essential for breaking symmetry between tensile and compressive forces developed by motor activities. We have shown that the free contraction of networks is inhibited if filament bending stiffness is increased substantially (J. Li et al., Soft Matter, 2017, 13: 3213-3220 and T. Bidone et al., PLOS Comput Biol, 2017, 13(1): e1005277). We expect that contractile forces generated by bundles or networks will be reduced significantly if we highly increase bending stiffness. However, considering the focus of the current study is on the structural properties of motors, we did not perform such simulations. 

(7) It would be interesting to see the simulated predictions of the bundle or network contraction dynamics. This can be done by changing to free boundary conditions so that the bundle can contract.

Thank you for the suggestion. We have previously investigated the free contraction of actomyosin networks with different motor density and ACP density (J Li et al., Soft Matter, 2017, 13: 3213). We observed that the rate of network contraction was higher with more motors and ACPs. However, we did not test the effects of the structural properties of thick filaments in the previous study. We plan to investigate the effects in future studies because the focus of the current study is the force generation process. Please note that in the discussion section of the original manuscript, we mentioned the following:

“Although we focused on force generation, the contractile behaviors of actomyosin structures (i.e., a decrease in length) have also been of great interest. Our model can be used to study such contractile behaviors by deactivating the periodic boundary condition and removing connection between one end of bundle/network and a domain boundary as done previously [20]. To achieve higher contractile speed with the same total number of myosin heads, the existence of multiple contractile units would be better as suggested in a previous work [4]. This means that there is a trade-off between force generation and contractile speed. Previous studies also showed that the contractile speed of networks is proportional to motor density [18, 43, 51]. We may be able to use our model to systematically investigate how the contractile speed is regulated by parameters that we tested in this study, including the number, distribution, length, and structure of motors.”

Minor suggestions for improvement:

(1) What are the vertical markers in Figures 1E and F? They should be labelled. if they are crosslinkers, it is not clear why the color is different from Figure 1A and B.

We believe that the reviewer meant Figs. 2E, F. Those vertical lines are indeed ACPs (crosslinkers). We changed the color of ACPs in Fig. 1A and Fig. 2B-D to purple to be consistent. In addition, we changed the colors of two filaments in Figs. 2B-D slightly to be consistent with Fig. 2E.

(2) To help understanding, please include a figure showing how forces are measured.

We added Fig. S1 in the revised manuscript to explain how the bundle force is calculated.

(3) It should be possible to extend the scaling arguments to predict what is the crossover myosin density (N_M) in Figure 4a at which the efficiency changes from going as 1/N_M to saturating. 

As the reviewer might have observed, the slope of the efficiency in Fig. 4A gradually changes, rather than showing a sharp transition. Thus, it is hard to define one crossover myosin density. 

Similarly, what are the slopes in Figure 6a-b?

We drew the reference lines in those two plots. Unfortunately, we do not have explanations about the origin of these slopes.

(4) Some more explanation for the observed values should be added. Figure 4: Why does efficiency plateau at a value close to 0.8 in (A)? 

We assume that the reviewer meant the plateau of η close to 0.08, not 0.8. Our speculation for the origin of this plateau value is related to L_M (= 462 nm under the reference condition). Ideally, ~43 motors are required to cover the entire length of the bundle (= 20 μm). Under this condition, η is ~0.023. Although this is not 0.08, we believe that these two values are related to each other. For example, if we increase L_M, this plateau level would increase. We added the following sentences in the result section of the revised manuscript:

“The plateau level of η at ~0.08 is related to the minimum number of motors required for saturating an entire bundle, implying that the plateau level would be higher if each motor is longer.”

Figure 5: Overlapping between motors seems to increase the total force applied by them because of cooperative effects. However, it is not abundantly clear why that should peak at a value of f = 0.06.

As shown in Fig. 5B, smaller f always results in higher F_tot due to higher level of cooperative overlap. The minimum value of f we tested in this study was 0.06, so F_tot was maximal at f = 0.06.

(5) Why is the network force expected to scale approximately as sqrt(N_M)? Is it because of the 2D geometry where the number of motors along the x or y-direction scale as sqrt(N_M)?

We initially thought that the weaker dependence of the total force on N_M was related to the random orientations of motors. However, if the network is fully saturated with motors, the inclusion of more motors will increase forces in both x and y directions almost linearly, resulting in the direct proportionality of F_tot to N_M. Our new hypothesis for weaker dependence is consistent with the reviewer’s speculation; the network is not fully saturated even with 1000 motors, so the entire regime shown in Fig. 7B corresponds to that with N_M < 100 in Fig. 4A where similar weaker dependence on N_M was observed. We added the following sentence in the result section of the revised manuscript to clarify this point:

“the average number of motors in each direction which can experience the cooperative overlap would be ~. Maximal N_M tested with the network was ~2,500, so the dependence of F_tot on N_M with the network is similar to that with N_M < ~50 with the bundle (Fig. 4A).”

(6) Figures 6 D and A: Figure 6D suggests that there is a more full overlap in the cases where there was a longer bare zone or larger spacing between motor arms. However, the quantification of the total force in A shows that the force is highest for the case where LM was increased by increasing the number of arms. Why do the authors think that is? I would expect from the explanation in Fig 6D that the Lsp and Lbz would be higher than Na in Fig 6A.

Fig. 6D shows a difference in the level of the cooperative overlap () between two motors. As the reviewer pointed out, the case with more arms shows the lowest , resulting in the lowest as we showed in Fig. S2B. However, as show in in Eq. 7, the total force is a function of both N_a and . Thus, due to higher N_a and lower , the force in the case with different N_a can be similar to that in the case with different L_bz. In the original manuscript, we had the following sentence to explain how the force can be similar between the two cases: 

“Thus, was higher (Fig. S2B, blue), resulting in higher F_tot and η despite smaller N_a.”

Reviewer #2 (Public review):

Summary:

In this study, the authors use a mechanical model to investigate how the geometry and deformations of myosin II filaments influence their force generation. They introduce a force generation efficiency that is defined as the ratio of the total generated force and the maximal force that the motors can generate. By changing the architecture of the myosin II filaments, they study the force generation efficiency in different systems: two filaments, a disorganized bundle, and a 2D network. In the simple two-filament systems, they found that in the presence of actin crosslinking proteins motors cannot add up their force because of steric hindrances. In the disorganized bundle, the authors identified a critical overlap of motors for cooperative force generation. This overlap is also influenced by the arrangement of the motor on the filaments and influenced by the length of the bare zone between the motor heads.

Strengths:

The strength of the study is the identification of organizational principles in myosin II filaments that influence force generation. It provides a complementary mechanistic perspective on the operation of these motor filaments. The force generation efficiency and the cooperative overlap number are quantitative ways to characterize the force generation of molecular motors in clusters and between filaments. These quantities and their conceptual implications are most likely also applicable in other systems.

Thank you for the comments about the strength of our study. 

Weaknesses:

The detailed model that the authors present relies on over 20 numerical parameters that are listed in the supplement. Because of this vast amount of parameters, it is not clear how general the findings are. On the other hand, it was not obvious how specific the model is to myosin II, meaning how well it can describe experimental findings or make measurable predictions. The model seems to be quantitative, but the interpretation and connection to real experiments are rather qualitative in my point of view.

As the reviewer mentioned, all agent-based computational models for simulating the actin cytoskeleton are inevitably involved with such a large number of parameters. Some of the parameter values are not known well, so we have tuned our parameter values carefully by comparing our results with experimental observations in our previous studies since 2009.We were aware of the importance of rigorous representation of unbinding and walking rates of myosin motors, so we implemented the parallel cluster model, which can predict those rates with consideration of the mechanochemical rates of myosin II, into our model. Thus, we are convincing that our motors represent myosin II.

In our manuscript, our results were compared with prior observations in Ref. 4 (Thoresen et al., Biophys J, 2013) several times. In particular, larger force generation with more myosin heads per thick filament was consistent between the experiment and our simulations. 

Our study can make various predictions. First, our study explains why non-muscle myosin II in stress fibers shows focal distributions rather than uniform distributions; if they stay closely, they can generate much larger forces in the stress fibers via the cooperative overlap. Our study also predicts a difference between bipolar structures (found in skeletal muscle myosins and nonmuscle myosins) and side polar structures (found in smooth muscle myosins) in terms of the likelihood of the cooperative overlap. As shown below, myosin filaments with the bipolar structure can add up their forces better than those with the side polar structure when their overlap level is the same.

Author response image 1.

It was often difficult for me to follow what parameters were changed and what parameters were set to what numerical values when inspecting the curve shown in the figures. The manuscript could be more specific by explicitly giving numbers. For example, in the caption for Figure 6, instead of saying "is varied by changing the number of motor arms, the bare zone length, the spacing between motor arms", the authors could be more specific and give the ranges: "is varied by changing the number of motor arms form ... to .., the bare zone length from .. to..., and the spacing between motor arms from .. to ..".

This unspecificity is also reflected in the text: "We ran simulations with a variation in either L_sp or L_bz" What is the range of this variation? "WhenL_M was similar" similar to what? "despite different N_M." What are the different values for N_M? These are only a few examples that show that the text could be way more specific and quantitative instead of qualitative descriptions.

We appreciate the comment. In the revised manuscript, we specified the range of the variation in each parameter.

In the text, after equation (2) the authors discuss assumptions about the binding of the motor to the actin filament. I think these model-related assumptions and explanations should be discussed not in the results section but rather in the "model overview" section.

Thank you for pointing this out. In the original manuscript, we described all the details of the model in Supplementary Material. We feel that the assumptions about interactions between motors and actin filaments are too detailed information to be included in the model overview section.

The lines with different colors in Figure 2A are not explained. What systems and parameters do they represent?

The different colors used in Fig. 2A were used for distinguishing 20 cases. We added the explanation about the colors in the figure caption in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

To guarantee the reproducibility of the results, I recommend that the authors publish their simulation code on GitHub.

We appreciate the reviewer’s suggestion. Following the suggestion, we prepared and posted the code on GitHub as mentioned in the Data Availability of the revised manuscript: The source code of our model is available on GitHub: https://github.com/ktyman2/ThickFilament”

Author Response

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

Reviewer 1

“The exact levels of inhibition, excitation, and neuromodulatory inputs to neural networks are unknown. Therefore, the work is based on fine-tuned measures that are indirectly based on experimental results. However, obtaining such physiological information is challenging and currently impossible. From a computational perspective it is a challenge that in theory can be solved. Thus, although we have no ground-truth evidence, this framework can provide compelling evidence for all hypothesis testing research and potentially solve this physiological problem with the use of computers.”

Response: We agree with the reviewer. This work was intended to determine the feasibility of reverse engineering motor unit firing patterns, using neuron models with a high degree realism. Given the results support this feasibility, our model and technique will therefore serve to construct new hypotheses as well as testing them.

• Common input structure lines 115

I agree with the following concepts, but I would specify that there is not only one dominant common input. It has been shown that there are multiple common inputs to the same motor nuclei (e.g., the two inputs are orthogonal and are shared with a subset of the active motoneurons) particularly for agonist motoneuron pools of synergistic muscles. On the hand muscles the authors are correct that there is only one dominant common input. Moreover, there is also some animal work suggesting that common inputs is just an epiphenomenon. This is completely in contradiction to what we observe in-vivo in the firing patterns of motor units, but perhaps worth mentioning and discussing.

Response: Thanks for emphasizing this point. We have cited a recent reference discussing the important issue of common drive and the possibility of more than one source. Our simulations assume the net form of the excitatory input to all motoneurons in the pool is the same, except for noise. This net form (which produces the linear CST output in each case) essentially represents the sum of all inputs, both descending and sensory. Our results show the same over pattern as human data, i.e. that all motor unit firing patterns have similar trajectories (again allowing for the impact of noise). Future studies will consider separating excitatory inputs into different sources.

It is interesting that the authors mention suprathreshold rate modulation. Could the authors just discuss more on how the model would respond to a simulated suprathreshold current for all simulated motoneurons (i.e., like the ones generated during a suprathreshold-injected current or voluntary maximal feedforward movement?)

Response: Thank you for this point. Our use of the term “suprathreshold” was not applied correctly. We meant “suprathreshold” to refer to amount of input above the recruitment threshold. We have decided to remove this term so now the sentence “…so less is available for rate modulation…”.

194 a full point is missing.

Response: We addressed the error.

204-231 and 232-259, these two paragraphs have been copied twice.

Response: We addressed the error.

Line 475 typo

Response: We addressed the error.

591 It would be interesting to add the me it takes a standard computer with known specs and a super computer to run over one batch of simulation (i.e., how long one of the 6,300,000 simulation takes).

Response: Each simulation took about 20 minutes of real me. Assuming a standard computer with 16 processor cores using a similar microarchitecture as Bebop (Intel Broadwell architecture), the standard computer could run 16 simulations at a me (one simulation assigned per core). This would take the standard computer about 15 years to complete all 6.3M simulations.

594 I don't understand why there are 6M simulations, could the authors provide more info on the combinations and why there are 6M simulations.

Response: The 6M simulations are the total number of simulations that were performed for this work. A detailed explanation can be found in section: “Machine learning inference of motor pool characteristics” at line 591. Briefly, there were 315,000 simulations of a pool of 20 motoneurons (20 x 315,000 = 6.3 million). The 315,000 simulations was required to run all possible combinations of 15 patens of inhibition, 5 of neuromodulation, 7 of distribution of excitatory inputs and 30 different repeats of synaptic noise with different seeds. In addition, there were 20 iterations for each of these combinations to generate a linear CST output (as illustrated in Fig. 3). 15 x5 x 7 x 30 x 20=315,000.

In several simulations it seems that there was a lot of fine-tuning of inputs to match the measured motor unit firing pattern. Have the authors ever considered a fully black-box AI approach? If they think is interesting maybe it could spice up the discussion.

Response: We agree that AI has potential for reverse engineering the whole system and we are looking into adding it to future version of this algorithm as an alternative. We started with a simple but powerful grid search to enhance our understanding of the interaction between inputs, neuron properties and outputs.

Reviewer 2

Comment 1:

“First, I believe that the relation between individual motor neuron behavioral characteristics (delta F, brace height etc.) and the motor neuron input properties can be illustrated more clearly. Although this is explained in the text, I believe that this is not optimally supported by figures. Figure 6 to some extent shows this, but figures 8 and 9 as well as Table 1 shows primarily the goodness of fit rather than the actual fit.”

Response: We agree with the reviewer that showing the relationship between the motor neuron behavioral characteristics (delta F, brace height etc.) and the motor neuron input properties would be a great addition to the manuscript. Because the regression models have multiple dimensions (7 inputs and 3 outputs) it is difficult to show the relationship in a static image. We thought it best to show the goodness of fit even though it is more abstract and less intuitive. We added a supplemental diagram to Figure 8 to show the structure of the reverse engineered model that was fit (see Figure 8D).

Author response image 1.

Figure 8. Residual plots showing the goodness of fit of the different predicted values: (A) Inhibition, (B) Neuromodulation and (C) excitatory Weight Rao. The summary plots are for the models showing highest 𝑅𝑅2 results in Table 1. The predicted values are calculated using the features extracted from the firing rates (see Figure 7, section Machine learning inference of motor pool characteristics and Regression using motoneuron outputs to predict input organization). Diagram (D) shows the multidimensionality of the RE models (see Model fits) which have 7 feature inputs (see Feature Extraction) predicting 3 outputs (Inhibition, Neuromodulation and Weight Rao).

Comment 2:

“Second, I would have expected the discussion to have addressed specifically the question of which of the two primary schemes (pushpull, balanced) is the most prevalent. This is the main research question of the study, but it is to some degree le unanswered. Now that the authors have identified the relation between the characteristics of motor neuron behaviors (which has been reported in many previous studies), why not exploit this finding by summarizing the results of previous studies (at least a few representative ones) and discuss the most likely underlying input scheme? Is there a consistent trend towards one of the schemes, or are both strategies commonly used?”

Response: We agree with the reviewer that our discussion should have addressed which of the two primary schemes – push-pull or balanced – is the most prevalent. At first glance, the upper right of Figure 6 looks the most realistic when compared to real data. We thus would expect that the push-pull scheme to dominate for the given task.

We added a brief section (Push-Pull vs Balance Motor Command) in the discussion to address the reviewer’s comments. This section is not exhaustive but frames the debate using relevant literature. We are also now preparing to deploy these techniques on real data.

Comment 3:

In addition, it seems striking to me that highly non-linear excitation profiles are necessary to obtain a linear CST ramp in many model configurations. Although somewhat speculative, one may expect that an approximately linear relation is desired for robust and intuitive motor control. It seems to me that humans generally have a good ability to accurately grade the magnitude of the motor output, which implies that either a non-linear relation has been learnt (complex task), or that the central nervous system can generally rely on a somewhat linear relation between the neural drive to the muscle and the output (simpler task).

Response: We agree with the reviewer, and we were surprised by these results. Our motoneuron pool is equipped with persistent inward currents (PICs) which are nonlinear. Therefore, for the motoneuron to produce a linear output the central nervous system would have to incorporate these nonlinearities into its commands.

Following this reasoning, it could be interesting to report also for which input scheme, the excitation profile is most linear. I understand that this is not the primary aim of the study, but it may be an interesting way to elaborate on the finding that in many cases non-linear excitation profiles were needed to produce the linear ramp.

This is a very interesting point. The most realistic firing patterns – with respect to human data – are found in the parameter regions in the upper right in Figure 6, which in fact produce the most nonlinear input (see push-pull pattern in Figure 4C). However, in future studies we hope to separate the total motor command illustrated here into descending and feedback commands. This may result in a more linear descending drive.

Author Response

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

eLife assessment

The study provides valuable insights into allosteric regulation of BTK, a non-receptor protein kinase, challenging previous models. Using a variety of biophysical and functional techniques, the paper presents evidence that the N-terminal PH-TH domain of BTK exists in a conformational ensemble surrounding a compact SH3-SH2-kinase core, that the BTK kinase domain can form partially active dimers, and that the PH domain can form a novel inhibitory interface after SH2/SH3 disengagement. Overall the presented evidence is solid, but the EM results may be over-interpreted and the work would benefit from additional functional validation.

We made every effort in our descriptions of the cryoEM data presented for full-length BTK to not overinterpret the results. In essence this is not an ideal EM target but given the failure by us and others to capture the full-length multi-domain protein crystallographically, we decided that the albeit low resolution cryoEM data are useful to the field.

Reviewer #1 (Public Review):

The manuscript by Lin et al describes a wide biophysical survey of the molecular mechanisms underlying full-length BTK regulation. This is a continuation of this lab's excellent work on deciphering the myriad levels of regulation of BTKs downstream of their activation by plasma membrane localised receptors.

The manuscript uses a synergy of cryo EM, HDX-MS and mutational analysis to delve into the role of how the accessory domains modify the activity of the kinase domain. The manuscript essentially has three main novel insights into BTK regulation.

1) Cryo EM and SAXS show that the PHTH region is dynamic compared to the conserved Src module.

2) A 2nd generation tethered PH-kinase construct crystal of BTK reveals a unique orientation of the PH domain relative to the kinase domain, that is different from previous structures.

3) A new structure of the kinase domain dimer shows how trans-phosphorylation can be achieved.

Excitingly these structural works allow for the generation of a model of how BTK can act as a strict coincidence sensor for both activated BCR complex as well as PIP3 before it obtains full activity. To my eye the most exciting result of this work is describing how the PH domain can inhibit activity once the SH3/SH2 domain is disengaged, allowing for an additional level of regulatory control.

I have very few experimental concerns as the methods and figures are well-described and clear. As the authors are potentially saying that the previously solved PH domain-kinase interface is artefactual, additional evidence strengthening their model would be helpful to resolve any possible controversies.

We do not argue that the previously solved PH domain-kinase interface is artefactual. Instead we point out that the PH/kinase interface identified in the prior structure is incompatible with the contacts between the SH3 and kinase domains in autoinhibited BTK. This then leads us to the suggestion that a PH/kinase inhibitory interaction may instead occur upon dissociation of the SH3-SH2 cassette from the kinase domain. Our data support that model. Moreover, our data suggest the PHTH domain is dynamic, likely not settling in to one particular autoinhibitory state. Thus, it is possible the previously solved PH/kinase structure exists within the conformational ensemble of a range PH/kinase domain interactions. In an effort to clarify our think we added two sentences to the Discussion (pg. 19).

Reviewer #2 (Public Review):

In this study, multiple biophysical techniques were employed to investigate the activation mechanism of BTK, a multi-domain non-receptor protein kinase. Previous studies have elucidated the inhibitory effects of the SH3 and SH2 domains on the kinase and the potential activation mechanism involving the membranebound PIP3 inducing transient dimerization of the PH-TH domain, which binds to lipids.

The primary focus of the present study was on three new constructs: a full-length BTK construct, a construct where the PH-TH domain is connected to the kinase domain, and a construct featuring a kinase domain with a phosphomimetic at the autophosphorylation site Y551. The authors aimed to provide new insights into the autoinhibition and allosteric control of BTK.

The study reports that SAXS analysis of the full-length BTK protein construct, along with cryoEM visualization of the PH-TH domain, supports a model in which the N-terminal PH-TH domain exists in a conformational ensemble surrounding a compact/autoinhibited SH3-SH2-kinase core. This finding is interesting because it contradicts previous models proposing that each globular domain is tightly packed within the core.

Furthermore, the authors present a model for an inhibitory interaction between the N-lobe of the kinase and the PH-TH domain. This model is based on a study using a tethered complex with a longer tether than a previously reported construct where the PH-TH domain was tightly attached to the kinase domain (ref 5). The authors argue that the new structure is relevant. However, this assertion requires further explanation and discussion, particularly considering that the functional assays used to assess the impact of mutating residues within the PH-TH/kinase domain contradict the results of the previous study (ref 5).

In our hands BTK activity is not significantly affected by mutation of just two residues, R133 and Y134. It is somewhat difficult to compare the previously reported activity assay for the same BTK mutant (Wang et al. ref 5, Figure 4D) with the data we report here. For unexplained reasons, the time scale for the quantitative assay in the previous work is truncated to 50 munutes for the R133/Y134 mutant data compared to 120 minutes for all of the other activity data reported in that figure. In our data, if we qualitatively examine the differences in a representative progress curve at 50 minutes between WT and the double R133/Y134 mutant (see Figure 6a, dark blue and pink traces) one might conclude that the R133/Y134 mutation is activating BTK. However, when we calculate the average kinase activity rate ± standard error for three independent experiments we find that the difference between WT and the double R133/Y134 mutant is not significant (see Figure 6b and c). Thus, instead of making any assertions about the previously published data we are trying to be as rigoruous as possible in presentation and interpretation of our own data.

In addition, throughout the manuscript we tried to be very careful in our discussion of our data and that published previously, to avoid conclusive statements about the previously described interface. Afterall, one of our overriding conclusions is that the N-terminal region of BTK is highly dynamic. See response to reviewer 1 above.

Additionally, the study presents the structure of the kinase domain with swapped activation loops in a dimeric form, representing a previously unseen structure along the trans-phosphorylation pathway. This structure holds potential relevance. To better understand its significance, employing a structure/function approach like the one described for the PH-TH/kinase domain interface would be beneficial.

We completely agree with this comment and are pursuing such studies now.

Overall, this study contributes to our understanding of the activation mechanism of BTK and sheds light on the autoinhibition and allosteric control of this protein kinase. It presents new structural insights and proposes novel models that challenge previous understandings. However, further investigation and discussion would significantly strengthen the study.

As indicated we are pursuing further investigation and felt that the body of work presented here is sufficient for a single manuscript.

Reviewer #3 (Public Review):

Yin-wei Lin et al set out to visualize the inactive conformation of full-length Bruton's Tyrosine Kinase (BTK), a molecule that has evaded high-resolution structural studies in its full-length form to this date. An open question in the field is how the Pleckstrin Homology-Tec Homology (PHTH) domain inhibits BTK activity, with multiple competing models in the field. The authors used a complimentary set of biophysical techniques combined with well-thought-out stabilizing mutations to obtain structural insights into BTK regulation in its full-length form. They were able to crystallize the full-length construct of BTK but unfortunately, the PHTH was not resolved yielding a structure similar to that previously obtained in the field. The investigation of the same construct by SAXS yielded an elongated structural model, consistent with previous SAXS studies. Using cryo-EM the authors obtained a low-resolution model for the FL BTK with a loosely connected density assigned to the dynamic PHTH around the compact SH2-SH3-Kinase Domain (KD) core. To gain further molecular insights into PHTH-KD interactions the authors followed a previously reported strategy and generated a fusion of PHTH-KD with a longer linker, yielding a crystal structure with a novel PHTH-KD interface which they tested in biochemical assays. Lastly, Yin-wei Lin et al crystallized the BTK KD in a novel partially active state in a "face-to-face" dimer with kinases exchanging the activation loops, although partially disordered, being theoretically perfectly positioned for transphosphorylation. Overall this presents a valiant effort to gain molecular insights into what clearly is a dynamic regulatory motif on BTK and is a valuable addition to the field.

However, this work can be improved by considering these points:

1) The cryo-EM reconstructions are potentially over-interpreted. The reported resolution for all of the analyzed reconstructions is better than 8Å, at which point helices should be recognized as well-resolved structural elements. In the current view/depiction of the cryo-EM maps/models it is hard to see such structural features and it would be great if the authors could include a panel showing maps at higher thresholds to show correspondence between the helices in the kinase C lobe and the cryo-EM maps. Otherwise, the overall positioning of the models within the cryo-EM maps is hard to evaluate and may very well be wrong. (Fig 4, S2).

First, we fully recognize the model is low-resolution and we are careful in our discussion of the cryo-EM data to use language that acknowledges the limitations of the model. Nevertheless, this is the model we have (specific data processing points are discussed below).

The resolution numbers are from the Fourier Shell Correlation (FSC) curve given by Cryosaprc at the end of refinement. We do acknowledge the reviewer’s comments that the resolution could be over estimated in that calculation, but our main focus is to show that the overall domain arrangement of the autoinhibited BTK core (Src-module) fits into the reconstructions.

We tested visualizing the maps at higher threshold, but the secondary structures of the reconstructions were still not well resolved. We do realize that with the current reconstructions, we do not have the structural details to correctly orientate and fit individual domains; this is why we chose to simply fit the available crystal structure of the autoinhibited BTK SH3-SH2-kinase core into the maps.

2) With the above in mind, if the maps are not at the point where helices are well resolved, it may be beneficial to low-pass filter the maps to a more conservative resolution for fitting, analysis, and representation. (Fig 4, S2).

Using low-pass filtered maps at 10Å or unsharpened maps, the fitting of the BTK model and map do not change significantly.

3) It would be valuable to get a quantitative metric on the model/map fitting for the cryo-EM work. One good package for this is Situs which provides cross-correlation values for the top orthogonal fits, without user input for initial fitting. This would again increase confidence in the correctness of model positioning on the map. (Fig 4, S2).

Thank you for this suggestion. We tested the colores feature (Exhaustive One-At-A-Time 6D Search) in Situs to perform model to map fitting without user input as the reviewer suggested. The highest ranked fitting is identical to what we presented in the manuscript. Following are the cross-corelation numbers calculated from “Fit-in-map” tool in chimera and from “collage” function in Situs. We now indicate this step in the caption to Figure 4.

Author response table 1.

4) It would be great to see 2D class averages from the particles contributing to each of the 3D classes. Theoretically, a clear bright "blob" (hypothesized to be the PHTH domain) should be observable in the 2D class averages. In the current 2D class averages that region is unconvincingly weak. (Fig 4, S2).

We attempted to improve both 2D and 3D reconstructitions by feeding the particles from each 3D class through many cycles of 2D classification and selection to exclude ‘bad’ paritcles, but neither the 2D class averages nor 3D reconstructions could be improved.

We agree the feature that appears in the 2D class averages is weak. The BTK protein is only 77kD in size and is highly dynamic and flexible. Thus, in reality this is not an ideal system for cryo-EM. As well, the PHTH domain itself is quite small and NMR data, acquired in the context of a different project, provides evidence that the isolated PHTH domain is dynamic in solution (NMR linewidths vary throughout the protein suggesting intermediate exchange). Nevertheless, given the inability to capture the PHTH domain in crystal structures of full-llength BTK we reasoned that cryo-EM could provide some insight. In the future we anticipate building on these data to include inhibitory binding partners of BTK; however such an effort is beyond the scope of the current work.

5) It seems like there was quite a large circular mask applied during 2D classification. Are authors confident that the weak density attributed to the PHTH domain is not neighboring particles making their way into the extraction box? It would be great if the authors would trim their particle stack with a very stringent interparticle distance cutoff (or report the cutoff in the manuscript if already done so) to minimize this possibility.

We initially picked particles using a small radius (100 Å), and stringently selected 2D classes with particles that contained only density aligning to the core SH3-SH2-kinase domains. We found, however, that 3D ab initio reconstruction always resulted in an additional density located at different positions around the larger core density. The structure of a single BTK PHTH domain fits into that additional remote density. Given the additional density that consistently appeared in 3D reconstructions, we went back and picked particles using a larger circular mask (200 A). Subsequent 2D classification and 3D reconstruction from this analysis gave similar results and are presented in the manuscript.

Regardless of the mask radius, we used stringent conditions for particle picking and checked for the presence of duplicates. An interparticle distance cutoff of 0.1 to 0.5 times the particle diameter was used and resulted in fewer number of particles, but the presence of the extended density remains. We also made use of template picking (2D class averages) to repick the particles and found no significant difference in the number of particles or quality of 2D classifications.

6) The cryo-EM processing may benefit from more stringent particle picking. The authors picked over 2M particles from 750 micrographs which likely represents very heavy overpicking. I would encourage the authors to re-pick the micrographs with 2D class averages and use more stringent metrics to reduce the overpicking. This may result in higher-resolution reconstructions. (Fig 4, S2).

This was an effort to maximize the number of particles extracted. After multiple rounds of 2D classification and selection to exclude empty and junk particles, the final number of particles selected for 3D ab-initio reconstructions were only 68,788, and only ~20K particles for each 3D reconstruction. Thus, we are not concerned that we overpicked particles. This approach is described in Supp Figure S2.

7) The Dmax from SAXS for the Full Length BTK is at 190Å. It would be great if the authors could make a cartoon of what domain arrangement may satisfy this distance, as it is quite extended for such a small particle. Can the authors rule out dimerization at SAXS concentrations? (Fig 1).

SAXS data for full-length, wild-type BTK has been previously published (Márquez et al, 2003 EMBO J. (2003) 22:4616-4624). Our data for WT BTK are consistent with that published previously (and we have cited this previous work). In that work, the authors attribute the ~200 Å Dmax value to an elongated BTK conformation where the domains of BTK are arranged in a linear fashion (a figure showing this domain arragement is provided by Marquez et al. precluding the need for such a cartoon here).

In the present work we take advantage of targeted mutations to stabilize the autoinhibted SH2-SH2-kinase core and the Dmax value that we report for this more autoinhibited version of full-length BTK (FL 4P1F) is ~150Å. Notwithstanding low resolution in both SAXS and cryoEM, it is notable that superposition of the cryoEM models in Figure 4c & d gives a distance of ~150Å between the PHTH domains from the two models.

Finally, we cannot completely rule out that a small fraction of full length BTK is forming dimers. However, in our experience purifying and working with this protein, we find that purified and concentrated monomeric fulllength Btk proteins (as high as 15mg/ml) are quite stable and remain monomeric and free of aggregation even after sitting at 4°C for more than a week. Here the BTK SAXS data were collected within 24 hours after the samples were thawed.

8) In Figure S1 (C) it seems that the curves are just scattering curves with Guinier plots in the inserts, but are labeled as Guinier plots in the legend. The Guinier plots for some samples (FL 4P1F) show signs of aggregation, which may complicate the analysis, it could be beneficial to redo.

We thank the reviewer for pointing out our mistake in presention of the SAXS data. We have now replaced plots in Figure S1c with the correct scattering profiles for each construct with the Guinier insets shown. We revised the label of this panel to “Scattering profile and Guinier plots (insets)”.

In addition, we re-processed the FL 4P1F data by performing buffer subtraction (using a different buffer alone scattering dataset (also collected during original data acquisition)). The data quality after reprocessing were significantly improved (see new scattering profiles and Guinier plots for full-length BTK in Supplementary Figure S1). Protein stability (see above) and the current data quality therefore suggest that aggregation is not complicating the SAXS analysis.

9) Have the authors verified that the activation loop mutations that they introduce do not disrupt the PHTH binding as they previously reported an activation loop on BTK to interact with PHTH, an interaction they do not see here? If so, a citation would be helpful in the text. If not, testing this would strengthen the paper.

The same activation loop mutations were included in the constructs used in the previous solution studies of the PHTH/kinase domain interaction by NMR and HDX (see ref [11]). We clarify this point in the methods section. As well, all but one of the sequence changes introduced into the activation loop are at positions at the ‘base’ of the activation loop and therefore are not surface exposed. Only one amino acid change is on the exposed part of the activation loop (V555T).

10) Can the authors comment on the surfaces which are accessible and inaccessible to the PHTH in the crystal (Fig 3E)? The fact that PHTH doesn't adopt a stable conformation in the solvent channel to some degree indicates that the accessible interaction surfaces are not suitable for PHTH interactions, as the "effective concentration" of the PHTH would be quite high. Are these surfaces consistent with the cryo-EM analysis?

This is an excellent point and we did state the following in describing the crystallization results:

“the crystallography results are consistent with a flexible N-terminal PHTH domain with the caveat that the domain swapped dimer organization might limit native autoinhibitory contacts between the PHTH and SH3SH2-kinase regions.”

In the domain swapped dimer seen in the crystal, a symmetry related molecule does partially block the Ghelix region of the kinase domain while the activation loop and C-helix in the N-lobe remain accessible. Our previous solution studies (ref [11]) pointed to the G helix as part of the interaction interface in addition to the activation loop and part of the N-lobe. We have now modified the sentence above to more clearly describe which parts of the kinase domain are inaccessible in the crystal and the possible ramifications of the steric environment on PHTH domain mobility in the crystal (see pg. 10). That said, all of our previous HDX data shows little protection in the PHTH domain in full-length BTK (mapping of the PHTH/kinase interaction was only possible in trans using excess PHTH domain) and so our data can be best summarized by concluding that the PHTH domain visits a number of conformational states and makes transient contacts with various regions of the kinase domain (dependent upon whether the SH3-SH2 region is engaged or not). This is similar to the ‘fuzzy’ intramolecular contacts described for the N-terminal region of the SRC family. Like the SRC family, BTK (and other TEC kinases) contain a long disordered linker between the N-terminal region and the compact SH3-SH2-kinase core.

11) For the novel active state dimer of the Kinase Domain it would be great to see some functional validation of the dimerization interface. It is structurally certainly quite suggestive, but without such experiments the functional significance is unclear. If appropriate mutations have been published previously a citation would be helpful.

We completely agree. We scoured the literature and our own facuntional assay results over many years but the appropriate mutations to test the functional significance of the kinase domain dimer have not been reported or previously studied in our lab. We are therefore actively pursuing this line of investigation now.

Reviewer #1 (Recommendations For The Authors):

I have the following proposed experiments/analysis that should help.

1) To better validate the putative PH-kinase interface seen, the authors should try some alphafold multimer / rosettaTTFold modelling of just the PHTH module with the kinase domain. The advantage of this is that it will test how conserved over evolution the potential interface is, and will help to decipher discrepancies between the two structures. This may end up being similar to what is seen in Akt (in this case the alphafold prediction does not match the allosteric inhibitor structure, or the nanobody bound structure), but this could help provide additional insight into how the PH domain interacts.

We have applied alphafold to this system. The PHTH-kinase fusion sequence was fed to Alphafold and the separate PHTH and kinase domains to Aphafold multimer. The results provide a range of ‘complexes’ none of which recapitulate the PHTH/kinase interface reported here or that reported by Wang et al in previous work. Three of five results from Alphafold Multimer place the PHTH domain on the activation loop face of the kinase domain consistent with the previous solution data pointing to a similar regulatory interface. This is interesting but our experience in applying alphafold to dynamic confromationally heterogeneous systems is that the results need to be considered with caution. For that reason we did not include any of the alphafold predictions in the manuscript.

Evolutionary conservation is discussed further in the next section:

2) Could the authors provide a detailed evolutionarily analysis of the binding surface between the PHTH and kinase domains and include this in Fig5, this also would help interpret the likelihood of this interface.

This is an excellent question and we have in fact previously published a detailed evolutionary analysis of the BTK kinase domain in collaboration with Kannan Natarajan (see Amatya et al., PNAS, 2019, [ref 11]). In that work we found that evolutionarily conserved residues on the kinase domain map to the activation loop face, supporting the solution data that the PHTH interacts with the kinase domain across the activation loop face. That work predated alphafold but it is interesting that, to the exent that alphafold predicts anything, it seems to converge on the PHTH domain containg the activation loop face.

In the context of our current work, and this question from the reviewer, we re-examined the evolutionary anlysis carried out previously and find that BTK (or TEC family) specific residues on the kinase domain do not appear at the newly identified PHTH/kinase interface we report here. We could speculate that since the ‘back’ of the kinase domain N-lobe interacts with multiple binding partners (SH3, SH2-linker and PHTH) evolutionary pressures may have resulted in a certain degree of plasticity to allow recognition of multiple binding partners.

Evolutionary analysis of the BTK PH domain was also carried out previously and shows that the conserved sites map to the phospholipid binding pocket of the PH domain. The analysis did not include TH domain residues. Since we find the TH domain contributes to the PHTH/kinase interface in our crystal structure, we do not have the data at this time to do a thourough anaylsis but we appreciate this comment and can address this in furture work with collaborators.

Author response:

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

We thank the reviewers and editors for their careful read of our paper, and appreciate the thoughtful comments.

Both reviewers agreed that our work had several major strengths: the large dataset collected in collaboration across ten labs, the streamlined processing pipelines, the release of code repositories, the multi-task neural network, and that we definitively determined that electrode placement is an important source of variability between datasets.

However, a number of key potential improvements were noted: the reviewers felt that a more standard model-based characterization of single neuron responses would benefit our reproducibility analysis, that more detail was needed about the number of cells, sessions, and animals, and that more information was needed to allow users to deploy the RIGOR standards and to understand their relationship to other metrics in the field.

We agree with these suggestions and have implemented many major updates in our revised manuscript. Some highlights include:

(1)  A new regression analysis that specifies the response profile of each neuron, allowing a comparison of how similar these are across labs and areas (See Figure 7 in the new section, “Single neuron coefficients from a regression-based analysis are rep oducible across labs”);

(2) A new decoding analysis (See Figure 9 in the section, “Decodability of task variables is consistent across labs, but varies by brain region”);

(3) A new RIGOR notebook to ease useability;

(4) A wealth of additional information about the cells, animals and sessions in each figure;

(5) Many new additional figure panels in the main text and supplementary material to clarify the specific points raised by the reviewers.

Again, we are grateful to the reviewers and editors for their helpful comments, which have significantly improved the work. We are hopeful that the many revisions we have implemented will be sufficient to change the “incomplete” designation that was originally assigned to the manuscript.

Reviewer #1 (Public review):

Summary:

The authors explore a large-scale electrophysiological dataset collected in 10 labs while mice performed the same behavioral task, and aim to establish guidelines to aid reproducibility of results collected across labs. They introduce a series of metrics for quality control of electrophysiological data and show that histological verification of recording sites is important for interpreting findings across labs and should be reported in addition to planned coordinates. Furthermore, the authors suggest that although basic electrophysiology features were comparable across labs, task modulation of single neurons can be variable, particularly for some brain regions. The authors then use a multi-task neural network model to examine how neural dynamics relate to multiple interacting task- and experimenter-related variables, and find that lab-specific differences contribute little to the variance observed. Therefore, analysis approaches that account for correlated behavioral variables are important for establishing reproducible results when working with electrophysiological data from animals performing decision-making tasks. This paper is very well-motivated and needed. However, what is missing is a direct comparison of task modulation of neurons across labs using standard analysis practice in the fields, such as generalized linear model (GLM). This can potentially clarify how much behavioral variance contributes to the neural variance across labs; and more accurately estimate the scale of the issues of reproducibility in behavioral systems neuroscience, where conclusions often depend on these standard analysis methods.

We fully agree that a comparison of task-modulation across labs is essential. To address this, we have performed two new analyses and added new corresponding figures to the main text (Figures 7 and 9). As the reviewer hoped, this analysis did indeed clarify how much behavioral variance contributes to the variance across labs. Critically, these analyses suggested that our results were more robust to reproducibility than the more traditional analyses would indicate.

Additional details are provided below (See detailed response to R1P1b).

Strengths:

(1) This is a well-motivated paper that addresses the critical question of reproducibility in behavioural systems neuroscience. The authors should be commended for their efforts.

(2) A key strength of this study comes from the large dataset collected in collaboration across ten labs. This allows the authors to assess lab-to-lab reproducibility of electrophysiological data in mice performing the same decision-making task.

(3) The authors' attempt to streamline preprocessing pipelines and quality metrics is highly relevant in a field that is collecting increasingly large-scale datasets where automation of these steps is increasingly needed.

(4) Another major strength is the release of code repositories to streamline preprocessing pipelines across labs collecting electrophysiological data.

(5) Finally, the application of MTNN for characterizing functional modulation of neurons, although not yet widely used in systems neuroscience, seems to have several advantages over traditional methods.

Thanks very much for noting these strengths of our work.

Weaknesses:

(1) In several places the assumptions about standard practices in the field, including preprocessing and analyses of electrophysiology data, seem to be inaccurately presented:

a) The estimation of how much the histologically verified recording location differs from the intended recording location is valuable information. Importantly, this paper provides citable evidence for why that is important. However, histological verification of recording sites is standard practice in the field, even if not all studies report them. Although we appreciate the authors' effort to further motivate this practice, the current description in the paper may give readers outside the field a false impression of the level of rigor in the field.

We agree that labs typically do perform histological verification. Still, our methods offer a substantial improvement over standard practice, and this was critical in allowing us to identify errors in targeting. For instance, we used new software, LASAGNA, which is an innovation over the traditional, more informal approach to localizing recording sites. Second, the requirement that two independent reviewers concur on each proposed location for a recording site is also an improvement over standard practice. Importantly, these reviewers use electrophysiological features to more precisely localize electrodes, when needed, which is an improvement over many labs. Finally, most labs use standard 2D atlases to identify recording location (a traditional approach); our use of a 3D atlas and a modern image registration pipeline has improved the accuracy of identifying the true placement of probes in 3D space.

Importantly, we don’t necessarily advocate that all labs adopt our pipeline; indeed, this would be infeasible for many labs. Instead, our hope is that the variability in probe trajectory that we uncovered will be taken into account in future studies. Here are 3 example ways in which that could happen. First, groups hoping to target a small area for an experiment might elect to use a larger cohort than previously planned, knowing that some insertions will miss their target. Second, our observation that some targeting error arose because experimenters had to move probes due to blood vessels will impact future surgeries: when an experimenter realizes that a blood vessel is in the way, they might still re-position the probe, but they can also adjust its trajectory (e.g., changing the angle) knowing that even little nudges to avoid blood vessels can have a large impact on the resulting insertion trajectory. Third, our observation of a 7 degree deviation between stereotaxic coordinates and Allen Institute coordinates can be used for future trajectory planning steps to improve accuracy of placement. Uncovering this deviation required many insertions and our standardized pipeline, but now that it is known, it can be easily corrected without needing such a pipeline.

We thank the reviewer for bringing up this issue and have added new text (and modified existing text) in the Discussion to highlight the innovations we introduced that allowed us to carefully quantify probe trajectory across labs (lines 500 - 515):

“Our ability to detect targeting error benefited from an automated histological pipeline combined with alignment and tracing that required agreement between multiple users, an approach that greatly exceeds the histological analyses done by most individual labs. Our approach, which enables scalability and standardization across labs while minimizing subjective variability, revealed that much of the variance in targeting was due to the probe entry positions at the brain surface, which were randomly displaced across the dataset. … Detecting this offset relied on a large cohort size and an automated histological pipeline, but now that we have identified the offset, it can be easily accounted for by any lab. Specifically, probe angles must be carefully computed from the CCF, as the CCF and stereotaxic coordinate systems do not define the same coronal plane angle. Minimizing variance in probe targeting is another important element in increasing reproducibility, as slight deviations in probe entry position and angle can lead to samples from different populations of neurons. Collecting structural MRI data in advance of implantation could reduce targeting error, although this is infeasible for most labs. A more feasible solution is to rely on stereotaxic coordinates but account for the inevitable off-target measurements by increasing cohort sizes and adjusting probe angles when blood vessels obscure the desired location.”

b) When identifying which and how neurons encode particular aspects of stimuli or behaviour in behaving animals (when variables are correlated by the nature of the animals behaviour), it has become the standard in behavioral systems neuroscience to use GLMs - indeed many labs participating in the IBL also has a long history of doing this (e.g., Steinmetz et al., 2019; Musall et al., 2023; Orsolic et al., 2021; Park et al., 2014). The reproducibility of results when using GLMs is never explicitly shown, but the supplementary figures to Figure 7 indicate that results may be reproducible across labs when using GLMs (as it has similar prediction performance to the MTNN). This should be introduced as the first analysis method used in a new dedicated figure (i.e., following Figure 3 and showing results of analyses similar to what was shown for the MTNN in Figure 7). This will help put into perspective the degree of reproducibility issues the field is facing when analyzing with appropriate and common methods. The authors can then go on to show how simpler approaches (currently in Figures 4 and 5) - not accounting for a lot of uncontrolled variabilities when working with behaving animals - may cause reproducibility issues.

We fully agree with the reviewer's suggestion. We have addressed their concern by implementing a Reduced-Rank Regression (RRR) model, which builds upon and extends the principles of Generalized Linear Models (GLMs). The RRR model retains the core regression framework of GLMs while introducing shared, trainable temporal bases across neurons, enhancing the model’s capacity to capture the structure in neural activity (Posani, Wang, et al., bioRxiv, 2024). Importantly, Posani, Wang et al compared the predictive performance of GLMs vs the RRR model, and found that the RRR model provided (slightly) improved performance, so we chose the RRR approach here.

We highlight this analysis in a new section (lines 350-377) titled, “Single neuron coefficients from a regression-based analysis are reproducible across labs”. This section includes an entirely new Figure (Fig. 7), where this new analysis felt most appropriate, since it is closer in spirit to the MTNN analysis that follows (rather than as a new Figure 3, as the reviewer suggested). As the reviewer hoped, this analysis provides some reassurance that including many variables when characterizing neural activity furnishes results with improved reproducibility. We now state this in the Results and the Discussion (line 456-457), highlighting that these analyses complement the more traditional selectivity analyses, and that using both methods together can be informative.

When the authors introduce a neural network approach (i.e. MTNN) as an alternative to the analyses in Figures 4 and 5, they suggest: 'generalized linear models (GLMs) are likely too inflexible to capture the nonlinear contributions that many of these variables, including lab identity and spatial positions of neurons, might make to neural activity'). This is despite the comparison between MTNN and GLM prediction performance (Supplement 1 to Figure 7) showing that the MTNN is only slightly better at predicting neural activity compared to standard GLMs. The introduction of new models to capture neural variability is always welcome, but the conclusion that standard analyses in the field are not reproducible can be unfair unless directly compared to GLMs.

In essence, it is really useful to demonstrate how different analysis methods and preprocessing approaches affect reproducibility. But the authors should highlight what is actually standard in the field, and then provide suggestions to improve from there.

Thanks again for these comments. We have also edited the MTNN section slightly to accommodate the addition of the previous new RRR section (line 401-402).

(2) The authors attempt to establish a series of new quality control metrics for the inclusion of recordings and single units. This is much needed, with the goal to standardize unit inclusion across labs that bypasses the manual process while keeping the nuances from manual curation. However, the authors should benchmark these metrics to other automated metrics and to manual curation, which is still a gold standard in the field. The authors did this for whole-session assessment but not for individual clusters. If the authors can find metrics that capture agreed-upon manual cluster labels, without the need for manual intervention, that would be extremely helpful for the field.

We thank the reviewer for their insightful suggestions regarding benchmarking our quality control metrics against manual curation and other automated methods at the level of individual clusters. We are indeed, as the reviewer notes, publishing results from spike sorting outputs that have been automatically but not manually verified on a neuron-by-neuron basis. To get to the point where we trust these results to be of publishable quality, we manually reviewed hundreds of recordings and thousands of neurons, refining both the preprocessing pipeline and the single-unit quality metrics along the way. All clusters, both those passing QCs and those not passing QCs, are available to review with detailed plots and quantifications at https://viz.internationalbrainlab.org/app (turn on “show advanced metrics” in the upper right, and navigate to the plots furthest down the page, which are at the individual unit level). We would emphasize that these metrics are definitely imperfect (and fully-automated spike sorting remains a work in progress), but so is manual clustering. Our fully automated approach has the advantage of being fully reproducible, which is absolutely critical for the analyses in the present paper. Indeed, if we had actually done manual clustering or curation, one would wonder whether our results were actually reproducible independently. Nevertheless, it is not part of the present manuscript’s objectives to validate or defend these specific choices for automated metrics, which have been described in detail elsewhere (see our Spike Sorting whitepaper, https://figshare.com/articles/online_resource/Spike_sorting_pipeline_for_the_International_Brain_La boratory/19705522?file=49783080). It would be a valuable exercise to thoroughly compare these metrics against a careful, large, manually-curated set, but doing this properly would be a paper in itself and is beyond the scope of the current paper. We also acknowledge that our analyses studying reproducibility across labs could, in principle, result in more or less reproducibility under a different choice of metrics, which we now describe in the Discussion (line 469-470)”:

“Another significant limitation of the analysis presented here is that we have not been able to assess the extent to which other choices of quality metrics and inclusion criteria might have led to greater or lesser reproducibility.”

(3) With the goal of improving reproducibility and providing new guidelines for standard practice for data analysis, the authors should report of n of cells, sessions, and animals used in plots and analyses throughout the paper to aid both understanding of the variability in the plots - but also to set a good example.

We wholeheartedly agree and have added the number of cells, mice and sessions for each figure. This information is included as new tabs in our quality control spreadsheet (https://docs.google.com/spreadsheets/d/1_bJLDG0HNLFx3SOb4GxLxL52H4R2uPRcpUlIw6n4 n-E/). This is referred to in line 158-159 (as well as its original location on line 554 in the section, “Quality control and data inclusion”).

Other general comments:

(1) In the discussion (line 383) the authors conclude: 'This is reassuring, but points to the need for large sample sizes of neurons to overcome the inherent variability of single neuron recording'. - Based on what is presented in this paper we would rather say that their results suggest that appropriate analytical choices are needed to ensure reproducibility, rather than large datasets - and they need to show whether using standard GLMs actually allows for reproducible results.

Thanks. The new GLM-style RRR analysis in Figure 7, following the reviewer’s suggestion, does indeed indicate improved reproducibility across labs. As described above, we see this new analysis as complementary to more traditional analyses of neural selectivity and argue that the two can be used together. The new text (line 461) states:

“This is reassuring, and points to the need for appropriate analytical choices to ensure reproducibility.”

(2) A general assumption in the across-lab reproducibility questions in the paper relies on intralab variability vs across-lab variability. An alternative measure that may better reflect experimental noise is across-researcher variability, as well as the amount of experimenter experience (if the latter is a factor, it could suggest researchers may need more training before collecting data for publication). The authors state in the discussion that this is not possible. But maybe certain measures can be used to assess this (e.g. years of conducting surgeries/ephys recordings etc)?

We agree that understanding experimenter-to-experimenter variability would be very interesting and indeed we had hoped to do this analysis for some time. The problem is that typically, each lab employed one trainee to conduct all the data collection. This prevents us from comparing outcomes from two different experimenters in the same lab. There are exceptions to this, such as the Churchland lab in which 3 personnel (two postdocs and a technician) collected the data. However, even this fortuitous situation did not lend itself well to assessing experimenter-to-experimenter variation: the Churchland lab moved from Cold Spring Harbor to UCLA during the data collection period, which might have caused variability that is totally independent of experimenter (e.g., different animal facilities). Further, once at UCLA, the postdoc and technician worked closely together- alternating roles in animal training, surgery and electrophysiology. We believe that the text in our current Discussion (line 465-468) accurately characterizes the situation:

“Our experimental design precludes an analysis of whether the reproducibility we observed was driven by person-to-person standardization or lab-to-lab standardization. Most likely, both factors contributed: all lab personnel received standardized instructions for how to implant head bars and train animals, which likely reduced personnel-driven differences.”

Quantifying the level of experience of each experimenter is an appealing idea and we share the reviewer’s curiosity about its impact on data quality. Unfortunately, quantifying experience is tricky. For instance, years of conducting surgeries is not an unambiguously determinable number. Would we count an experimenter who did surgery every day for a year as having the same experience as an experimenter who did surgery once/month for a year? Would we count a surgeon with expertise in other areas (e.g., windows for imaging) in the same way as surgeons with expertise in ephys-specific surgeries? Because of the ambiguities, we leave this analysis to be the subject of future work; this is now stated in the Discussion (line 476).

(3) Figure 3b and c: Are these plots before or after the probe depth has been adjusted based on physiological features such as the LFP power? In other words, is the IBL electrophysiological alignment toolbox used here and is the reliability of location before using physiological criteria or after? Beyond clarification, showing both before and after would help the readers to understand how much the additional alignment based on electrophysiological features adjusts probe location. It would also be informative if they sorted these penetrations by which penetrations were closest to the planned trajectory after histological verification.

The plots in Figure 3b and 3c reflect data after the probe depth has been adjusted based on electrophysiological features. This adjustment incorporates criteria such as LFP power and spiking activity to refine the trajectory and ensure precise alignment with anatomical landmarks. The trajectories have also been reviewed and confirmed by two independent reviewers. We have clarified this in line 180 and in the caption of Figure 3.

To address this concern, we have added a new panel c in Figure 3 supplementary 1 (also shown below) that shows the LFP features along the probes prior to using the IBL alignment toolbox. We hope the reviewer agrees that a comparison of panels (a) and (c) below make clear the improvement afforded by our alignment tools.

In Figure 3 and Figure 3 supplementary 1, as suggested, we have also now sorted the probes by those that were closest to the planned trajectory. This way of visualizing the data makes it clear that as the distance from the planned trajectory increases, the power spectral density in the hippocampal regions becomes less pronounced and the number of probes that have a large portion of the channels localized to VISa/am, LP and PO decreases. We have added text to the caption to describe this. We thank the reviewer for this suggestion and agree that it will help readers to understand how much the additional alignment (based on electrophysiological features) adjusts probe location.

(4) In Figures 4 and 6: If the authors use a 0.05 threshold (alpha) and a cell simply has to be significant on 1/6 tests to be considered task modulated, that means that they have a false positive rate of ~30% (0.05*6=0.3). We ran a simple simulation looking for significant units (from random null distribution) from these criteria which shows that out of 100.000 units, 26500 units would come out significant (false error rate: 26.5%). That is very high (and unlikely to be accepted in most papers), and therefore not surprising that the fraction of task-modulated units across labs is highly variable. This high false error rate may also have implications for the investigation of the spatial position of task-modulated units (as effects of the spatial position may drown in falsely labelled 'task-modulated' cells).

Thank you for this concern. The different tests were kept separate, so we did not consider a neuron modulated if it was significant in only one out of six tests, but instead we asked whether a neuron was modulated according to test one, whether it was modulated according to test two, etc., and performed further analyses separately for each test. Thus, we are only vulnerable to the ‘typical’ false positive rate of 0.05 for any given test. We made this clearer in the text (lines 232-236) and hope that the 5% false positive rate seems more acceptable.

(5) The authors state from Figure 5b that the majority of cells could be well described by 2 PCs. The distribution of R2 across neurons is almost uniform, so depending on what R2 value one considers a 'good' description, that is the fraction of 'good' cells. Furthermore, movement onset has now been well-established to be affecting cells widely and in large fractions, so while this analysis may work for something with global influence - like movement - more sparsely encoded variables (as many are in the brain) may not be well approximated with this suggestion. The authors could expand this analysis into other epochs like activity around stimulus presentation, to better understand how this type of analysis reproduces across labs for features that have a less global influence.

We thank the reviewer for the suggestion and fully agree that the window used in our original analysis would tend to favor movement-driven neurons. To address this, we repeated the analysis, this time using a window centered around stimulus onset (from -0.5 s prior to stimulus onset until 0.1 s after stimulus onset). As the reviewer suspected, far fewer neurons were active in this window and consequently far fewer were modelled well by the first two PCs, as shown in Author response image 1b (below). Similar to our original analysis using the post-movement window, we found mixed results for the stimulus-centered window across labs. Interestingly, regional differences were weaker in this new analysis compared to the original analysis of the post-movement window. We have added a sentence to the results describing this. Because the results are similar to the post-movement window main figure, we would prefer to restrict the new analysis only to this point-by-point response, in the hopes of streamlining the paper.

Author response image 1.

PCA analysis applied to a stimulus-aligned window ([-0.5, 0.1] sec relative to stim onset). Figure conventions as in main text Fig 5. Results are comparable to the post-movement window analysis, however regional differences are weaker here, possibly because fewer cells were active in the pre-movement window. We added panel j here and in the main figure, showing cell-number-controlled results. I.e. for each test, the minimum neuron number of the compared classes was sampled from all classes (say labs in a region), this sampling was repeated 1000 times and p-values combined via Fisher’s method, overall resulting in much fewer significant differences across laboratories and, independently, regions.

(6) Additionally, in Figure 5i: could the finding that one can only distinguish labs when taking cells from all regions, simply be a result of a different number of cells recorded in each region for each lab? It makes more sense to focus on the lab/area pairing as the authors also do, but not to make their main conclusion from it. If the authors wish to do the comparison across regions, they will need to correct for the number of cells recorded in each region for each lab. In general, it was a struggle to fully understand the purpose of Figure 5. While population analysis and dimensionality reduction are commonplace, this seems to be a very unusual use of it.

We agree that controlling for varying cell numbers is a valuable addition to this analysis. We added panel j in Fig. 5 showing cell-number-controlled test results of panel i. I.e. for a given statistical comparison, we sample the lowest number of cells of compared classes from the others, do the test, and repeat this sampling 1000 times, before combining the p-values using Fisher’s method. This cell-number controlled version of the tests resulted in clearly fewer significant differences across distributions - seen similarly for the pre-movement window shown in j in Author response image 1. We hope this clarified our aim to illustrate that low-dimensional embedding of cells’ trial-averaged activity can show how regional differences compare with laboratory differences.

As a complementary statistical analysis to the shown KS tests, we fitted a linear-mixed-effects model (statsmodels.formula.api mixedlm), to the first and second PC for both activity windows (“Move”: [-0.5,1] first movement aligned; “Stim”: [-0.5,0.1] stimulus onset aligned), independently. Author response image 2 (in this rebuttal only) is broadly in line with the KS results, showing more regional than lab influences on the distributions of first PCs for the post-movement window.

Author response image 2:

Linear mixed effects model results for two PCs and two activity windows. For the post-movement window (“Move”), regional influences are significant (red color in plots) for all but one region while only one lab has a significant model coefficient for PC1. For PC2 more labs and three regions have significant coefficients. For the pre-movement window (“Stim”) one region for PC1 or PC2 has significant coefficients. The variance due to session id was smaller than all other effects (“eids Var”). “Intercept” shows the expected value of the response variable (PC1, PC2) before accounting for any fixed or random effects. All p-values were grouped as one hypothesis family and corrected for multiple comparisons via Benjamini-Hochberg.

(7) In the discussion the authors state: " Indeed this approach is a more effective and streamlined way of doing it, but it is questionable whether it 'exceeds' what is done in many labs.

Classically, scientists trace each probe manually with light microscopy and designate each area based on anatomical landmarks identified with nissl or dapi stains together with gross landmarks. When not automated with 2-PI serial tomography and anatomically aligned to a standard atlas, this is a less effective process, but it is not clear that it is less precise, especially in studies before neuropixels where active electrodes were located in a much smaller area. While more effective, transforming into a common atlas does make additional assumptions about warping the brain into the standard atlas - especially in cases where the brain has been damaged/lesioned. Readers can appreciate the effectiveness and streamlining provided by these new tools without the need to invalidate previous approaches.

We thank the reviewer for highlighting the effectiveness of manual tracing methods used traditionally. Our intention in the statement was not to invalidate the precision or value of these classical methods but rather to emphasize the scalability and streamlining offered by our pipeline. We have revised the language to more accurately reflect this (line 500-504):

“Our ability to detect targeting error benefited from an automated histological pipeline combined with alignment and tracing that required agreement between multiple users, an approach that greatly exceeds the histological analyses done by most individual labs. Our approach, which enables scalability and standardization across labs while minimizing subjective variability, revealed that much of the variance in targeting was due to the probe entry positions at the brain surface, which were randomly displaced across the dataset.”

(8) What about across-lab population-level representation of task variables, such as in the coding direction for stimulus or choice? Is the general decodability of task variables from the population comparable across labs?

Excellent question, thanks! We have added the new section “Decodability of task variables is consistent across labs, but varies by brain region” (line 423-448) and Figure 9 in the revised manuscript to address this question. In short, yes, the general decodability of task variables from the population is comparable across labs, providing additional reassurance of reproducibility.

Reviewer #2 (Public review):

Summary:

The authors sought to evaluate whether observations made in separate individual laboratories are reproducible when they use standardized procedures and quality control measures. This is a key question for the field. If ten systems neuroscience labs try very hard to do the exact same experiment and analyses, do they get the same core results? If the answer is no, this is very bad news for everyone else! Fortunately, they were able to reproduce most of their experimental findings across all labs. Despite attempting to target the same brain areas in each recording, variability in electrode targeting was a source of some differences between datasets.

Major Comments:

The paper had two principal goals:

(1) to assess reproducibility between labs on a carefully coordinated experiment

(2) distill the knowledge learned into a set of standards that can be applied across the field.

The manuscript made progress towards both of these goals but leaves room for improvement.

(1) The first goal of the study was to perform exactly the same experiment and analyses across 10 different labs and see if you got the same results. The rationale for doing this was to test how reproducible large-scale rodent systems neuroscience experiments really are. In this, the study did a great job showing that when a consortium of labs went to great lengths to do everything the same, even decoding algorithms could not discern laboratory identity was not clearly from looking at the raw data. However, the amount of coordination between the labs was so great that these findings are hard to generalize to the situation where similar (or conflicting!) results are generated by two labs working independently.

Importantly, the study found that electrode placement (and thus likely also errors inherent to the electrode placement reconstruction pipeline) was a key source of variability between datasets. To remedy this, they implemented a very sophisticated electrode reconstruction pipeline (involving two-photon tomography and multiple blinded data validators) in just one lab-and all brains were sliced and reconstructed in this one location. This is a fantastic approach for ensuring similar results within the IBL collaboration, but makes it unclear how much variance would have been observed if each lab had attempted to reconstruct their probe trajectories themselves using a mix of histology techniques from conventional brain slicing, to light sheet microscopy, to MRI imaging.

This approach also raises a few questions. The use of standard procedures, pipelines, etc. is a great goal, but most labs are trying to do something unique with their setup. Bigger picture, shouldn't highly "significant" biological findings akin to the discovery of place cells or grid cells, be so clear and robust that they can be identified with different recording modalities and analysis pipelines?

We agree, and hope that this work may help readers understand what effect sizes may be considered “clear and robust” from datasets like these. We certainly support the reviewer’s point that multiple approaches and modalities can help to confirm any biological findings, but we would contend that a clear understanding of the capabilities and limitations of each approach is valuable, and we hope that our paper helps to achieve this.

Related to this, how many labs outside of the IBL collaboration have implemented the IBL pipeline for their own purposes? In what aspects do these other labs find it challenging to reproduce the approaches presented in the paper? If labs were supposed to perform this same experiment, but without coordinating directly, how much more variance between labs would have been seen? Obviously investigating these topics is beyond the scope of this paper. The current manuscript is well-written and clear as is, and I think it is a valuable contribution to the field. However, some additional discussion of these issues would be helpful.

We thank the reviewer for raising this important issue. We know of at least 13 labs that have implemented the behavioral task software and hardware that we published in eLife in 2021, and we expect that over the next several years labs will also implement these analysis pipelines (note that it is considerably cheaper and faster to implement software pipelines than hardware). In particular, a major goal of the staff in the coming years is to continue and improve the support for pipeline deployment and use. However, our goal in this work, which we have aimed to state more clearly in the revised manuscript, was not so much to advocate that others adopt our pipeline, but instead to use our standardized approach as a means of assessing reproducibility under the best of circumstances (see lines 48-52): “A high level of reproducibility of results across laboratories when procedures are carefully matched is a prerequisite to reproducibility in the more common scenario in which two investigators approach the same high-level question with slightly different experimental protocols.”

Further, a number of our findings are relevant to other labs regardless of whether they implement our exact pipeline, a modified version of our pipeline, or something else entirely. For example, we found probe targeting to be a large source of variability. Our ability to detect targeting error benefited from an automated histological pipeline combined with alignment and tracing that required agreement between multiple users, but now that we have identified the offset, it can be easily accounted for by any lab. Specifically, probe angles must be carefully computed from the CCF, as the CCF and stereotaxic coordinate systems do not define the same coronal plane angle. Relatedly, we found that slight deviations in probe entry position can lead to samples from different populations of neurons. Although this took large cohort sizes to discover, knowledge of this discovery means that future experiments can plan for larger cohort sizes to allow for off-target trajectories, and can re-compute probe angle when the presence of blood vessels necessitates moving probes slightly. These points are now highlighted in the Discussion (lines 500-515).

Second, the proportion of responsive neurons (a quantity often used to determine that a particular area subserves a particular function), sometimes failed to reproduce across labs. For example, for movement-driven activity in PO, UCLA reported an average change of 0 spikes/s, while CCU reported a large and consistent change (Figure 4d, right most panel, compare orange vs. yellow traces). This argues that neuron-to-neuron variability means that comparisons across labs require large cohort sizes. A small number of outlier neurons in a session can heavily bias responses. We anticipate that this problem will be remedied as tools for large scale neural recordings become more widely used. Indeed, the use of 4-shank instead of single-shank Neuropixels (as we used here) would have greatly enhanced the number of PO neurons we measured in each session. We have added new text to Results explaining this (lines 264-268):

“We anticipate that the feasibility of even larger scale recordings will make lab-to-lab comparisons easier in future experiments; multi-shank probes could be especially beneficial for cortical recordings, which tend to be the most vulnerable to low cell counts since the cortex is thin and is the most superficial structure in the brain and thus the most vulnerable to damage. Analyses that characterize responses to multiple parameters are another possible solution (See Figure 7).”

(2) The second goal of the study was to present a set of data curation standards (RIGOR) that could be applied widely across the field. This is a great idea, but its implementation needs to be improved if adoption outside of the IBL is to be expected. Here are three issues:

(a) The GitHub repo for this project (https://github.com/int-brain-lab/paper-reproducible-ephys/) is nicely documented if the reader's goal is to reproduce the figures in the manuscript. Consequently, the code for producing the RIGOR statistics seems mostly designed for re-computing statistics on the existing IBL-formatted datasets. There doesn't appear to be any clear documentation about how to run it on arbitrary outputs from a spike sorter (i.e. the inputs to Phy).

We agree that clear documentation is key for others to adopt our standards. To address this, we have added a section at the end of the README of the repository that links to a jupyter notebook (https://github.com/int-brain-lab/paper-reproducible-ephys/blob/master/RIGOR_script.ipynb) that runs the RIGOR metrics on a user’s own spike sorted dataset. The notebook also contains a tutorial that walks through how to visually assess the quality of the raw and spike sorted data, and computes the noise level metrics on the raw data as well as the single cell metrics on the spike sorted data.

(b) Other sets of spike sorting metrics that are more easily computed for labs that are not using the IBL pipeline already exist (e.g. "quality_metrics" from the Allen Institute ecephys pipeline [https://github.com/AllenInstitute/ecephys_spike_sorting/blob/main/ecephys_spike_sorting/m odules/quality_metrics/README.md] and the similar module in the Spike Interface package [https://spikeinterface.readthedocs.io/en/latest/modules/qualitymetrics.html]). The manuscript does not compare these approaches to those proposed here, but some of the same statistics already exist (amplitude cutoff, median spike amplitude, refractory period violation).

There is a long history of researchers providing analysis algorithms and code for spike sorting quality metrics, and we agree that the Allen Institute’s ecephys code and the Spike Interface package are the current options most widely used (but see also, for example, Fabre et al. https://github.com/Julie-Fabre/bombcell). Our primary goal in the present work is not to advocate for a particular implementation of any quality metrics (or any spike sorting algorithm, for that matter), but instead to assess reproducibility of results, given one specific choice of spike sorting algorithm and quality metrics. That is why, in our comparison of yield across datasets (Fig 1F), we downloaded the raw data from those comparison datasets and re-ran them under our single fixed pipeline, to establish a fair standard of comparison. A full comparison of the analyses presented here under different choices of quality metrics and spike sorting algorithms would undoubtedly be interesting and useful for the field - however, we consider it to be beyond the scope of the present work. It is therefore an important assumption of our work that the result would not differ materially under a different choice of sorting algorithm and quality metrics. We have added text to the Discussion to clarify this limitation:

“Another significant limitation of the analysis presented here is that we have not been able to assess the extent to which other choices of quality metrics and inclusion criteria might have led to greater or lesser reproducibility.”

That said, we still intend for external users to be able to easily run our pipelines and quality metrics.

(c) Some of the RIGOR criteria are qualitative and must be visually assessed manually. Conceptually, these features make sense to include as metrics to examine, but would ideally be applied in a standardized way across the field. The manuscript doesn't appear to contain a detailed protocol for how to assess these features. A procedure for how to apply these criteria for curating non-IBL data (or for implementing an automated classifier) would be helpful.

We agree. To address this, we have provided a notebook that runs the RIGOR metrics on a user’s own dataset, and contains a tutorial on how to interpret the resulting plots and metrics (https://github.com/int-brain-lab/paper-reproducible-ephys/blob/master/RIGOR_script.ipynb).

Within this notebook there is a section focused on visually assessing the quality of both the raw data and the spike sorted data. The code in this section can be used to generate plots, such as raw data snippets or the raster map of the spiking activity, which are typically used to visually assess the quality of the data. In Figure 1 Supplement 2 we have provided examples of such plots that show different types of artifactual activity that should be inspected.

Other Comments:

(1) How did the authors select the metrics they would use to evaluate reproducibility? Was this selection made before doing the study?

Our metrics were selected on the basis of our experience and expertise with extracellular electrophysiology. For example: some of us previously published on epileptiform activity and its characteristics in some mice (Steinmetz et al. 2017), so we included detection of that type of artifact here; and, some of us previously published detailed investigations of instability in extracellular electrophysiological recordings and methods for correcting them (Steinmetz et al. 2021, Windolf et al. 2024), so we included assessment of that property here. These metrics therefore represent our best expert knowledge about the kinds of quality issues that can affect this type of dataset, but it is certainly possible that future investigators will discover and characterize other quality issues.

The selection of metrics was primarily performed before the study (we used these assessments internally before embarking on the extensive quantifications reported here), and in cases where we refined them further during the course of preparing this work, it was done without reference to statistical results on reproducibility but instead on the basis of manual inspection of data quality and metric performance.

(2) Was reproducibility within-lab dependent on experimenter identity?

We thank the reviewer for this question. We have addressed it in our response to R1 General comment 2, as follows:

We agree that understanding experimenter-to-experimenter variability would be very interesting and indeed we had hoped to do this analysis for some time. The problem is that typically, each lab employed one trainee to conduct all the data collection. This prevents us from comparing outcomes from two different experimenters in the same lab. There are exceptions to this, such as the Churchland lab in which 3 personnel (two postdocs and a technician) collected the data. However, even this fortuitous situation did not lend itself well to assessing experimenter-to-experimenter variation: the Churchland lab moved from Cold Spring Harbor to UCLA during the data collection period, which might have caused variability that is totally independent of experimenter (e.g., different animal facilities). Further, once at UCLA, the postdoc and technician worked closely together- alternating roles in animal training, surgery and electrophysiology. We believe that the text in our current Discussion (line 465-468) accurately characterizes the situation:

“Our experimental design precludes an analysis of whether the reproducibility we observed was driven by person-to-person standardization or lab-to-lab standardization. Most likely, both factors contributed: all lab personnel received standardized instructions for how to implant head bars and train animals, which likely reduced personnel-driven differences.”

Quantifying the level of experience of each experimenter is an appealing idea and we share the reviewer’s curiosity about its impact on data quality. Unfortunately, quantifying experience is tricky. For instance, years of conducting surgeries is not an unambiguously determinable number. Would we count an experimenter who did surgery every day for a year as having the same experience as an experimenter who did surgery once/month for a year? Would we count a surgeon with expertise in other areas (e.g., windows for imaging) in the same way as surgeons with expertise in ephys-specific surgeries? Because of the ambiguities, we leave this analysis to be the subject of future work; this is now stated in the Discussion (line 476).

(3) They note that UCLA and UW datasets tended to miss deeper brain region targets (lines 185-188) - they do not speculate why these labs show systematic differences. Were they not following standardized procedures?

Thank you for raising this point. All researchers across labs were indeed following standardised procedures. We note that our statistical analysis of probe targeting coordinates and angles did not reveal a significant effect of lab identity on targeting error, even though we noted the large number of mis-targeted recordings in UCLA and UW to help draw attention to the appropriate feature in the figure. Given that these differences were not statistically significant, we can see how it was misleading to call out these two labs specifically. While the overall probe placement surface error and angle error both show no such systematic difference, the magnitude of surface error showed a non-significant tendency to be higher for samples in UCLA & UW, which, compounded with the direction of probe angle error, caused these probe insertions to land in a final location outside LP & PO.

This shows how subtle differences in probe placement & angle accuracy can lead to compounded inaccuracies at the probe tip, especially when targeting deep brain regions, even when following standard procedures. We believe this is driven partly by the accuracy limit or resolution of the stereotaxic system, along with slight deviations in probe angle, occurring during the setup of the stereotaxic coordinate system during these recordings.

We have updated the relevant text in lines 187-190 as follows, to clarify:

“Several trajectories missed their targets in deeper brain regions (LP, PO), as indicated by gray blocks, despite the lack of significant lab-dependent effects in targeting as reported above. These off-target trajectories tended to have both a large displacement from the target insertion coordinates and a probe angle that unfavorably drew the insertions away from thalamic nuclei (Figure 2f).”

(4) The authors suggest that geometrical variance (difference between planned and final identified probe position acquired from reconstructed histology) in probe placement at the brain surface is driven by inaccuracies in defining the stereotaxic coordinate system, including discrepancies between skull landmarks and the underlying brain structures. In this case, the use of skull landmarks (e.g. bregma) to determine locations of brain structures might be unreliable and provide an error of ~360 microns. While it is known that there is indeed variance in the position between skull landmarks and brain areas in different animals, the quantification of this error is a useful value for the field.

We thank the reviewer for their thoughtful comment and are glad that they found the quantification of variance useful for the field.

(5) Why are the thalamic recording results particularly hard to reproduce? Does the anatomy of the thalamus simply make it more sensitive to small errors in probe positioning relative to the other recorded areas?

We thank the reviewer for raising this interesting question. We believe that they are referring to Figure 4: indeed when we analyzed the distribution of firing rate modulations, we saw some failures of reproducibility in area PO (bottom panel, Figure 4h). However, the thalamic nuclei were not, in other analyses, more vulnerable to failures in reproducibility. For example, in the top panel of Figure 4h, VisAM shows failures of reproducibility for modulation by the visual stimulus. In Fig. 5i, area CA1 showed a failure of reproducibility. We fear that the figure legend title in the previous version (which referred to the thalamus specifically) was misleading, and we have revised this. The new title is, “Neural activity is modulated during decision-making in five neural structures and is variable between laboratories.” This new text more accurately reflects that there were a number of small, idiosyncratic failures of reproducibility, but that these were not restricted to a specific structure. The new analysis requested by R1 (now in Figure 7) provides further reassurance of overall reproducibility, including in the thalamus (see Fig. 7a, right panels; lab identity could not be decoded from single neuron metrics, even in the thalamus).

Reviewer #1 (Recommendations for the authors):

(1) Figure font sizes and formatting are variable across panels and figures. Please streamline the presentation of results.

Thank you for your feedback. We have remade all figures with the same standardized font sizes and formatting.

(2) Please correct the noncontinuous color scales in Figures 3b and 3d.

Thank you for pointing this out, we fixed the color bar.

(3) In Figures 5d and g, the error bars are described as: 'Error bands are standard deviation across cells normalised by the square root of the number of sessions in the region'. How does one interpret this error? It seems to be related to the standard error of the mean (std/sqrt(n)) but instead of using the n from which the standard deviation is calculated (in this case across cells), the authors use the number of sessions as n. If they took the standard deviation across sessions this would be the sem across sessions, and interpretable (as sem*1.96 is the 95% parametric confidence interval of the mean). Please justify why these error bands are used here and how they can be interpreted - it also seems like it is the only time these types of error bands are used.

We agree and for clarity use standard error across cells now, as the error bars do not change dramatically either way.

(4) It is difficult to understand what is plotted in Figures 5e,h, please unpack this further and clarify.

Thank you for pointing this out. We have added additional explanation in the figure caption (See caption for Figure 5c) to explain the KS test.

(5) In lines 198-201 the authors state that they were worried that Bonferroni correction with 5 criteria would be too lenient, and therefore used 0.01 as alpha. I am unsure whether the authors mean that they are correcting for multiple comparisons across features or areas. Either way, 0.01 alpha is exactly what a Bonferroni corrected alpha would be when correcting for either 5 features or 5 areas: 0.05/5=0.01. Or do they mean they apply the Bonferroni correction to the new 0.01 alpha: i.e., 0.01/5=0.002? Please clarify.

Thank you, that was indeed written confusingly. We considered all tests and regions as whole, so 7 tests * 5 regions = 35 tests, which would result in a very strong Bonferroni correction. Indeed, if one considers the different tests individually, the correction we apply from 0.05 to 0.01 can be considered as correcting for the number of regions, which we now highlight better. We apply no further corrections of any kind to our alpha=0.01. We clarified this in the manuscript in all relevant places (lines 205-208, 246, 297-298, and 726-727).

(6) Did the authors take into account how many times a probe was used/how clean the probe was before each recording. Was this streamlined between labs? This can have an effect on yield and quality of recording.

We appreciate the reviewer highlighting the potential impact of probe use and cleanliness on recording quality and yield. While we did not track the number of times each probe was used, we ensured that all probes were cleaned thoroughly after each use using a standardized cleaning protocol (Section 16: Cleaning the electrode after data acquisition in Appendix 2: IBL protocol for electrophysiology recording using Neuropixels probe). We acknowledge that tracking the specific usage history of each probe could provide additional insights, but unfortunately we did not track this information for this project. In prior work the re-usability of probes has been quantified, showing insignificant degradation with use (e.g. Extended Data Fig 7d from Jun et al. 2017).

(7) Figure 3, Supplement1: DY_013 missed DG entirely? Was this included in the analysis?

Thank you for this question. We believe the reviewer is referring to the lack of a prominent high-amplitude LFP band in this mouse, and lack of high-quality sorted units in that region. Despite this, our histology did localize the recording trajectory to DG. This recording did pass our quality control criteria overall, as indicated by the green label, and was used in relevant analyses.

The lack of normal LFP features and neuron yield might reflect the range of biological variability (several other sessions also have relatively weak DG LFP and yield, though DY_013 is the weakest), or could reflect some damage to the tissue, for example as caused by local bleeding. Because we could not conclusively identify the source of this observation, we did not exclude it.

(8) Given that the authors argue for using the MTNN over GLMs, it would be useful to know exactly how much better the MTNN is at predicting activity in the held-out dataset (shown in Figure 7, Supplement 1). It looks like a very small increase in prediction performance between MTNN and GLMs, is it significantly different?

The average variance explained on the held-out dataset, as shown in Figure 8–Figure Supplement 1 Panel B, is 0.065 for the GLMs and 0.071 for the MTNN. As the reviewer correctly noted, this difference is not significant. However, one of the key advantages of the MTNN over GLMs lies in its flexibility to easily incorporate covariates, such as electrophysiological characteristics or session/lab IDs, directly into the analysis. This feature is particularly valuable for assessing effect sizes and understanding the contributions of various factors.

(9) In line 723: why is the threshold for mean firing rate for a unit to be included in the MTNN results so high (>5Hz), and how does it perform on units with lower firing rates?      

We thank the reviewer for pointing this out. The threshold for including units with a mean firing rate above 5 Hz was set because most units with firing rates below this threshold were silent in many trials, and reducing the number of units helped keep the MTNN training time reasonable. Based on this comment, we ran the MTNN experiments including all units with firing rates above 1 Hz, and the results remained consistent with our previous conclusions (Figure 8). Crucially, the leave-one-out analysis consistently showed that lab and session IDs had effect sizes close to zero, indicating that both within-lab and between-lab random effects are small and comparable.

Reviewer #2 (Recommendations for the authors):

(1) Most of the more major issues were already listed in the above comments. The strongest recommendation for additional work would be to improve the description and implementation of the RIGOR statistics such that non-IBL labs that might use Neuropixels probes but not use the entire IBL pipeline might be able to apply the RIGOR framework to their own data.

We thank the reviewer for highlighting the importance of making the RIGOR statistics more accessible to a broader audience. We agree that improving the description and implementation of the RIGOR framework is essential for facilitation of non-IBL labs using Neuropixels probes. To address this we created a jupyter notebook with step-by-step guidance that is not dependent on the IBL pipeline. This tool (https://github.com/int-brain-lab/paper-reproducible-ephys/blob/develop/RIGOR_script.ipynb) is publicly available through the repository, accompanied by example datasets and usage tutorials.

(2) Table 1: How are qualitative features like "drift" defined? Some quantitative statistics like "presence ratio" (the fraction of the dataset where spikes are present) already exist in packages like ecephys_spike_sorting. Who measured these qualitative features? What are the best practices for doing these qualitative analyses?

At the probe level, we compute the estimate of the relative motion of the electrodes to the brain tissue at multiple depths along the electrode. We overlay the drift estimation over a raster plot to detect sharp displacements as a function of time. Quantitatively, the drift is the cumulative absolute electrode motion estimated during spike sorting (µm). We clarified the corresponding text in Table 1.

The qualitative assessments were carried out by IBL staff and experimentalists. We have now provided code to run the RIGOR metrics along with an embedded tutorial, to complement the supplemental figures we have shown about qualitative metric interpretation.

(3) Table 1: What are the units for the LFP derivative?

We thank the reviewer for noting that the unit was missing. The unit (decibel per unit of space) is now in the table.

(4) Table 1: For "amplitude cutoff", the table says that "each neuron must pass a metric". What is the metric?

We have revised the table to include this information. This metric was designed to detect potential issues in amplitude distributions caused by thresholding during deconvolution, which could result in missed spikes. There are quantitative thresholds on the distribution of the low tail of the amplitude histogram relative to the high tail, and on the relative magnitude of the bins in the low tail. We now reference the methods text from the table, which includes a more extended description and gives the specific threshold numbers. Also, the metric and thresholds are more easily understood with graphical assistance; see the IBL Spike Sorting Whitepaper for this (Fig. 17 in that document and nearby text; https://doi.org/10.6084/m9.figshare.19705522.v4). This reference is now also cited in the text.

(5) Figure 2: In panel A, the brain images look corrupted.

Thanks; in the revised version we have changed the filetype to improve the quality of the panel image.

(6) Figure 7: In panel D, make R2 into R^2 (with a superscript)

Panel D y-axis label has been revised to include superscript (note that this figure is now Figure 8).

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This study extends the previous interesting work of this group to address the potentially differential control of movement and posture. Their earlier work explored a broad range of data to make the case for a downstream neural integrator hypothesized to convert descending velocity movement commands into postural holding commands. Included in that data were observations from people with hemiparesis due to stroke. The current study uses similar data but pushes into a different, but closely related direction, suggesting that these data may address the independence of these two fundamental components of motor control. I find the logic laid out in the second sentence of the abstract ("The paretic arm after stroke is notable for abnormalities both at rest and during movement, thus it provides an opportunity to address the relationships between control of reaching, stopping, and stabilizing") less than compelling, but the study does make some interesting observations. Foremost among them, is the relation between the resting force postural bias and the effect of force perturbations during the target hold periods, but not during movement. While this interesting observation is consistent with the central mechanism the authors suggest, it seems hard to me to rule out other mechanisms, including peripheral ones. 

Response 1.1. Thank you for your comments, which we address in detail below and in our response to Recommendations to the authors (see pp. 15-19 of this letter). We would first like to clarify the motivation behind our use of a stroke population to understand the interactions between the control of reaching in and holding. We agree that this idea can be laid out in a more compelling way.

The fact that stroke patients usually display issues with their control of both reaching and holding, allows for within-individual comparisons of those two modes of control. Further, the magnitude of abnormalities is relatively large, making it easier to measure, compare and investigate effects. And, importantly, these two modes of control can be differentially affected after stroke (also pointed out by Reviewer 2, point 4 in Comments to the Authors). Finally, this kind of work – examining interactions between positive signs of stroke (such as abnormal posture or synergy) vs. negative signs (such as loss of motor control) – needs to be done in humans, as positive signs are relatively absent even in primates (Tower, 1940).

We have changed our abstract (changes shown below in red), and our intro (expanding the second paragraph, lines 75-76), to lay out our motivation more clearly.

From the abstract:

“The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke. “

On the other hand, the relation between force bias and the well-recognized flexor synergy seems rather self-evident, and I don't see that these results add much to that story.

Response 1.2. While it seems natural that these biases would be the resting expression of abnormal flexor synergies (given their directionality towards the body, as shown in Figures 2-3, and the other similarities we demonstrate in Figure 8), we do not believe it is self-evident. These biases are measured at rest, with the patient passively moved and held still, whereas abnormal synergies emerge when the patient actively tries to move. The lack of relationship we find between these resting force biases and active movement underlines that the relation between force bias and flexor synergy should not be taken as self-evident, making it worthwhile to examine it (as we motivate in lines 589-596 and show in Figure 8).

The paradox here is that, in spite of a relationship between force bias and flexor synergy (itself manifesting during attempted movement), there seems to be no relationship between force bias and direct measures of active movement (Figures 5,6). This is the paradox that inspired our conceptual model (Figure 9) and inspires to further investigate the factors under which these two systems are intermingled or kept separate. We thus find it to be a helpful element in the story.

I am also struck by what seems to be a contradiction between the conclusions of the current and former studies: "These findings in stroke suggest that moving and holding still are functionally separable modes of control" and "the commands that hold the arm and finger at a target location depend on the mathematical integration of the commands that moved the limb to that location." The former study is mentioned here only in passing, in a single phrase in the discussion, with no consideration of the relation between the two studies. This is odd and should be addressed. 

Response 1.3. While these two sets of findings are not contradictory, we understand how they can appear as such without providing context. We now discuss the relationship between our present study and the previous one more directly (lines 66-70 and 663-669 of the revised manuscript).

The previous study examined how the control of movement informs the control of holding after the movement was over; the current study examines whether abnormalities in holding measured at rest with the movement leading to the rest position being passive. There are thus two important distinctions:

First, directionality of potential effects: here we examine the effect of (abnormalities in) holding control upon movement, but the 2020 study (Albert et al., 2020) examines the effects of movement upon holding control. Stroke patient data in the 2020 study showed that, under CST damage, while the reach controller is disrupted, the hold controller can continue to integrate the malformed reach commands faithfully. In line with this, we proposed a model where the postural controller system sits downstream of the moving controller (Figure 7G in the 2020 paper). We thus did not claim, in 2020, that integration of movement commands is the only way to do determine posture control, as we stated explicitly back then, e.g. (emphasis ours):

“Equations (1) and (2) describe how the integration of move activity may relate to changes in hold commands, but does not specify the hold command at the target.”

In short, finding no effect of holding abnormalities upon movement (present finding) does not mean there is no potential effect of movement upon holding (2020 finding). This is something we had alluded to in the Discussion but not clarified, which we do now (see edits at the end of our response to this point).

Second, active vs. passive movement: here, we measure holding control at rest (Experiment 1). The 2020 study shows that endpoint forces reflect the integration of learned dynamics exerted during active movement that led to the endpoint position. However, in Experiment 1, there is no active reaching to integrate, as the robot passively moves the arm to the held position. Thus, resting postural forces measured in Experiment 1 could not reflect the integration of reach commands that led to each rest position.  

Thus, the two sets of findings are not contradictory. Taking our current and 2020 findings together suggests that active holding control would comprise would reflect both the integration of movement control that led to assuming the held position, plus the force biases measured at rest.

Hence our decision to describe these two systems as functionally separable: while these systems can interact, the effects of post-stroke malfunctions in each can be independent depending on the function and conditions at hand. This does not make this a limited finding: being able to dissociate post-stroke impairment based on each of these two modes of control may inform rehabilitation, and also importantly, understanding the conditions in which these two modes of control become separable can substantially advance our understanding of both how different stroke signs interact with each other and how motor control is assembled in the healthy motor system. Figure 9 illustrates our conceptual model behind this and may serve as a blueprint to further dissect these circuits in the future.

We discuss these issues briefly in lines 663-669 in our Discussion section, reproduced below for convenience:

“It should be noted, however, that having distinct neural circuits for reaching and holding does not rule out interactions between them. For example, we recently demonstrated how arm holding control reflects the integration of motor commands driving the preceding active movement that led to the hold position, in both healthy participants and patients with hemiparesis (Albert et al., 2020). However, in that paper, we did not claim that this integration is the only source of holding control. Indeed, in Experiment 1 of the current study, we used passive movement to bring the arm to each probed position, which means that the postural biases could not be the result of integration of motor commands.” 

And, we have adjusted our Introduction to provide pertinent context regarding our 2020 work (first paragraph, lines 66-70 of the updated manuscript).

A minor wording concern I had is that the term "holding still" is frequently hard to parse. A couple of examples: "These findings in stroke suggest that moving and holding still are functionally separable modes of control." This example is easily read, "moving and holding [continue to be] functionally separable". Another: "...active reaching and holding still in the same workspace, " could be "...active reaching and holding [are] still in the same workspace." Simply "holding", "posture" or "posture maintenance" would all be better options.

Response 1.4. Thank you for your suggestion. Following your comment, we have abbreviated this term to simply “holding”, both on the title and throughout the text.

Reviewer #2 (Public Review):

Summary: 

Here the authors address the idea that postural and movement control are differentially impacted with stroke. Specifically, they examined whether resting postural forces influenced several metrics of sensorimotor control (e.g., initial reach angle, maximum lateral hand deviation following a perturbation, etc.) during movement or posture. The authors found that resting postural forces influenced control only following the posture perturbation for the paretic arm of stroke patients, but not during movement. They also found that resting postural forces were greater when the arm was unsupported, which correlated with abnormal synergies (as assessed by the Fugl-Meyer). The authors suggest that these findings can be explained by the idea that the neural circuitry associated with posture is relatively more impacted by stroke than the neural circuitry associated with movement. They also propose a conceptual model that differentially weights the reticulospinal tract (RST) and corticospinal tract (CST) to explain greater relative impairments with posture control relative to movement control, due to abnormal synergies, in those with stroke.

Strengths: 

The strength of the paper is that they clearly demonstrate with the posture task (i.e., active holding against a load) that the resting postural forces influence subsequent control (i.e., the path to stabilize, time to stabilize, max. deviation) following a sudden perturbation (i.e., suddenly removal of the load). Further, they can explain their findings with a conceptual model, which is depicted in Figure 9. 

Weaknesses: 

Current weaknesses and potential concerns relate to i) not displaying or reporting the results of healthy controls and non-paretic arm in Experiment 2 and ii) large differences in force perturbation waveforms between movement (sudden onset) and posture (sudden release), which could potentially influence the results and or interpretation. 

Response 2.0. Thank you for your assessment, and for pointing out ways to improve our paper. We address the weakness and potential concerns in detail below.

Larger concerns

(1) Additional analyses to further support the interpretation. In Experiment 1 the authors present the results for the paretic arm, non-paretic arm, and controls. However, in Experiment 2 for several key analyses, they only report summary statistics for the paretic arm (Figure 5D-I; Figure 6D-E; Figure 7F). It is understood that the controls have much smaller resting postural force biases, but they are still present (Figure 3B). It would strengthen the position of the paper to show that controls and the non-paretic arm are not influenced by resting postural force biases during movement and particularly during posture, while acknowledging the caveat that the resting positional forces are smaller in these groups. It is recommended that the authors report and display the results shown in Figure 5D-I; Figure 6D-E; Figure 7F for the controls and non-paretic arm. If these results are all null, the authors could alternatively place these results in an additional supplementary. 

Response 2.1a. Thank you for your recommendations. We agree both on the value of these analyses and the caveat associated with them: these resting postural force biases are substantially smaller for the non-paretic and control data (for example, the magnitude of resting biases in the supported condition is 2.8±0.4N for the paretic data, but only 1.8±0.4N and 1.3±0.2N for the non-paretic and control data, respectively; the difference is even greater in the unsupported condition, though this is not the one being compared to Experiment 2).

We now conduct a comprehensive series of supplementary analyses, including the examination of non-paretic and control data for all three components of Experiment 2 (unperturbed reaches; pulse perturbations; and active holding control). These are mentioned in the Results (lines 422-424, 512513, and 574-574 of the revised manuscript) and illustrated in the supplementary materials: Supplementary Figures S5-1, S6-1, and S7-1 contain the main analyses (comparisons of instances with the most extreme resting biases for each individual) for the unperturbed reach analysis, pulse perturbation analysis, and active holding control analysis, respectively.

We find that non-paretic and control data do not display effects of resting biases upon unperturbed reaching control (Figure S5-1) or control against a pulse perturbation early during movement (Figure S6-1) – as is the case with the paretic data. Non-paretic and control data do not display evidence of influence of their resting force biases upon active holding control either (Figure S7-1), unlike the paretic data. For the non-paretic data, however, these influences are nominally towards the same direction as in the paretic data. Given that resting biases are substantially weaker for the non-paretic case, it is possible a similar relationship exists but requires increased statistical power to discern. Moreover, it is possible that the effect of resting biases is non-linear, with small biases effectively kept under check so that their impact upon active holding control is even less than a linearly scaled version of the impact of the stronger, paretic-side biases. This can be the subject of future work.

Please also note that, following your recommendation (Recommendations to the Authors, point 2.1), we have conducted secondary analyses which estimate sensitivity to resting bias using all datapoints, validating our main analyses; these analyses were also performed for control and non-paretic data, with similar results (Response 2.A.1).

Further, the results could be further boosted by reporting/displaying additional analyses. In Figure 6D the authors performed a correlation analysis. Can they also display the same analysis for initial deviation and endpoint deviation for the data shown in Figure 5D-F & 5G-I, as well for 7F for the path to stabilization, time to stabilization, and max deviation? This will also create consistency in the analyses performed for each dependent variable across the paper.

Response 2.1b. Here, we set to test whether resting biases affect movement. It is best to do this using a within-individual comparison design, rather than using across-individual correlations: while correlation analyses can in general be informative, they obscure within-individual effects which are the main comparisons of interest in our study. Consider a participant with strong resting bias towards one direction, tested on opposing perturbations; averaging these responses for each individual would mostly cancel out any effects of resting biases. Even if we were to align responses to the direction of the perturbation before averaging, the power of correlation analyses may be diluted by inter-individual differences in other factors, such as overall stiffness.

Thus, our analysis design was instead focused on examining the differential effects of resting posture biases within each individual’s data. We compared the most extreme opposing/aligned or clockwise/counter-clockwise instances within each individual, specifically to assess these differential effects. In our revised version, we have further reinforced these analyses to include all data rather than the most extreme instances (see response 2.A.1.a to the Reviewer’s recommendation to the authors) where we performed correlations of within-individual resting posture vs. the corresponding dependent variables and compared the resulting slopes. 

The across-individual correlation analyses add little to that for the reasons we outlined above. At the same time, it is possible they can be helpful in e.g. illustrating across-individual variability. We thus now include across-individual correlation analyses for all dependent variables, but, given their limited value, only in the supplementary material. This also means that, for consistency, we moved the correlation analysis in Figure 6 to the corresponding supplementary figure as well (Figure S6-3).

In addition, following the Reviewer’s comment about consistency in the analyses performed for each dependent variable across the paper, we added within-individual comparisons for settling time following the pulse perturbations (Figure 6D, right).

(2) Inconsistency in perturbations that would differentially impact muscle and limb states during movement and posture. It is well known that differences in muscle state (activation / preloaded, muscle fiber length and velocity) and limb state (position and velocity) impact sensorimotor control (Pruszynski, J. A., & Scott, S. H. (2012). Experimental brain research, 218, 341-359.). Of course, it is appreciated that it is not possible to completely control all states when comparing movement and posture (i.e., muscle and limb velocity). However, using different perturbations differentially impacts muscle and limb states. Within this paper, the authors used very different force waveforms for movement perturbations (i.e., 12 N peak, bell-shaped, 0.7ms duration -> sudden force onset to push the limb; Figure 6A) and posture perturbations (i.e., 6N, 2s ramp up -> 3s hold -> sudden force release that resulted in limb movement; Figure 4) that would differentially impact muscle (and limb) states. Preloaded muscle (as in the posture perturbation) has a very different response compared to muscle that has little preload (as in the movement perturbations, where muscles that would resist a sudden lateral perturbation would likely be less activated since they are not contributing to the forward movement). Would the results hold if the same perturbation had been used for both posture and movement (e.g., 12 N pulse for both experiments)? It is recommended that the authors comment and discuss in the paper why they chose different perturbations and how that might impact the results. 

Response 2.2a. We agree that it can be impossible to completely control all states when comparing movement and posture. We would also like to stress that these perturbations were not designed so that responses are directly compared to each other (though of course there is an indirect comparison in the sense that we show influence of biases in one type of perturbation but not the other). Instead, Experiment 2 tried to implement a probe optimized for each motor control modality (moving vs. holding). However, the Reviewer has a point that the potential impact of differences between the perturbations is important to discuss in the paper.

The Reviewer points out two potentially interesting differences between the two perturbations. First, the magnitude (6N for the posture perturbation vs. 12N for the pulse perturbation); second, the presence of background load in the posture perturbation, in contrast to the pulse perturbation.

For the movement perturbation, we used a 12-N, 70ms pulse. This perturbation and scaled versions have been tested before in both control and patient populations (Smith et al., 2000; Fine and Thoroughman, 2006). For the holding perturbation, we used a background load to ensure that active holding control is engaged, and the duration of the probe (holding for about 5s) made using a stronger perturbation impractical –maintaining a background load at, say, 12N for that long could lead to increased fatigue.

The question raised by the Reviewer, whether the findings would be the same if the same, 12-N pulse were used to probe both moving and holding control, is interesting to investigate. We would expect the same qualitative findings (i.e. there would still be a connection between resting posture and active holding control when the latter were probed with a 12N pulse). Recent work provides more specific insight into what to expect. Our posture perturbation task is similar to the Unload Task in (Lowrey et al., 2019), whereby a background torque is released, whereas our pulse perturbation is more similar to their Load Task, whereby a torque is imposed against no background load (though it is a step perturbation rather than a pulse). Lowrey et al., 2019 find that their Unload task is harder than the Load task, with 2x the fraction of patient trials classified as failed (with failure defined as task performance being outside of the 95% confidence interval for controls), though there are still clear effects for the Load task. 

This suggests that the potential effects of using a pulse-like perturbation to probe posture control would likely be weaker in magnitude, all other things being equal. At the same time, however, the Load and Unload tasks in Lowrey et al., 2019 were perturbations of the same magnitude; it is thus also likely that the reduction in effect would be mitigated, or reversed, by the fact that we would be using a 12N instead of a 6N perturbation.

A relevant consequence of the Lowrey et al., 2019 findings is that the Unload paradigm is superior in its ability to detect impairment in static, posture perturbations, and thus provides a better signal to detect potential relationships with resting posture biases. This is not surprising, as a background load further engages the control of active holding, which what we were trying to probe in the first place.

But then why not use the same paradigm (preloading and release) for movement? There are two main reasons. First, requiring a background load throughout the experiment is unfeasible due to fatigue. Second, for the holding perturbation, we wanted to ensure that the postural control system is meaningfully engaged when the perturbation hits, hence we picked the background load. Were we to impose the same during moving – i.e. impose a lateral background load on the movement - we could be engaging posture control on top of movement control. This preloading would reduce the degree to which the pulse probe isolates movement control, and lead to intrusion of the posture control system in the movement task by design. This relates to what the Reviewer proposes in the comment below: preloading may result in postural biases i.e. engage posture control; see below where we argue this interpretation is within the scope of our conceptual model rather a counter to it.

We now explain the rationale behind our perturbation design in the Methods section (lines 211-220).

Relatedly, an alternative interpretation of the results is that preloading muscle for stroke patients, whether by supporting the weight of one's arm (experiment 1) or statically resisting a load prior to force release (experiment 2), leads to a greater postural force bias that can subsequently influence control. It is recommended that the authors comment on this. 

Response 2.2b. We find this interpretation valid, but we do not see how it meaningfully differs from the framework we propose. We already state that the RST may be tailored for both posture/holding control and the production of large forces (which would include muscle preloading):

“Thus, the accumulated evidence suggests that the RST could control posture and large force production in the upper limb.“ (lines 698-699 in the current version)

“the RST, in contrast, is weighted more towards slower postural control and generation of large isometric forces” (lines 724-726 in the current version)

And, we discuss other conditions where the RST is involved in large force production, such as power grip, and how these interact with the role of the RST in posture/holding control (lines 758-768 in the current version).

To better explain our model, we now provide the two examples mentioned by the reviewer along with our description of the proposed role for the RST (lines 726-727):

“…the RST, in contrast, is weighted more towards slower postural control and generation of large isometric forces (such as vertical forces for arm support, or horizontal forces for holding the arm still against a background load like in our posture/release perturbation trials).”

We note, however, that we find resting posture abnormalities even in the presence of arm support, suggesting the involvement of the RST in holding control even when the forces involved (and the need to preload the muscle) are small.

Reviewer #3 (Public Review): 

The authors attempt to dissociate differences in resting vs active vs perturbed movement biases in people with motor deficits resulting from stroke. The analysis of movement utilizes techniques that are similar to previous motor control in both humans and non-human primates, to assess impairments related to sensorimotor injuries. In this regard, the authors provide additional support to the extensive literature describing movement abnormalities in patients with hemiparesis both at rest and during active movement. The authors describe their intention to separate out the contribution of holding still at a position vs active movement as a demonstration that these two aspects of motor control are controlled by two separate control regimes.

Strengths: 

(1) The authors utilize a device that is the same or similar to devices previously used to investigate motor control of movement in normal and impaired conditions in humans and non-human primates. This allows comparisons to existing motor control studies. 

(2) Experiment 1 demonstrates resting flexion biases both in supported and unsupported forelimb conditions. These biases show a correlated relationship with FM-UE scores, suggesting that the degree of motor impairment and the degree of resting bias are related.

(3) The stroke patient participant population had a wide range of both levels of impairment and time since stroke, including both sub-acute and chronic cases allowing the results to be compared across impairment levels.

The authors describe several results from their study: 1. Postural biases were systematically toward the body (flexion) and increased with distance from the body (when the arm was more extended) and were stronger when the arm was unsupported. 2. These postural biases were correlated with FM-UE score. 3. They found no evidence of postural biases impacting movement, even when that movement was perturbed. 4. When holding a position at the end of a movement, if the position was perturbed opposite of the direction of bias, movement back to the target was improved compared to the perturbation in the direction of bias. Taken together, the authors suggest that there are at least two separate motor controls for tasks at rest versus with motion. Further, the authors propose that these results indicate that there is an imbalance between cortical control of movement (through the corticospinal tracts) and postural control (through the reticulospinal tract).

Response 3.1. Thank you for pointing out some of the strengths of our work and summarizing our findings. A minor clarification we would like to make, related to (3), is that, while our study did enroll two patients towards the end of the subacute stage (2-3 months), the rest of the population were at the chronic stage, at one year and beyond. We thus find it very unlikely that time after stroke was the primary driver of differences in impairment in the population we studied.

There are several weaknesses related to the interpretation of the results:

In Experiment 1, the participants are instructed to keep their limbs in a passive position after being moved. The authors show that, in the impaired limb, these resting biases are significantly higher when the limb is unsupported and increase when the arm is moved to a more extended position.

When supported by the air sled, the arm is in a purely passive position, not requiring the same antigravity response so will have less RST but also less CST involvement. While the unsupported task invokes more involvement of the reticulospinal tract (RST), it likely also has significantly higher CST involvement due to the increased difficulty and novelty of the task.

If there were an imbalance in CST regulating RST as proposed by the authors, the bias should be higher in the supported condition as there should be relatively less CST activation/involvement/ modulation leading to less moderating input onto the RST and introducing postural biases. In the unsupported condition, there is likely more CST involvement, potentially leading to an increased modulatory effect on RST. If the proportion of CST involvement significantly outweighs the RST activation in the unsupported task, then it isn't obvious that there is a clear differentiation of motor control. As the degree of resting force bias and FM-UE score are correlated, an argument could be made that they are both measuring the impairment of the CST unrelated to any RST output. If it is purely the balance of CST integrity compared to RST, then the degree of bias should have been the same in both conditions. In this idea of controller vs modulator, it is unclear when this switch occurs or how to weigh individual contributions of CST vs. extrapyramidal tracts. Further, it isn't clear why less modulation on the RST would lead only to abnormal flexion.

Response 3.2. Our model posits two mechanisms by which CST impairment would lead to increased RST involvement. The first – which is the one discussed by the Reviewer here - is a direct one, whereby weaker modulation of the RST by the CST leads to increased RST involvement. The second is an indirect one, whereby the incapacity of CST to drive sufficient motor output to deal with tasks eventually leads to increased RST drive.

The reviewer suggests it is likely that the unsupported task demands increased activation through both the CST and the RST. If that were the case, however, it would exaggerate the effects of CST/RST imbalance after stroke compared to healthy motor control: if task conditions (lack of support) required higher CST involvement, then CST damage would have an even larger effect. In turn, this would lead to even higher RST involvement and further diminishing the ability of CST to moderate RST. Thus, RST-driven biases would be higher in the unsupported condition.

And, given that the CST itself is damaged and has to deal with an even-increased RST activation, we would not expect that the proportion of CST involvement would outweigh RST activation, but the opposite. In fact, a series of relatively recent findings suggest just this. For example,

• Zaaimi et al., 2012  showed that unilateral CST lesions in monkeys lead to significant increases in the excitability of the contralesional RST (Zaaimi et al., 2012). Interestingly, this effect was present in flexors but not extensors, potentially explaining why less modulation and/or overactivation of the RST would primarily lead to abnormal flexion. 

• McPherson et al. (further discussed in point 2.A.23, by Reviewer 2 – Recommendations to the Authors) showed that, after stroke, contralesional activity (which would include the ipsilateral RST) increases relative to ipsilesional activity (which would include the contralateral CST)

(McPherson et al., 2018). The same study also provides evidence that FM-UE may primarily reflect RST-driven impairment. The ipsilateral(RST)/contralateral(CST) balance, expressed as a laterality index, correlated with FM-UE, with lower FM-UE for indices indicating higher RST involvement. (Interestingly, the slope of this relationship was steeper when the laterality of brain activation patterns was examined under tasks with less arm support, mirroring the steeper FM-UE vs resting bias slope when arm support is absent, as shown in our Figure 8).

• Wilkins et al., 2020 (Wilkins et al., 2020) found that providing less support (i.e. requiring increased shoulder abduction) increases ipsilateral activation (representing RST) relative to contralateral activation (representing CST).

This resting bias could be explained by an imbalance in the activation of flexors vs extensors which follows the results that this bias is larger as the arm is extended further, and/or in a disconnect in sensory integration that is overcome during active movement. Neither would necessitate separate motor control for holding vs active movement. 

Response 3.3. We do not think that either of these points necessarily argue against our model. First, the resting biases we observe are clearly pointed towards increased flexion, and can thus be seen as the outcome of an imbalance in the activation of flexors vs. extensors at rest. This imbalance between flexors/extensors can also be explained by the CST/RST imbalance posited by our conceptual model: in their study of CST lesions in the monkey, Zaaimi et al., 2012 found increased RST activation for flexors but not extensors, suggesting that RST over-involvement may specifically lead to flexor abnormalities (Zaaimi et al., 2012). Second, overcoming a disconnect in sensory integration may be one way the motor system switches between separate controllers; how this switch happens is not examined by our conceptual model.

In Experiment 2, the participants are actively moving to and holding at targets for all trials while being supported by the air sled. Even with the support, the paretic participants all showed start- and endpoint force biases around the movement despite not showing systematic deviations in force direction during active movement start or stop. There could be several factors that limit systematic deviations in force direction. The most obvious is that the measured biases are significantly higher when the limb is unsupported and by testing with a supported limb the authors are artificially limiting any effect of the bias.

Response 3.4. We do expect, in line with what the reviewer suggests, that any potential effects would be stronger in the unsupported condition. The decision to test active motor control with arm support was done as running the same Experiment 2 would pose challenges, particularly with our most impaired patients, given the duration of Experiment 2 (~2 hours, about 1 hour with each arm) and the expected fatigue that would ensue.

However, a key characteristic of our comparisons is that we are comparing Experiment 2 active control data under arm support, against Experiment 1 resting bias data also under arm support. While Experiment 1 measured biases without arm support as well, these are not used for this comparison. And, while resting biases are weaker with arm support, they are still clear and significant; yet they do not lead to detectable changes in active movement.

At the same time, we do not rule out that, if we were to repeat Experiment 2 without arm support, we could find some systematic deviation in the direction of resting bias in movement control. Our conceptual model, in fact, suggests that this may be the case, as we described in lines 618-620 of our original manuscript. The idea here is that, when arm support is not provided, the increased strength requirements lead to increased drive through the RST, to the point that posture control (and its abnormalities) spills into movement control (Figure 9). We now better clarify this position in our Discussion (lines 744-750):

“The interesting implication of this conceptual model is that synergies are in fact postural abnormalities that spill over into active movement when the CST can no longer modulate the increased RST activation that occurs when weight support is removed (i.e. resting biases may influence active reaching in absence of weight support). Supporting this idea, a study found increased ipsilateral activity (which primarily represents activation via the descending ipsilateral RST (Zaaimi et al., 2012)) when the paretic arm had reduced support compared to full support (McPherson et al., 2018).”

It is also possible that significant adaptation or plasticity with the CST or rubrospinal tracts could give rise to motor output that already accounts for any intrinsic resting bias.  

Response 3.5. This kind of adaptation – regardless of the tracts potentially involved – is an issue we examined in our experiment. As we talk about in our Results (lines 458-460 in the updated manuscript), with most of our patient population in the chronic stage, it could be likely that their motor system adapted to those biases to the point that movement planning took them into account, thereby limiting their effect. This motivated us to examine responses to unpredictable perturbations during movement (Figure 6) where we still find lack of an obvious effect of resting biases upon reaching control. We thus believe that our findings are not explained by this kind of adaptation, though we agree it would be of great interest for future work to compare resting biases and reaching control in acute vs. chronic stroke populations to examine the degree to which stroke patients adapt to these biases as they recover.

In any case, the results from the reaching phase of Experiment 2 do not definitively show that directional biases are not present during active reaching, just that the authors were unable to detect them with their design. The authors do acknowledge the limitations in this design (a 2D constrained task) in explaining motor impairment in 3D unconstrained tasks. 

Response 3.6. It is, of course, an inherent limitation of a negative finding is that it cannot be proven. What we show here is that, there is no hint of intrusion of resting posture abnormalities upon active movement in spite of these resting posture abnormalities being substantial and clearly demonstrated even under arm support. To allow for the maximum bandwidth to detect any such effects, we specifically chose to compare the most extreme instances (resting bias-wise) for each individual, and yet we did not find any relationship between biases and active reaching.

This suggests that, even if these biases could be in some form present during active movement, their effect would be minimal and thus limited in meaningfully explaining post-stroke impairment in active movement under arm support.

Note that, as we already discuss, our conceptual model (Figure 9) suggests that the degree to which directional biases would be present in active reaching may be influenced by arm support (or the specific movements examined – hence our limitation in not examining 3D movement). Thus we do not claim that this independence is absolute. Examples include the last line of the passage quoted right above, and the summary statement of our Discussion quoted below (lines 639-641):

“…which raises the possibility that the observed dissociation of movement and posture control for planar weight-supported movements may break down for unsupported 3D arm movements.”

Finally, we now more explicitly acknowledge that abnormal resting biases may influence active movement in the absence of arm support (see Response 3.4).

It would have been useful, in Experiment 2, to use FM-UE scores (and time from injury) as a factor to determine the relationship between movement and rest biases. Using a GLMM would have allowed a similar comparison to Experiment 1 of how impairment level is related to static perturbation responses. While not a surrogate for imaging tractography data showing a degree of CST involvement in stroke, FM-UE may serve as an appropriate proxy so that this perturbation at hold responses may be put into context relative to impairment.

Response 3.7. Here the Reviewer suggests we use FM-UE scores as a proxy for CST integrity. We do not think this analysis would be particularly helpful in our case for a number of reasons:

First, while FM-UE is a general measure of post-stroke impairment, it was designed to track - among other things - the emergence and resolution of abnormal synergies, a sign assumed to result from abnormally high RST outflow (McPherson et al., 2018; McPherson and Dewald, 2022). In line with this, the FM-UE scales with EMG-based measures of synergy abnormality (Bourbonnais et al., 1989). Impairments in dexterity, a sign associated with damage to the CST (Lawrence and Kuypers, 1968; Porter and Lemon, 1995; Duque et al., 2003), dissociate with synergy abnormalities when compared under arm support as we do here (Levin, 1996; Hadjiosif et al., 2022). This means that FM-UE would be a stronger proxy for RST activity and thus not a direct proxy for CST integrity particularly when one wants to dissociate RST-specific vs. CST-specific abnormalities. In fact, as we discuss in Response 3.2 above, there is a number of studies supporting this idea: for example, Zaaimi et al., 2012 show that relative RST activation – the balance between ipsilateral excitability, primarily reflecting RST, and contralateral excitability, primarily reflecting the CST, scales with FM-UE (Zaaimi et al., 2012).

Second, this kind of analysis would obscure within-individual effects, since FM-UE scores are, of course, assigned to each individual. This is the same issue as doing across-individual correlation analyses in general (see response 2.1b).Strong resting force bias would have opposite effects on opposing perturbations, averaging across subjects would occlude these effects.

Third, while FM-UE is a good measure of synergy abnormality, weakness alone could also give an abnormal FM-UE (Avni et al., 2024).

The Reviewer also suggests we use time from injury for this analysis. Time from injury can indeed potentially be an important factor. However, this analysis would not be appropriate for our dataset, since the effective variation in recovery stage within our population is limited: our sample is essentially chronic (only two patients were examined within the subacute stage – at 2 and 3 months after stroke - with everybody else examined more than a year after stroke) with the “positive” elements of their phenotype (and FM-UE itself) essentially plateaued (Twitchell, 1951; Cortes et al., 2017). We thus would not expect to see any meaningful effects of time from injury within our population. It would be an excellent question for future work to investigate both resting biases and their relationship to reaching in acute/subacute patients, and examine whether the trajectory of resting biases (both emergence and abatement due to recovery) follows the one for abnormal synergies.

It is not clear that even in the static perturbation trials that the hold (and subsequent move from perturbation) is being driven by reticulospinal projections. Given a task where ~20% of the trials are going to be perturbed, there is likely a significant amount of anticipatory or preparatory signaling from the CST. How does this balance with any proposed contribution that the RST may have with increased grip?

Response 3.8. We included our response to this as part of Response 3.2. In brief, while we cannot rule out that these tasks may recruit increased CST signaling, this would tend to increase, rather than reduce, the effects of post-stroke impairment: the requirement for increased signaling from a CST that is damaged would magnify the effects of this damage, in turn leading to increased recruitment of other tracts, such as the RST.

In general, the weakness of the interpretation of the results with respect to the CST/RST framework is that it is necessary to ascribe relative contributions of different tracts to different phases of movement and hold using limited or indirect measures. Barring any quantification of this data during these tasks, different investigators are likely to assess these contributions in different ways and proportions limiting the framework's utility.

Response 3.9. We believe that our Reponses 3.2-3.6 put our findings in fair perspective, and the edits undertaken based on the Reviewer’s comments have clarified our position as to how the dissociation between holding and moving control may break down. We do agree, however, that our framework would be strengthened by the use of direct measures of CST/RST connectivity in future research. We present our conceptual model as a comprehensive explanation of our findings and how they blend with current hypotheses regarding the role of these two tracts in motor control after stroke.  As such, it provides a blueprint towards future research that more directly measures or modulates CST and RST involvement, using tools such as tractography or non-invasive brain stimulation.

Recommendations for the authors:   

Reviewer #1 (Recommendations For The Authors):

L226 “…of this issue, we repeated the analysis of Figure 7F (a) by excluding these four patients…”.  Should this be three, based on the previous sentence? 

Response 1.A.1. Thank you for pointing this typo, which is now corrected. The analysis in question (Figure S1 in the original submission, now re-numbered as Figure S7-4), excluded the three patients mentioned in the previous sentence.

L254 “…the hand was held in a more distal position. The postural force biases were strongest when…”  Could this be "extended" rather than distal? See my later comment about the inadequate description of targets.

Response 1.A.2. The reviewer is correct that, the arm will tend to be more extended in the distal targets. However, since these positions were defined in extrinsic coordinates, we think the terms distal/proximal are also appropriate. In either case, we now clarify these definitions in the text (see Response 1.A.3 below).

L263 “…contained both distal and proximal targets, and, importantly, they were also the movement…”.  Distal/proximal targets were never described as part of the task. 

Response 1.A.3. We improved our description by (i) changing the wording above to “represented positions both distal and proximal to the body,”, (ii) doing the same in our Methods (line 175) and (iii) indicating distal/proximal targets in Figure 3A (bottom right of panel A).

L378 “…the pulse perturbation. We hypothesized that, should resting postural forces play a role, they…”  L379 “…would tend to reduce the effect of the pulse if they were in the opposite direction, and…”  Not really obvious why. A reduction in the displacement caused by a force pulse might be caused by different stiffness or viscosity, but not by a linear, time-invariant force bias. This situation is different from that of "moving the arm through a high-postural bias area vs. a low-postural bias area" where it would encounter time- (actually spatially) varying forces and varying amounts of displacement. Clarify the logic if this is a critical point.

Response 1.A.4. We thank the Reviewer for highlighting this point of potential confusion. We now clarify that these postural bias forces are neuromuscular in origin (Kanade-Mehta et al., 2023), and likely result from an expression of abnormal synergy, at least under static conditions. In this case, we hypothesized that force pulses acting against the gradient of the postural bias field would act to stretch the already active muscles, which would lead to a further increase in postural resistance due to inherent length-tension properties of active muscle. By contrast, force pulses acting along the gradient of the postural bias field would act to shorten the same active muscles, which would lead to a reduction in postural resistance. The data did not support this in the case of force pulses imposed during movement. We note, however, that similar effects would affect responses to static perturbations as well, wherein we do find an effect of resting biases. We now better explain this reasoning (lines 479482).

L466 “resting postural force). In short, our perturbations revealed that resting flexor biases switched  467 on after movement was over, providing evidence for separate control between moving” and 

L468 “holding still.”

I do not think the authors have presented clear evidence that forces, "switch on", implying the switch to a different controller which they posit. This could as easily be a nonlinear or time-varying property of a single controller (admittedly, the latter possibility overlaps broadly with their idea of distinct, interacting controllers). An example that the authors are certainly aware of is that of muscle "thixotropy" a purely peripheral mechanism due to the dynamics of crossbridge cycling that causes resting muscle to be stiffer than moving muscle, changing with a time constant of ~1-2 seconds. Neither this particular example nor changing levels of contraction (more likely during the unpredictable force perturbations) would be in the direction to explain the main observation here -- a point perhaps worth making, together with the stretch reflex comments. 

Response 1.A.5. Thank you for this perspective. Indeed, it might be that “switching on” represents a shift along a nonlinear property of the same controller: in the extreme, if this nonlinearity is a step (on/off) function, this single controller would be functionally identical to two separate controllers. We thus cannot tell if these controllers are distinct in the strict sense. What we argue here is that, no matter the underlying controller architecture - two distinct controllers or two distinct modes of the same controller - is that the control of reaching vs. holding can be functionally separable even after stroke. In line with this idea, we used a more nuanced phrasing (e.g. “separable functional modes for moving vs. holding”) throughout our manuscript, and we have now edited out a mention of “separate controllers” to be consistent with this.

Moreover, thank you for pointing out the example of thixotropy, showing how peripheral mechanisms could interact with central control. As you point out, this effect would not explain the main observation here: in fact, if stiffness were substantially higher during rest or holding (instead of moving) that would reduce the impact of the static perturbation, making it harder to detect any effects of resting biases compared to the moving perturbation case.

L480 “…during movement (Sukal et al., 2007). Yet, Experiment 2 found no relationship between resting…” L481”… postural force biases and active movement control. To further investigate this apparent…”  The methods of the two studies seem fairly similar, but this question warrants a more careful comparison. How did the size of the two workspaces compare? What about the magnitude of the exerted forces? The movement condition in this study was done with the limb entirely supported. Under that condition, the Sukal study also found fairly small effects of the range of motion.

Response 1.A.6. Sukal et al., 2007 did not directly measure exerted forces, but instead compared the active range of motion under different loading conditions. They used the extent of reach area to quantify the effect of abnormal synergies, with a more extended active range of motion signifying reduced effect of abnormal synergies. As the Reviewer points out, Sukal et al. found fairly small effects of synergies upon the range of motion when arm support was provided (the reach area for the paretic side was found to be about 85% of the nonparetic side under full arm support, though they were statistically significantly different, Figure 5 of their paper). They found increasing effect of synergies as arm support was reduced: on average, the reach area when participants had to fully support the arm was less than 50% the reach area when full arm support was given (comparing the 0% vs. 100% active support conditions [i.e. 100% vs. 0% external support] in their Figure 5). As we discuss in our paper, this effect of arm support upon synergy mirrors the one we found for resting postures.

To compare our workspace with the one in Sukal et al., we overlaid our workspace (the array of positions for which the posture biases were measured, for a typical participant from Experiment 1) on the one they used as shown in their Figure 4. Note that their figure only shows an example participant, and thus our ability to compare is limited by the fact that each participant can vary widely in terms of their impairment, and assumptions had to be made to prepare this overlay (e.g. that (0,0) represents the position of the right acromion point). 

For this example, and our assumptions, our workspace was smaller, with the main points of interest (red dots, the movement start/end points used for Experiment 2) within the Sukal et al. workspace. That our workspace is smaller is not surprising, given that the area in Sukal et al. represents the limit of what can be reached, and thus motor control *has* to be examined in a subset of that area.

Author response image 1.

Comparing the two study methodologies, however, suggests an advantage of measuring resting biases in terms of sensitivity and granularity: first, resting biases can be clearly detected even under arm support (something we point out in our Discussion, lines 715-717); second, they can measure abnormalities at any point in the workspace, rather than a binary within/without the reach area. The resting bias approach may thus be a more potent tool to probe the shared bias/synergy mechanisms we propose here.

Figure 2 

Needs color code. 

The red dots could be bigger.

Response 1.A.7. We have increased the size of the red dots and added a color code to explain the levels illustrated by the contours. We also expanded our caption to better explain this illustration.

Figure 3

Labeling is confusing. Drop the colored words (from both A and B), and stick to the color legend. Consider using open and filled symbols (and bars) to represent arm support or lack thereof. The different colored ovals are very hard to distinguish.

Response 1.A.8. We find these recommendations improve the readability of Figure 3 and we have thus adopted them - see updated Figure 3.

Figure 4

Not terribly necessary.  

Response 1.A.9. While this figure is indeed redundant based our descriptions in the text, we kept it as we believe it can be useful in clarifying the different stages of movement we examine.

Figure 5 

Tiny blue and green arrows are impossible to distinguish. 

Although the general idea is clear, E and H are not terribly intuitive.  Add distance scale bars for D-I. 

Response 1.A.10. For improved contrast, we now use red and blue (also in line with comment below regarding Figure 7), and switched to brighter colors in general. To make E and H more intuitive and easier to follow, we expanded the on-panel legend. Thank you for pointing out that distance scale bars are missing; we have now added them (panels EFHI).

Figure 6 

Panel E inset is too small. 

Response 1.A.11. We have now moved the inset to the right and enlarged it.

Figure 7 

Green and blue colors are not good. 

Response 1.A.12. For improved contrast, we now use red and blue.

Figure 8 

Delete or move to supplement? 

Response 1.A.13. We respectfully disagree. While the relationships on these data are also captured by the ANOVA, we believe these scatter plots offer a better overview of the relationships between force biases and FM-UE across different conditions.

Really minor

L113 “…participants' lower arm was supported using a custom-made air-sled (Figure 1C). Above the  participant's…” 

Response 1.A.14. We put the apostrophe after the s so to refer to participants in general (plural).

L117 ”…subject-produced forces on the handle were recorder using a 6-axis force transducer.”  recorded 

Response 1.A.14. Thank you for pointing out this error which we have now corrected.

L136 “…2013), Experiment 1 assessed resting postural forces by passively moving participants to>…”  The experiment did not move the participant. 

Response 1.A.15. We now fix this issue: “by having the robot passively move…”

L248 “…experiment blocks: two with each arm, with or without arm weight support (provided by an air experimental…”

Response 1.A.16. We have now corrected this.

L364 “…responses to mid-movement perturbations. In 1/3 of randomly selected reaching movements…”  Obviously, you mean 1/3 of all movements: "One-third of the reaching movements were chosen randomly"  

Response 1.A.17. We now clarify: “In 1/3 of reaching movements in Experiment 2, chosen randomly”. Also please note our response to Reviewer 2, point 10: we now report the exact number of trials for which each kind of perturbation was present.

L609 “Damage to the CST after stroke reduces its moderating influence upon the RST (Figure 9,…”  "its" refers to the subject, "Damage", not "CST".

Response 1.A.18. We have changed this to “Post-stroke damage to the CST reduces the moderating influence the CST has upon the RST”.

Reviewer #2 (Recommendations For The Authors):

(1) Throughout, the authors cleverly selected the most opposed and most aligned resting postural force biases to perform a within-subject analysis. However, this approach excludes a lot of data. The authors could perform an additional within-subject analysis. For each participant they could correlate lateral resting posture force bias to each dependent variable, utilizing all the trials of a participant. 

Response 2.A.1a. Thank you for your appreciating our analysis design, and suggesting additional analyses. We focused our within-subject analysis design on the most extreme instances, as we believe that this approach would offer the best opportunity to detect any potential effects of resting biases. We reasoned that, since resting biases tend to be relatively small for most locations in the workspace, taking all biases into account would inject a disproportionate amount of noise in our analysis, which would in turn diminish our ability to detect any potential relationships. This could be because small biases lead to small effects but also small biases may themselves be more likely to reflect measurement noise in the first place. Note that our study talks about separability of active reaching from resting abnormalities based on lack of relationships between the two. While one cannot definitely prove a negative, it is also important to take the approach that maximizes the ability to detect any such relationship if there were one. We believe taking the most extreme instances fulfills that role.

However, as the Reviewer points out, this approach also excludes a substantial amount of data. We agree that our findings could be further strengthened by exploring additional within-subject analyses that utilize all trials. Thus, following the reviewer’s suggestion, we estimated the sensitivity of each dependent variable to lateral resting posture force bias. Specifically, we estimated the slope of this relationship for each individual (separately for paretic and non-paretic data) using linear regression, and assessed whether the average slope is significant for each group (paretic data, non-paretic data, and control data).

This secondary analysis replicated our main findings: lack of relationship between posture biases and active reaching control (both for unperturbed and perturbed movement), and a significant relationship between posture biases and active holding control. In addition, in line with main point 2.1 by the reviewer, we performed the same analyses for non-paretic and control data. While there are no definitive conclusions to be made for these cases (as was likely, given that the resting force biases are smaller, as also pointed out by the Reviewer in 2.1) these data are worthy of discussion, with potentially interesting insights (for example, there are hints that the connection between resting biases and active holding control is present in the non-paretic arm as well, and may be explored in future research).

We have included these analyses in the supplementary materials, and we point to them in the main text. Specifically:

First, in line with our main analyses in Figure 5, we find no effect (the average slope is insignificant) for start and endpoint biases upon the corresponding reaching angles. This is now mentioned in lines 425-434 of the Results, and illustrated in Figure S5-2. There was a lack of effect for the non-paretic and control data as well.

Second, in line with our main analyses in Figure 6, we find no effect of start biases upon responses to the pulse (Figure S6-2, mentioned in lines 513-517 of the Results). As above, there was no effect of non-paretic or control data either.

And, finally, in line with our main analysis in Figure 7, we find an effect of resting biases upon performance for the static perturbation (Figure S7-2, mentioned in lines 578-586 of the Results). Interestingly, there is a suggestion that resting biases may affect static perturbation responses in the non-paretic data as well based on the relationship between posture bias and maximum deviation, but not the other two metrics. Given the lack of consistency of resting bias effects for all three different dependent variables examined, however, our current data are thus unable to give a definite answer as to whether there is the connection between resting biases and active holding control is also present in the non-paretic side. Our hypothesis is that, since resting abnormalities and their effects are the pathological over-manifestations of mechanisms inherent in the motor system in general, then such a relationship would exist. Answering this question, however, would require an experiment design better tailored to detect relationships in the non-paretic arm, where resting biases are weaker.

We thank the Reviewer for their suggestions and believe that these additional analyses provide a more complete picture of the data, and their consistency with our main results reinforces the message of the paper.

Then, they can report the percentage of participants that display significant correlations separately for the paretic, nonparetic, and control arms. 

Response 2.A.1b. We note that, even in cases where the average slope (across individuals) is significant, the individual slopes themselves are usually not significant, likely due to the large amount of noise for datapoints corresponding to weak resting biases. To further examine this, we performed additional analyses whereby we examined slopes by (a) pooling all participant data together (centered separately for each individual), and then (b) took a further step to normalize each participant’s data not only by centering but by also adjusting by each individual’s variability along each axis (i.e. assess the slope between z-scores of resting bias vs. z-scores of each dependent variable). These two analyses confirmed our finding that resting biases interacted with active motor control, with significant slopes between resting biases and outcome variables. (a) Pooling all data together: path to stabilization: p = 0.032; time to stabilization: p = 1.4x10-5; maximum deviation: p = 0.021. (b) Pooling and normalizing: path to stabilization: p = 0.0013; time to stabilization: p = 8.6x10-6; maximum deviation: p = 0.00056. The latter analysis showed even stronger connection between resting bias and active holding control, probably due to better accounting for differences in the range of resting biases across participants). For simplicity, however, we only provide the across-individual slope comparisons in the paper.

(2) An important aspect of all the analyses is that they rely heavily on estimates of the resting postural force bias. How stable are these resting postural force biases at the individual level? The authors could assess this by reporting within-subject variance for both the magnitude and direction of the resting postural force bias.

Response 2.A.2. Thank you for your suggestion. We now assess the individual-level variance in error across measurements for patients’ paretic data using an ANOVA: the variance that remains after all other factors (same probe location; same arm support condition; same participant) are taken into account. We found that individual level measurement variance explained a mere 9.0% of total variance for resting bias magnitude. (We note that the same figure was 20.2% for the non-paretic data, in line with the weaker average biases which would be more susceptible to noise). We now note this in the Methods, as part of the new subsection “Stability of resting posture bias measurements in Experiment 1” (lines 266-273).

(3) Does resting postural force bias influence hand movement immediately following force release from the postural perturbation? This could be assessed before any volitional responses by examining the velocity of the hand during the first 50 ms following the postural perturbation.

Response 2.A.3. The influence seems fairly rapid, within the first 100ms as shown to the right. Here we plot hand deviation in the direction of the perturbation for the most-opposed (red) vs. most-aligned (blue) instances to examine when these curves become different. The bottom plots show the difference between these two, whereas shading indicates SEM (note that these curves are referenced to the average deviation in the last 0.5 s before force release). The rightmost plots zoom in to make it easier to see how responses to the most opposed vs. most aligned instances diverge.

To detect the earliest post-perturbation timepoint for which this effect was significant, we performed paired t-tests at each timestep, and found that the two responses were systematically statistically different 95ms after perturbation onset onwards. For reference, the same method detected a response at 25ms for the most aligned instances and 40ms for the most opposed instances.

We have now added Supplementary Figure S7-4 with short commentary in the Supplementary Materials.

(4) Abstract. lines 7-9. At a glance (and when reading the manuscript linearly) this sentence is unclear. If the paretic arm is compromised across rest and movement, how does that afford the opportunity to address the relationship between reaching, stopping, and stabilizing when all could be impacted? It might be useful to specify that these factors may impacted differently relative to one another with stroke, providing an opportunity to better understand the differences between movement and postural control. 

Response 2.A.4. Thank you for pointing out this issue (also related to Reviewer 1’s point – Response 1.1). We have changed this to more clearly reflect our reasoning and highlight that the issue is that stroke can differentially impact reaching vs. holding, copied below:

“The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke.”

(5) Line 27. It is perhaps more appropriate to say conceptual model than simply 'model'.  

Response 2.A.5. Thank you for your suggestion, which we have adopted throughout the manuscript.

(6) Line 122-125. Figure 1A caption. The authors should specify that resting posture force biases occur when the limb or hand is physically constrained in a specific position. 

Response 2.A.6. Thank you for pointing this out – we have clarified the caption:

“If one were to physically constrain the hand in a position away from the resting posture, the torques involved in each component of the abnormal resting posture translate to a force on the hand (blue arrow);”

(7) Line 147. Why was the order not randomized or counterbalanced? 

Response 2.A.7. We prioritized paretic data, as the primary analyses and comparisons in our paper involved resting posture biases and active movement with the paretic arm. We note that our primary analyses, which rely on paretic-paretic comparisons, would not be affected by paretic vs. non-paretic ordering effects. However, ordering effects could potentially affect comparisons between paretic and non-paretic data. We now note the reasoning behind the absence of counterbalancing, and mention the potential limitation in interpreting paretic to non-paretic comparisons in lines 124-129 of the Methods.

(8) Line 172. 12N is the peak force of the pulse?

Response 2.A.8. The reviewer is correct; we have clarified our description (line 463 in the updated manuscript):

“a 70 ms bell-shaped force pulse which was 12N at its peak”

(9) Line 175. What is a clockwise pulse? Was the force vector rotating in direction over time so that it was always acting orthogonally to the movement, or did it always act leftwards or rightwards?

Response 2.A.9. The force vector was not rotating in direction over time. Here, we used clockwise/counterclockwise to indicate rightwards/leftwards with respect to the ideal movement direction – the line from start position to target (which is what we understand the Reviewer means by “always act rightwards or leftwards”). We have clarified the text to indicate this (lines 193-195):

…was applied by the robot lateral to the ideal movement direction (i.e. the direction formed between the center of the start position and the center of the target) after participants reached 2cm away from the starting position (Smith and Shadmehr, 2005; Fine and Thoroughman, 2006).

(10) Lines 177-182. It might be useful to explicitly mention the frequency of each of the perturbations, just for ease of the reader. 

Response 2.A.10. We have added this information to our Methods (lines 206-210):

Thus, in summary, each 96-movement block consisted of 64 unperturbed movements and 32 movements perturbed with a force pulse (16 clockwise, and 16 counter-clockwise). For 20 out of the 96 movements in each block, the hold period was extended to test the hold perturbation (4 trials for each of the 5 target locations, each one of the 4 trials testing one perturbation direction as shown in Figure 7C).

(11) Line 191. Lines 188-190. It would be useful to see a sample of several of these force traces over time (0-5s) that were used to make the average for a position. That would give insight into the stability of the forces of a participant for one of the postures. These traces could be shown in Figure 2.

Response 2.A.11. Thank you for your suggestion. We have added these panels to Figure 1, (as Figure 2 was already large). Each panel illustrates the three measurements taken at similar positions (closest to midline, distal from the body) and the same condition (paretic arm, with arm support given) for one participant (same participants as in Figure 2). Solid lines indicate the force on the x-axis (positive values indicate forces towards the left), whereas dashed lines indicate the force on the y-axis (positive values indicate forces towards the body). The shaded area indicates the part averaged in order to estimate the resting bias, illustrating how resting biases were relatively stable by the 2s mark. Note that these examples include one trial (blue traces in the third panel) which was rejected following visual inspection as described in Materials and Methods – Data Exclusion Criteria (“trials where forces appeared unstable and/or there was movement during the robot hold period”). We find this helpful as this illustrates (and motivates) one component of our methodology. 

(12) Line 196. Figure 1D (not 1E).  

Response 2.A.12. Thank you for catching this error, which we have now corrected.

(13) Line 215: The authors mentioned similar results. Were there any different results that impacted interpretation? Some evidence of this, similar to and in addition to Supplementary 1, would be helpful. 

Response 2.A.13. We repeated our analyses without these exclusion criteria, with no impact to the interpretation. We now include versions of the main outcome panels from Figures 5, 6, and 7 in the supplementary materials calculated without this outlier exclusion (Figures S5-E, S6-E, and S7-E, respectively). 

(14) Line 231: Perhaps better to explicitly state the furthest three positions are being across as the distal targets for the ANOVA. 

Response 2.A.14. Thank you for your suggestion. We now explicitly clarify this in line 276:

“distal targets [furthest three positions] vs. proximal targets [closest two positions]”

(15) Figure 3B, lines 265. Clearly, these are different, but the authors should report statistics. 

Response 2.A.15. We now report these numbers (lines 339-346 of the revised manuscript, which also include statistics related to bias direction as described in 2.A.17 below).

(16) Figure 2 should have a heat map scale.  

Response 2.A.16. We have now added this (also Response 1.A.7), including an explanation of what the heat map represents in the caption.

(17) Figure 3C: It would be useful to quantify and plot the direction of the resting force bias vector. 

Response 2.A.17. Thank you for your suggestion. We have expanded Figure 3 to include the average direction of the resting force bias vector (note the readjustment of colors following Reviewer 1’s comment: striped bars indicate No Support data, and full bars indicate Support data, with the colors being the same). The direction of the force bias vector, however, may not be very informative in cases where the magnitude is small (and the signal-to-noise ratio is small), whereas averaging the direction of the force bias vector across different positions for one participant may average out systematic variations in this direction across different locations. Nevertheless, the average direction appears generally towards the body (around -90°, or 6 o’clock) even in the non-paretic and control data (though the noise – as suggested by the size of the errorbars – is much higher in the latter cases, especially when the arm is supported). This is a (weak) suggestion that these resting biases may be present, though much subdued, in the nonparetic limb and healthy individuals; further work will be needed to elucidate this.

(18) Line 428. It is not significantly longer compared to controls. Can the authors slightly revise this sentence?

Response 2.A.18. We have revised this sentence (lines 529-532):

Patients showed impaired capacity to resist and recover from this perturbation (the abrupt release of the imposed force). The time to stabilization for the paretic side (0.94±0.05s) was longer compared to the non-paretic side (0.79±0.03s, p = 0.024) and controls (0.78±0.06s, though this was statistically marginal, p = 0.061) as shown in Figure 7E, left.

(19) Line 541. It is unclear how these data support the idea of three distinct controllers. Can the authors please clarify? 

Response 2.A.19. Here, we compared our findings to previous ideas about distinct controllers, and discuss a potential fusion of these ideas with ours. Specifically, we find that holding is distinct from both initial reaching and coming to a stop. Previous work argues that initial reaching and coming to a stop are themselves distinct (Ghez et al., 2007; Jayasinghe et al., 2022). Combining these two sets of arguments, we arrive at the possibility of three distinct controllers. 

(20) It would be useful if the authors provided a definition of synergy, as well as distinguishing between muscle and movement synergies. 

Response 2.A.20. We now provide this in lines 591-594:

Here, “synergies” refer to abnormal co-activation patterns across joints that manifest as the patient tries to move – for example, the elbow involuntarily flexing as the patient tries to abduct their shoulder (Twitchell, 1951; Brunnstrom, 1966). 

(21) Line 592-593. The wording of this sentence could be improved. 

Response 2.A.21. We have switched this sentence to active voice for more clarity:

Thus, while full weight support reduces both resting flexor biases and movement-related flexor synergies, this reduction seems more complete for synergies rather than resting biases.

(22) Figure 9. In the left column, it should read normal synergies and normal resting posture.  

Response 2.A.22. We intentionally used the same terminology, as the idea behind our conceptual model is that these patterns, which manifest as well-recognized abnormal synergies and abnormal resting postures in stroke, may be present in the healthy motor system as well, but kept in check by CST moderating the RST. At the same time, we recognize that, by definition, synergies and posture in controls are the “normal” reference point against which “abnormal” synergies and posture are defined after stroke. To clarify this issue, we thus decided to forgo the use of the terms “abnormal” in the figure, and instead refer to “synergistic movement ” and “synergistic resting posture”.

(23) Figure 9. With stroke, is RST upregulated, a decreased influence of CST, or both? All seem plausible.

Response 2.A.23a. We believe both can be happening. From previous work (e.g. McPherson et al., 2018) it seems safe to say that RST upregulation is the case, whereas one would also expect a decreased CST influence due to its damage due to the stroke. The relative weight of these influences would be interesting to elucidate in future work.

I have not read the paper, but did McPherson et al., 2018 test these different hypotheses?  

Response 2.A.23b. The main point of McPherson et al., 2018 is that increased synergy expression is due to increased RST involvement, rather than reduced CST influence. However, McPherson et al. do not show separate increases/reductions in RST/CST activity; they show that contralesional activity relative to ipsilesional activity is increased (using a laterality index). While it does seem that RST is upregulated in this case, this does not exclude the possibility that CST influence is reduced as well.

We also noticed that the citation itself, while mentioned in the text, was missing from the bibliography. This is now fixed.

For Figure 9, McPherson is cited as they provide evidence for the idea that RST involvement increases when arm support is decreased. This evidence is both direct (e.g. in their Figure 3 where they show that “Stroke participants exhibited increased activity in the contralesional (R) hemisphere as SABD loading increased” [i.e. arm support was reduced]) and indirect: they connect synergies to RST involvement, and also show increased synergies with reduced arm support (also shown multiple times previously). Both these arguments suggest that arm support reduces RST involvement. We have clarified the relevant sentence:

The interesting implication of this conceptual model is that synergies are in fact postural abnormalities that spill over into active movement when the CST can no longer modulate the increased RST activation that occurs when weight support is removed. Supporting this idea, McPherson et al. found increased ipsilateral activity (which primarily represents activation via the descending RST (Zaaimi et al., 2012)) when the paretic arm had reduced support compared to full support (McPherson et al., 2018).

Reviewer #3 (Recommendations For The Authors):

For Experiment 2, it is not immediately clear how the within-subject values are being pooled and compared across the different conditions. For instance, in the static perturbation trials, there are four blocks with 20 perturbation trials per block per arm (80 total per arm) with each location and direction once per block. For each participant, the comparison is between the location/direction that was most opposed (although this doesn't look accurately represented in Fig 7F). Therefore, the within-subject comparison is 4 trials per participant? Were these values averaged or pooled? It is a little odd that the SD for all the within-subjects trials are identical or nearly identical across conditions especially when looking at the example patient data in 7B and 7F.  

Response 3.A.1. For static perturbation trials, the within-subject comparison involves 8 trials per participant: 4 trials corresponding to the perturbation direction/position combination with resting bias most opposed to the perturbation, and 4 trials corresponding to the perturbation direction/position combination with resting bias most aligned with the perturbation. These values were averaged for each individual. We have expanded our methods to make this part of our data analysis clear (lines 284-296) for all types of comparisons (unperturbed movement, pulse perturbation, static perturbations – now referred to as “release perturbation”).

The across-subject SDs for the average resting forces for each one of these two conditions, shown in Figure 7F are indeed identical. This is due to how these two instances (most aligned vs. most resistive) were selected: because the perturbation directions come in pairs that exactly oppose each other (Figure 7B), if one were to select the position with the most opposing resting bias, that would mean that the combination with same position and the oppositely-directed perturbation would be the one with the most assistive resting bias. Hence the resting biases selected for the most opposing/assistive instances would be equal in magnitude and opposite to each other for each participant, as illustrated in Figure 7F, whereby the most-opposed bias for each individual is exactly opposite to the corresponding most-aligned bias for the same individual. We have added a brief commentary about this on the caption (lines 551-554), reproduced below:

Note how the most-opposed resting bias for each patient is equal and opposite to the their mostaligned resting bias. This is because the same resting bias, when projected along the direction of two oppositely-directed perturbations (illustrated in C), it would oppose one with the same magnitude it would align with the other.

Importantly, following suggestions by Reviewer 2 (see point 2.A.1), we now provide supplementary analyses that use the entirety of the relevant data, rather than the most extreme instances, which provide evidence supporting our main findings (Figures S5-2, S6-2, and S7-2).

The printed colors in Figure 3 are very muddled and hard to read/interpret, especially in panel A. 

Response 3.A.2. Thank you for pointing out this issue, also raised by Reviewer 1. We have adjusted the colors to be more distinct from each other and look clear both in print and on-screen, making use of dashed lines and stripes rather than different shades.

I think it would improve readability and interpretation if Figure 8 and the results related to FM-UE were contained within the description of results for Experiment 1.

Response 3.A.3. Thank you for this suggestion. This is actually a debate we had among ourselves earlier, and we can see merits to either ordering. It is very arguable that moving Figure 8 and the FMUE results within the rest of Experiment 1 may improve readability somewhat. However, we believe that presenting these results at the end better serves to illustrate the apparent paradox between the lack of direct connection between resting biases and active movement on one hand, and the relationship between resting biases and abnormal synergies on the other. We believe that this better sets the stage to present our conceptual model, which explains this paradox based on the role arm support plays in modulating the expression of both resting biases and abnormal synergies.

Additional changes/corrections not outlined above

Figure 1D displayed a right arm, but showed a target array (red dots) for a left arm paradigm. We now flip the target array shown for consistency.

We corrected Figure 6C, which accidentally used an earlier definition of settling time which was based on lateral stabilization throughout the entire movement, rather focus on the period immediately following the pulse. The intended definition of settling time (as we had described in the Methods, lines 204-206 of original submission) focuses on lateral corrections specific to the pulse (rather than corrections when the participant approaches the endpoint) and better matches the one for settling time for the release (static) perturbation trials. Note that this change did not affect the (lack of) relationship between settling time and resting force bias, both across individuals (correlation plots now in Figure S6-1) and within individuals (now shown in the right part of panel 6D). Also in panel C, an error in the scaling for the maximum lateral deviation in the pulse direction (right side of the panel) is also now corrected.

In addition, we made minor edits throughout the text to improve readability.

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Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

In their manuscript, Gerlevik et al. performed an integrative analysis of clinical, genetic and transcriptomic data to identify MDS subgroups with distinct outcomes. The study was based on the building of an "immunoscore" and then combined with genotype and clinical data to analyze patient outcomes using multi-omics factor analysis. 

Strengths: Integrative analysis of RNA-seq, genotyping and clinical data 

Weaknesses: Validation of the bioinformatic pipeline is incomplete 

Major comments: 

(1) This study considered two RNA-seq data sets publicly available and generated in two distinct laboratories. Are they comparable in terms of RNA-seq technique: polyA versus rRNA depletion, paired-end sequencing, fragment length? 

We want to reemphasize that the main point of this study is not to compare the BMMNC with the HSPC cohort. These datasets are not comparable because they were

collected from different cell types, and we should not expect them to be matched. We just analysed them in parallel to check how much HSPCs contribute to the molecular signatures we see in BMMNC samples. However, we agree with the reviewer that similar RNA-seq experimental techniques should be employed to control for confounding factors. Here is the information that we found for HSPC and BMMNC RNA-seq studies:

HSPC RNA-seq cohort: Total RNA was extracted using TRIzol (Thermo Scientific), and Sequencing was performed on an Illumina HiSeq4000 with 100-bp paired-end reads.

BMMNC RNA-seq cohort: The RNA was extracted with TRIzol reagent (Thermo Scientific). RNA-sequencing libraries were prepared from poly(A)-selected RNA and were sequenced using Illumina HiSeq 2000 or 2500 platform with 100-bp paired-end reads. 

The only difference between the two cohorts is that one cohort includes total RNAs, whereas the other has polyA-selected RNAs. Since the gene set signatures use the expression of proteincoding genes, which all have polyA tails and are included in total RNA libraries, the analysis will not be affected by total vs. polyA-selected RNA-seq techniques. 

(2) Data quality control (figure 1): the authors must show in a graph whether the features (dimensions) of factor 1 were available for each BMMNC and CD34+ samples.  

By features of Factor 1, we think the reviewer means the features with high weights for Factor 1 in BMMNC and CD34+ samples. Figure 2c-d clearly illustrates the important features and their associations with Factor 1 for all samples in both cohorts. The samples are the columns of the two heatmaps.

(3) How to validate the importance of "immunoscore"? If GSEA of RNA-seq data was performed in the entire cohort, in the SF3B1-mutated samples or SRSF2-mutated samples (instead of patients having a high versus low level of factor 1 shown in Sup Fig. 4), what would be the ranking of Hallmarks or Reactome inflammatory terms among the others? 

Our GSEA analysis was an attempt to validate the importance of our identified factors. As described in the paper, Factor 1 represents a combination of immunology scores (or  “immunoscores”) in CD34+ cohort. Applying GSEA, we identified upregulation of inflammation related pathways, chemokines, and Neutrophils in patients having high (4th quartile) versus low (1st quartile) levels of Factor 1. Interestingly, sorting patients by Factor 1 resulted in similar pattern based on gene signature scores (Figure 2d).    

To show that Factor1 generated by MOFA is important and different from known MDS categories such as SF3B1 and SRSF2 mutants, we performed GSEA in SF3B1-mutated vs. SF3B1-WT samples and SRSF2-mutated vs. SRSF2-WT samples in the CD34+ cohort. As shown in Author response image 1, we did not see the upregulation of inflammation and interferon pathways in SF3B1 and SRSF2 mutant MDS.

Author response image 1.

GSEA showed no upregulation of inflammation and interferon pathways for SF3B1 and SRSF2 mutant in CD34+ cohort.  

(4) To decipher cell-type composition of BMMNC and CD34+ samples, the authors used van Galen's data (2019; supplementary table 3). Cell composition is expressed as the proportion of each cell population among the others. Surprisingly, the authors found that the promonocytelike score was increased in SF3B1-mutated samples and not in SRSF2-mutated samples, which are frequently co-mutated with TET2 and associated with a CMML-like phenotype. Is there a risk of bias if bone marrow subpopulations such as megakaryocytic-erythroid progenitors or early erythroid precursors are not considered? 

We thank the reviewer for their insightful comment about CMML and the high prevalence of SRSF2 mutation (> 45%) in CMML cases. Using single-cell RNA sequencing and high-parameter flow cytometry, Ferrall-Fairbanks et al. (DOI: 10.1158/2643-3230.BCD-21-0217) recently showed that CMML can be classified into three differentiation trajectories: monocytic, megakaryocyte-erythroid progenitor (MEP), and normal-like. One hallmark of monocytic-biased trajectory was the enrichment of inflammatory granulocyte–macrophage progenitor (GMP)-like cells, which we observed through our analysis for SRSF2 mutants (Figure 6a).

Unfortunately,  van Galen's data does not provide any gene set for MEP, and there is no singlecell RNA-seq atlas for MDS to employ to calculate the MEP score. Also, we compared the Promono-like and GMP-like gene sets from van Galen's data, and we could not find any overlap, meaning that Promono-like is not specific enough to capture the signatures coming from the more differentiated progenitors such as GMPs. Therefore, as described in the paper, we focused on GMP-like rather than Promono-like.

(5) Figures 2a and 2b indicated that the nature of retrotransposons identified in BMMNC and CD34+ was dicerent. ERVs were not detected in CD34+ cells. Are ERVs not reactivated in CD34+ cells? Is there a bias in the sequencing or bioinformatic method?  

As described above, the two cohorts' sequencing methods, read length, etc., are identical.

CD34+ RNA-seq is total RNA-seq that includes both polyA and non-polyA RTE transcripts.

Therefore, the chance of bias and missing RTE signatures in CD34+ cohort is very low. L1 and Alu, which are shared between the two cohorts, are the two RTE families that are still active and make new insertions in humans. Our interpretation is that ERV activation in BM is associated with immune cells. As shown by Au et al. (DOI: 10.1016/j.ccell.2021.10.001), several ERV loci had expression in purified immune cell subsets in renal cell carcinoma samples, potentially explaining ERV upregulation in tumours responding to treatment as those biopsies had increased tumour infiltration.

(6) What is the impact of factor 1 on survival? Is it dicerent between BMMNC and CD34+ cells considering the distinct composition of factor 1 in CD34+ and BMMNC? 

As shown in Table 1, Factor 1 in the BMMNC cohort is associated with overall survival (P-val < 0.05) when we did multivariate analysis but not univariate analysis. We did not observe any association between Factor 1 and event-free survival in the BMMNC cohort. Also, The 10 factors identified by MOFA in BM CD34+ cohort did not show any significance associated with MDS overall survival (Supplementary Table 5). 

(7) In Figure 1e, genotype contributed to the variance of in the CD34+ cell analyses more importantly than in the BMMNC. Because the patients are dicerent in the two cohorts, dicerences in the variance could be explained either by a greater variability of the type of mutations in CD34 or an increased frequency of poor prognosis mutations in CD34+ compared to BMMNC. The genotyping data must be shown.  

The genotype has already been reported in Supplementary Table 2. In fact, the number of inspected genes was much higher in the BMMNC cohort (17 genes) compared to the CD34+ cohort (3 genes). Therefore, we have more significant variability of the type of mutations in the BMMNC cohort compared to the CD34+ cohort. For the CD34+ cohort, we only had mutations for three spliceosome genes, where most cases (n=28) were SF3B1 mutants with good prognosis. We think that the result makes sense because the less genetic variability, the more homogenous groups and the more chance that one factor or a group of factors can explain the genetic variance.   

(8) Fig. 2a-b: Features with high weight are shown for each factor. For factor 9, features seemed to have a low weight (Fig. 1b and 1c). However, factor 9 was predictive of EFS and OS in the BMMNC cohort. What are the features driving the prognostic value of factor 9? 

As shown in Figure 3b, The main features are RTE expression from LTR:ERV1, SINE:MIR, and SINE:Alu family.  

(9) The authors also provided microarray analyses of CD34+ cell. It could be interesting to test more broadly the correlation between features identified by RNA-seq or microarrays. 

The microarray data did not come with any genetic information or clinical data except survival information. Therefore, we could not apply MOFA on Microarray data. However, we did generate gene signature scores from Microarray data and investigated the relationship between inflammatory chemokines and cytokines, and IFN-I signature scores with MDS survival (Figure 3c and 4c).    

(10) The authors should discuss the relevance of immunosenescence features in the context of SRSF2 mutation and extend the discussion to the interest of their pipeline for patient diagnosis and follow up under treatments. 

We have added the below text to the discussion:

Recent studies have shown that the expression of programmed death-ligand 1 (PD-L1) protein is significantly elevated in senescent cells (DOIs: 10.1128/mcb.00171-22, 10.1172/JCI156250, 10.1038/s41586-022-05388-4). Increased PD-L1 protein levels protect senescent cells from being cleared by cytotoxic immune cells that express the PD-1 checkpoint receptor. In fact, activation of the PD-1 receptor inhibits the cytotoxic capabilities of CD8 + T and NK cells, increasing immunosenescence.   

Notably, patients with MDS who possess particular somatic mutations, such as those in the TP53, ASXL1, SETBP1, TET2, SRSF2, and RUNX1 genes, have an increased propensity to react favourably to PD-1/PD-L1 inhibitors (DOIs: 10.1111/bjh.17689, https://doi.org/10.1182/blood2020-141100) confirming that many cellular and molecular mechanisms, known to promote cellular senescence, including alteration of splicing machinery, are crucial stimulators of the expression of PD-L1 protein. Interestingly, in our analysis, we also observed a correlation between the senescence gene signature score and the expression of the PD-L1 gene in CD34+ cells (Supplementary Figure 7), supporting the previous findings linking PD-L1 gene expression to cellular senescence.

The immunology and ageing features extracted from the MDS transcriptomic data used in our analysis pipeline can enhance the conventional risk-scoring systems for MDS by providing new insights into this disease, particularly in the context of inflammation and ageing. For some patients, the clinical and genetic features may remain relatively the same until follow-up. Still, the transcriptomic features might differ considerably from the baseline diagnosis, affecting the course of treatment.    

Reviewer #2 (Public Review): 

The authors performed a Multi-Omics Factor Analysis (MOFA) on analysis of two published MDS patient cohorts-1 from bone marrow mononuclear cells (BMMNCs) and CD34 cells (ref 17) and another from CD34+ cells (ref 15) --with three data modalities (clinical, genotype, and transcriptomics). Seven different views, including immune profile, inflammation/aging, Retrotransposon (RTE) expression, and cell-type composition, were derived from these modalities to attempt to identify the latent factors with significant impact on MDS prognosis. 

SF3B1 was found to be the only mutation among 13 mutations in the BMMNC cohort that indicated a significant association with high inflammation. This trend was also observed to a lesser extent in the CD34+ cohort. The MOFA factor representing inflammation showed a good prognosis for MDS patients with high inflammation. In contrast, SRSF2 mutant cases showed a granulocyte-monocyte progenitor (GMP) pattern and high levels of senescence, immunosenescence, and malignant myeloid cells, consistent with their poor prognosis. Also, MOFA identified RTE expression as a risk factor for MDS. They proposed that this work showed the efficacy of their integrative approach to assess MDS prognostic risk that 'goes beyond all the scoring systems described thus far for MDS'. 

Several issues need clarification and response: 

(1) The authors do not provide adequate known clinical and molecular information which demonstrates prognostic risk of their sample cohorts in order to determine whether their data and approach 'goes 'beyond all the scoring systems described thus far for MDS'. For example, what data have the authors that their features provide prognostic data independent of the prior known factors related to prognosis (eg, marrow blasts, mutational, cytogenetic features, ring sideroblasts, IPSS-R, IPSS-M, MDA-SS)? 

We agree with the reviewer that we did not generate a new cumulative risk score and compare it with the conventional risk scores for MDS. However, we identified individual MOFA factors, which are risk or protective factors for MDS, based on survival analysis in the BMMNC cohort. One reason that we did not generate our independent, cumulative score and compare it with other scores was that we did not receive any conventional risk score for the BMMNC cohort. However, we had access to all the clinical and genetic variables from the BMMNC cohort (except for three patients) that were required to calculate IPSS-R; hence, we calculated the IPSS-R in our resubmission for the BMMNC cohort. We made three IPSS-R risk categories by combining low and very low as low risk, and high and very high as high risk, and keeping intermediate as intermediate risk. Our survival analysis of these three categories showed a clear match between IPSS-R score and MDS survival (Author response image 2a).

We then investigated the relationship between factors 2, 4, and 9 from MOFA with three IPSS-R risk groups.  Integration of IPSS-R risk groups with factor values confirmed the finding in the manuscript that Factors 4 and 9 generally exert a protective influence over the MDS risk, whilst higher levels of Factor 2 predict a high-risk MDS (Author response image 2b). However, we see so many outliers in all three factors, indicating that some patients were assigned to the wrong IPSS-R categories because IPSS-R calculation is based on clinical and genetic variables and does not include the transcriptomics data for coding and non-coding genomic regions. 

Author response image 2.

Comparison of IPSS-R risk categories and MOFA risk and protective factors.

(2) A major issue in analyzing this paper relates to the specific patient composition from whom the samples and data were obtained. The cells from the Shiozawa paper (ref 17) is comprised of a substantial number of CMML patients. Thus, what evidence have the authors that much of the data from the BMMNCs from these patients and mutant SRSF2 related predominantly to their monocytic dicerentiation state?  

We thank the reviewer for the insightful comment about the monocytic differentiation state of CMML and SRSF2 mutant cases. The BMMNC cohort has 11 CMML and 17 SRSF2 mutant cases, of which six are shared between the two groups. We have divided the patients into four groups: CMML only, SRSF2 mutant only, CCML and SRSF2 mutant, and others. We have generated boxplots for all cellular composition gene signature scores for these groups and compared the scores between these groups. As explained above, Ferrall-Fairbanks et al. (DOI: 10.1158/2643-3230.BCD-21-0217) recently showed that CMML can be classified into three differentiation trajectories: monocytic, megakaryocyte-erythroid progenitor (MEP), and normal-like. One hallmark of monocytic-biased trajectory was the enrichment of inflammatory granulocyte–macrophage progenitor (GMP)-like cells, which we observed through our analysis for the CMML cases with SRSF2 mutation (Author response image 3.).

Author response image 3.

Cellular composition gene signature scores for CMML and SRSF2 mutant versus other cases. CMML cases with SRSF2 mutation show a significant higher level of GMP and GMP-like scores compared to other MDS cases.  

(3) In addition, as the majority of patients in the Shiozawa paper have ring sideroblasts (n=59), thus potentially skewing the data toward consideration mainly of these patients, for whom better outcomes are well known.  

We disagree with the reviewer. We used 94 BMMNC samples from Shiozawa’s paper, of which 19 cases had Refractory Anemia with Ring Sideroblasts (RARS), 4 cases had Refractory Anemia with Ring Sideroblasts and thrombocytosis (RARS-T), and 5 cases had Refractory cytopenia with multilineage dysplasia and ring sideroblasts (RCMD-RS). In total, we had 28 cases (~30%) with Ring Sideroblasts (RS), which are not large enough to skew the data.

(4) Further, regarding this patient subset, what evidence have the authors that the importance of the SF3B1 mutation was merely related to the preponderance of sideroblastic patients from whom the samples were analyzed? 

We had 34 SF3B1 mutant cases, of which 25 had Ring Sideroblasts (RS). The total number of cases with RS in the BMMNC cohort was 28. Therefore, the BMMNC cohort is not an RSdominant cohort, and RS cases did not include all SF3B1 mutants. Furthermore, it was recently shown by Ochi et al. (DOI: 10.1038/s41598-022-18921-2) that RS is a consequence of SF3B1K700E mutation, and it is not a cause to affect the SF3B1 importance.

(5) An Erratum was reported for the Shiozawa paper (Shiozawa Y, Malcovati L, Gallì A, et al. Gene expression and risk of leukemic transformation in myelodysplasia. Blood. 2018 Aug 23;132(8):869-875. doi: 10.1182/blood-2018-07-863134) that resulted from a coding error in the construction of the logistic regression model for subgroup prediction based on the gene expression profiles of BMMNCs. This coding error was identified after the publication of the article. The authors should indicate the ecect this error may have had on the data they now report.  

Thank you for bringing this important issue to our attention. The error resulted from a mistake in the construction of the logistic regression model for subgroup prediction based on the gene expression profiles of BMMNCs. However, this issue does not affect our result because we analysed the expression data from scratch and generated our own gene signature scores. Also, the error has no impact on the genetics and clinical information that we received from the authors.

(6) What information have the authors as to whether the dicering RTE findings were not predominantly related to the dicerentiation state of the cell population analyzed (ie higher in BM MNCs vs CD34, Fig 1)? What control data have the authors regarding these values from normal (non-malignant) cell populations? 

As described above, L1 and Alu, the two RTE families shared between the two cohorts, are still active and make new insertions in humans (Figure 2.a-b). Our interpretation is that ERV activation in BM is associated with immune cells. This interpretation is further supported by the findings of Au et al. (DOI: 10.1016/j.ccell.2021.10.001), where several ERV loci had expression in purified immune cell subsets in renal cell carcinoma samples. 

Unfortunately, none of these two cohorts had normal (non-malignant) cell populations. We think that the MOFA unbiased way of modelling the heterogeneity is su@icient to capture the RTE derepressed phenotype of a subset of MDS cases compared to others, and we do not need normal cases to further support the finding. 

(7) The statement in the Discussion regarding the ecects of SRSF2 mutation is speculative and should be avoided. Many other somatic gene mutations have known stronger ecects on prognosis for MDS. 

One aim of this study is to identify specific immune signatures associated with SRSF2 and SF3B1 mutations, which are highly prevalent in MDS. Although other mutations, such as TP53, may have a stronger correlation with poor survival, numerous studies have demonstrated a clear link between SRSF2 mutations and poor prognosis.

Author response:

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

eLife assessment

This study provides an important cell atlas of the gill of the mussel Gigantidas platifrons using a single nucleus RNA-seq dataset, a resource for the community of scientists studying deep sea physiology and metabolism and intracellular host-symbiont relationships. The work, which offers solid insights into cellular responses to starvation stress and molecular mechanisms behind deep-sea chemosymbiosis, is of relevance to scientists interested in host-symbiont relationships across ecosystems.

Public Reviews:

Reviewer #1 (Public Review):

Wang et al have constructed a comprehensive single nucleus atlas for the gills of the deep sea Bathymodioline mussels, which possess intracellular symbionts that provide a key source of carbon and allow them to live in these extreme environments. They provide annotations of the different cell states within the gills, shedding light on how multiple cell types cooperate to give rise to the emergent functions of the composite tissues and the gills as a whole. They pay special attention to characterizing the bacteriocyte cell populations and identifying sets of genes that may play a role in their interaction with the symbiotes.

Wang et al sample mussels from 3 different environments: animals from their native methane-rich environment, animals transplanted to a methane-poor environment to induce starvation, and animals that have been starved in the methane-poor environment and then moved back to the methane-rich environment. They demonstrated that starvation had the biggest impact on bacteriocyte transcriptomes. They hypothesize that the upregulation of genes associated with lysosomal digestion leads to the digestion of the intracellular symbiont during starvation, while the non-starved and reacclimated groups more readily harvest the nutrients from symbiotes without destroying them.

Strengths:

This paper makes available a high-quality dataset that is of interest to many disciplines of biology. The unique qualities of this non-model organism and the collection of conditions sampled make it of special interest to those studying deep sea adaptation, the impact of environmental perturbation on Bathymodioline mussels populations, and intracellular symbiotes. The authors do an excellent job of making all their data and analysis available, making this not only an important dataset but a readily accessible and understandable one.

The authors also use a diverse array of tools to explore their data. For example, the quality of the data is augmented by the use of in situ hybridizations to validate cluster identity and KEGG analysis provides key insights into how the transcriptomes of bacteriocytes change.

The authors also do a great job of providing diagrams and schematics to help orient non-mussel experts, thereby widening the audience of the paper.

Thank the reviewer for the valuable feedback on our study. We are grateful that the reviewers found our work to be interesting and we appreciate their thorough evaluation of our research. Their constructive comments will be considered as we continue to develop and improve our study.

Weaknesses:

One of the main weaknesses of this paper is the lack of coherence between the images and the text, with some parts of the figures never being referenced in the body of the text. This makes it difficult for the reader to interpret how they fit in with the author's discussion and assess confidence in their analysis and interpretation of data. This is especially apparent in the cluster annotation section of the paper.

We appreciate the feedback and suggestions provided by the reviewer, and we have revised our manuscript to make it more accessible to general audiences.

Another concern is the linking of the transcriptomic shifts associated with starvation with changes in interactions with the symbiotes. Without examining and comparing the symbiote population between the different samples, it cannot be concluded that the transcriptomic shifts correlate with a shift to the 'milking' pathway and not other environmental factors. Without comparing the symbiote abundance between samples, it is difficult to disentangle changes in cell state that are due to their changing interactions with the symbiotes from other environmental factors.

We are grateful for the valuable feedback and suggestions provided by the reviewer. Our keen interest lies in understanding symbiont responses, particularly at the single-cell level. However, it's worth noting that existing commercial single-cell RNA-seq technologies rely on oligo dT priming for reverse transcription and barcoding, thus omitting bacterial gene expression information from our dataset. We hope that advancements in technology will soon enable us to perform an integrated analysis encompassing both host and symbiont gene expression.

Additionally, conclusions in this area are further complicated by using only snRNA-seq to study intracellular processes. This is limiting since cytoplasmic mRNA is excluded and only nuclear reads are sequenced after the organisms have had several days to acclimate to their environment and major transcriptomic shifts have occurred.

We appreciate the comments shared by the reviewer and agree that scRNA-seq provides more comprehensive transcriptional information by targeting the entire mRNA of the cell. However, we would like to highlight that snRNA-seq has some unique advantages over scRNA-seq. Notably, snRNA-seq allows for simple snap-freezing of collected samples, facilitating easier storage, particularly for samples obtained during field trips involving deep-sea animals and other ecologically significant non-model animal samples. Additionally, unlike scRNA-seq, snRNA-seq eliminates the need for tissue dissociation, which often involves prolonged enzymatic treatment of deep-sea animal tissue/cells under atmospheric pressure. This process can potentially lead to the loss of sensitive cells or alterations in gene expression. Moreover, snRNA-seq procedures disregard the size and shape of animal cells, rendering it a superior technology for constructing the cell atlas of animal tissues. Consequently, we assert that snRNA-seq offers flexibility and represents a suitable choice for the research objects of our current research.

Reviewer #2 (Public Review):

Wang, He et al. shed insight into the molecular mechanisms of deep-sea chemosymbiosis at the single-cell level. They do so by producing a comprehensive cell atlas of the gill of Gigantidas platifrons, a chemosymbiotic mussel that dominates the deep-sea ecosystem. They uncover novel cell types and find that the gene expression of bacteriocytes, the symbiont-hosting cells, supports two hypotheses of host-symbiont interactions: the "farming" pathway, where symbionts are directly digested, and the "milking" pathway, where nutrients released by the symbionts are used by the host. They perform an in situ transplantation experiment in the deep sea and reveal transitional changes in gene expression that support a model where starvation stress induces bacteriocytes to "farm" their symbionts, while recovery leads to the restoration of the "farming" and "milking" pathways.

A major strength of this study includes the successful application of advanced single-nucleus techniques to a non-model, deep-sea organism that remains challenging to sample. I also applaud the authors for performing an in situ transplantation experiment in a deep-sea environment. From gene expression profiles, the authors deftly provide a rich functional description of G. platifrons cell types that is well-contextualized within the unique biology of chemosymbiosis. These findings offer significant insight into the molecular mechanisms of deep-sea host-symbiont ecology, and will serve as a valuable resource for future studies into the striking biology of G. platifrons.

The authors' conclusions are generally well-supported by their results. However, I recognize that the difficulty of obtaining deep-sea specimens may have impacted experimental design. In this area, I would appreciate more in-depth discussion of these impacts when interpreting the data.

Thank the reviewer for their valuable feedback on our study. We're grateful that the reviewers found our work interesting, and we appreciate their thorough evaluation of our research. We'll consider their constructive comments as we continue to develop and improve our study.

Because cells from multiple individuals were combined before sequencing, the in situ transplantation experiment lacks clear biological replicates. This may potentially result in technical variation (ie. batch effects) confounding biological variation, directly impacting the interpretation of observed changes between the Fanmao, Reconstitution, and Starvation conditions. It is notable that Fanmao cells were much more sparsely sampled. It appears that fewer cells were sequenced, resulting in the Starvation and Reconstitution conditions having 2-3x more cells after doublet filtering. It is not clear whether this is due to a technical factor impacting sequencing or whether these numbers are the result of the unique biology of Fanmao cells. Furthermore, from Table S19 it appears that while 98% of Fanmao cells survived doublet filtering, only ~40% and ~70% survived for the Starvation and Reconstitution conditions respectively, suggesting some kind of distinction in quality or approach.

There is a pronounced divergence in the relative proportions of cells per cell type cluster in Fanmao compared to Reconstitution and Starvation (Fig. S11). This is potentially a very interesting finding, but it is difficult to know if these differences are the expected biological outcome of the experiment or the fact that Fanmao cells are much more sparsely sampled. The study also finds notable differences in gene expression between Fanmao and the other two conditions- a key finding is that bacteriocytes had the largest Fanmao-vs-starvation distance (Fig. 6B). But it is also notable that for every cell type, one or both comparisons against Fanmao produced greater distances than comparisons between Starvation and Reconstitution (Fig. 6B). Again, it is difficult to interpret whether Fanmao's distinctiveness from the other two conditions is underlain by fascinating biology or technical batch effects. Without biological replicates, it remains challenging to disentangle the two.

As highlighted by the reviewer, our experimental design involves pooling multiple biological samples within a single treatment state before sequencing. We acknowledge the concern regarding the absence of distinct biological replicates and the potential impact of batch effects on result interpretation. While we recognize the merit of conducting multiple sequencing runs for a single treatment to provide genuine biological replicates, we contend that batch effects may not exert a strong influence on the observed patterns.

In addition, we applied a bootstrap sampling algorithm to assess whether the gene expression patterns within a cluster are more similar than those between clusters. This algorithm involves selecting a portion of cells per cluster and examining whether this subset remains distinguishable from other clusters. Our assumption was that if different samples exhibited distinct expression patterns due to batch effect, the co-assignment probabilities of a cluster would be very low. This expectation was not met in our data, as illustrated in Fig. S2. The lack of significantly low co-assignment probabilities within clusters suggests that batch effects may not exert a strong influence on our results.

Indeed, we acknowledge a noticeable shift in the expression patterns of certain cell types, such as the bacteriocyte. However, this is not universally applicable across all cell types. For instance, the UMAP figure in Fig. 6A illustrates a substantial overlap among basal membrane cell 2 from Fanmao, Starvation, and Reconstitution treatments, and the centroid distances between the three treatments are subtle, as depicted in Fig. 6B. This consistent pattern is also observed in DEPC, smooth muscle cells, and the food groove ciliary cells.

The reviewer also noted variations in the number of cells per treatment. Specifically, Fanmao sequencing yielded fewer than 10 thousand cells, whereas the other two treatments produced 2-3 times more cells after quality control (QC). It is highly probable that the technician loaded different quantities of cells into the machine for single-nucleus sequencing—a not uncommon occurrence in this methodology. While loading more cells may increase the likelihood of doublets, it is crucial to emphasize that this should not significantly impact the expression patterns post-QC. It's worth noting that overloading samples has been employed as a strategic approach to capture rare cell types, as discussed in a previous study (reference: 10.1126/science.aay0267).

The reviewer highlighted the discrepancy in cell survival rates during the 'doublet filtering' process, with 98% of Fanmao cells surviving compared to approximately 40% and 70% for the Starvation and Reconstitution conditions, respectively. It's important to clarify that the reported percentages reflect the survival of cells through a multi-step QC process employing various filtering strategies.

Post-doublet removal, we filtered out cells with <100 or >2500 genes and <100 or >6000 unique molecular identifiers (UMIs). Additionally, genes with <10 UMIs in each data matrix were excluded. The observed differences in survival rates for Starvation and Reconstitution cells can be attributed to the total volume of data generated in Illumina sequencing. Specifically, we sequenced approximately 91 GB of data for Fanmao, ~196 GB for Starvation, and ~249 GB for Reconstitution. As a result, the qualified data obtained for Starvation and Reconstitution conditions was only about twice that of Fanmao due to the limited data volume.

The reviewer also observed a divergence in the relative proportions of cells per cell type cluster in Fanmao compared to Reconstitution and Starvation, as depicted in Fig. S1. This discrepancy may hold true biological significance, presenting a potentially intriguing finding. However, our discussion on this pattern was rather brief, as we acknowledge that the observed differences could be influenced by the sample preparation process for dissection and digestion. It is crucial to consider that cutting a slightly different area during dissection may result in variations in the proportion of cells obtained. While we recognize the potential impact of this factor, we do not think that the sparsity of sampling alone could significantly affect the relative proportions of cells per cell type.

In conclusion, we acknowledge the reviewer's suggestion that sequencing multiple individual samples per treatment condition would have been ideal, rather than pooling them together. However, the homogenous distribution observed in UMAP and the consistent results obtained from bootstrap sampling suggest that the impact of batch effects on our analyses is likely not substantial. Additionally, based on our understanding, the smaller number of cells in the Fanmao sample should not have any significant effect on the resulting different proportion of cells or the expression patterns per each cluster.

Reviewer #3 (Public Review):

Wang et al. explored the unique biology of the deep-sea mussel Gigantidas platifrons to understand the fundamental principles of animal-symbiont relationships. They used single-nucleus RNA sequencing and validation and visualization of many of the important cellular and molecular players that allow these organisms to survive in the deep sea. They demonstrate that a diversity of cell types that support the structure and function of the gill including bacteriocytes, specialized epithelial cells that host sulfur-oxidizing or methane-oxidizing symbionts as well as a suite of other cell types including supportive cells, ciliary, and smooth muscle cells. By performing experiments of transplanting mussels from one habitat which is rich in methane to methane-limited environments, the authors showed that starved mussels may consume endosymbionts versus in methane-rich environments upregulated genes involved in glutamate synthesis. These data add to the growing body of literature that organisms control their endosymbionts in response to environmental change.

The conclusions of the data are well supported. The authors adapted a technique that would have been technically impossible in their field environment by preserving the tissue and then performing nuclear isolation after the fact. The use of single-nucleus sequencing opens the possibility of new cellular and molecular biology that is not possible to study in the field. Additionally, the in-situ data (both WISH and FISH) are high-quality and easy to interpret. The use of cell-type-specific markers along with a symbiont-specific probe was effective. Finally, the SEM and TEM were used convincingly for specific purposes in the case of showing the cilia that may support water movement.

We appreciate the valuable feedback provided by the reviewer on our study. It is encouraging to know that our work was found to be interesting and that they conducted a thorough evaluation of our research. We will take their constructive comments into account as we strive to develop and enhance our study. Thank the reviewer for all the input.

The one particular area for clarification and improvement surrounds the concept of a proliferative progenitor population within the gill. The authors imply that three types of proliferative cells within gills have long been known, but their study may be the first to recover molecular markers for these putative populations. The markers the authors present for gill posterior end budding zone cells (PEBZCs) and dorsal end proliferation cells (DEPCs) are not intuitively associated with cell proliferation and some additional exploration of the data could be performed to strengthen the argument that these are indeed proliferative cells. The authors do utilize a trajectory analysis tool called Slingshot which they claim may suggest that PEBZCs could be the origin of all gill epithelial cells, however, one of the assumptions of this analysis is that differentiated cells are developed from the same precursor PEBZC population.

However, these conclusions do not detract from the overall significance of the work of identifying the relationship between symbionts and bacteriocytes and how these host bacteriocytes modulate their gene expression in response to environmental change. It will be interesting to see how similar or different these data are across animal phyla. For instance, the work of symbiosis in cnidarians may converge on similar principles or there may be independent ways in which organisms have been able to solve these problems.

We are grateful for the valuable comments and suggestions provided by the reviewer. All suggestions have been carefully considered, and the manuscript has been revised accordingly. We particularly value the reviewer's insights regarding the characterization of the G. platifrons gill proliferative cell populations. In a separate research endeavor, we have conducted experiments utilizing both cell division and cell proliferation markers on these proliferative cell populations. While these results are not incorporated into the current manuscript, we would be delighted to share our preliminary findings with the reviewer. Our preliminary results indicate that the proliferative cell populations exhibit positivity for cell proliferation markers and contain a significant number of mitotic cells..

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Further experiments are needed to link the changes in transcriptomes of Bathymodioline mussels in the different environmental conditions to changes in their interactions with symbiotes. For example, quantifying the abundance and comparing the morphology of symbiotes between the environmental conditions would lend much support for shifting between milking and farming strategies. Without analyzing the symbiotes and comparing them across populations, it is difficult to comment on the mechanisms of interactions between symbiotes and the hosts. Without this analysis, this data is better suited towards comments about the general effect of environmental perturbation and stress on gene expression in these mussels.

We appreciate the reviewer’s comments. We are also very curious about the symbiont responses, especially at the single-cell level. However, all the current commercial single-cell RNA-seq technologies are based on oligo dT priming for reverse transcription and barcoding. Therefore, the bacterial gene expression information is omitted from our dataset. Hopefully, with the development of technology, we could conduct an integrated analysis of both host and symbiont gene expression soon.

Additionally, clarification is needed on which types of symbiotes are being looked at. Are they MOX or SOX populations? Are they homogenous? What are the concentrations of sulfur at the sampled sites?

We thank you for your valuable comments and suggestions. Gigantidas platifrons harbors a MOX endosymbiont population characterized by a single 16S rRNA phylotype. We apologize for any confusion resulting from our previous wording. To clarify, we have revised lines 57-59 of our introduction

In the text and images, consider using standardized gene names and leaving out the genome coordinates. This would greatly help with readability. Also, be careful to properly follow gene naming and formatting conventions (ie italicizing gene names and symbols).

We appreciate the reviewer’s insightful comments. In model animals, gene nomenclature often stems from forward genetic approaches, such as the identification of loss-of-function mutants. These gene names, along with their protein products, typically correspond to unique genome coordinates. Conversely, in non-model invertebrates (e.g., Gigantidas platifrons of present study), gene prediction relies on a combination of bioinformatics methods, including de novo prediction, homolog-based prediction, and transcriptomics mapping. Subsequently, the genes are annotated by identifying their best homologs in well-characterized databases. Given that different genes may encode proteins with similar annotated functions, we chose to include both the gene ID (genome coordinates) and the gene name in our manuscript. This dual labeling approach ensures that our audience receives accurate and comprehensive information regarding gene identification and annotation.

Additionally, extending KEGG analysis to the atlas annotation section could help strengthen the confidence of annotations. For example, when identifying bacteriocyte populations, the functional categories of individual marker genes (lysosomal proteases, lysosomal traffic regulators, etc) are used to justify the annotation. Presenting KEGG support that these functional categories are upregulated in this population relative to others would help further support how you characterize this cluster by showing it's not just a few specific genes that are enriched in this cell group, but rather an overall functionality.

We appreciate the valuable suggestion provided by the reviewer. Indeed, incorporating KEGG analysis into the atlas annotation section could further enhance the confidence in our annotations. However, in our study, we encountered some limitations that impeded us from conducting a comprehensive KEGG enrichment analysis.

Firstly, the number of differentially expressed genes (DEGs) that we identified for certain cell populations was relatively small, making it challenging to meet the threshold required for meaningful KEGG enrichment analysis. For instance, among the 97 marker genes identified for the Bacteriocyte cluster, only two genes, Bpl_scaf_59648-4.5 (lysosomal alpha-glucosidase-like) and Bpl_scaf_52809-1.6 (lysosomal-trafficking regulator-like isoform X1), were identified as lysosomal genes. To generate reliable KEGG enrichments, a larger number of genes is typically required.

Secondly, single-nucleus sequencing, as employed in our study, tends to yield a relatively smaller number of genes per cell compared to bulk RNA sequencing. This limited gene yield can make it challenging to achieve sufficient gene representation for rigorous KEGG enrichment analysis.

Furthermore, many genes in the genome still lack comprehensive annotation, both in terms of KEGG and GO annotations. In our dataset, out of the 33,584 genes obtained through single-nuclei sequencing, 26,514 genes have NO KEGG annotation, and 25,087 genes have NO GO annotation. This lack of annotations further restricts the comprehensive application of KEGG analysis in our study.

The claim that VEPCs are symbiote free is not demonstrated. Additional double in situs are needed to show that markers of this cell type localize in regions free of symbiotes.

We appreciate your comments and suggestions. In Figure 5B, our results demonstrate that the bacteriocytes (green fluorescent signal) are distant from the VEPCs, which are located around the tip of the gill filaments (close to the food groove). We have revised our Figure 5B to make it clear.

Additionally, it does not seem like trajectory analysis is appropriate for these sampling conditions. Generally, to create trajectories confidently, more closely sampled time points are needed to sufficiently parse out the changes in expression. More justification is needed for the use of this type of analysis here and a discussion of the limitations should be mentioned, especially when discussing the hypotheses relating to PEBZCs, VEPCs, and DEPCs.

We greatly appreciate your thoughtful commentary. It is important to acknowledge that in the context of a developmental study, incorporating more closely spaced time points indeed holds great value. In our ongoing project investigating mouse development, for instance, we have implemented time points at 24-hour intervals. However, in the case of deep-sea adult animals, we hypothesized a slower transcriptional shift in such extreme environment, which led us to opt for a time interval of 3-7 days. Examining the differential expression profiles among the three treatments, we observed that most cell types exhibited minimal changes in their expression profiles. For the cell types strongly impacted by in situ transplantation, their expression profiles per cell type still exhibited highly overlap in the UMAP analysis (Figure 6a), thus enabling meaningful comparisons. Nevertheless, we recognize that our sampling strategy may not be flawless. Additionally, the challenging nature of conducting in situ transplantation in 1000-meter depths limited the number of sampling occasions available to us. We sincerely appreciate your input and understanding.

Finally, more detail should be added on the computational methods used in this paper. For example, the single-cell genomics analysis protocol should be expanded on so that readers unfamiliar with BD single-cell genomics handbooks could replicate the analysis. More detail is also needed on what criteria and cutoffs were used to calculate marker genes. Also, please be careful to cite the algorithms and software packages mentioned in the text.

Acknowledged, thank you for highlighting this. In essence, the workflow closely resembles that of the 10x Genomics workflow (despite the use of a different software, i.e., Cell Ranger). We better explain the workflow below, and also noting that this information may no longer be relevant for newer users of BD or individuals who are not acquainted with BD, given that the workflow underwent a complete overhaul in the summer of 2023.

References to lines

Line 32: typo "..uncovered unknown tissue heterogeny" should read "uncovering" or "and uncovered")

Overall abstract could include more detail of findings (ex: what are the "shifts in cell state" in line 36 that were observed)

We apologize for the mistakes, and have revised the manuscript accordingly.

Line 60: missing comma "...gill filament structure, but also"

We apologize for the mistakes, and have revised the manuscript accordingly.

Line 62-63: further discussion here, or in the relevant sections of the specific genes identified in the referenced bulk RNA-seq project could help strengthen confidence in annotation

We appreciate the comment, and have revised the manuscript accordingly.

Line 112: what bootstrapping strategy? Applied to what?

This is a bootstrap sampling algorithm to assess the robustness of each cell cluster developed in a recent biorxiv paper. (Singh, P. & Zhai, Y. Deciphering Hematopoiesis at single cell level through the lens of reduced dimensions. bioRxiv, 2022.2006.2007.495099 (2022). https://doi.org:10.1101/2022.06.07.495099)

Lines 127-129: What figures demonstrate the location of the inter lamina cells? Are there in situs that show this?

We apologize for any errors; the referencing of figures in the manuscript has been revised for clarity

Lines 185-190: does literature support these as markers of SMCs? Are they known smooth muscle markers in other systems?

We characterized the SMCs by the expression of LDL-associated protein, angiotensin-converting enzyme-like protein, and the "molecular spring" titin-like protein, all of which are commonly found in human vascular smooth muscle cells. Based on this analysis, we hypothesize that these cells belong to the smooth muscle cell category.

Line 201: What is meant by "regulatory roles"?

In this context, we are discussing the expression of genes encoding regulatory proteins, such as SOX transcription factors and secreted-frizzled proteins.

Line 211: which markers disappeared? What in situs show this?

We apologize for the mistakes, and have revised the manuscript accordingly.

Line 211: typo, "role" → "roll"

We apologize for the mistakes, and have revised the manuscript accordingly.

Line 214: what are these "hallmark genes"

We apologize for the mistakes, here we are referring to the genes listed in figure 4B. We have revised the manuscript accordingly.

Line 220: are there meristem-like cells in metazoans? If so, this would be preferable to a comparison with plants.

In this context, we are discussing the morphological characteristics of gill proliferative cell populations found in filibranch bivalves. These populations, namely PEPC, VEPC, and DEPC, consist of cells exhibiting morphological traits akin to those of plant cambial-zone meristem cells. These cells typically display small, round shapes with a high nucleus-to-plasma ratio. We acknowledge that while these terms are utilized in bivalve studies (citations below), they lack the robust support seen in model systems backed by molecular biology evidences. The present snRNA-seq data, however, may offer valuable cell markers for future comprehensive investigations.

Leibson, N. L. & Movchan, O. T. Cambial zones in gills of Bivalvia. Mar. Biol. 31, 175-180 (1975). https://doi.org:10.1007/BF00391629

Wentrup, C., Wendeberg, A., Schimak, M., Borowski, C. & Dubilier, N. Forever competent: deep-sea bivalves are colonized by their chemosynthetic symbionts throughout their lifetime. Environ. Microbiol. 16, 3699-3713 (2014). https://doi.org:10.1111/1462-2920.12597

Cannuel, R., Beninger, P. G., McCombie, H. & Boudry, P. Gill Development and its functional and evolutionary implications in the blue mussel Mytilus edulis (Bivalvia: Mytilidae). Biol. Bull. 217, 173-188 (2009). https://doi.org:10.1086/BBLv217n2p173

Line 335: what is slingshot trajectory analysis? Does this differ from the pseudotime analysis?

Slingshot is an algorithm that uses the principal graph of the cells to infer trajectories. It models trajectories as curves on the principal graph, capturing the progression and transitions between different cellular states.

Both Slingshot and pseudotime aim to infer cellular trajectories. Slingshot focuses on capturing branching patterns which is fully compatible with the graph generated using dimensionality reduction such as UMAP and PHATE, while pseudotime analysis aims to order cells along a continuous trajectory. It does not rely on dimensionality reduction graphs. We used both in the MS for different purposes.

Line 241: introduce FISH methodology earlier in the paper, when in situ images are first referenced

We appreciate the comment, and have revised the manuscript accordingly.

Line 246-249: can you quantify the decrease in signal or calculate the concentration of symbiotes in the cells? Was 5C imaged whole? This can impact the fluorescent intensity in tissues of different thicknesses.

We appreciate your comment. In Figure 5C, most of the typical gill filament region is visible (the ventral tip of the gill filament, and the mid part of the gill filament) except for the dorsal end. The gill filament of bathymodioline mussels exhibits a simple structure: a single layer of bacteriocytes grow on the basal membrane. Consequently, the gill slices have a fairly uniform thickness (with two layers of bacteriocytes and one layer of interlamina cells in between), minimizing any potential impact on fluorescent intensity. As of now, detailed quantification of intracellular symbionts may necessitate continuous TEM or ultra-resolution confocal sections to 3D reconstruct the bacteriocytes, which may exceed the scope of the current study. Therefore, fluorescent intensity remains the only method available to us for estimating bacterial density/distribution across the gill filament.

Line 249: What is meant by 'environmental gradient?'

Here we are refereeing the gases need for symbiont’s chemosynthesis. We have revised the manuscript to make it clear.

Lines 255-256: Were the results shown in the TEM images previously known? Not clear what novel information is conveyed in images Fig 5 C and D

In the Fig 5 C and D, we’ve delivered a high-quality SEM TEM image of a typical bacteriocyte, showcasing its morphology and subcellular machinery with clarity. These electron microscopy images offer the audience a comprehensive introduction to the cellular function of bacteriocytes. Additionally, they serve as supportive evidence for the bacteriocytes' snRNA-seq data.

Line 295-296: Can you elaborate on what types of solute carrier genes have been shown to be involved with symbioses?

We appreciate the comment, and have revised the manuscript accordingly. The putative functions of the solute carriers could be found in Figure 5I.

Line 297-301: Which genes from the bulk RNA-seq study? Adding more detail and references in cluster annotation would help readers better understand the justifications.

We appreciate the comment, and have revised the manuscript accordingly.

Line 316 -322: Can you provide the values of the distances?

We also provide values in the main text, in addition to the Fig6b. We also provide a supplementary Table (Supplementary Table S19).

Line 328: What are the gene expression patterns?

We observed genes that are up- and down-regulated in Starvation and reconstitution.

LIne 334-337: A visualization of the different expression levels of the specific genes in clusters between sites might be helpful to demonstrate the degree of difference between sites.

We have prepared a new supplementary file showing the different expression levels.

Line 337: Citation needed

We appreciate the comment. Here, we hypothesize the cellular responds based on the gene’s function and their expression patterns.

Line 402-403: Cannot determine lineages from data presented. Need lineage tracing over time to determine this

We acknowledge the necessity of conducting lineage tracing over time to validate this hypothesis. Nonetheless, in practical terms, it is difficult to obtain samples for testing this. Perhaps, it is easier to use their shallow sea relatives to test this hypothesis. However, in practice, it is very difficult.

413-414: What are the "cell-type specific responses to environmental change"? It could be interesting to present these results in the "results and discussion" section

These results are shown in Supplementary Figure S8.

Line 419-424: Sampling details might go better earlier on in the paper, when the sampling scheme is introduced.

We appreciate the comments. Here, we are discussing the limitations of our current study, not sampling details.

Line 552: What type of sequencing? Paired end? How long?

We conducted 150bp paired-end sequencing.

556-563: More detail here would be useful to readers not familiar with the BD guide. Also be careful to cite the software used in analysis!

The provided guide and handbook elucidate the intricacies of gene name preparation, data alignment to the genome, and the generation of an expression matrix. It is worth mentioning that we relied upon outdated versions of the aforementioned resources during our data analysis phase, as they were the only ones accessible to us at the time. However, we have since become aware of a newer pipeline available this year, rendering the information presented here of limited significance to other researchers utilizing BD.

Many thanks for your kind reminding. We have now included a reference for STAR. All other software was cited accordingly. There are no scholarly papers or publications to refer to for the BD pipeline that we can cite.

Line 577-578: How was the number of clusters determined? What is meant by "manually combine the clusters?" If cells were clustered by hand, more detail on the method is needed, as well as direct discussion and justification in the body of the paper.

It would be more appropriate to emphasize the determination of cell types rather than clusters. The clusters were identified using a clustering function, as mentioned in the manuscript. It's important to note that the clustering function (in our case, the FindClusters function of Seurat) provides a general overview based on diffuse gene expression. Technically speaking, there is no guarantee that one cluster corresponds to a single cell type. Therefore, it is crucial to manually inspect the clustering results to assign clusters to the appropriate cell types. In some cases, multiple clusters may be assigned to the same cell type, while in other cases, a single cluster may need to be further subdivided into two or more cell types or sub-cell types, depending on the specific circumstances.

For studies conducted on model species such as humans or mice, highly and specifically expressed genes within each cluster can be compared to known marker genes of cell types mentioned in previous publications, which generally suffices for annotation purposes. However, in the case of non-model species like Bathymodioline mussels, there is often limited information available about marker genes, making it challenging to confidently assign clusters to specific cell types. In such situations, in situ hybridisation proves to be incredibly valuable. In our study, WISH was employed to visualise the expression and morphology of marker genes within clusters. When WISH revealed the expression of marker genes from a cluster in a specific type of cell, we classified that cluster as a genuine cell type. Moreover, if WISH demonstrated uniform expression of marker genes from different clusters in the same cell, we assigned both clusters to the same cell type.

We expanded the description of the strategy in the Method section.

LIne 690-692: When slices were used, what part of the gill were they taken from?

We sectioned the gill around the mid part which could represent the mature bacteriocytes.

References to figures:

General

Please split the fluorescent images into different channels with an additional composite. It is difficult to see some of the expression patterns. It would also make it accessible to colorblind readers.

We appreciate the comments and suggestions from the reviewer. We have converted our figures to CMYK colour which will help the colorblind audiences to read our paper.

Please provide the number of replicates for each in situ and what proportion of those displayed the presented pattern.

We appreciate the reviewer’s comments. We have explained in the material and methods part of the manuscript.

Figure 2.C' is a fantastic summary and really helps the non-mussel audience understand the results. Adding schematics like this to Figures 3-5 would be helpful as well.

We value the reviewer's comments. We propose that Figures 3K, 4C, and 5A-D could offer similar schematic explanations to assist the audience.

Figure 2:

Figures 2.C-F, 2.C', 2.H-J are not referenced in the text. Adding in discussions of them would help strengthen your discussions on the cluster annotation

We appreciate the reviewer's comments. We have revise the manuscript accordingly.

In 2.B. 6 genes are highlighted in red and said to be shown in in situs, but only 5 are shown.

We apology for the mistake. We didn’t include the result 20639-0.0 WISH in present study. We have changed the label to black.

Figure 3:

FIg 2C-E not mentioned.

We appreciate the reviewer's comments. We have revise the manuscript accordingly.

In 3.B 8 genes are highlighted in red and said to be shown in in situs. Only 6 are.

The result of the WISH were provided in Supplementary Figures S4 and S5.

FIgure 3.K is not referenced in the legend.

We appreciate the comment, and have revised the manuscript accordingly.

Figure 4:

In Figure D, it might be helpful to indicate the growth direction.

We appreciate the comment, and have revised the manuscript accordingly by adding an arrow in panel D to indicate growth direction.

4F: A double in situ with the symbiote marker is needed to demonstrate the nucleolin-like positive cells are symbiote free.

We appreciate the comment. The symbiont free region could be found in Figure 5A.

Figure 5:

In 5.A, quantification of symbiote concentration would help support your conclusion that they are denser around the edges.

We appreciate the comment, as we mentioned above, detailed quantification of intracellular symbionts may necessitate continuous TEM or ultra-resolution confocal sections to 3D reconstruct the bacteriocytes, which may exceed the scope of the current study. Therefore, fluorescent intensity remains the only method available to us for estimating bacterial density/distribution across the gill filament.

In 5.D, the annotation is not clear. Adding arrows like in 5.C would be helpful.

We appreciate the comment, and have revised the manuscript accordingly.

A few genes in 5.F are not mentioned in the paper body when listing other genes. Mentioning them would help provide more support for your clustering.

We appreciate the comment, and have revised the manuscript accordingly.

Is 5.I meant to be color coded with the gene groups from 5.F? Color Coding the gene names, rather than organelles or cellular structures might portray this better and help visually strengthen the link between the diagram and your dot plot.

We appreciate the suggestions. We've experimented with color-coding the gene names, but some colors are less discernible against a white background.

Figure 6:

6.B Is there a better way to visualize this data? The color coding is confusing given the pairwise distances. Maybe heatmaps?

We attempted a heatmap, as shown in the figure below. However, all co-authors agree that a bar plot provides clearer visualization compared to the heatmap. We agree that the color scheme maya be confusing because they use the same color as for individual treatment. So we change the colors.

Author response image 1.

Figure 6.D: Why is the fanmao sample divided in the middle?

Fig6C show that single-cell trajectories include branches. The branches occur because cells execute alternative gene expression programs. Thus, in Fig 6D, we show changes for genes that are significantly branch dependent in both lineages at the same time. Specifically, in cluster 2, the genes are upregulated during starvation but downregulated during reconstitution. Conversely, genes in cluster 1 are downregulated during starvation but upregulated during reconstitution. It's of note that Fig 6D displays only a small subset of significantly branch-dependent genes.

FIgure 6.D: Can you visualize the expression in the same format as in figures 2-5?

We appreciate the comments from the reviewer. As far as we know, this heatmap are the best format to demonstrate this type of gene expression profile.

Supplementary Figure S2:

Please provide a key for the cell type abbreviations

We appreciate the comment, and have added the abbreviations of cell types accordingly.

Supplementary Figures S4 and S5:

What part of the larger images are the subsetted image taken from?

We appreciate the comment, these images were taken from the ventral tip and mid of the gill slices, respectively. We have revised the figure legends to make it clear.

Supplemental Figure S7:

If clusters 1 and 2 show genes up and downregulated during starvation, what do clusters 4 and 3 represent?

Cluster 1: Genes that are obviously upregulated during Starvation, and downregulated during reconstitution; luster4： genes are downregulated during reconstitution but not obviously upregulated during Starvation.

Cluster 2 show genes upregulated during reconstitution, and cluster 3 obviously downregulated during Starvation.

Author response table 1.

Supplemental Figure S8:

This is a really interesting figure that I think shows some of the results really well! Maybe consider moving it to the main figures of the paper?

We appreciate the comments and suggestions. We concur with the reviewer on the significance of the results presented. However, consider the length of this manuscript, we have prioritized the inclusion of the most pertinent information in the main figures. Supplementary materials containing additional figures and details on the genes involved in these pathways are provided for interested readers.

Supplemental Figure S11:

Switching the axes might make this image easier for the reader to interpret. Additionally, calculating the normalized contribution of each sample to each cluster could help quantify the extent to which bacteriocytes are reduced when starving.

Thank you for the insightful suggestion, which we have implemented as detailed below. We acknowledge the importance of understanding the changes in bacteriocyte proportions across different treatments. However, it's crucial to note that the percentage of cells per treatment is highly influenced by factors such as the location of digestion and sequencing, as previously mentioned.

Author response image 2.

Reviewer #2 (Recommendations For The Authors):

The following are minor recommendations for the text and figures that may help with clarity:

Fig. 3K: This figure describes water flow induced by different ciliary cells. It is not clear what the color of the arrows corresponds to, as they do not match the UMAP (i.e. the red arrow) and this is not indicated in the legend. Are these colours meant to indicate the different ciliary cell types? If so it would be helpful to include this in the legend.

We appreciate the reviewer's comments and suggestions. The arrows indicate the water flow that might be agitated by the certain types of cilium. We have revised our figure and figure legends to make it clear.

Line 369: The incorrect gene identifier is given for the mitochondrial trifunctional enzyme. This gene identifier is identical to the one given in line 366, which describes long-chain-fatty-acid-ligase ACSBG2-like (Bpl_scaf_28862-1.5).

We appreciate the reviewer's comments and suggestions. We have revised our manuscript accordingly.

Line 554: The Bioproject accession number (PRJNA779258) does not appear to lead to an existing page in any database.

We appreciate the reviewer's comments and suggestions. We have released this Bioproject to the public.

Line 597-598: it would be helpful to know the specific number of cells that the three sample types were downsampled to, and the number of cells remaining in each cluster, as this can affect the statistical interpretation of differential expression analyses.

The number of cells per cluster in our analysis ranged from 766 to 14633. To mitigate potential bias introduced by varying cell numbers, we implemented downsampling, restricting the number of cells per cluster to no more than 3500. This was done to ensure that the differences between clusters remained less than 5 times. We experimented with several downsampling strategies, exploring cell limits of 4500 and 2500, and consistently observed similar patterns across these variations.

Data and code availability:

The supplementary tables and supplementary data S1 appear to be the final output of the differential expression analyses. Including the raw data (e.g. reads) and/or intermediate data objects (e.g. count matrices, R objects), in addition to the code used to perform the analyses, may be very helpful for replication and downstream use of this dataset. As mentioned above, the Bioproject accession number appears to be incorrect.

We appreciate the reviewer's comments and suggestions. Regarding our sequencing data, we have deposited all relevant information with the National Center for Biotechnology Information (NCBI) under Bioproject PRJNA779258. Additionally, we have requested the release of the Bioproject. Furthermore, as part of this round of revision, we have included the count matrices for reference.

Reviewer #3 (Recommendations For The Authors):

As noted in the public review, my only major concerns are around the treatment of progenitor cell populations. I am sympathetic to the challenges of these experiments but suggest a few possible avenues to the authors.

First, there could be some demonstration that these cells in G. platifrons are indeed proliferative, using EdU incorporation labeling or a conserved epitope such as the phosphorylation of serine 10 in histone 3. It appears in Mytilus galloprovincialis that proliferating cell nuclear antigen (PCNA) and phospho-histone H3 have previously been used as good markers for proliferative cells (Maiorova and Odintsova 2016). The use of any of these markers along with the cell type markers the authors recover for PEBZCs for example would greatly strengthen the argument that these are proliferative cells.

If performing these experiments would not be currently possible, the authors could use some computation approaches to strengthen their arguments. Based on conserved cell cycle markers and the use of Cell-Cycle feature analysis in Seurat could the authors provide evidence that these progenitors occupy the G2/M phase at a greater percentage than other cells? Other than the physical position of the cells is there much that suggests that these are proliferative? While I am more convinced by markers in VEPCs the markers for PEBZCs and DEPCs are not particularly compelling.

While I do not think the major findings of the paper hinge on this, comments such as "the PBEZCs gave rise to new bacteriocytes that allowed symbiont colonization" should be taken with care. It is not clear that the PBEZCs are proliferative and there does not seem to be any direct evidence that PBEZCs (or DEPCs or VEPCS for that manner) are the progenitor cells through any sort of labeling or co-expression studies.

We appreciate the comments and suggestions from the reviewer. We have considered all the suggestions and have revised the manuscript accordingly. We especially appreciate the reviewer’s suggestions about the characterisations of the G. platifrons gill proliferative cell populations. In a separate research project, we have tested both cell division and cell proliferation markers on the proliferation cell populations. Though we are not able to include these results in the current manuscript, we are happy to share our preliminary results with the reviewer. Our results demonstrate the proliferative cell populations, particularly the VEPCs, are cell proliferation marker positive, and contains high amount of mitotic cells.

Author response image 3.

Finally, there is a body of literature that has examined cell proliferation and zones of proliferation in mussels (such as Piquet, B., Lallier, F.H., André, C. et al. Regionalized cell proliferation in the symbiont-bearing gill of the hydrothermal vent mussel Bathymodiolus azoricus. Symbiosis 2020) or other organisms (such as Bird, A. M., von Dassow, G., & Maslakova, S. A. How the pilidium larva grows. EvoDevo. 2014) that could be discussed.

We appreciate the comments and suggestions from the reviewer. We have considered all the suggestions and have revised the manuscript accordingly (line 226-229).

Minor comments also include:

Consider changing the orientation of diagrams in Figure 2C' in relationship to Figure 2C and 2D-K.

We appreciate the comments and suggestions from the reviewer. The Figure 2 has been reorganized.

For the diagram in Figure 3K, please clarify if the arrows drawn for the direction of inter lamina water flow is based on gene expression, SEM, or some previous study.

We are grateful for the reviewer's valuable feedback and suggestions. The arrows in the figure indicate the direction of water flow that could be affected by specific types of cilium. Our prediction is based on both gene expression and SEM results. To further clarify this point, we have revised the figure legend of Fig. 3.

Please include a label for the clusters in Figure 5E for consistency.

We have revised our Figure 5E to keep our figures consistent.

Please include a note in the Materials and Methods for Monocle analysis in Figure 6.

We conducted Monocle analyses using Monocle2 and Monocle 3 in R environment. We have revised our material and methods with further information of Figure 6.

In Supplement 2, the first column is labeled PEBC while the first row is labeled PEBZ versus all other rows and columns have corresponding names. I am guessing this is a typo and not different clusters?

We appreciate the great effort of the reviewer in reviewing our manuscript. We have corrected the typo in the revised version.

Author Response

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

Reviewer #1:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We thank the Reviewer for pointing this out.

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

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

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

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

b) We have changed the wording accordingly.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2:

(Numbers indicate the line number.)

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

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

62: should cite Zilman et al 2010

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

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

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

67: Schleicher 2014 does not provide evidence of dedicated channels

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

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

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

77: Probably Jovanovic- Talisman (2009)?

We thank the Reviewer for pointing out this typo.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Author response image 1.

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

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

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

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

Figure 4: how was transport rate for Kaps calculated?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Recommendations For The Authors):

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

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

Author Response

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

eLife assessment

This study and associated data is compelling, novel, important, and well-carried out. The study demonstrates a novel finding that different chemotherapeutic agents can induce nucleolar stress, which manifests with varying cellular and molecular characteristics. The study also proposes a mechanism for how a novel type of nucleolar stress driven by CDK inhibitors may be regulated. The study sheds light on the importance of nucleolar stress in defining the on-target and offtarget effects of chemotherapy in normal and cancer cells.

We are thankful to the reviewers and the editor for their feedback and thorough assessment of our work. Our responses to the comments and suggestions are below.

Reviewer #1 (Public Review):

The study titled "Distinct states of nucleolar stress induced by anti-cancer drugs" by Potapova and colleagues demonstrates that different chemotherapeutic agents can induce nucleolar stress, which manifests with varying cellular and molecular characteristics. The study also proposes a mechanism for how a novel type of nucleolar stress driven by CDK inhibitors may be regulated. As a reviewer, I appreciate the unbiased screening approach and I am enthusiastic about the novel insights into cell biology and the implications for cancer research and treatment. The study has several significant strengths: i) it highlights the understudied role of nucleolar stress in the on- and off-target effects of chemotherapy; ii) it defines novel molecular and cellular characteristics of the different types of nucleolar stress phenotypes; iii) it proposes novel modes of action for well-known drugs. However, there are several important points that should be addressed:

• The rationale behind choosing RPE cells for the screen is unclear. It might be more informative to use cancer cells to study the effects of chemotherapeutic agents. Alternatively, were RPE cells selected to evaluate the side effects of these agents on normal cells? Clarifying these points in the introduction and discussion would guide the reader.

RPE1, a non-cancer-derived cell line, was chosen for this study to evaluate the effects of anticancer drugs on normal nucleolar function, with the underlying premise that nucleolar stress in normal cells can contribute to non-specific toxicity. This clarification is added to the introduction. Another factor that played in selecting a normal cell line for the drug screen and subsequent experiments was the spectrum of known and unknown genetic and metabolic alterations present in various cancer cell lines. These variables are often unique to a particular cancer cell line and may or may not impact nucleolar proteome and function. Therefore, the nucleolar stress response can be influenced by the spectrum of alterations inherent to each cancer. Our primary focus was to determine the impact of these drugs under normal conditions.

That said, the selected hits of main drug classes were validated in a panel of cell lines that included two other hTERT lines (BJ5TA and CHON-002) and two cancer lines (DLD1 and HCT116). In cancer cells starting nucleolar normality scores were lower than in hTERT cells, suggesting that genetic and metabolic changes in these cells may indeed affect nucleolar morphology. Nonetheless, all drugs from a panel of selected hits from different target classes validated in both cancer cell lines (Fig. 2F).

• Figure 2F indicates that DLD1 and HCT116 cells are less sensitive to nucleolar changes induced by several inhibitors, including CDK inhibitors. It would be crucial to correlate these differences with cell viability. Are these differences due to cell-type sensitivity or variations in intracellular drug levels? Assessing cell viability and intracellular drug concentration for the same drugs and cells would provide valuable insights.

One of the reasons for the reduced magnitude of the effects of selected drugs in DLD1 and HCT116 cells is their lower baseline normality scores compared to hTERT cells (now shown in Sup. Fig. 1B-C). Other potential factors include proteomic and metabolic shifts and alterations in signaling pathways that control ribosome production. The less-likely possibility of variations in intracellular drug levels cannot be excluded, but measuring this for every compound in every cell line was not feasible in this study. These limitations are now noted in the results section.

Regarding the point about viability - our initial screen output, in addition to normality scores, included cell count (cumulative count of cells in all imaged fields), which serves as a proxy for viability. By this measure, all hit compounds in our screen were cytostatic or cytotoxic in RPE1 cells (Fig. 2C). The impact of these drugs on the viability of cancer cells that can have various degrees of addiction to ribosome biogenesis merits a separate study of a large cancer cell line panel.

• Have the authors interpreted nucleolar stress as the primary cause of cell death induced by these drugs? When cells treated with CDK inhibitors exhibit the dissociated nucleoli phenotype, is this effect reversible? Is this phenotype indicative of cell death commitment? Conducting a washout experiment to measure the recovery of nucleolar function and cell viability would address these questions.

Whether nucleolar toxicity is the primary cause of cytotoxicity for a given chemotherapy drug is an incisive and thought-provoking question. Our screen did not discern whether the cytotoxic effects of our hits were due to inhibition of their intended targets, their impact on the nucleolus, or a combined effect. This point is now mentioned in the results section. Regarding the reversibility of the nucleolar disassembly phenotype seen in CDK inhibitors –in the case of flavopiridol, which is a reversible CDK inhibitor, we demonstrated that nucleoli re-assembled within 4-6 hours after the drug was washed out. An example of this is shown in Sup. Figure 3 and in Video 5. For these experiments, cells were pretreated with the drug for 5 hours, not long enough to cause cell death.

• The correlation between the loss of Treacle phosphorylation and nucleolar stress upon CDK inhibition is intriguing. However, it remains unclear how these two events are related. Would Treacle knockdown yield the same nucleolar phenotype as CDK inhibition? Moreover, would point mutations that abolish Treacle phosphorylation prevent its interaction with Pol-I? Experiments addressing these questions would enhance our understanding of the correlation/causation between Treacle phosphorylation and the effects of CDK inhibition on nucleolar stress.

We agree that the Treacle finding is interesting and warrants further investigation. In our attempts to knock down Treacle with siRNA, its protein levels were reduced by no more than 50%, which was not sufficient to cause a strong nucleolar stress response. Therefore, these data were not incorporated into the manuscript. However, in our view, Treacle is unlikely to be the only nucleolar CDK substrate whose dephosphorylation is causing the “bare scaffold” phenotype caused by the transcriptional CDK inhibitors. Our phospho-proteomics studies identified multiple nucleolar CDK substrates with established roles in the formation of the nucleolus. For instance, the granular component protein Ki-67 was also dephosphorylated on multiple sites and dispersed throughout the nucleus (shown in Sup. Fig 4). Given that CDKs typically phosphorylate many substrates that can have multiple phosphorylation sites, identifying a sole protein or phosphorylation site responsible for nucleolar disassembly may be an unattainable target.

Overall, this study is significant and novel as it sheds light on the importance of nucleolar stress in defining the on-target and off-target effects of chemotherapy in normal and cancer cells.

Thank you, we appreciate the positive and constructive assessment of our study.

Reviewer #2 (Public Review):

This is an interesting study with high-quality imaging and quantitative data. The authors devise a robust quantitative parameter that is easily applicable to any experimental system. The drug screen data can potentially be helpful to the wider community studying nucleolar architecture and the effects of chemotherapy drugs. Additionally, the authors find Treacle phosphorylation as a potential link between CDK9 inhibition, rDNA transcription, and nucleolar stress. Therefore I think this would be of broad interest to researchers studying transcription, CDKs, nucleolus, and chemotherapy drug mechanisms. However, the study has several weaknesses in its current form as outlined below.

1) Overall the study seems to suffer from a lack of focus. At first, it feels like a descriptive study aimed at characterizing the effect of chemotherapy drugs on the nucleolar state. But then the authors dive into the mechanism of CDK inhibition and then suddenly switch to studying biophysical properties of nucleolus using NPM1. Figure 6 does not enhance the story in any way; on the contrary, the findings from Fig. 6 are inconclusive and therefore could lead to some confusion.

This study was specifically designed to examine a broad range of chemotherapy drugs. The newly created nucleolar normality score enabled us to measure nucleolar stress precisely and in high throughput. Our primary objective was to find drugs that disrupt the normal nucleolar morphology and then study in-depth the most interesting and novel hits. We have made revisions to emphasize that these are the primary focal points of the manuscript.

As context, we were motivated to explore the biophysical properties of the nucleolus because they are thought to underlie its formation and function, which also suggested a potential predictive value for modeling nucleolar responses to drug treatments. For this, we edited the RPE1 cell line by endogenously tagging NPM1, a granular component protein that behaves in line with the phase-separation paradigm in vitro and when over-expressed. We fully expected to confirm that its behavior in vivo would be consistent with LLPS, but instead found that even in an untreated scenario, the dynamics of endogenous NPM1 could not be fully explained by the phase separation theory (Fig. 6 A-C). Our message is that accurately predicting drug responses using the nucleolar normality score as a readout, based on our current understanding of the biophysical forces governing nucleolar assembly, is unworkable. For instance, normality scores decrease and NPM1 dynamics increase radically when CDKs are inhibited, without changes in NPM1 concentration or concentrations of other protein components (Fig.6 E-H). These observations are important because they highlight our gaps in understanding the relative contribution of phase separation versus active assembly in nucleolar formation. We believe that these observations are worth sharing with the scientific community.

2) The justification for pursuing CDK inhibitors is not clear. Some of the top hits in the screen were mTOR, PI3K, HSP90, Topoisomerases, but the authors fail to properly justify why they chose CDKi over other inhibitors.

We decided to focus on CDK inhibitors for several reasons. First, their effects were completely new and unexpected, suggesting the existence of an unknown mechanism regulating nucleolar structure and function. In addition, CDK inhibitors caused a very strong and distinct nucleolar stress phenotype with the lowest normality scores that merited its own term, the “bare scaffold” phenotype. One more reason for pursuing CDK-inhibiting drugs was their high rate of failure in clinics because of the intense and hard-to-explain toxicity. We suspect that this toxicity may be due at least in part to their profound effect on nucleolar organization and ribosome production throughout the body. We stated this rationale more explicitly in the manuscript.

3) In addition to poor justification, it seems like a very superficial attempt at deciphering the mechanism of CDK9imediated nucleolar stress. I think the most interesting part of the study is the link between CDK9, Pol I transcription, and nucleolar stress. But the data presented is not entirely convincing. There are several important controls missing as detailed below.

We agree with the reviewer that follow-up studies of CDK9, Pol I, and nucleolar stress connection are important long-term goals. However, the primary objective of this study was to ascertain the scope of anticancer agents that can cause nucleolar stress and the establishment of nucleolar stress categories. This is an important advance and could serve as the foundation for a standalone in-depth study or multiple studies. We have included the complete screen, proteomics, and phospho-proteomics results (Sup. Tables 1, 2, and 3), which will enable other investigators to mine the screen information based on their specific interests. Furthermore, we have made multiple text revisions to clarify rationale and interpretation, and incorporated additional data that strengthen the manuscript.

4) The authors did not test if inhibition of CDK7 and/or CDK12 also induces nucleolar stress. CDK7 and CDK12 are also major kinases of RNAPII CTD, just like CDK9. Importantly, there are well-established inhibitors against both these kinases. It is not clear from the text whether these inhibitors were included in the screen library.

Our anticancer compound library contained CDK7 inhibitor THZ1⦁2HCL, and it was a hit at both 1 and 10 uM concentrations (Sup. Table 1). However, its nucleolar stress phenotype was morphologically distinct from CDK9 inhibitors, resembling the stress caps phenotype instead of the bare scaffold phenotype. We did not pursue CDK7 because of its two hard-to-separate functions: in addition to its role as an RNAPII CTD kinase, it also acts as a CDK-activating kinase (CAK) by promoting the associations of multiple CDKs with their cyclin partners. This dual role of CDK7 makes the interpretation of THZ1-induced nucleolar stress phenotype difficult because it could be attributed to either or both of these functions. Moreover, it was reported to cause DNA damage, which may explain why it causes stress caps. An image depicting nucleolar stress phenotype caused by THZ1⦁2HCL is provided in Author response image 1.

Author response image 1.

Control and THZ1 - treated RPE1 cells, images from screen plates.

We are not aware of specific inhibitors of CDK12, as they also reportedly inhibit CDK13. None of the CDK12/CDK13 inhibitors were present in our library, therefore we can neither confirm nor exclude the possible involvement of these kinases in regulating nucleolar structure. Many other existing CDK inhibitors were absent from our library. Our work highlights the importance of assessing their potential to induce nucleolar stress and offers an approach for this assessment.

5) In Figure 4E, the authors show that Pol I is reduced in nucleolus/on rDNA. The authors should include an orthogonal method like chromatin fractionation and/or ChIP

We acknowledge the reviewer’s request for additional validation of reduced occupancy of rDNA by Pol I.<br /> Nucleolar chromatin fractionation in cells treated with CDK inhibitors is unlikely to work due to nearly complete nucleolar disassembly. Chromatin immunoprecipitation would require finding and validating a suitable ChIP-grade antibody. Moreover, the evaluation of repetitive regions by ChIP is non-trivial and error-prone. To help address this request and further confirm the POLR1A immunofluorescence results in 4E, we included additional immunofluorescence data obtained with a different POLR1A antibody (Sup. Fig. 3D), and the results were similar.

6) In Fig. 5D, in vitro kinase lacks important controls. The authors should include S to A mutants of Treacle S1299A/S1301A to demonstrate that CDK9 phosphorylates these two residues specifically.

7) To support their model, the authors should test if overexpression of Treacle mutants S1299A/S1301A can partially phenocopy the nucleolar stress seen upon CDK9 inhibition. This would considerably strengthen the author's claim that reduced Treacle phosphorylation leads to Pol I disassociation from rDNA and consequently leads to nucleolar stress.

8) Additionally, it would be interesting if S1299D/S1301D mutants could partially rescue CDK9 inhibition.

Points (6-8):

We reiterate that transcriptional CDKs target multiple nucleolar proteins, and the observed phenotype might be due to the combined effects of de-phosphorylation of multiple substrates. We concur that deconstructing the role of Treacle phosphorylation sites is very interesting and warrants further in-depth studies. The phospho-proteomics enrichment method, while an effective first-pass strategy, might not capture 100% of the phosphorylated sites. Treacle is a phospho-protein with an abundance of serine and threonine residues. It could potentially have been selectively dephosphorylated on more sites than were detected by this method. Therefore, the suggested mutations may not be the exclusive contributors responsible for the functional phenotype. Additionally, overexpressing Treacle impairs the viability of RPE1 cells, complicating the interpretation of experiments involving overexpression of both wild-type and mutant proteins. A conceivable strategy would involve generating phosphomimetic and non-phosphorylatable mutants by gene editing, studying their interactions by biochemical approaches, and determining their impact on nucleolar function, but this may take years of additional work. We hope that our work will inspire further studies that explore Treacle phosphorylation and other functions of transcriptional CDKs in nucleolar formation.

Thank you for the thoughtful review and suggestions.

Reviewer #2 (Recommendations For The Authors):

1) The manuscript could be re-organized to focus on 'CDK9-Treacle-Pol I-nucleolar stress' as the central part of the story.

While we acknowledge this suggestion, it's important to emphasize that the primary focus of this manuscript is on the identification of anticancer drugs that induce nucleolar stress and the establishment of nucleolar stress categories.

2) Include a "no ATP" control in the in vitro kinase assay and indicate molecular sizes.

We provided an additional kinase assay (Sup. Fig. 4B) that includes no ATP control lanes and a fragment of a Coomassie blue stained gel showing molecular weight markers. No ATP control assays (lanes 4 and 5) were blank as expected. Molecular weight markers were added to all other kinase assays based on the known sizes of isolated Pol II holoenzyme subunits Rbp1 (191 kDa) and Rbp2 (138 kDa).

3) For in vitro phosphorylation, please provide an explanation for using CDK9/cyclin K instead of Cyclin T1 which is the predominant cyclin for CDK9

Recombinant CDK9/cyclin K complex was used for in vitro kinase assays for a technical reason: CDK9/cyclin T obtained from the same vendor appeared to be low quality, as it showed only minimal activity toward our positive control, the isolated Pol II complex. The kinase assays using recombinant CDK9/cyclin T in parallel with CDK9/cyclin K are now presented it Sup. Fig. 4B. The first two assays in this experiment contained Pol II as a substrate, and it is evident that Pol II was phosphorylated much stronger by CDK9/cyclin K than CDK9/cyclin T (comparing lane 1 vs lane 2). Therefore, the lack of detectable Treacle phosphorylation by CDK9/Cyclin T (lane 7), in contrast to strong phosphorylation by CDK9/cyclin K (lane 6), was likely attributable to poor reagent quality rather than physiological differences. We can conclude that CDK9/cyclin K reliably phosphorylates Treacle in vitro, but CDK9/cyclin T kinase assays were inconclusive.

Author response:

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

Reviewer #1:

Point 1: While the manuscript is methodologically sound, the following aspects of image acquisition and data analysis need to be clarified to ensure replicability and reproducibility. The authors state that the sample is a "population-derived adult lifespan sample", the lack of demographic information makes it impossible to know if the sample is truly representative. Though this may seem inconsequential, education may impact both cognitive performance and functional activation patterns. Moreover, the authors do not report race/ethnicity in the manuscript. This information is essential to ensure representativeness in the sample. It is imperative that barriers to study participation within minoritized groups are addressed to ensure rigor and reproducibility of findings.

First, the section Methods-Participants has been updated to refer readers to a prior article where the sample’s demographics are broken down into nine decile age groups (see Wu et al. 2023 Table 1), including information about their education levels. Secondly, we have updated the Data Availability section text to indicate that all Cam-CAN IDs are included in the available OSF datasets, allowing anyone to verify additional participant demographics described in the Cam-CAN protocol article (Shafto et al., 2014). Third, we have updated the Participants section text to refer to another prior study that reported on the representativeness of the Cam-CAN sample indicating that at least some elements of the sample have been independently deemed as representative (e.g., Sex).

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“A healthy population-derived adult lifespan human sample (N = 223; ages approximately uniformly distributed from 19 - 87 years; females = 112; 50.2%) was collected as part of the Cam-CAN study (Stage 3 cohort; Shafto et al., 2014). Participants were fluent English speakers in good physical and mental health, based on the Cam-CAN cohort’s exclusion criteria which includes poor mini mental state examination, ineligibility for MRI and medical, psychiatric, hearing or visual problems. Throughout analyses, age is defined at the Home Interview (Stage 1; Shafto et al., 2014). The study was approved by the Cambridgeshire 2 (now East of England–Cambridge Central) Research Ethics Committee and participants provided informed written consent. Further demographic information of the sample is reported in Wu et al. (2023) and is openly available (see section Data Availability) with a recent report indicating the representativeness of the sample across sexes (Green et al., 2018).”

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“Raw and minimally pre-processed MRI (i.e., from automatic analysis; Taylor et al., 2017) and behavioural data are available by submitting a data request to Cam-CAN (https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/). The univariate and multivariate ROI data, and behavioural data, can be downloaded from the Open Science Framework, which includes Cam-CAN participant identifiers allowing the retrieval of any additional demographic data (https://osf.io/v7kmh), while the analysis code is available on GitHub.”

Point 2: For the whole-brain analysis in which the ROIs were derived, the authors used a threshold-free cluster enhancement (TFCE; Smith & Nichols 2009). The methodological paper cited suggests that individuals' TCFE image should still be corrected for multiple comparisons using the following: "to correct for multiple comparisons, one [...] has to build up the null distribution (across permutations of the input data) of the maximum (across voxels) TFCE score, and then test the actual TFCE image against that. Once the 95th percentile in the null distribution is found then the TFCE image is simply thresholded at this level to give inference at the p < 0.05 (corrected) level." (Smith & Nichols, 2009). Although the authors mention that clusters were estimated using 2000 permutations, there is no mention of the TFCE image itself being thresholded. While this would impact the overall size of the ROIs used in the study, the remaining analyses are methodologically sound.

We have updated the text to detail the t=1.97 (i.e., p = .05) threshold we applied before interpretation of the resultant TFCE images to the section: Experimental Design & Statistical Analysis. This threshold value can also be verified in the analytics code that is referenced on GitHub from the section Data Availability within the requisite toolbox functions: https://github.com/kamentsvetanov/CommonalityAnalysis/blob/main/code/ca_vba_tfce_threshold.m#L24 and https://github.com/kamentsvetanov/CommonalityAnalysis/blob/main/code/external/ca_matlab_tfce_transform.m

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“For whole-brain voxelwise analyses, clusters were estimated using threshold-free cluster enhancement (TFCE; Smith & Nichols 2009) with 2000 permutations and the resulting images were thresholded at a t-statistic of 1.97 before interpretation.”

Point 3: The authors should consider moving the ROI section to results. The way the manuscript currently reads, the ROIs seem to be derived a priori as opposed to being derived from activation maps in the current study.

After consideration of this point, we have decided to leave the methodological details regarding the definition of ROIs in the methods, to maintain the focus of the Results section. However, we have improved signposting in the results section to highlight that the ROIs were derived from the overlapped activation maps.

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“Crucially, two areas of the brain showed spatially-overlapping positive effects of age and performance, which is suggestive of an age-related compensatory response (Figure 2A yellow intersection). These were in bilateral cuneal cortex (Figure 2B magenta) and bilateral frontal cortex (Figure 2B brown), the latter incorporating parts of the middle frontal gyri and anterior cingulate. Therefore, based on traditional univariate analyses, these are two candidate regions for age-related functional compensation (Cabeza et al. 2013; 2018). Accordingly, we defined regions of interest within these two regions using the overlap activation maps (see section: ROIs) to be used for subsequent univariate and multivariate analysis.”

Point 4: The manuscript can be strengthened by explaining why the authors chose a greedy search algorithm over a dynamic Bayesian model.

The text is updated to refer to appropriateness of the computationally efficient greedy search implementation, due to the size of the fMRI cohort dataset.

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“The pattern weights specifying the mapping of data features to the target variable are optimized with a greedy search algorithm using a standard variational scheme (Friston et al., 2007) which was particularly appropriate given the large dataset.”

Reviewer #2:

Point 1: However, it might have been nice to see an analysis of a more crystallised intelligence task included too, as a contrast since this is an area that does not demonstrate such a decline (and perhaps continues to improve over aging).

We (Samu et al., 2017) have previously investigated, but failed to find, univariate evidence for functional compensation in this cohort’s performance on a sentence comprehension task that is more closely aligned to a measure of crystallised intelligence. Based on the additional previous studies where we have applied these types of univariate and multivariate criteria of functional compensation (Morcom & Henson, 2018; Knights et al., 2021), we have consistently observed that the uni-/multivariate effects are in the same direction. Therefore, we would not initially expect a different conclusion here, where the univariate and multivariate effects suggest different outcomes. Notably, the univariate analysis approach in Samu et al. (2017) did differ from focusing on the age x behaviour interaction term here, so it could still be worth future investigation, but it does seem less likely that evidence of compensation would be observed than for fluid intelligence. However, as the Reviewer suggests, such a task may make another good contrast to show evidence against the existence of functional compensation (as in Morcom & Henson, 2018; Knights et al., 2021).

Point 2: Figure 1B: Consider adding coefficients describing relationships to plots.

Annotations of the coefficients have been added to Figure 1B:

Point 3: Figure 2C. The scale of the axis for RSFA-Scales cuneal cortex ROI activations should be the same as the other 3 plots.

Figure axes are updated such that ROIs are on matching scales, according to whether data were RSFA-scaled or not.

Point 4: Figure 2C. Adding in the age ranges for each of the three groups following the tertile split may be informative to the reader.

The age group tertile definition used for Figure 2C visualisations is now added to the Figure description.

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“Figure 2. Univariate analysis. (A) Whole-brain effects of age and performance. Age (green) and performance (red) positively predicted unique aspects of increased task activation, with their spatial overlap (yellow) being overlaid on a template MNI brain, using p < 0.05 TFCE. (B) Intersection ROIs. A bilateral cuneal (magenta) and frontal cortex (brown) ROI were defined from voxels that showed a positive and unique effect of both age and performance (yellow map in Figure 2A). (C) ROI Activation. Activation (raw = left; RSFA-scaled = right) is plotted against behavioural performance based on a tertile split between three age groups (19-44, 45-63 & 64-87 years).”

Reviewer #3:

Point 1: [Public Review] 1) I don't quite follow the argumentation that compensatory recruitment would need to show via non-redundant information carried by any given non-MDN region (cf. p14). Wouldn't the fact that a non-MDN region carries task-related information be sufficient to infer that it is involved in the task and, if activated increasingly with increasing age, that its stronger recruitment reflects compensation, rather than inefficiency or dedifferentiation? Put differently, wouldn't "more of the same" in an additional region suffice to qualify as compensation, as compared to the "additional information in an additional region" requirement set by the authors? As a consequence, in my honest opinion, showing that decoding task difficulty from non-MDN ROIs works better with higher age would already count as evidence for compensation, rather than asking for age-related increases in decoding boosts obtained from adding such ROIs. It would be interesting to see whether the arguably redundant frontal ROI would satisfy this less demanding criterion. At any rate, it seems useful to show whether the difference in log evidence for the real vs. shuffled models is also related to age.

We agree with the logic for conducting a weaker assessment of functional compensation whereby a brain region does not necessarily have to provide a unique contribution beyond that of the ordinarily activated task-relevant network. However, although non-unique recruitment is predicted by a compensation theory, it can also be explained by a nonspecific mechanism that recruits multiple regions in tandem. In contrast, unique additional recruitment is compatible with compensation but not with nonspecific recruitment. In this article, and those prior (Morcom & Henson, 2018; Knights et al. 2021), we have also deliberately avoided using the specific kind of analysis proposed (i.e., testing for an effect of age on differential log evidence) because these would involve applying statistical tests directly to the log evidence, a variable that is already a statistical test output.

Nevertheless, temporarily putting these caveats aside, we did run the suggested test. Results from multiple regression showed that using log evidence from frontal cortex models still did not meet this less demanding criterion for functional compensation as there was an effect of age in the opposite direction to that expected by functional compensation: there was a significant negative effect of age (t(218) = -7.95, p = < .001) indicating that as age increased, the difference in log evidence decreased. This effect is visualised below for transparency, but we preferred not to add this information to the article because we do not wish to encourage using this kind of analysis for the reason mentioned above. Thus, although our main multivariate test of interest is stringent, the additional step of mapping log evidence back to the boost-likelihood categories (e.g., boost vs. no difference to model performance) lends itself to the more appropriate logistic regression statistical approach.

Author response image 1.

Negative effect of age on MVB log evidence model outcomes for frontal cortex.

A different approach that could be taken to assess a more lenient definition of functional compensation would be to analyse the effects of age on the spread of multivariate responses predicting task difficulty (i.e., standard deviation of fitted MVB voxel weights; also see Morcom & Henson, 2018; Knights et al., 2021) specifically from models that only include the candidate ‘compensation’ ROIs.

Accordingly, these analyses and their discussion have been added to the article. To summarise, these analyses showed that (1) the frontal cortex still did not show evidence of functional compensation (i.e., a negative effect of age like in Morcom & Henson, 2018) and (2) no effect of age on the cuneal ROI, implying that the original model comparison approach (i.e., Figure 2C in the manuscript now) can provide more sensitivity for detecting evidence of functional compensation (perhaps because of the importance of including task-relevant network responses when building decoding models).

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“As a final analysis, we also tested a more lenient definition of functional compensation, whereby the multivariate contribution from the “compensation ROI” does not necessarily need to be above and beyond that of the task-relevant network (Morcom & Henson, 2018; Knights et al., 2021). To do this, we again assessed whether age was associated with an increase in the spread (standard deviation) of the weights over voxels, for smaller models containing only the cuneal or frontal ROI. This tested whether increased age led to more voxels carrying substantial information about task difficulty, a pattern predicted by functional compensation (but also consistent with non-specific additional recruitment). In this case, the results of this test did not support functional compensation, as there was no effect detected for the cuneal cortex and even a negative effect of age for the frontal cortex where the spread of the information across voxels was lower for older age (Figure 3C; Table 2).”

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“The age- and performance-related activation in our frontal region satisfied the traditional univariate criteria for functional compensation, but our multivariate (MVB) model comparison analysis showed that additional multivariate information beyond that in the MDN was absent in this region, which is inconsistent with the strongest definition of compensation. In fact, the results from the spread analysis showed that as age increased, this frontal area processed less, rather than more, multivariate information about the cognitive outcome (Figure 3C) as previously observed in two (memory) tasks for a comparable ROI within the same Cam-CAN cohort (Morcom & Henson, 2018).”

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“This said, univariate criteria for functional compensation will continue to play a role in hypothesis testing. For instance, the over-additive interaction observed in the cuneal cortex - where the increase in activity with better performance is more pronounced in older adults - offers stronger evidence of compensation compared to the simple additive effect of age and performance observed in the frontal cortex (Figure 2C). So far, the two studies that have combined these rigorous univariate, behavioral and multivariate approaches to assess functional compensation (i.e., Knights et al., 2021; the present study) have generally found converging evidence regardless of the method used. However, it is important to note that the MVB approach uniquely shifts the focus from individual differences to the specific task-related information that compensatory neural activations are assumed to carry and provides a specific test of region- (or network-) unique information. With further studies, it may also be that multivariate approaches prove more sensitive for detecting compensation effects than when using mean responses over voxels (e.g., Friston et al., 1995) particularly since over-additive effects are challenging to observe because compensatory effects are typically ‘partial’ and do not fully restore function (for review see Scheller et al., 2014; Morcom & Johnson, 2015). Within the multivariate analysis options themselves, it is also interesting to highlight that the stringent MVB boost likelihood analysis could detect functional compensation unlike the more lenient analysis focusing on the spread of MVB voxel weights. This suggests the importance of including task-relevant network responses when building decoding models to assess compensation.”

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“Alongside the MVB boost analysis, we also included an additional measure using the spread (standard deviation) of voxel classification weights (Morcom & Henson, 2018). This measure indexes the absolute amplitude of voxel contributions to the task, reflecting the degree to which multiple voxels carry substantial task-related information. When related to age this can serve as a multivariate index of information distribution, unlike univariate analyses. However, it is worth highlighting that even if an ROI shows an effect of age on this spread measure, such an effect could instead be explained by a non-specific mechanism that represents the same information in tandem across multiple regions (rather than reflecting compensation) as seen previously (Knights et al., 2021; also see Morcom & Johnson, 2015). Thus, it is the MVB boost analysis that is the most compelling assessment of functional compensation because it can directly detect novel information representation.”

Point 2: [Public Review] 2) Relatedly, does the observed boost in decoding by adding the cuneal ROI (in older adults) really reflect "additional, non-redundant" information carried by this ROI? Or could it be that this boost is just a statistical phenomenon that is obtained because the cuneus just happens to show a more clear-cut, less noisy difference in hard vs. easy task activation patterns than does the MDN (which itself may suffer from increased neural inefficiency in older age), and thus the cuneaus improves decoding performance without containing additional (novel) pieces of information (but just more reliable ones)? If so, the compensation account could still be maintained by reference to the less demanding rationale for what constitutes compensation laid out above.

We agree that this is a possibility and have added this as an additional explanation to the Discussion. We have also discussed why we think it is a less likely possibility, but do concede that it cannot be ruled out currently.

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“Another possibility is that the age-related increases in fMRI activations (for hard versus easy) in one or both of our ROIs do not reflect greater fMRI signal for hard problems in older than younger people, but rather lower fMRI signal for easy problems in the older. Without a third baseline condition, we cannot distinguish these two possibilities in our data. However, a reduced “baseline” level of fMRI signal (e.g., for easy problems) in older people is consistent with other studies showing an age-related decline in baseline perfusion levels, coupled with preserved capacity of cerebrovascular reactivity to meet metabolic demands of neuronal activity at higher cognitive load  (Calautti et al., 2001; Jennings et al., 2005). Though age-related decline in baseline perfusion occurs in the cuneal cortex (Tsvetanov et al., 2021), the brain regions showing modulation of behaviourally-relevant Cattell fMRI activity by perfusion levels did not include the cuneal cortex (Wu et al., 2023). This suggests that the compensatory effects in the cuneus are unlikely to be explained by age-related hypo-perfusion, consistent with the minimal effect here of adjusting for RSFA (Figure 2C).

One final possibility is whether the observed boost in decoding from adding the cuneal ROI simply reflects less noisy task-related information (i.e., a better signal-to-noise ratio (SNR)) than the MDN and, consequently, the boosted decoding is the result of more resilient patterns of information (rather than the representation of additional information) based on a steeper age-related decline of SNR in the MDN. Overall then, as none of the explanations above agree with all aspects of the results, to functionally explain the role of the cuneal cortex in this task would require further investigation.”

Point 3: [Public Review] 3) On page 21, the authors state that "...traditional univariate criteria alone are not sufficient for identifying functional compensation." To me, this conclusion is quite bold as I'd think that this depends on the unvariate criterion used. For instance, it could be argued that compensation should be more clearly indicated by an over additive interaction as observed for the relationship of cuneal activity with age and performance (i.e., the activity increase with better performance becomes stronger with age), rather than by an additive effect of age and performance as observed for the prefrontal ROI (see Fig. 2C). In any case, I'd appreciate it if the authors discussed this issue and the relationship between univariate and multivariate results in more detail (e.g. how many differences in sensitivity between the two approaches have contributed), in particular since the sophisticated multivariate approach used here is not widely established in the field yet.

We have now considered this point further in a section of the Discussion (which is merged with points 1 & 2 above) about the relevance and distinction of univariate / multivariate criteria for functional compensation. As described in text below, whilst we agree that univariate / behavioural approaches have a role in testing functional compensation, we still view the MVB boost analysis to be a particularly compelling approach for assessing this theory.

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“This said, univariate criteria for functional compensation will continue to play a role in hypothesis testing. For instance, the over-additive interaction observed in the cuneal cortex - where the increase in activity with better performance is more pronounced in older adults - offers evidence of compensation compared to the simple additive effect of age and performance observed in the frontal cortex (Figure 2C). However, the conclusions that can be drawn from age-related differences in cross-sectional associations of brain and behaviour are limited, mainly because individual performance differences are largely lifespan-stable (see Lindenberger et al., 2011; Morcom & Johnson, 2015). So far, the two studies that have combined these univariate-behavioral and multivariate approaches to assess functional compensation (i.e., Knights et al., 2021; the present study) have generally found converging evidence regardless of the method used. However, it is important to note that the MVB approach uniquely shifts the focus from individual differences to the specific task-related information that compensatory neural activations are assumed to carry. With further studies, it may also be that multivariate approaches prove more sensitive for detecting compensation effects than when using mean responses over voxels (e.g., Friston et al., 1995) particularly since over-additive effects are challenging to observe because compensatory effects are typically ‘partial’ and do not fully restore function. Within the multivariate analysis options themselves, it is also interesting to highlight that the stringent MVB boost likelihood analysis could detect functional compensation unlike the more lenient analysis focusing on the spread of MVB voxel weights. This suggests the importance of including task-relevant network responses when building decoding models to asses compensation.”

Point 4: [Public Review] 4) As to the exclusion of poorly performing participants (see p24): If only based on the absolute number of errors, wouldn't you miss those who worked (overly) slowly but made few errors (possibly because of adjusting their speed-accuracy tradeoff)? Wouldn't it be reasonable to define a criterion based on the same performance measure (correct - incorrect) as used in the main behavioural analyses?

This is a good point, though if we were to exclude participants using a chance level exclusion rate based on the formulae used for measuring behavioural performance, this removes identical subjects to those originally excluded. Based on this, the text has been updated to reflect this more parsimonious approach for defining exclusion criteria.

Page-25

“In a block design, participants completed eight 30-second blocks which contained a series of puzzles from one of two difficulty levels (i.e., four hard and four easy blocks completed in an alternating block order; Figure 1A). The fixed block time allowed participants to attempt as many trials as possible. Therefore, to balance speed and accuracy, behavioural performance was measured by subtracting the number of incorrect from correct trials and averaging over the hard and easy blocks independently (i.e., ((hard correct - hard incorrect) + (easy correct - easy incorrect))/2; Samu et al., 2017). For assessing reliability and validity, behavioural performance (total number of puzzles correct) was also collected from the same participants during a full version of the Cattell task (Scale 2 Form A) administered outside the scanner at Stage 2 of the Cam-CAN study (Shafto et al., 2014). Both the in- and out-of-scanner measures were z-scored. We excluded participants (N = 28; 17 females) who performed at chance level ((correct + incorrect) / incorrect < 0.5) on the fMRI task, leading to the same subset as reported in Samu et al. (2017).”

Point 5: [Public Review] 5) Did the authors consider testing for negative relationships between performance and brain activity, given that there is some literature arguing that neural efficiency (i.e. less activation) is the hallmark of high intelligence (i.e. high performance levels in the Cattell task)? If that were true, at least for some regions, the set of ROIs putatively carrying task-related information could be expanded beyond that examined here. If no such regions were found, it would provide some evidence bearing on the neural efficiency hypothesis.

No, we did not test for negative relationships between performance and brain activity in this study. However, In Wu et al. (2023) we did specifically test for this and neither of the relevant results reported in section 3.3.1 (i.e., unique relationship between activity and performance) nor section 3.3.2 (i.e., age-related relationship between activity and performance) showed the queried direction of effects. Note that the negative effect in section 3.3.2 (Age U Performance) is a more unique suppression effect representing a positive relationship between performance and activity where this becomes stronger as age is added to the model.

Point 6: [Recommendations for the authors] 1) Page 26: It is not quite clear how the authors made sure their age and performance covariates functioned as independent regressors in the univariate group-level GLM, given the correlation between age and performance (i.e. shared variance).

We included age and performance as covariates (of the age x performance effect of interest) by simply including these as independent regressors in the group-level GLM design matrix in addition to the interaction term (i.e., activity ~ age*performance + covariates equivalent to activity ~ age:performance + age + performance + covariates; Wilkinson & Roger 1973 notation), allowing us to examine the unique variance explained by each predictor (Table 1 and Table 2) and to control for their shared variance.

We should note that while the GLM approach we used accounts for unique and shared effects, it does not explicitly report shared effects in its standard output. To directly examine shared variance, one would need to employ commonality analysis. For reference, results from a commonality analysis on this task have been previously reported in Wu et al. (2023).

Prompted by this point, we have made some further minor improvements to help ensure our methodological steps are reproducible, as highlighted below.

Page-30

“Continuous age and behavioural performance variables were standardised and treated as linear predictors in multiple regression throughout the behavioural (Figure 1B), wholebrain voxelwise (Figure 1C/2A), univariate (Table 1; Figure 1B/2B) and MVB (Table 2; Figure 3) analyses. Throughout, sex was included as a covariate. The models, including interaction terms, can be described, according to Wilkinson & Roger’s (1973) notation, as activity ~ age * performance + covariates (which is equivalent to activity ~ age:performance + age + performance + covariates), allowing us to examine the unique variance explained by each predictor (Table 1) and to control for their shared variance. For whole-brain voxelwise analyses, clusters were estimated using threshold-free cluster enhancement (TFCE; Smith & Nichols 2009) with 2000 permutations and the resulting images were thresholded at a t-statistic of 1.97 before interpretation. Bonferroni correction was applied to a standard alpha = 0.05 based on the two ROIs (cuneal and frontal) that were examined. For Bayes Factors, interpretation criteria norms were drawn from Jarosz & Wiley (2014).”

Point 7: [Recommendations for the authors] 2) Figure 3: I suggest changing the subheading in panel B to "Joint vs. MDN-only Model," in line with the wording in the main text.

The subheading of Figure 3B is updated as suggested to `Joint vs. MDN-only Model`.

Point 8: [Recommendations for the authors] 3) In Figures 1C and 2A, MNI z coordinates should be added to the section views. The appreciation of Figure 2B could be enhanced by adding some rendering with a saggital (medial and/or lateral) view.

The slice mosaics in Figure 1C and 2A are now updated with each slice’s MNI Z coordinates and mentioned in the figure descriptions.

Point 9: [Recommendations for the authors] 4) Page 7 (l. 135): What exactly is meant by "lateral occipital temporal cortex"?

The text is updated to specify the anatomical landmarks that were used for guidance when referring to activation within the lateral occipital temporal cortex, based on ROI criteria definitions used in Knights, Mansfield et al. (2021):

Page-7 Line-135:

“Additional activation was observed bilaterally in the inferior/ventral and lateral occipital temporal cortex (i.e., a cluster around the lateral occipital sulcus that extended anteriorly beyond the anterior occipital sulcus), likely due to the visual nature of the task.”

Point 10: [Recommendations for the authors] 5) On p18ff. (ll. 259-318) the authors discuss in quite some detail how the age-related decoding boost seen with the cuneus ROI can be functionally explained, but it seems like none of the explanations agrees with all aspects of the results. While this is not a major problem for the paper, it may be advisable if this part of the discussion ends with a clearer statement that this issue is not fully solved yet and provides material for future research.

A more direct sentence has been added to make it clear that future investigation will be needed to explain the role of the cuneal cortex here.

Page-20 Line-322:

“Another possibility is that the age-related increases in fMRI activations (for hard versus easy) in one or both of our ROIs do not reflect greater fMRI signal for hard problems in older than younger people, but rather lower fMRI signal for easy problems in the older. Without a third baseline condition, we cannot distinguish these two possibilities in our data. However, a reduced “baseline” level of fMRI signal (e.g., for easy problems) in older people is consistent with other studies showing an age-related decline in baseline perfusion levels, coupled with preserved capacity of cerebrovascular reactivity to meet metabolic demands of neuronal activity at higher cognitive load  (Calautti et al., 2001; Jennings et al., 2005). Though age-related decline in baseline perfusion occurs in the cuneal cortex (Tsvetanov et al., 2021), the brain regions showing modulation of behaviourally-relevant Cattell fMRI activity by perfusion levels did not include the cuneal cortex (Wu et al., 2021). This suggests that the compensatory effects in the cuneus are unlikely to be explained by age-related hypo-perfusion, consistent with the minimal effect here of adjusting for RSFA (Figure 2C). Overall then, as none of the explanations above agree with all aspects of the results, to functionally explain the role of the cuneal cortex in this task will require further investigation.”

Point 11: [Recommendations for the authors] 6) The threshold choice for Bayesian log evidence (> 3) should be motivated in some more detail, rather than just pointing to a book reference, as there is no established convention in the field, the choice may depend on the type of data and/or analysis, and a sizeable part of the readership may not be deeply familiar with the particular Bayesian approach used here.

Text is updated to further clarify our motivation for using the log evidence BF>3 criterion:

Page-29

“The outcome measure was the log evidence for each model (Morcom & Henson, 2018; Knights et al., 2021). To test whether activity from an ROI is compensatory, we used an ordinal boost measure (Morcom & Henson, 2018; Knights et al., 2021) to assess the contribution of that ROI for the decoding of task-relevant information (Figure 3B). Specifically, Bayesian model comparison assessed whether a model that contains activity patterns from a compensatory ROI and the MDN (i.e., a joint model) boosted the prediction of task-relevant information relative to a model containing the MDN only. The compensatory hypothesis predicts that the likelihood of a boost to model decoding will increase with older age. The dependent measure, for each participant, was a categorical recoding of the relative model evidence to indicate the outcome of the model comparison. The three possible outcomes were: a boost to model evidence for the joint vs. MDN-only model (difference in log evidence > 3), ambiguous evidence for the two models (difference in log evidence between -3 to 3), or a reduction in evidence for the joint vs. MDN-only model (difference in log evidence < -3).These values were selected because a log difference of three corresponds to a Bayes Factor of 20, which is generally considered strong evidence (Lee & Wagenmakers, 2014). Further, with uniform priors, this chosen criterion (Bayes Factor > 3) corresponds to a p-value of p<~.05 (since the natural logarithm of 20 equals three, as evidence for the alternative hypothesis).”

Point 12: [Recommendations for the authors] 7) Adding page numbers would be helpful.

Page numbers have been added to the manuscript file – apologies for this oversight.

References

Green, E., Bennett, H., Brayne, C., & Matthews, F. E. (2018). Exploring patterns of response across the lifespan: The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study. BMC Public Health, 18, 1-7.

Knights, E., Mansfield, C., Tonin, D., Saada, J., Smith, F. W., & Rossit, S. (2021). Hand-selective visual regions represent how to grasp 3D tools: brain decoding during real actions. Journal of Neuroscience, 41(24), 5263-5273.

Samu, D., Campbell, K. L., Tsvetanov, K. A., Shafto, M. A., & Tyler, L. K. (2017). Preserved cognitive functions with age are determined by domain-dependent shifts in network responsivity. Nature communications, 8(1), 14743.

Shafto, M. A., Tyler, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., ... & Cam-CAN. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC neurology, 14, 1-25.

Wu, S., Tyler, L. K., Henson, R. N., Rowe, J. B., & Tsvetanov, K. A. (2023). Cerebral blood flow predicts multiple demand network activity and fluid intelligence across the adult lifespan. Neurobiology of aging, 121, 1-14.

Author response:

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

Reviewer #1 (Public Reviewer):

It is not clear from the analysis presented in the paper how persistent those environmentally induced changes, do they remain with the bats till the end of their lives.

Currently, the long-term effects of enrichment on the bats remain uncertain. Preliminary results suggest that these differences may persist throughout the bats’ lifetimes; however, further data analysis is ongoing to determine the extent of these effects. We also addressed now at the manuscript discussion

Reviewer #2 (Public Reviewer):

(1) Assessing personality metrics and the indoor paradigm: While I applaud this effort and think the metrics used are justified, I see a few issues in the results as they are currently presented:

(a) [Major] I am somewhat concerned that here, the foraging box paradigm is being used for two somewhat conflicting purposes: (1) assessing innate personality and (2) measuring changes in personality as a result of experience. If the indoor foraging task is indeed meant to measure and reflect both at the same time, then perhaps this can be made more explicit throughout the manuscript. In this circumstance, I think the authors could place more emphasis on the fact that the task, at later trials/measurements, begins to take on the character of a "composite" measure of personality and experience.

Personality traits should generally be stable over time, but personality can also somewhat change with experience. We used the foraging box to assess individual personality, but we also examined the assumption that what we are measuring is a proxy of personality and hence is stable over time. We now clarify this in the manuscript. 

(b) [Major] Although you only refer to results obtained in trials 1 and 2 when trying to estimate "innate personality" effects, I am a little worried that the paradigm used to measure personality, i.e. the stable components of behavior, is itself affected by other factors such as age (in the case of activity, Fig. 1C3, S1C1-2), the environment (see data re trial 3), and experience outdoors (see data re trials 4/5).

We found that boldness was the most consistent trait, showing persistence between trials 1 to 5, i.e., 144 days apart on average. We thus also used Boldness as the primary parameter for assessing the effects of personality on the outdoors behavior. While we evaluated other traits for completeness, boldness was the only one that consistently met the criteria for personality, which is why we focused on it in our analyses. The other traits which were not stable over time could be used to assess the effects of experience on behavior

Ideally, a study that aims to disentangle the role of predisposition from early-life experience would have a metric for predisposition that is relatively unchanging for individuals, which can stand as a baseline against a separate metric that reflects behavioral differences accumulated as a result of experience.

I would find it more convincing that the foraging box paradigm can be used to measure personality if it could be shown that young bats' behavior was consistent across retests in the box paradigm prior to any environmental exposure across many baseline trials (i.e. more than 2), and that these "initial settings" were constant for individuals. I think it would be important to show that personality is consistent across baseline trials 1 and 2. This could be done, for example, by reproducing the plots in Fig. 1C1-3 while plotting trial 1 against trial 2. (I would note here that if a significant, positive correlation were to be found (as I would expect) between the measures across trial 1 and 2, it is likely that we would see the "habituation effect" the authors refer to expressed as a steep positive slope on the correlation line (indicating that bold individuals on trial 1 are much bolder on trial 2).)

We agree and thus used boldness which was found to be stable over five trials (three of which were without external experience). We note that if Boldness as we measured it increased over time, the differences between individuals remained similar and this is what is expected from personality traits measured in the same paradigm several times (after the animal acquires experience).  

(c) Related to the previous point, it was not clear to me why the data from trial 2 (the second baseline trial) was not presented in the main body of the paper, and only data from trial 1 was used as a baseline.

We added a main figure, showing the correlation between the two baseline trials

In the supplementary figure and table, you show that the bats tended to exhibit more boldness and exploratory behavior, but fewer actions, in trial 2 as compared with trial 1. You explain that this may be due to habituation to the experimental setup, however, the precise motivation for excluding data from trial 2 from the primary analyses is not stated. I would strongly encourage the authors to include a comparison of the data between the baseline trials in their primary analysis (see above), combine the information from these trials to form a composite baseline against which further analyses are performed, or further justify the exclusion of data as a baseline.

We had no intention of excluding data from baseline 2. As we have shown several times before (e.g., Harten, 2021) bats’ boldness as we measure it in the box experiment increases over sessions performed nearby in time. This means that trial 2’s boldness was higher than that of trial 1 and trial 3 which made the data less suitable for a Linear model. Moreover, our measurement of boldness is capped (with a maximum of 1) again making it less suitable for a Linear model. However, following the reviewer’s question we now ran all analyses with trial 2’s data included and not only that the results remained the same, some of the models fit better (based on the AIC criterion). We added this information to the revised manuscript.  

(2) Comparison of indoor behavioral measures and outdoor behavioral measures Regarding the final point in the results, correlation between indoor personality on Trial 4 and outdoor foraging behavior: It is not entirely clear to me what is being tested (neither the details of the tests nor the data or a figure are plotted). Given some of the strong trends in the data - namely, (1) how strongly early environment seems to affect outdoor behavior, (2) how strongly outdoor experience affects boldness, measured on indoor behavior (Fig. 1D) - I am not convinced that there is no relationship, as is stated here, between indoor and outdoor behavior. If this conclusion is made purely on the basis of a p-value, I would suggest revisiting this analysis.

We agree that the relationship between indoor personality measures and outdoor foraging behavior is of great interest and had expected to find some correspondence between the two. To test this, we conducted multiple GLM analyses using the different indoor behavioral traits as predictors of outdoor behaviors. These analyses did not reveal any significant correlations. We also performed a separate analysis using PC1 (derived from the indoor behavioral variables) as a predictor, and again found no significant associations with outdoor behavior.

We were indeed surprised by this outcome. It is possible that the behavioral traits we assessed indoors (boldness, exploration, and activity) do not fully capture the dimensions of behavior that are most relevant to foraging in the wild. For example, traits such as neophobia or decisionmaking under risk, which we did not assess directly, may have had stronger predictive value for outdoor behavior. We now highlight this point more clearly in the Discussion and acknowledge the possibility that alternative or additional personality traits might have revealed meaningful relationships.

(3) Use of statistics/points regarding the generalized linear models While I think the implementation of the GLMM models is correct, I am not certain that the interpretation of the GLMM results is entirely correct for cases where multivariate regression has been performed (Tables 4s and S1, and possibly Table 3). (You do not present the exact equation they used for each model (this would be a helpful addition to the methods), therefore it is somewhat difficult to evaluate if the following critique properly applies, however...)

The "estimate" for a fixed effect in a regression table gives the difference in the outcome variable for a 1 unit increase in the predictor variable (in the case of numeric predictors) or for each successive "level" or treatment (in the case of categorical variables), compared to the baseline, the intercept, which reflects the value of the outcome variable given by the combination of the first value/level of all predictors. Therefore, for example, in Table 4a - Time spend outside: the estimate for Bat sex: male indicates (I believe) the difference in time spent outside for an enriched male vs. an enriched female, not, as the authors seem to aim to explain, the effect of sex overall. Note that the interpretation of the first entry, Environmental condition: impoverished, is correct. I refer the authors to the section "Multiple treatments and interactions" on p. 11 of this guide to evaluating contrasts in G/LMMS: https://bbolker.github.io/mixedmodelsmisc/notes/contrasts.pdf

We are not certain we fully understand the comment; however, if our understanding is correct, we respectfully disagree. A GLM analysis without interaction terms—as conducted in our study—functions as a multiple linear regression, wherein each factor's estimate reflects its individual effect on the dependent variable. For example in the case of sex, it examines he effect of sex on the tie spent out independently of enrichment. An interaction term would be needed to test sex*enrichment. We have added the models’ formula, and we hope this clarifies our approach

Reviewer #1 (Recommendations for the authors):

I would recommend the following:

(1) As video tracking and behavioral analysis softwares are wide spread, it would be great to see this applied to the bat behavior indoor to answer questions like how does the bat velocity or heading or acceleration correlate with the behavioral measures boldness , activity or exploration? In the same gist, can one infer boldness, activity or exploration from measured bat velocity or other parameters? I think this will further make the indoor behavior more quantitative.

In a tent of the size used in our study, bats’ flight behavior tends to be highly stereotypical: they typically perch on the wall, take off, circle the tent—sometimes multiple times—and then either land or not, and enter or not. Flight velocity is largely determined by individual maneuverability and the physical constraints of the space; thus, precise tracking is unlikely to provide further insight into boldness. In contrast, decision-making behaviors—such as whether to land or enter—more accurately reflect personality traits, as we have shown previously (Harten et al., 2018). Moreover, accurate 3D tracking in such an environment is possible but definitely not easy due to the many blind-spots resulting from the cameras being inside the 3D volume.  Nonetheless, we quantified flight activity and assessed its correlation with the other behavioral axes. As it was highly correlated with general activity, we did not include it as an independent parameter in the main analysis. However, in response to the reviewer’s suggestion, we now present this analysis in the Supplementary Materials.

(2) It is not clear whether the bats come from the same genetic background. they might be but it is not mentioned in the methods under the experimental subjects.

We have shown in the past that there is no familial relations in a randomly caught sample of bats in the colony where we usually work (Harten et al., 2018). The bats were caught in three, not related wild colonies. The text referring to the table was clarified in the revised manuscript

(3) It will be great to include the author's thoughts about mechanisms underlying those environmentally induced changes in behavior in the discussion section along with how this will affect the bats' social foraging abilities. Another question that comes to mind is whether growing up with a large number of bats constitute an enriched environment in itself.

We agree that this could count as an enrichment, and we thus ensured similar group sizes in both groups for this reason. We clarify this in the revised manuscript. 

We have elaborated on the underlying mechanisms in the discussion, focusing on how they contribute to behavioral changes.

Reviewer #2 (Recommendations for the authors):

(1) Outdoor foraging behavior

If I understand correctly, the data you display in Fig. 3A is only from the 2nd to 3rd weeks of exploration, i.e. just before the first post-exploration trial.

What does the data look like for the second outdoor exploration data, i.e. before the final trial?

Is there a specific reason why these measures were only computed on the GPS data from the 3rd week outside? If so, can this sampling of the data be motivated or briefly addressed (in the methods and wherever else necessary)?

In order to allow a comparison between individuals, we had to restrict ourself to a period we had data from many individuals (some dissapeared later on).

Following the reviewer suggestion – we added a supplemenry figure including days 21-26

I would find it important and of great interest to see movement maps for more animals, as these give very rich information that is not entirely captured by the three proxies of outdoor activity.

Are these four exemplary animals sampled from both seasons?

Did you check to see if there were any overall differences in outdoor foraging behavior as a function of the season in which the bats were captured?

Yes, the samples represent individuals from both tested years. This was clarified, and additional examples were included in a supplementary figure.

Variable of time spent outdoors: You mention that you did not include the nights that the bat spent in the colony in these calculations. Did you also look to see if 'the number of nights when the bats left the colony' predicted the bat's earlier enrichment treatment? This could also be interesting to consider.

In response to the reviewer’s comment, we conducted an additional analysis to test whether the proportion of nights each bat spent foraging outside the roost was predicted by its earlier environmental condition (enriched vs. impoverished). We also examined whether sex or age influenced this variable. This analysis showed no significant effect of environmental condition, sex, or age on the proportion of nights spent foraging outside the roost

[Following on point 3 in public review...]

When wishing to discuss the effect/significance of predictors overall, it is common to present the modelling results as an analysis of variance table. See, for example, the two-way anova section (p. 182) in the book Practical Regression and ANOVA using R: https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf

I think the output of passing the model object to an "anova" yields the table that you may be looking for, where the variance accounted for by a predictor is given overall, and not just relative to the first level of all predictors. Naturally, this information can be used in combination with the information provided by the raw model output presented in the paper.

I assume you have done this analysis in R, but am not sure, as the statistical software used is not mentioned. There are several packages in R that allow users to quickly plot the graphical interaction of the parameters they use in models, which aids in interpreting results. It would be good to check results of model fitting in this manner.

Relatedly, I was unable to locate the data and code for this paper using the DOI provided. Neither searching the internet using the doi nor entering the doi on the Mendeley Data website returned the right results. I tried searching Mendeley Data using the senior author's last name, but the most recent entry does not appear to be from this paper. https://data.mendeley.com/datasets/fr48bmnhxj/1

We thank the reviewer for the helpful comment. The analysis was indeed conducted in MATLAB, and this has now been clarified in the manuscript. We have also revised the result tables to improve clarity and included the exact formulas used for each model. Regarding the data availability, the reviewer is correct — the dataset had not yet been published at the time of submission. It is now available at the provided DOI link.

### Suggestions and questions for the present paper, grouped thematically:

[Major] Expansion and development of results: I thought there were many interesting and suggestive points in this data that could be expanded upon. I mention some of these here. While the authors of course do not need to implement all of these suggestions, I think the paper would benefit from a more substantial presentation of this rich data set:

(a) Individual differences as such are not emphasized in the paper so much, as the analyses, particularly those expressed as boxplots, are grouped. The scatter plots in Figure 1 give the richest insight into how individual behavior changes throughout the course of the experiment. I would advocate for the authors to show additional comparisons using such scatter plots (perhaps in the supplementary, if needed).

We thank the reviewer and added scatter plots to figure 2

(b) In the second paragraph of the results, the authors introduce the concept of a pareto front and that of personality archetypes (lines 101-107). I found this very interesting, but these concepts were never reiterated upon later in the results or in the discussion. In fact, at many points, I found myself curious as to how the three indoor measures of personality might be combined to form a composite measure of personality (and likewise for outdoor measures). Have you tried to combine measures into a composite and tried to measure whether this composite metric provides any additional insight into these phenomena? For example, what if you mapped the starting position of each bat as a point in a three-dimensional space, given by the three personality measures, and then evaluated their trajectory through this space with measurements taken at later trials. Could innate personality be interpreted as the starting vector in this space (measured across the two baseline trials)? 

Following the reviewer’s (justified) curiosity we ran a PCA analysis on the behavioral data from trials 1 and 5 and found that there is a significant correlation between the individual scores on PC1. This can be thought of as a measurement that takes both boldness and exploration into account (the weight of activity was very low). We added this information to the revised manuscript and also use this new behavioral parameter as a predisposition in the models (instead of exploration and activity). 

Could environmental exposure be quantified as a warping of the trajectory through this space? Finally, could outdoor experience also be incorporated to evaluate how an individual arrives at its final measurement of personality combined with experience (trial 5)?

The paper currently tries to explain outdoors behavior given personality and not vice versa. While this is a very interesting suggestion, we feel that adding this analysis would make the premise of the paper less clear and since the paper is already somewhat complex, we prefer to leave this analysis for a future study. 

Examining the 3D trajectories of the individuals through the personality space did not reveal any immediate clear pattern (triangles mark the first trial and colours depict the environmental treatment) – 

Author response image 1.

Related to this point: I think the strongest part of the paper is the result showing that bats exposed to enriched environments explore farther, more often, and over larger distances than bats that were raised in an impoverished environment.

We completely agree and tried to further emphasize this  

(c) While these results of the outdoor GPS tracking are very clear, I wish that more information were extracted from the tracking data, which is incredibly rich and certainly can be used to derive many interest parameters beyond those that the authors have shown here. Examples might include: distance travelled (as opposed to estimated km2 or farthest point), a metric of navigational ability (how much "dead reckoning" the animal engages in). I even wonder if the areas or landmarks visited by the enriched bats might be found to be more complex, challenging, or richer by some measure.

This study was a first step, aiming to establish a connection between early exposure and outdoors foraging

We agree that there are many more analyses that can be done and indeed that ones related to navigation capabilities are missing. We are still collecting data on these bats and hope to present a more advanced analysis with a time span of years. 

(d) Related to the above point: I find it very interesting that in 3 of the 4 bats for which you show exemplary movement data (Fig. 3, panels B and C), they appear to travel to the farthest distances and cover the most ground early on, and become more "conservative" in their flight paths on later evenings. This point is not explored in the discussion, nor related to earlier measurements.

During the first months of exploration, bats will occasionally perform long exploratory flights in between bouts of shorter flights where they return to nearby familiar trees. This behavior can be seen in more detail in Harten et al Science 2020. We are currently quantifying this more carefully for another study. 

(e) Finally, my points about the possible strength of a composite measure of the three personality metrics is related to my concern about one of the conclusions, which is that innate personality does not have an effect on outdoor foraging behavior. I think the manner in which this was tested statistically is likely to bias the results against finding such a result given that personality metrics are used to predict outdoor behaviors in an individual manner (6 models in total, each examining a single comparison of predisposition to outdoor behavior), while both indoor personality metrics (Fig 1B) and outdoor behaviors appear to be correlated with each other (Table 5).

Are there other analyses you have performed that are not presented in the paper and that have led you to conclude that there is no relationship here?

We agree with the reviewer, that our findings do not exclude an effect of innate personality on foraging but only suggest no such affect for the parameter we measured. That said, we did expect to find an effect of boldness because this parameter has been shown to differentiate much between groups (Harten et al., 2018), and to correlate with other parameters of behavior. We were therefore surprised to find no significant effects, as we had anticipated observing some differences.

Following the reviewer’s previous comment we now also tested another predisposition parameter – the PC1 score and also found that it did not explain foraging. 

(f) Personality measured before and after early environmental exposure (related to point (a) above): I find it interesting that the positive correlation in boldness between baseline and post-enrichment or baseline and post-release suggests that the individuals that were the most bold remained bold (and likewise for less adventurous individuals). The correlation for activity, too, still suggests that more active individuals early in life are likely to remain very active after enrichment, even accounting for the fact that activity is confounded with age.

Perhaps you could place some emphasis on the fact that the initial variation between individuals also appears to be relatively stable over repeated trials. You might also consider measuring this directly (population variance over successive trials; relationship of population variance on indoor measures vs. outdoor measures...)

Yes – this is a main point of interest. We further emphasize that in the revised manuscript 

(g) Effect of indoor behavior following early experience on outdoor behavior: You evaluate the effect of predisposition (measured on baseline trial 1) and environmental condition on measures of outdoor activity (Table 4). I wonder if you also tried using indoor behavioral measures measured on the post-enrichment trial 3 to predict outdoor foraging behavior.

Assuming that these measures are in fact reflecting a combination of predisposition and accumulated experience, then measurements at this closer time point may tell you how the combination of innate traits and early acquired experience affect behavior in the wild.

We appreciate the reviewer’s insightful suggestion to test whether indoor behavior from post-enrichment Trial 3, reflecting both innate traits and experience, predicts outdoor foraging behavior. We conducted this analysis, but found that the boldness in Trial 3 did not significantly predict any of the outdoor activity measures.

(2) [Minor] Age/development: While the authors discuss the effect of their manipulations on behavioral measures, they do not much discuss the effect of age.

I think it would be important to include at some point a mention of the developmental stages of Rousettus, giving labels to certain age ranges, e.g. pup, juvenile, adult, and to provide more context about the stages at which bats were tested in the discussion. Presently, age is only really mentioned as an explanation for declining activity levels, but I wonder if it might also have an influence on boldness.

It would also be very elegant for figures where age is given in days, to additional label then with these stages.

All bats were juveniles during the trials (approximately 4 to 8 months old), so they could not be divided into distinct age groups. To assess the effect of age, it was included as a predictor (in days) in the GLM analysis.

(3) [Major] Effect of early experience and outdoor experience on the indoor task: In the paragraph on lines 278-285, you argue that the effect of seeing earlyenriched bats exhibit more boldness in trial 5 was likely due to post-sampling bias...

I tend to disagree with this conclusion. I actually find this result both interesting and intuitive - that bats that were exposed to an enriched environment and have had experience in the wild, show much bolder activity on a familiar indoor foraging test (i.e. outside experience has made the animals bolder than before) (Fig 1, lines 159-161, Fig. S1). I did not notice this possibility mentioned in the discussion of the results.

I also do not fully understand this argument. Could you please explain further?

We accept the reviewer's comment and updated the manuscript (lines 336346) explaining the two hypotheses more clearly and arguing that it is difficult to tell them apart with the current data.

[Minor] You also say that "this difference... can be seen in Figure 2 when examining only the bats that had remained until the last trial (Figure 2A2)." Do you mean supplementary Figure S1 A2? In fact, I am entirely unclear on what data is plotted in the supplementary Figure S1 and what differentiates the two columns of figures and the two models presented in the supplementary table. Did you plot data similar to that in Figure 2, with only bats that were present for all trials, but not show this data?

There was a mistake: what was previously referred to as 2A2 is actually S2 A2.

On the right side—only among the individuals with GPS data—the change is already evident at Baseline 2, where only the bolder individuals remain. If you have suggestions for a better analysis approach, we would be happy to hear them.

### Minor points

General points regarding figures:

For Figures 2 and 3A1-3 (as well as Fig. S1): Authors must show the raw data points over the box plots. It is very difficult to interpret the data and conclusions without being able to see the true distribution.

Done

For all figures showing grouped individual data, please annotate all panels or sets of boxplots with the number of bats whose data entered into each, as it is a little difficult to keep track of the changing sample sizes across experimental stages.

To enhance transparency, we have added individual data points to all boxplots, allowing visual estimation of sample sizes across experimental stages. While numerical annotations are not included on the figures, the exact number of bats contributing to each group is provided in the Methods section (Table 8), ensuring this information is readily accessible to readers.In response to the reviewer’s request, we have updated all relevant figures to display individual data points within each boxplot. This addition makes it easier to track changes in sample size across different experimental stages.

Unless I've missed the reason behind differences in axis labelling across the figures, it seems that trials are not always referred to consistently. E.g. Fig. 1 labels say "Trial 1 (baseline)" and fig. 2 labels say "Baseline 1 0 days." I'm not entirely sure if these correspond to exactly the same data. If so, perhaps the labels can be made uniform. I think the descriptive ones (Baseline 1, Postenrichment...) may be more helpful to the reader than providing the trial number (Trial 1, etc....).

Done

Figure 1:

Very good Fig. 1A and 1B.

For panels C1-3 & D, I think it would make it easier for the reader if the personality measure labels were placed at the top of each panel, e.g. "Boldness (entrance proportion)". The double axis labels are not only harder to read, they are also redundant, as the personality measure label repeats on both axes.

Done

Panel C1: For the first panel in this sequence, I think it would be elegant to include an annotation in the figure that indicates what the datapoints lying on either side of the dashed line means, i.e. "bolder after enrichment treatment" in the upper left corner, and "bolder before enrichment treatment" in the bottom right corner.

Panel C2: It appears as though many of the data points in this panel overlap, and it appears to me that the blue data points in particular are overlaid by the orange ones. I am guessing this happens because proportion values based on entrances to only 6 boxes end up giving a more "discrete" looking distribution. I wonder if you can find a way to allow all the data to be visible by, e.g., jittering the data slightly; if there is rounding being done to the proportions, perhaps don't round them so that minute differences will allow them to escape the overlap; or possibly split the panel by enrichment treatment.

Caption for C1-3: it may be helpful to mention the correlation line color scheme: "enriched (blue lines), the impoverished (orange lines)". The caption also says positive correlations were found for "both environments together," but this correlation line is not shown. Perhaps mention "(not shown)" or show line. Please rephrase the sentence "Dashed line represents the Y=X line." for more transparency and clarity. I understand you mean an "equality" or "unity" line, but perhaps you can explicitly state the information that this line provides, something like e.g. "Dashed line indicates equal values measured on both trials."

We added the line for a reference, the caption was corrected

Figure 3:

Panels B1-C2: I would suggest giving these panels supertitles that indicate that B panels are enriched, C panels are impoverished, and that each panel is data from a different individual.

The legend was corrected to be more clear about the figure

General points regarding tables:

Please revisit tables for formatting and typos, particularly in Table 4. Please also revise table captions for clarity. E.g. "first exploration as predisposition" to "Exploration (Baseline 1)" or similar

Done

Supplementary Tables and Figure: these are missing captions and explanations.

The missing parts were adddad and corrected

Points of clarification/style:

It would seem to me more logical to present the results shown in Table 3 before those in Table 2, given that the primary in-lab manipulation is discussed with relation to Table 3, and the analysis in Table 2 is discussed rather as a limitation (though I believe this result can be expanded upon further, see above).

For the activity metric, I would suggest showing this data as actions/hour instead of actions/minute. I think it is much more intuitive to consider, for example, that a bat makes 2 actions every hour, than that it makes 0.002 actions per minute.

Done

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors present a model for multisensory correlation detection that is based on the neurobiologically plausible Hassenstein Reichardt detector. It modifies their previously reported model (Parise & Ernst, 2016) in two ways: a bandpass (rather than lowpass) filter is initially applied and the filtered signals are then squared. The study shows that this model can account for synchrony judgement, temporal order judgement, etc in two new data sets (acquired in this study) and a range of previous data sets.

Strengths:

(1) The model goes beyond descriptive models such as cumulative Gaussians for TOJ and differences in cumulative Gaussians for SJ tasks by providing a mechanism that builds on the neurobiologically plausible Hassenstein-Reichardt detector.

(2) This modified model can account for results from two new experiments that focus on the detection of correlated transients and frequency doubling. The model also accounts for several behavioural results from experiments including stochastic sequences of A/V events and sine wave modulations.

Additional thoughts:

(1) The model introduces two changes: bandpass filtering and squaring of the inputs. The authors emphasize that these changes allow the model to focus selectively on transient rather than sustained channels. But shouldn't the two changes be introduced separately? Transients may also be detected for signed signals.

We updated the original model because our new psychophysical evidence demonstrates the fundamental role of unsigned transient for multisensory perception. While the original model received input from sustained unimodal channels (low-pass filters), the new version receives input from unsigned unimodal transient channels. Transient channels are normally modelled through bandpass filters (to remove the DC and high-frequency signal components) and squaring (to remove the sign). While these may appear as two separate changes in the model, they are, in fact, a single one: the substitution of sustained with unsigned transient channels (for a similar approach, see Stigliani et al. 2017, PNAS). Either change alone would not be sufficient to implement a transient channel that accounts for the present results.

That said, we were also concerned with introducing too many changes in the model at once. Indeed, we simply modelled the unimodal transient channels as a single band-pass filter followed by squaring. This is already a stripped-down version of the unsigned transient detectors proposed by Adelson and Bergen in their classic Motion Energy model. The original model consisted of two biphasic temporal filters 90 degrees out of phase (i.e., quadrature filters), whose output is later combined. While a simpler implementation of the transient channels was sufficient in the present study, the full model may be necessary for other classes of stimuli (including speech, Parise, 2024, BiorXiv). Therefore, for completeness, we now include in the Supplementary Information a formal description of the full model, and validate it by simulating our two novel psychophysical studies. See Supplementary Information “The quadrature MCD model” section and Supplementary Figure S8.

(2) Because the model is applied only to rather simple artificial signals, it remains unclear to what extent it can account for AV correlation detection for naturalistic signals. In particular, speech appears to rely on correlation detection of signed signals. Can this modified model account for SJ or TOJ judgments for naturalistic signals?

It can. In a recent series of studies we have demonstrated that a population of spatially-tuned MCD units can account for audiovisual correlation detection for naturalistic stimuli, including speech (e.g. the McGurk Illusion). Once again, unsigned transients were sufficient to replicate a variety of previous findings. We have now extended the discussion to cover this recent research: Parise, C. V. (2024). Spatiotemporal models for multisensory integration. bioRxiv, 2023-12.

Even Nidiffer et al. (2018) which is explicitly modelled by the authors report a significant difference in performance for correlated and anti-correlated signals. This seems to disagree with the results of study 1 reported in the current paper and the model's predictions. How can these contradicting results be explained? If the brain detects correlation on signed and unsigned signals, is a more complex mechanism needed to arbitrate between those two?

We believe the reviewer here refers to our Experiment 2 (where, like Nidiffer at al. (2018) we used periodic stimuli, not Experiment 1, which consists of step stimuli). We were also puzzled by the difference between our Experiment 2 and Nidiffer et al. (2018): we induced frequency doubling, Nidiffer did not. Based on quantitative simulations, we concluded that this difference could be attributed to the fact that while Nidiffer included on each trial an intensity ramp in their periodic audiovisual stimuli, we did not. As a result, when considering the ramp (unlike in Nidiffer’s analyses), all audiovisual signals used by Nidiffer were positively correlated (irrespective of frequency and phase offset), while our signals in Experiment 2 were sometimes correlated and other times not (depending on the phase offset). This important simulation is included in Supplementary Figure S7; we also have now updated the text to better highlight the role of the pedestal in determining the direction of the correlation.

(3) The number of parameters seems quite comparable for the authors' model and descriptive models (e.g. PSF models). This is because time constants require refitting (at least for some experimental data sets) and the correlation values need to be passed through a response mode (i.e. probit function) to account for behavioural data. It remains unclear how the brain adjusts the time constants to different sensory signals.

This is a deep question. For simplicity, here the temporal constants were fitted to the empirical psychometric functions. To avoid overfitting, whenever possible we fitted such parameters over some training datasets, while trying to predict others. However, in some cases, it was necessary to fit the temporal constants to specific datasets. This may suggest that the temporal tuning of those units is not crystalised to some pre-defined values, but is adjusted based on recent perceptual history (e.g., the sequence of trials and stimuli participants are exposed to during the various experiments).

For transparency, here we show how varying the tuning of the temporal constants of the filters affects the goodness of fit of our new psychophysical experiments (Supplementary Figure S8). As it can be readily appreciated, the relative temporal tuning of the unimodal transient detector was critical, though their absolute values could vary over a range of about 15 to over 100ms. The tuning of the low-pass filters of the correlation detector (not shown here) displayed much lower temporal sensitivity over a range between 0.1s to over 1s.

This simulation shows the impact of temporal tuning in our simulations, however, the question remains as to how such a tuning gets selected in the first place. An appealing explanation relies on natural scene statistics: units are temporally tuned to the most common audiovisual stimuli. Although our current empirical evidence does not allow us to quantitatively address this question, in previous simulations (see Parise & Ernst, 2016, Supplementary Figure 8), by analogy with visual motion adaptation, we show how the temporal constants of our model can dynamically adjust and adapt to recent perceptual history. We hope these new and previous simulations address the question about the nature of the temporal tuning of the MCD units.

(4) Fujisaki and Nishida (2005, 2006) proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector (though not formalized as a computational model).

This is correct, Fujisaki and Nishida (2005, 2007) also hypothesized that AV synchrony could be detected using a mechanism analogous to motion detection. Interestingly, however, they ruled out such a hypothesis, as their “data do not support the existence of specialized low-level audio-visual synchrony detectors”. Yet, along with our previous work (Parise & Ernst, 2016, where we explicitly modelled the experiments of Fujisaki and Nishida), the present simulations quantitatively demonstrate that a low-level AV synchrony detector is instead sufficient to account for audiovisual synchrony perception and correlation detection. We now credit Fujusaki and Nishida in the modelling section for proposing that AV synchrony can be detected by a cross-correlator.

Finally, we believe the reviewer is referring to the 2005 and 2007 studies of Fujisaki and Nishida (not 2006); here are the full references of the two articles we are referring to:

Fujisaki, W., & Nishida, S. Y. (2005). Temporal frequency characteristics of synchrony–asynchrony discrimination of audio-visual signals. Experimental Brain Research, 166, 455-464.

Fujisaki, W., & Nishida, S. Y. (2007). Feature-based processing of audio-visual synchrony perception revealed by random pulse trains. Vision Research, 47(8), 1075-1093.

Reviewer #2 (Public Review):

Summary:

This is an interesting and well-written manuscript that seeks to detail the performance of two human psychophysical experiments designed to look at the relative contributions of transient and sustained components of a multisensory (i.e., audiovisual) stimulus to their integration. The work is framed within the context of a model previously developed by the authors and is now somewhat revised to better incorporate the experimental findings. The major takeaway from the paper is that transient signals carry the vast majority of the information related to the integration of auditory and visual cues, and that the Multisensory Correlation Detector (MCD) model not only captures the results of the current study but is also highly effective in capturing the results of prior studies focused on temporal and causal judgments.

Strengths:

Overall the experimental design is sound and the analyses are well performed. The extension of the MCD model to better capture transients makes a great deal of sense in the current context, and it is very nice to see the model applied to a variety of previous studies.

Weaknesses:

My one major issue with the paper revolves around its significance. In the context of a temporal task(s), is it in any way surprising that the important information is carried by stimulus transients? Stated a bit differently, isn't all of the important information needed to solve the task embedded in the temporal dimension? I think the authors need to better address this issue to punch up the significance of their work.

In hindsight, it may appear unsurprising that transient signals carry most information for audiovisual integration. Yet, so somewhat unexpectedly, this has never been investigated using perhaps the most diagnostic psychophysical tools for perceived crossmodal timing; namely temporal order and simultaneity judgments–along with carefully designed experiments with quantitative predictions for the effect of either channel. The fact that the results conform to intuitive expectations further supports the value of the present work: grounding empirically with what is intuitively expected. This offers solid psychophysical evidence that one can build on for future advancements. Importantly, developing a model that builds on our new results and uses the same parameters to predict a variety of classic experiments in the field, further supports the current approach.

If “significance” is intended as shaking previous intuitions or theories, then no: this is not a significant contribution. If instead, by significance we intend to build a solid empirical and theoretical ground for future work, then we believe this study is not significant, it is foundational. We hope that this work's significance is better captured in our discussion.

On a side note, there is an intriguing factor around transient vs. sustained channels: what matters is the amount of change, not the absolute stimulus intensity. Previous studies, for example, have suggested a positive cross modal mapping between auditory loudness and visual lightness or brightness [Odegaard et al., 2004]. This study, conversely, challenges this view and demonstrates that what matters for multisensory integration in time is not the intensity of a stimulus, but changes thereof.

In a more minor comment, I think there also needs to be a bit more effort into articulating the biological plausibility/potential instantiations of this sustained versus transient dichotomy. As written, the paper suggests that these are different "channels" in sensory systems, when in reality many neurons (and neural circuits) carry both on the same lines.

The reviewer is right, in our original manuscript we glossed over this aspect. We have now expanded the introduction to discuss their anatomical basis. However, we are not assuming any strict dichotomy between transient and sustained channels; rather, our results and simulations demonstrate that transient information is sufficient to account for audiovisual temporal integration.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Related to point 2 of the public review, can the authors provide additional results showing that the model can also account for naturalistic signals and more complex stochastic signals?

While working on this manuscript, we were also working in parallel on a project related to audiovisual integration of naturalistic signals. A pre-print is available online [Parise, 2024, BiorXiv], and the related study is now discussed in the conclusions.

(2) As noted in the public review, Fujisaki and Nishida (2005, 2006) already proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector. Their work should be referenced and discussed.

We have now acknowledged the contribution of Fujisaki and Nishida in the modelling section, when we first introduce the link between our model and the Hassenstein-Reichardt detectors.

(3) Experimental parameters: Was the phase shift manipulated in blocks? If yes, what about temporal recalibration?

To minimise the effect of temporal recalibration, the order of trials in our experiments was randomised. Nonetheless, we can directly assess potential short-term recalibration effects by plotting our psychophysical responses against both the current SOA, and that of the previous trials. The resulting (raw) psychometric surfaces below are averaged across observers (and conditions for Experiment 1). In all our experiments, responses are obviously dependent on the current SOA (x-axis). However, the SOA of the previous trials (y-axis) does not seem to meaningfully affect simultaneity and temporal order judgments. The psychometric curves above the heatmaps represent the average psychometric functions (marginalized over the SOA of the previous trial).

All in all, the present analyses demonstrate negligible temporal recalibration across trials, likely induced by a random sequence of lags or phase shifts. Therefore, when estimating the temporal constants of the model, it seems reasonable to ignore the potential effects of temporal recalibration. To avoid increasing the complexity of the present manuscript, we would prefer not to include the present analyses in the revised version.

Author response image 1.

Effect of previous trial. Psychometric surfaces for Experiments 1 and 2 plotted against the lag in the current vs. the previous trial. While psychophysical responses are strongly modulated by the lag in the last trial (horizontal axis), they are relatively unaffected by the lag in the previous trial (vertical axis).

(4) The model predicts no differences for experiment 1 and this is what is empirically observed. Can the authors support these null results with Bayes factors?

This is a good suggestion: we have now included a Bayesian repeated measures ANOVA to the analyses of Experiment 1. As expected, these analyses provide further, though mild evidence in support for the null hypothesis (See Table S2). For completeness, the new Bayesian analyses are presented alongside the previous frequentist ones in the revised manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aim to consider the effects of phonotactics on the effectiveness of memory reactivation during sleep. They have created artificial words that are either typical or atypical and showed that reactivation improves memory for the latter but not the former.

Comment 1:

Strengths:

This is an interesting design and a creative way of manipulating memory strength and typicality. In addition, the spectral analysis on both the wakefulness data and the sleep data is well done. The article is clearly written and provides a relevant and comprehensive of the literature and of how the results contribute to it.

We thank the reviewer for his/her positive evaluation of our manuscript. 

Comment 2:

Weaknesses:

(1) Unlike most research involving artificial language or language in general, the task engaged in this manuscript did not require (or test) learning of meaning or translation. Instead, the artificial words were arbitrarily categorised and memory was tested for that categorisation. This somewhat limits the interpretation of the results as they pertain to language science, and qualifies comparisons with other language-related sleep studies that the manuscript builds on.

We thank the reviewer for this comment. We agree that we did not test for meaning or translation but used a categorization task in which we trained subjects to discriminate artificial words according to their reward associations (rewarded vs. non-rewarded). Previous language studies (Batterink et al., 2014; Batterink and Paller, 2017; Reber, 1967) used artificial words to investigate implicit learning of hidden grammar rules. Here, the language researchers studied generalization of the previously learned grammar knowledge by testing subject’s ability to categorize correctly a novel set of artificial words into rule-congruent versus rule-incongruent words. These differences to our study design might limit the comparability between the results of previous language studies of artificial grammar learning and our findings. We discussed now this aspect as a limitation of our novel paradigm. 

We added the following sentences to the discussion on p.14, ll. 481-488:

Based on our paradigm, we investigated categorization learning of artificial words according to their reward associations (rewarded vs. unrewarded) and did not studied aspects of generalization learning of artificial grammar rules (Batterink et al., 2014; Batterink and Paller, 2017; Reber, 1967). This difference might limit the comparability between these previous language-related studies and our findings. However, the usage of artificial words with distinct phonotactical properties provided a successful way to manipulate learning difficulty and to investigate word properties on TMR, whereas our reward categorization learning paradigm had the advantage to increase the relevance of the word learnings due to incentives.    

Comment 3:

(2) The details of the behavioural task are hard to understand as described in the manuscript. Specifically, I wasn't able to understand when words were to be responded to with the left or right button. What were the instructions? Were half of the words randomly paired with left and half with right and then half of each rewarded and half unrewarded? Or was the task to know if a word was rewarded or not and right/left responses reflected the participants' guesses as to the reward (yes/no)? Please explain this fully in the methods, but also briefly in the caption to Figure 1 (e.g., panel C) and in the Results section.

We thank the reviewer for this comment and added additional sentences into the document to provide additional explanations. We instructed the participants to respond to each word by left- and right-hand button presses, whereas one button means the word is rewarded and the other button means the word is unrewarded. The assignment of left- and right-hand button presses to their meanings (rewarded versus unrewarded) differed across subjects. In the beginning, they had to guess. Then over trial repetitions with feedback at the end of each trial, they learned to respond correctly according to the rewarded/unrewarded associations of the words.        

We added the following sentences to the results section on p.5, ll. 161-168: 

As a two alternative forced-choice task, we assigned left- and right-hand button presses to the rewarded and the unrewarded word category, counterbalanced across subjects. We instructed the participants to respond to each word by left- or right-hand button presses, whereas one button means the word is rewarded (gain of money points) and the other button means the word is unrewarded (avoid the loss of money points). In the beginning, they had to guess. By three presentations of each word in randomized order and by feedback at the end of each trial, they learned to respond correctly according to the rewarded/unrewarded associations of the words (Fig. 1c). 

We added the following sentences to the caption of Figure 1 on p.6, ll. 188-194:

As a two alternative forced-choice task, responses of left- and right-hand button presses were assigned to the rewarded and the unrewarded word category, respectively. The participants were instructed to respond to each word by left- or right-hand button presses, whereas one button means the word is rewarded (gain of money points) and the other button means the word is unrewarded (avoid the loss of money points). d) Feedback matrix with the four answer types (hits: rewarded and correct; CR, correct rejections: unrewarded and correct; misses: rewarded and incorrect; FA, false alarms: unrewarded and incorrect) regarding to response and reward assignment of the word.

We added the following sentences to the methods on p.19, ll. 687-692:  

As a two alternative forced-choice task, we assigned left- and right-hand button presses to the rewarded and the unrewarded word category, counterbalanced across subjects. We instructed the participants to respond to each word by left- or right-hand button presses, whereas one button means the word is rewarded (gain of money points) and the other button means the word is unrewarded (avoid the loss of money points).

Comment 4:  

(3) Relatedly, it is unclear how reward or lack thereof would translate cleanly into a categorisation of hits/misses/correct rejections/false alarms, as explained in the text and shown in Figure 1D. If the item was of the non-rewarded class and the participant got it correct, they avoided loss. Why would that be considered a correct rejection, as the text suggests? It is no less of a hit than the rewarded-correct, it's just the trial was set up in a way that limits gains. This seems to mix together signal detection nomenclature (in which reward is uniform and there are two options, one of which is correct and one isn't) and loss-aversion types of studies (in which reward is different for two types of stimuli, but for each type you can have H/M/CR/FA separably). Again, it might all stem from me not understanding the task, but at the very least this required extended explanations. Once the authors address this, they should also update Fig 1D. This complexity makes the results relatively hard to interpret and the merit of the manuscript hard to access. Unless there are strong hypotheses about reward's impact on memory (which, as far as I can see, are not at the core of the paper), there should be no difference in the manner in which the currently labelled "hits" and "CR" are deemed - both are correct memories. Treating them differently may have implications on the d', which is the main memory measure in the paper, and possibly on measures of decision bias that are used as well.

We thank the reviewer for this comment giving us the opportunity to clarify. As explained in the previous comment, for our two alternative forced-choice task, we instructed the participants to press one button when they were thinking the presented word is rewarded and the other button, when they were thinking the word is unrewarded. Based on this instruction, we applied the signal detection theory (SDT), because the subjects had the task to detect when reward was present or to reject when reward was absent. Therefore, we considered correct responses of words of the rewarded category as hits and words of the unrewarded category as correct rejections (see Table below). However, the reviewer is correct because in addition to false alarms, we punished here the incorrect responses by subtraction of money points to control for alternative task strategies of the participants instead of reward association learning of words. We agree that further explanation/argumentation to introduce our nomenclature is necessary.  

Author response table 1.

We adjusted the results section on p.5, ll. 169-177:

To obtain a measurement of discrimination memory with respect to the potential influence of the response bias, we applied the signal detection theory (Green and Swets, 1966). Because, we instructed the participants to respond to each word by left- or right-hand button presses and that one button means reward is present whereas the other button means reward is absent, we considered correct responses of words of the rewarded category as hits and words of the unrewarded category as correct rejections. Accordingly, we assigned the responses with regard to the reward associations of the words to the following four response types: hits (rewarded, correct); correct rejections (unrewarded, correct); misses (rewarded, incorrect); and false alarms (unrewarded, incorrect). Dependent on responses, subjects received money points (Fig. 1d). 

Comment 5:

(4) The study starts off with a sample size of N=39 but excludes 17 participants for some crucial analyses. This is a high number, and it's not entirely clear from the text whether exclusion criteria were pre-registered or decided upon before looking at the data. Having said that, some criteria seem very reasonable (e.g., excluding participants who were not fully exposed to words during sleep). It would still be helpful to see that the trend remains when including all participants who had sufficient exposure during sleep. Also, please carefully mention for each analysis what the N was.

Our study was not pre-registered. Including all the subjects independent of low prememory performance, but with respect to a decent number of reactivations (> 160 reactivations, every word at least 2 times), resulted in a new dataset with 15 and 13 participants of the high- and low-PP cueing condition, respectively. Here, statistical analyses revealed no significant overnight change anymore in memory performance in the high-PP cueing condition (Δ memory (d'): t(14) = 1.67, p = 0.12), whereas the increase of the bias in decision making towards risk avoidance still remained significant (Δ bias (c-criterion): t(14) = 3.36, p = 0.005).

We modified and added the following sentences to the discussion on p.13, ll. 456-458:

Our study has limitations due to a small sample size and between-subject comparisons. The criteria of data analyses were not pre-registered and the p-values of our behavior analyses were not corrected for multiple comparisons.

Comment 6:             

(5) Relatedly, the final N is low for a between-subjects study (N=11 per group). This is adequately mentioned as a limitation, but since it does qualify the results, it seemed important to mention it in the public review.

We agree with the reviewer that the small sample size and the between subject comparisons represent major limitations of our study. Accordingly, we now discussed these limitations in more detail by adding alternative explanations and further suggestions for future research to overcome these limitations.        

We added the following sentences to the discussion about the limitations on p.14, ll. 465-488: 

To control for potential confounders despite the influence of difficulty in word learning on TMR, we compared parameters of sleep, the pre-sleep memory performance and the vigilance shortly before the post-sleep memory test, revealing no significant group differences (see Table S1 and S2). Nevertheless, we cannot rule out that other individual trait factors differed between the groups, such as the individual susceptibility to TMR. To rule out these alternative explanations based on individual factors, we suggest for future research to replicate our study by conducting a within-subject design with cueing of subsets of previously learned low- and high-PP words providing all conditions within the same individuals as shown in other TMR studies (Cairney et al., 2018; Schreiner and Rasch, 2015).

Comment 7:

(6) The linguistic statistics used for establishing the artificial words are all based on American English, and are therefore in misalignment with the spoken language of the participants (which was German). The authors should address this limitation and discuss possible differences between the languages. Also, if the authors checked whether participants were fluent in English they should report these results and possibly consider them in their analyses. In all fairness, the behavioural effects presented in Figure 2A are convincing, providing a valuable manipulation test.

We thank the reviewer pointing to the misalignment between the German-speaking participants and the used artificial words based on American English. Further, we did not assessed the English language capability of the participants to control it as a potential confounder, whereas comparative control analyses revealed no significant differences between the both cueing groups in pre-sleep memory performance (see Table S1). 

We now discussed these comments as limitations on p.14, ll. 473-481: 

Further, we used artificial words based on American English in combination with German speaking participants, whereas language differences of pronunciation and phoneme structures might affect word perception and memory processing (Bohn and Best, 2012). On the other hand, both languages are considered to have the same language family (Eberhard et al., 2019) and the phonological distance between English and German is quite short compared for example to Korean (Luef and Resnik, 2023). Thus, major common phonological characteristics across both languages are still preserved. In addition, our behavior analyses revealed robust word discrimination learning and distinct memory performance according to different levels of phonotactic probabilities providing evidence of successful experimental manipulation. 

Comment 8:

(7) With regard to the higher probability of nested spindles for the high- vs low-PP cueing conditions, the authors should try and explore whether what the results show is a general increase for spindles altogether (as has been reported in the past to be correlated with TMR benefit and sleep more generally) or a specific increase in nested spindles (with no significant change in the absolute numbers of post-cue spindles). In both cases, the results would be interesting, but differentiating the two is necessary in order to make the claim that nesting is what increased rather than spindle density altogether, regardless of the SW phase.

We conducted additional analyses based on detected sleep spindles to provide additional data according to this question. 

We added the following section to the supplementary data on pp. 31-32, ll. 1007-1045:  

After conducting a sleep spindle detection (frequency range of 12-16Hz, see methods for details), we compared the sleep spindle density between the TMR conditions of high- and lowPP showing no significant difference (see Fig. S8a and Table S9). Next, we subdivided the detected sleep spindles into coupled and uncoupled sleep spindles with the previously detected slow waves (SW; analyses of Fig. 4). Sleep spindles were defined as coupled when their amplitude peak occurred during the SW up-state phase (0.3 to 0.8s time-locked to the SW troughs). A two-way mixed design ANOVA on the amplitude size of the sleep spindles with the cueing group as a between-subject factor (high-PP-cued vs. low-PP-cued) and SW-coupling as a within-subject factor (coupled vs. uncoupled) showed a significant interaction effect (cueing group × SW-coupling: F(1,20) = 4.51, p = 0.046, η2 = 0.18), a significant main effect of SW-coupling (F(1,20) = 85.02, p < 0.001, η2 = 0.81), and a trend of significance of the main effect of the cueing group (F(1,20) = 3.54, p = 0.08). Post-hoc unpaired t-tests revealed a significant higher amplitude size of the coupled sleep spindles of the cueing group of high- compared to low-PP (t(20) = 2.13, p = 0.046, Cohen’s d = 0.91; Fig. S8b) and no significant group difference of the uncoupled sleep spindles (t(20) = 1.62, p = 0.12). An additional comparison of the amount of coupled sleep spindles between the cueing groups revealed no significant difference (see Table S9). 

Here, we found that detected sleep spindles coupled to the SW up-state phase occurred with higher amplitude after TMR presentations of the high-PP words in comparison to the low-PP words, whereas the sleep spindle density and the amount of sleep spindles coupled to the SW up-state phase did not differed between the cueing conditions.     

We added the following sentences to the methods on pp. 22-23, ll. 822-839:  

Sleep spindle analyses 

We detected fast sleep spindles by band-pass filtering (12-16Hz) the signal of the Pz electrode during the auditory cueing trials in the time windows of -2 to 8s according to stimulus onsets. The amplitude threshold was calculated individually for each subject as 1.25 standard deviations (SDs) from the mean. The beginning and end times of the sleep spindles were then defined as the points at which the amplitude fell below 0.75 SDs before and after the detected sleep spindle. Only sleep spindles with a duration of 0.5-3 s were included in subsequent analyses. 

To compare the sleep spindle densities between the different cueing conditions of high- and low-PP, we computed the grand average sleep spindle density distribution in number per trial with a bin size of 0.5s from -0.5 to 6s time-locked to stimulus onset in each condition (see Fig. S8a and Table S9).     

Based on the detected slow waves and sleep spindles, we defined coupling events when the positive amplitude peak of a detected sleep spindle was occurring during the slow wave upstate phase in a time window of 0.3 to 0.8s according to the trough of a slow wave. 

We computed the averaged amplitude size of each detected sleep spindle by calculating the mean of the absolute amplitude values of all negative and positive peaks within a detected sleep spindle (see Fig. S8b).

We added the following sentences to the results on p.10, ll. 338-343:  

By conducting an additional analyses based on detection of fast sleep spindles (12-16Hz; see methods), we confirmed that fast sleep spindles during the SW up-states (from 0.3 to 0.8s after the SW trough) occurred with significantly higher amplitude after the cueing presentation of high- compared to low-PP words, whereas parameters of sleep spindle density and the amount sleep spindles coupled to the SW up-state did not differed between the cueing conditions (see Fig. S8 and Table S9).       

Reviewer #2 (Public Review):

Summary:

The work by Klaassen & Rasch investigates the influence of word learning difficulty on sleepassociated consolidation and reactivation. They elicited reactivation during sleep by applying targeted memory reactivation (TMR) and manipulated word learning difficulty by creating words more similar (easy) or more dissimilar (difficult) to our language. In one group of participants, they applied TMR of easy words and in another group of participants, they applied TMR of difficult words (between-subjects design). They showed that TMR leads to higher memory benefits in the easy compared to the difficult word group. On a neural level, they showed an increase in spindle power (in the up-state of an evoked response) when easy words were presented during sleep.

Comment 9:

Strengths:

The authors investigate a research question relevant to the field, that is, which experiences are actually consolidated during sleep. To address this question, they developed an innovative task and manipulated difficulty in an elegant way.

Overall, the paper is clearly structured, and results and methods are described in an understandable way. The analysis approach is solid.

We thank the reviewer for his/her positive evaluation of our manuscript.

Weaknesses:

Comment 10:

(1) Sample size

For a between-subjects design, the sample size is too small (N = 22). The main finding (also found in the title "Difficulty in artificial word learning impacts targeted memory reactivation") is based on an independent samples t-test with 11 participants/group.

The authors explicitly mention the small sample size and the between-subjects design as a limitation in their discussion. Nevertheless, making meaningful inferences based on studies with such a small sample size is difficult, if not impossible.

We agree with the reviewer that the small sample size and the between subject comparisons represent major limitations of our study. Accordingly, we now discussed these limitations in more detail by adding alternative explanations and further suggestions for future research to overcome these limitations.        

We added the following sentences to the discussion about the limitations on p.14, ll. 465-473: 

To control for potential confounders despite the influence of difficulty in word learning on TMR, we compared parameters of sleep, the pre-sleep memory performance and the vigilance shortly before the post-sleep memory test, revealing no significant group differences (see Table

S1 and S2). Nevertheless, we cannot rule out that other individual trait factors differed between the groups, such as the individual susceptibility to TMR. To rule out these alternative explanations based on individual factors, we suggest for future research to replicate our study by conducting a within-subject design with cueing of subsets of previously learned low- and high-PP words providing all conditions within the same individuals as shown in other TMR studies (Cairney et al., 2018; Schreiner and Rasch, 2015).

Comment 11:

(2) Choice of task

though the task itself is innovative, there would have been tasks better suited to address the research question. The main disadvantage the task and the operationalisation of memory performance (d') have is that single-trial performance cannot be calculated. Consequently, choosing individual items for TMR is not possible.

Additionally, TMR of low vs. high difficulty is conducted between subjects (and independently of pre-sleep memory performance) which is a consequence of the task design.

The motivation for why this task has been used is missing in the paper.

We used a reward task combined with TMR because previous studies revealed beneficial effects of reward related information on sleep dependent memory consolidation and reactivation (Asfestani et al., 2020; Fischer and Born, 2009; Lansink et al., 2009; Sterpenich et al., 2021). In addition, we wanted to increase the motivation of the participants, as they could receive additional monetary compensation according to their learning and memory task performances. Furthermore, we designed the task, with the overall possibility to translate this task to operant conditioning in rats (see research proposal: https://data.snf.ch/grants/grant/168602). However, the task turned out to be too difficult to translate to rats, whereas we developed a different learning paradigm for the animal study (Klaassen et al., 2021) of this cross-species research project.       

We added the following sentence to the introduction on p.4, ll. 134-137:

To consider the beneficial effect of reward related information on sleep dependent memory consolidation and reactivation (Asfestani et al., 2020; Fischer and Born, 2009; Lansink et al., 2009; Sterpenich et al., 2021), we trained healthy young participants to categorize these words into rewarded and unrewarded words to gain and to avoid losses of money points.  

Reviewer #3 (Public Review):

Summary:

In this study, the authors investigated the effects of targeted memory reactivation (TMR) during sleep on memory retention for artificial words with varying levels of phonotactical similarity to real words. The authors report that the high phonotactic probability (PP) words showed a more pronounced EEG alpha decrease during encoding and were more easily learned than the low PP words. Following TMR during sleep, participants who had been cued with the high PP TMR, remembered those words better than 0, whilst no such difference was found in the other conditions. Accordingly, the authors report higher EEG spindle band power during slow-wave up-states for the high PP as compared to low PP TMR trials. Overall, the authors conclude that artificial words that are easier to learn, benefit more from TMR than those which are difficult to learn.

Comment 12 & 13:

Strengths:

(1) The authors have carefully designed the artificial stimuli to investigate the effectiveness of TMR on words that are easy to learn and difficult to learn due to their levels of similarity with prior wordsound knowledge. Their approach of varying the level of phonotactic probability enables them to have better control over phonotactical familiarity than in a natural language and are thus able to disentangle which properties of word learning contribute to TMR success.

(2) The use of EEG during wakeful encoding and sleep TMR sheds new light on the neural correlates of high PP vs. low PP both during wakeful encoding and cue-induced retrieval during sleep.

We thank the reviewer for his/her positive evaluation of our manuscript.

Weaknesses:

Comment 14:

(1) The present analyses are based on a small sample and comparisons between participants. Considering that the TMR benefits are based on changes in memory categorization between participants, it could be argued that the individuals in the high PP group were more susceptible to TMR than those in the low PP group for reasons other than the phonotactic probabilities of the stimuli (e.g., these individuals might be more attentive to sounds in the environment during sleep). While the authors acknowledge the small sample size and between-subjects comparison as a limitation, a discussion of an alternative interpretation of the data is missing.

We agree with the reviewer that the small sample size and the between subject comparisons represent major limitations of our study. We thank the reviewer for this helpful comment and now discussed these limitations in more detail by adding alternative explanations and further suggestions for future research to overcome these limitations.

We added the following sentences to the discussion on p.14, ll. 465-473: 

To control for potential confounders despite the influence of difficulty in word learning on TMR, we compared parameters of sleep, the pre-sleep memory performance and the vigilance shortly before the post-sleep memory test, revealing no significant group differences (see Table S1 and S2). Nevertheless, we cannot rule out that other individual trait factors differed between the groups, such as the individual susceptibility to TMR. To rule out these alternative explanations based on individual factors, we suggest for future research to replicate our study by conducting a within-subject design with cueing of subsets of previously learned low- and high-PP words providing all conditions within the same individuals as shown in other TMR studies (Cairney et al., 2018; Schreiner and Rasch, 2015).

Comment 15:

(2) While the one-tailed comparison between the high PP condition and 0 is significant, the ANOVA comparing the four conditions (between subjects: cued/non-cued, within-subjects: high/low PP) does not show a significant effect. With a non-significant interaction, I would consider it statistically inappropriate to conduct post-hoc tests comparing the conditions against each other. Furthermore, it is unclear whether the p-values reported for the t-tests have been corrected for multiple comparisons. Thus, these findings should be interpreted with caution.

We thank the reviewer for this comment giving us the opportunity to correct our analyses and clarify with additional description. Indeed, we investigated at first overnight changes in behavior performance within the four conditions, conducting t-tests against 0 of Δ-values of d' and c-criterion. Whereas for all our statistical analyses the p-value was set at p < 0.05 for two-tailed testing, we did not corrected the p-value of our behavior analyses for multiple comparisons. To investigate subsequently differences between conditions, we conducted additional ANOVAs. We agree with the reviewer that without significant of results of the ANOVA, post-hoc analyses should not be conducted. Taken in account as well the recommendation of reviewer 1, we included now only post-hoc pairwise comparisons when the interaction effect of the ANOVA revealed at least a trend of significance (p < 0.1). 

We removed the following post-hoc analyses from the results section on p.9, ll. 291-295: 

Additional post-hoc pairwise comparisons revealed a significant difference between the highPP cued and low-PP uncued (high-PP cued vs. low-PP uncued: t(10) = 2.43, p = 0.04), and no difference to other conditions (high-PP cued vs.: high-PP uncued t(20) = 1.28, p = 0.22; lowPP cued t(20) = 1.57, p = 0.13).  

Further, we mentioned the lack of correction for multiple comparisons as a limitation of our results in the discussion on p.13, ll. 456-458:  

The criteria of data analyses were not pre-registered and the p-values of our behavior analyses were not corrected for multiple comparisons.

We added the following sentences to the methods p.23, ll. 842-849:

To analyze overnight changes of sleep behavioral data within TMR conditions, we conducted at first dependent sample t-tests against 0 of Δ-values (post-sleep test minus pre-sleep test) of d' and c-criterion (see Fig. 3). Two-way mixed design ANOVAs were computed to compare Δvalues between TMR conditions. After confirming at least a trend of significance (p < 0.1) for the interaction effect, we conducted post-hoc pairwise comparisons by independent and dependent sample t-tests. For all behavior statistical analyses, the p-value was set at p < 0.05 for two-tailed testing. A p-value < 0.1 and > 0.05 was reported as a trend of significance.

Comment 16:

(3) With the assumption that the artificial words in the study have different levels of phonotactic similarity to prior word-sound knowledge, it was surprising to find that the phonotactic probabilities were calculated based on an American English lexicon whilst the participants were German speakers. While it may be the case that the between-language lexicons overlap, it would be reassuring to see some evidence of this, as the level of phonotactic probability is a key manipulation in the study.

We thank the reviewer pointing to the misalignment between the German-speaking participants and the used artificial words based on American English. In line with this recommendation, we added a more outlined argumentation to the manuscript about the assumption of our study that major common phonetic characteristics across both languages are still preserved.       

We now discussed these aspects on p.14, ll. 473-481:

Further, we used artificial words based on American English in combination with German speaking participants, whereas language differences of pronunciation and phoneme structures might affect word perception and memory processing (Bohn and Best, 2012). On the other hand, both languages are considered to have the same language family (Eberhard et al., 2019) and the phonological distance between English and German is quite short compared for example to Korean (Luef and Resnik, 2023). Thus, major common phonological characteristics across both languages are still preserved. In addition, our behavior analyses revealed robust word discrimination learning and distinct memory performance according to different levels of phonotactic probabilities providing evidence of successful experimental manipulation. 

Comment 17:

(4) Another manipulation in the study is that participants learn whether the words are linked to a monetary reward or not, however, the rationale for this manipulation is unclear. For instance, it is unclear whether the authors expect the reward to interact with the TMR effects.

We used a reward task combined with TMR because previous studies revealed beneficial effects of reward related information on sleep dependent memory consolidation and reactivation (Asfestani et al., 2020; Fischer and Born, 2009; Lansink et al., 2009; Sterpenich et al., 2021). In addition, we wanted to increase the motivation of the participants, as they could receive additional monetary compensation according to their learning and memory task performances. Furthermore, we designed the task, with the overall possibility to translate this task to operant conditioning in rats (see research proposal: https://data.snf.ch/grants/grant/168602). However, the task turned out to be too difficult to translate to rats, whereas we developed a different learning paradigm for the animal study (Klaassen et al., 2021) of this cross-species research project.       

We added the following sentence to the introduction on p.4, ll. 134-137:

To consider the beneficial effect of reward related information on sleep dependent memory consolidation and reactivation (Asfestani et al., 2020; Fischer and Born, 2009; Lansink et al., 2009; Sterpenich et al., 2021), we trained healthy young participants to categorize these words into rewarded and unrewarded words to gain and to avoid losses of money points.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Comment 18:

(1) Please clearly define all linguistics terms - and most importantly the term "phonotactics" - at first use.

We thank the reviewer for this recommendation and we added the definition of phonotactics and further reduced the diversity of linguistic terms to improve readability. 

We added the following sentences to the beginning of the introduction on p.3, ll. 72-76:

One critical characteristic of similarity to pre-existing knowledge in auditory word processing is its speech sound (phoneme) pattern. In phonology as the field of language specific phoneme structures, phonotactics determines the constraints of word phoneme composition of a specific language.

Comment 19:

(2) Some critical details about the methods should be included in the Results section to make it comprehensible. For example, the way the crucial differences between G1-4 words should be addressed in the Results, not only in Figure 1.

According to the recommendation, we added this information to the results section.  We added the following sentences to the results section on p.4, ll. 145-154:

To study the impact of difficulty in word learning on TMR, we developed a novel learning paradigm. We formed four sets of artificial words (40 words per set; see Table S3 and S4) consisting of different sequences of two vowels and two consonants. Here, we subdivided the alphabet into two groups of consonants (C1: b, c, d, f, g, h, j, k, l, m; C2: n, p, q, r, s, t, v, w, x, z) and vowels (V1: a, e, I; V2: o, u, y). Four-letter-words were created by selecting letters from the vowel and consonant groups according to four different sequences (G1:C1, V1, V2, C2; G2: C1, V1, C2, V2; G3: V1, C1, C2, V2; G4: V1, C1, V2, C2; Fig. 1a; see methods for further details). Comparison analyses between the sets revealed significant differences in phonotactic probability (PP; Fig. 1b; unpaired t-tests: G1 / G2 > G3 / G4, p < 0.005, values of Cohen’s d > 0.71).

Comment 20

(3) Was scoring done both online and then verified offline? If so, please note that.

We included now this information.  

We adjusted the method section on p.21, ll. 765-769:   

The sleep stages of NREM 1 to 3 (N1 to N3), wake, and REM sleep were scored offline and manually according to the criteria of the American Academy of Sleep Medicine (AASM) by visual inspection of the signals of the frontal, central, and occipital electrodes over 30s epochs (Iber et al., 2007). Based on offline scoring, we confirmed TMR exposure during N2 and N3 and no significant differences (p-values > 0.05) of sleep parameters between the cueing groups (see Table S2).  

Comment 21:

(4) In Figure 2, please arrange the panel letters in an easier-to-read way (e.g., label upper right panel b with a different letter).

Now we rearranged the panel letters according to the recommendation.

We adjusted Figure 2 on p.8, ll. 242-258:     

Comment 22

(5) In the first paragraph on TMR effects, please note which memory measure you are comparing (i.e., d').

We added this information according to the recommendation.  

We adjusted the sentence of the results on p.8, ll. 260-263:

To examine whether TMR during sleep impacts memory consolidation of discrimination learning with respect to learning difficulty, we calculated the overnight changes by subtracting the pre- from the post-sleep memory performance based on d'-values of the reactivated sequences (cued) and non-reactivated sequences (uncued).

Comment 23:

(6) Please show the pre-sleep and post-sleep test scores for both word categories (not only the delta). It may be best to show this as another data point in Fig 2a, but it may be helpful to also see this split between cued and uncued.

We added the pre-sleep and post-sleep test scores with the individual data points as an additional figure. 

We added the following figure to the supplementary data on p.28, ll. 936-940:  

Comment 24:

(7) In the sentence "An additional two-way mixed design ANOVA on the same values with cueing as a between-subject factor (cued vs. uncued) ...", a more exact phrasing for the last parentheses would probably be "(high-PP-Cued vs Low-PP-Cued)". Both groups were cued.

We thank the reviewer pointing this out. According to the recommendation, we corrected the descriptions of the two-way mixed design ANOVAs. In addition, we detected a mistake of wrong assignments of the conditions to ANOVAs and corrected the reported values.   

We adjusted the sentences and corrected the values on p.9, ll. 271-275 and ll. 289-291: 

An additional two-way mixed design ANOVA on the same values with the factor cueing (cued vs. uncued) as a within-subject factor and group as a between-subject factor revealed trends of significance (p < 0.1) for the interaction (cueing × group: F(1,20) = 3.47, p = 0.08) and the main effect of group (F(1,20) = 3.28, p = 0.09). The main effect of cueing was not significant (F(1,20) = 0.58, p = 0.46).

An ANOVA on c-criterion changes showed no significant effects (interaction cueing × group: F(1,20) = 2.66, p = 0.12; main effect cueing  F(1,20) = 2.08, p = 0.17; main effect group F(1,20) = 0.38, p = 0.55).

Comment 25:

(8) In the same ANOVA, please mention that there is a trend toward an interaction effect. If there wasn't one, the post-hoc comparison would be unwarranted. Please consider noting other p<0.1 pvalues as a trend as well, for consistency.

Regarding this recommendation, we included now only post-hoc pairwise comparisons after confirming at least a trend toward an interaction effect of these ANOVAs and reported consistently a p-value < 0.1 and > 0.05 as a trend of significance.

We added the following sentences to the methods p.23, ll. 844-849:

Two-way mixed design ANOVAs were computed to compare Δ-values between TMR conditions. After confirming at least a trend of significance (p < 0.1) for the interaction effect, we conducted post-hoc pairwise comparisons by independent and dependent sample t-tests. For all behavior statistical analyses, the p-value was set at p < 0.05 for two-tailed testing. A p-value < 0.1 and > 0.05 was reported as a trend of significance.

We removed the following post-hoc analyses from the results section on p.9, ll. 291-295: 

Additional post-hoc pairwise comparisons revealed a significant difference between the highPP cued and low-PP uncued (high-PP cued vs. low-PP uncued: t(10) = 2.43, p = 0.04), and no difference to other conditions (high-PP cued vs.: high-PP uncued t(20) = 1.28, p = 0.22; lowPP cued t(20) = 1.57, p = 0.13).          

Comment 26:      

(9) Please consider adding an analysis correlating spindle power with memory benefit across participants. Even if it is non-significant, it is important to report given that some studies have found such a relationship.

According to this recommendation, we conducted an additional correlation analyses.

We added the following sentences to the manuscript into the results (pp. 10-11, ll. 346-349), the discussion (p.12, ll. 413-417), and the methods (p.23, ll. 864-867):   

Whereas we found a significant group difference in spindle power nested during SW up-states,   conducting further whole sample (n = 22) correlation analyses between the individual spindle power values of the significant cluster and the overnight changes of behavior measurements revealed no significant correlations (Δ d': r = 0.16, p = 0.48; Δ c-criterion: r = 0.19, p = 0.40).

In addition to our result of the significant group difference, we failed to find significant correlations between SW nested spindle power values and overnight changes in behavior measurements, whereas previous studies reported associations of SW and spindle activities during sleep with the integration of new memories in pre-existing knowledge networks (Tamminen et al., 2013, 2010).

By using the same extracted power values (0.3 to 0.8s; 11-14Hz; Pz, P3, P4, O2, P7) per subject, we performed whole sample (n = 22) Pearson correlation analyses between these power values and the overnight changes of behavior measurements of the cued condition (Δ d' and Δ ccriterion).

Reviewer #2 (Recommendations For The Authors):

(1) Choice of task

Comment 27:      

In general, I find your task well-designed and novel. In light of your research question, however, I wonder why you chose this task. When you outlined the research question in the introduction, I expected a task similar to Schreiner et al. (2015). For example, participants have to associate high PP words with each other and low PP words. The advantage here would be that you could test the benefits of TMR in a within-subjects design (for example, cueing half of the remembered high and half of the remembered low PP words).

Please see our previous response at comment 14.    

Comment 28:

Why did you decide to introduce a reward manipulation?

Please see our previous response at comment 11.    

Comment 29:

Why did you do the cueing on a category level (cueing all high PP or all low PP words instead of single word cueing or instead of cueing 20 reward high-PP, 20 unrewarded high-PP plus 20 reward low-PP and 20 unrewarded low-PP)? Both alternatives would have provided you the option to run your statistics within participants.

Please see our previous response at comment 14.    

Comment 30:

(2) Between-subjects design and small sample size.

Why did you decide on a between-subjects design that severely reduces your power?

Why did you just collect 22 participants with such a design? Were there any reasons for this small sample size? Honestly, I think publishing a TMR study with healthy participants and such a small sample size (11 participants for some comparisons) is not advisable.

Please see our previous response at comment 14.

Comment 31:

(3) Encoding performance.

Is d' significantly above 0 in the first repetition round? I would assume that the distinction between rewarded and non-rewarded words is just possible after the first round of feedback.

Indeed, conducting t-tests against 0 revealed significantly increased d'-values in the first repetition round (2nd presentation) in both PP conditions (high-PP: 0.85 ± 0.09, t(32) = 9.17, p < 0.001; low-PP: 0.62 ± 0.09, t(32) = 6.83, p < 0.001).  

Comment 32:

(4) Encoding response options

If you want to you could make it more explicit what exactly the response options are. I assume that one button means a word has a high reward and the other button means a word has a low reward. Making it explicit increases the understanding of the results section.

Please see our previous response at comment 3.

Comment 33:           

(5) Alpha desynchronisation.

Relative change

Why did you subtract alpha power during the 1st presentation from alpha power during 2nd and 3rd presentation? You baseline-corrected already and individually included the 1st, 2nd, and 3rd repetition in your behavioural analysis.

Based on this analysis, we aimed to examine the relative change in alpha power between PP-conditions of memory-relevant word repetitions. Therefore, to extract memory relevant changes of EEG activities, the first word presentation of naive stimulus processing could serve as a more representative baseline condition covering the time-window of interest of 0.7 to 1.9 s after the stimulus onset compared to a baseline condition before stimulus onset (-1 to -0.1s). 

To explain the rational of the analyses with the baseline condition more clearly, we added this information to the results section on p.7, ll. 222-226: 

We obtained the changes in power values by subtracting the first from the second and third presentation for the high- and low-PP condition, respectively. Here, the first word presentation of naive stimulus processing served us with a more representative baseline condition covering the time-window of interest of 0.7 to 1.9 s after the stimulus onset to examine relevant changes of encoding.  

Comment 34:

(6) Alpha desynchronisation as a neural correlate of encoding depth & difficulty?

"In addition to the behavior results, these EEG results indicate differences between PP conditions in desynchronization of alpha oscillations, as an assumed neural correlate of encoding depth. In addition to the behavior results, these EEG results indicate differences between PP conditions in desynchronization of alpha oscillations, as an assumed neural correlate of encoding depth."

Given that the low-PP words are more difficult to learn, I was expecting to see higher alpha desynchronisation in the low-PP relative to the high-PP words. Could you outline in a bit more detail how your findings fit into the literature (e.g., Simon Hanslmayr did a lot of work on this)?

I would also advise you to add citations e.g., after your sentence in the quote above ("as an assumed neural correlate of encoding depth").

We thank the reviewer for the recommendation giving us the opportunity to discuss in more detail how our results relate to previous findings. 

We added additional sentences to the discussion on p.13, ll. 441-455:    

Additional studies linked alpha desynchronization to cognitive effort and cognitive load (Proskovec et al., 2019; Zhu et al., 2021). So, one could assume to observe higher alpha desynchronization in the more difficult to learn condition of low-PP compared to high-PP. On the other hand numerous studies investigating oscillatory correlates of learning and memory showed that alpha desynchronization is associated with memory across different tasks, modalities and experimental phases of encoding and retrieval (Griffiths et al., 2016, 2021, 2019a, 2019b; Hanslmayr et al., 2009; Michelmann et al., 2016). Strikingly, Griffith and colleagues (Griffiths et al., 2019a) revealed by simultaneous EEG-fMRI recordings a negative correlation between the occurrence of patterns of stimulus-specific information detected by fMRI and cortical alpha/beta suppression. Here, the authors suggested that a decrease of alpha/beta oscillations might represent the neuronal mechanism of unmasking the task-critical signal by simultaneous suppression of task-irrelevant neuronal activities to promote information processing. Following this interpretation, we assume that over the course of learning elevated memory processing of the easier to learn stimuli is associated with enhanced information processing and thus accompanied by higher cortical alpha desynchronization in comparison of the more difficult to learn stimuli.

In addition, we added the mentioned quote on p.7, ll. 239-240:

In addition to the behavior results, these EEG results indicate differences between PP conditions in desynchronization of alpha oscillations, as an assumed neural correlate of encoding depth (Griffiths et al., 2021; Hanslmayr et al., 2009).

Comment 35:

(7) Exclusion criterion.

Why did you use a d' > 0.9 as a criterion for data inclusion?

This criterion ensured that each included subject had at least in one PP-condition a d' > 1.05 of pre-sleep memory performance, which corresponds to a general accuracy rate of 70%. 

Accordingly, we adjusted these sentences of the method section on p.19, ll. 677-680: 

Data were excluded from subjects who did not reach the minimal learning performance of d' > 1.05 during the pre-sleep memory test in at least one of the two PP conditions, whereas this threshold value corresponds to accuracy rates of 70% (n = 5). In addition, we excluded one subject who showed a negative d' in one PP condition of the pre-sleep memory test (n = 1). 

Comment 36:

(8) Coherence of wording.

When you talk about your dependent variable (d') you sometimes use sensitivity. I would stick to one term.

We replaced the word sensitivity with d'.    

(9) Criterion

Comment 37:

Why do you refer to a change in criterion (Figure 3b, axis labels) as a change in memory? Do you think the criterion says something about memory?

We corrected the axis label of Figure 3b and deleted here the word memory.

Comment 38:

Additionally, why did you analyse the effect of TMR on the criterion? Do you expect the criterion to change due to sleep-dependent memory consolidation? This section would benefit from more explanation. Personally, I am very interested in your thoughts and your hypothesis (if you had one, if not that is also fine but then, make it explicit that it was an exploratory analysis).

By conducting exploratory analyses of overnight changes of the c-criterion measurements, we aimed to examine the bias of decision-making to provide comprehensive data according to the framework of the signal detection theory. Regarding the previous literature showing mainly beneficial effects of sleep on learning and memory, we focused with our hypothesis on d' and explored additionally the c-criterion.

Despite our task design with gains/hits of +10 money points and losses/FAs of -8 (instead of -10), the subjects showed already during the pre-sleep memory task significant biases towards loss avoidance in both PP conditions (t-tests against 0: high-PP: 0.44 ± 0.07, t(21) = 5.63, p < 0.001; low-PP: 0.47 ± 0.09, t(21) = 5.51, p < 0.001). As already reported in the preprint, we found an additional significant increase of c-criterion by TMR solely for the high-PP words (see Fig. 3b). Even by integrating subjects with poor pre-sleep memory performance (high-PP-cueing group: n = 15; low-PP-cueing group: n = 13), t-tests against 0 revealed a significant increase of the high-PP cueing condition (t(14) = 3.36, p = 0.005) and no significant overnight changes in the other conditions (high-PP uncued: t(12) = 1.39, p = 0.19; low-PP cued: t(12) = 1.47, p = 0.17; low-PP uncued: t(14) = -0.20, p = 0.84). These exploratory findings on c-criterion suggest potential applications of TMR to affect decision-making biases in combination with reward learning.      

We revised the manuscript mentioning the exploratory character of the c-criterion analyses of the results on p.9, ll. 282-283 and of the discussion on p.12, ll. 400-402:  

We examined next as an exploratory analysis whether TMR conditions influence biases in decision-making.

By conducting an additional exploratory analysis, we observed a significant change of the decision bias in the cueing condition of the easy to learn words and no overnight changes in the other conditions.

Comment 39:

(10) You detected SWs in the time range of 0-6 sec post sound stimulation. How was the distribution of all detected SW down-states in this time range? (You could plot a histogram for this.)

We illustrated now the detected SWs in the time range of 0 to 6 s after stimulus onset. 

We added a histogram to the supplementary section on p.30, ll. 982-986:  

Reviewer #3 (Recommendations For The Authors):

Comment 40:

(1) In line with the weakness outlined above, I would recommend including a discussion of how the between-subject comparison and small sample size could affect the results and provide alternative interpretations.

Please see our previous response at comment 14.

Comment 41:

(2) Regarding my point about statistical comparisons, I would recommend that the authors follow best practice guidelines for post-hoc tests and multiple comparisons. In Figures 3a and b, I would also recommend removing the stars indicating significance from the post-hoc tests (if this is what they reflect). Perhaps this link will be useful: https://www.statology.org/anova-post-hoc-tests/

Please see our previous response at comment 15.    

Comment 42:

(3) Furthermore, to address any doubts about the possible phonotactic probability differences between languages, I would recommend that the authors show whether the languages overlap, the level of English fluency in the German-speaking participants, and/or another way of reassuring that this is unlikely to have affected the results.

Please see our previous response at comment 7.    

Comment 43:

(4) In the introduction, I would recommend that the authors outline a clear rationale for the reward/no reward manipulation.

Please see our previous response at comment 11.    

Comment 44:

(5) Figure 1c: Please include what response options participants had, e.g., 'rewarded/not rewarded'. This would make the type of categorization clearer to the reader.

Please see our previous response at comment 3.

Comment 45:

(6) It is unclear whether the additional ANOVA conducted on the time and frequency of the identified clusters included all channels or only the channels contributing to the cluster. Consider clarifying this in the relevant methods and results. Furthermore, I would recommend labelling this as a posthoc test as this analysis was guided by an initial peak at the data and the timings, frequencies, and channels of interest were not selected a-priori.

We thank the reviewer for this recommendation and labelled the additional repeatedmeasure ANOVA as a post-hoc test. Further, we mentioned the used channels (Pz and Cz) for this analyses.

We adjusted the results section on p.7, ll. 230-233 and the methods section on p.23, ll. 858-860:            

A post-hoc repeated-measure ANOVA on alpha power changes (merged over Pz and Cz electrodes) with PP (high vs. low) and presentations (2 to 3) as within-subjects factors revealed a main effect of PP (F(1,32) = 5.42, p = 0.03, η2 = 0.15), and a significant interaction (F(1,32)  = 7.38, p = 0.01, η2 = 0.19; Fig. 2e).

After confirming the existence of a significant cluster, we conducted an additional post-hoc repeated-measure ANOVA with averaged values of the identified time and frequency range of interest and merged over the Pz and Cz electrodes (see Fig. 2e).

Comment 46:

(7) Figure 3: To better illustrate within- vs. between-subjects comparisons and promote transparency, please add individual points and lines between the within-subjects conditions.

According to this recommendation, we changed Figure 3 to add the individual data points by lines.  

We modified Figure 3 on p.9, ll. 299-303:  

Comment 47:

(8) For the SW density time-bin analyses, please include statistics for all comparisons (i.e., through 0 s to 3 s) and say whether these were corrected for multiple comparisons.

According to this recommendation, we included now statistics for all comparisons. 

We added table S6 table to the supplementary data on p.29, l.962:     

Comment 48:

(9) Consider reporting effect sizes.

We thank the reviewer for this recommendation and we added now effect sizes of significant results. 

Comment 49:

(10) For transparency and replicability, consider including a list of the four stimulus sets including their phoneme and biphone probabilities.

We included a list of the four stimulus sets with their phoneme and biphone probabilities  

We added table S3 and table S4 to the supplementary data on pp. 26-27:       

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Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, the researchers aimed to investigate the cellular landscape and cell-cell interactions in cavernous tissues under diabetic conditions, specifically focusing on erectile dysfunction (ED). They employed single-cell RNA sequencing to analyze gene expression patterns in various cell types within the cavernous tissues of diabetic individuals. The researchers identified decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in several cell types, including fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. They also discovered a newly identified marker, LBH, that distinguishes pericytes from smooth muscle cells in mouse and human cavernous tissues. Furthermore, the study revealed that pericytes play a role in angiogenesis, adhesion, and migration by communicating with other cell types within the corpus cavernosum. However, these interactions were found to be significantly reduced under diabetic conditions. The study also investigated the role of LBH and its interactions with other proteins (CRYAB and VIM) in maintaining pericyte function and highlighted their potential involvement in regulating neurovascular regeneration. Overall, the manuscript is well-written and the study provides novel insights into the pathogenesis of ED in patients with diabetes and identifies potential therapeutic targets for further investigation.

Reviewer #2 (Public Review):

Summary: In this manuscript, the authors performed single cell RNA-sequencing of cells from the penises of healthy and diabetes mellitus model (STZ injection-based) mice, identified Lbh as a marker of penis pericytes, and report that penis-specific overexpression of Lbh is sufficient to rescue erectile function in diabetic animals. In public human single cell RNA-sea datasets, the authors report that LBH is similarly specific to pericytes and down regulated in diabetic patients. Additionally, the authors report discovery of CRYAB and VIM1 as protein interacting partners with LBH.

The authors contributions are of interest to the erectile dysfunction community and their Lbh overexpression experiments are especially interesting and well-conducted. However, claims in the manuscript regarding the specificity of Lbh as a pericyte marker, the mechanism by which Lbh overexpression rescues erectile function, cell-cell interactions impaired by diabetes, and protein-interaction partners require qualification or further evidence to justify.

Major claims and evidence:

1) Marker gene specificity and quantification: One of the authors' major contributions is the identification of Lbh as a marker of pericytes in their data. The authors present qualitative evidence for this marker gene relationship, but it is unclear from the data presented if Lbh is truly a specific marker gene for the pericyte lineage (either based on gene expression or IF presented in Fig. 2D, E). Prior results (see Tabula Muris Consortium, 2018) suggest that Lbh is widely expressed in non-pericyte cell types, so the claims presented in the manuscript may be overly broad. Even if Lbh is not a globally specific marker, the authors' subsequent intervention experiments argue that it is still an important gene worth studying.

Answer: We appreciate this comment. In our scRNAseq data for the mouse cavernosum tissues, previously known markers such as Rgs5, Pdgfrb, Cspg4, Kcnj8, Higd1b, and Cox4i2 were found to be expressed not exclusively in pericytes, while Lbh exhibited specific expression patterns in pericytes (Fig. 2 and Supplementary Fig. 5). LBH expression was easily distinguishable from α-SMA, not only in mouse cavernosum but also in dorsal artery and dorsal vein tissues within penile tissues. This distinctive expression pattern of LBH was also observed in the human cavernous pericytes (Fig. 5). Then, we examined Lbh expression patterns in various mouse tissues using the mouse single-cell atlas (Tabula Muris), although endothelial and pericyte clusters were not subclustered in most tissues from Tabula Muris. To identify pericytes, we relied on the expression pattern of known marker genes (Pecam1 for endothelial cells, Rgs5, Pdgfrb, and Cspg4 for pericytes). Lbh was expressed in pericytes of the bladder, heart and aorta, kidney, and trachea but not as specifically in penile pericytes (Supplementary Fig. 6A-D). However, it is worth noting that other known pericyte markers were also did not exhibit exclusive expression in pericytes across all the tissues we analyzed. Therefore, in certain tissues, particularly in mouse penile tissues, Lbh may be a valuable marker in conjunction with other established pericyte marker genes for distinguishing pericytes.

2) Cell-cell communication and regulon activity changes in the diabetic penis: The authors present cell-cell communication analysis and TF regulon analysis in Fig 3 and report differential activities in healthy and DM mice. These results are certainly interesting, however, no statistical analyses are performed to justify claimed changes in the disease state and no validations are performed. It is therefore challenging to interpret these results, and the relevant claims do not seem well supported.

Answer: In response to these helpful suggestions, we calculated statistical significance and performed experimental validation. CellphoneDB permutes the cluster labels of all cells 1000 times and calculates the mean(mean(molecule 1 in cluster X), mean(molecule 2 in cluster Y)) at each time for each interaction pair, for each pairwise comparison between two cell types. We only considered interactions in which the difference in means calculated by these permutations were greater than 0.25-fold between diabetes and normal. Also, we considered that the interactions with P-value < 0.05 were significant.

To assess differential regulon activities of transcription factor (SCENIC) between diabetic and normal pericytes, we utilized a generalized linear model with scaled activity scores for each cell as input. These scaled regulon activity values for angiogenesis-related TFs exhibited differences between diabetic and normal pericytes. The results of the generalized linear model revealed that Klf5, Egr1, and Junb were TFs with significantly altered regulon activities in diabetic pericytes. Experimental data indicated that the expression level of Lmo2, Junb, Elk1, and Hoxd10 was higher (Hoxd10) or lower (Lmo2, Junb, Elk1) in diabetic pericytes compared to normal pericytes (Supplementary Fig. 9). We have added the scaled regulon activity values and statistical significance in Fig. 3E.

3) Rescue of ED by Lbh overexpression: This is a striking and very interesting result that warrants attention. By simple overexpression of the pericyte marker gene Lbh, the authors report rescue of erectile function in diabetic animals. While mechanistic details are lacking, the phenomenon appears to have a large effect size and the experiments appear sophisticated and well conducted. If anything, the authors appear to underplay the magnitude of this result.

Answer: We appreciate this comment. Therefore, we have added relevant clarification in the revised manuscript discussion section to emphasize the importance of LBH overexpression on rescuing ED as follows: “To test our hypothesis, we utilized the diabetes-induced ED mouse model, commonly employed in various studies focusing on microvascular complications associated with type 1 diabetes. We observed that the overexpression of LBH in diabetic mice led to the restoration of reduced erectile function by enhancing neurovascular regeneration. However, this study primarily demonstrated the observed phenomenon without delving into the detailed mechanisms. Nonetheless, these results of LBH on erections provide us with new strategies for treating ED and should be of considerable concern.” (Please see revised ‘Discussion’)

4) Mechanistic claims for rescue of ED by Lbh overexpression: The authors claim that cell type-specific effects on MPCs are responsible for the rescue of erectile function induced by Lbh overexpression. This causal claim is unsupported by the data, which only show that Lbh overexpression influences MPC performance. In vivo, it's likely that Lbh is being over expressed by diverse cell types, any of which could be the causal driver of ED rescue. In fact, the authors report rescue of cell type abundance in endothelial cells and neuronal cells. Therefore, it cannot be concluded that MPC effects alone or in principal are responsible for ED rescue.

Answer: We agree with these claims. Therefore, we have added relevant clarifications in the discussion section of the revised manuscript. Our findings suggest that LBH can affect the function of cavernous pericytes, although we cannot definitively specify which particular cavernous cell types are affected by the overexpressed LBH, whether it be cavernous endothelial cells, smooth muscle cells, or others. Subsequent research will be required to conduct more comprehensive mechanistic investigations, such as in vitro studies using cavernous endothelial cells, smooth muscle cells, and fibroblasts to address these knowledge gaps. (Please see revised ‘Discussion’)

5) Protein interaction data: The authors claim that CRYAB and VIM1 are novel interacting partners of LBH. However, the evidence presented (2 blots in Fig. 6A,B) lack the relevant controls. It is possible that CRYAB and VIM1 are cross-reactive with the anti-LBH antibody or were not washed out completely. The abundance of bands on the Coomassie stain in Fig. 6A suggests that either event is plausible. Therefore, the evidence presented is insufficient to support the claim that CRYAB and VIM1 are protein interacting partners of LBH.

Answer: We agree with these claims. Therefore, we have added the relevant controls(Input) and performed Co-IP (IP: CRYAB or VIM, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Our results show that we can detect the expression of CRYAB and VIM after LBH IP, and we also detect the expression of LBH after CRYAB and VIM IP. In addition, it can be seen from our results that the binding of LBH to VIM is higher than that of CRYAB. Regardless, these results indicate that the binding of CRYAB or VIM to LBH is not a random phenomenon. (Please see revised ‘Result’ and ‘Figure 6B’)